The value of risk scores for predicting postoperative nausea and vomiting when used to compare patient groups in a randomised controlled trial

FORUM

The in vitro effects of crystalloids and colloids on

coagulation

K. Ekseth,1 L. Abildgaard,2 M. Vegfors,3 J. Berg-Johnsen1 and O. Engdahl2

1 Consultant Neurosurgeon, Department of Neurosurgery, The National Hospital, 0027 Oslo, Norway,

2 Consultant Anaesthesiologist, and 3 Head of Neuroanaesthesia, Department of Anaesthesiology, University Hospital,

58085 Linkoping, Sweden

Summary

Classically haemodilution is regarded as causing coagulopathy. However, haemodilution with

saline seems to cause a hypercoagulable state both in vivo and in vitro. The aim of the present study

was to measure the effect of mild to severe haemodilution using thrombelastography. Blood

samples were taken in 12 healthy volunteers and divided into seven aliquots. One aliquot was

undiluted and acted as control. The other six were diluted with normal saline, Ringer Acetate,

4% albumin, Dextran 70, 6% and 10% hydroxyethylstarch to 10%, 20%, 40%, 50% and 60%

dilution. The dilution was checked by measuring the haemoglobin concentration. Each aliquot was

placed in a temperature-controlled thrombelastography channel. Increased coagulation activity, as

measured by thrombelastography changes, was detected at low and medium levels of dilution with

all the tested solutions. At more than 40% dilution, coagulation returned to normal while in the

case of dextran and hydroxyethylstarch coagulopathy developed. For crystalloids and albumin,

dilution had to exceed 50% before coagulation was impaired. If these findings can be reproduced

in vivo, they may have implications for transfusion practice and prophylaxis against thrombosis.

Keywords Coagulation: haemodilution; thrombelastography.

........................................................................................................

Correspondence to: Dr K. Ekseth

E-mail: [email protected]

Accepted: 14 May 2002

Haemodilution is common following blood loss. Tradi-

tionally the focus during haemodilution has been on the

oxygen carrying capacity of blood [1], and haemodilution

has generally been regarded as a cause of hypocoagulation.

However, in 1950 Tocantins et al. reported that moderate

blood dilution with normal saline caused accelerated

coagulation. Numerous reports describing alterations of

coagulation during haemodilution followed [2–6]. Dur-

ing normovolaemic haemodilution in patients under

anaesthesia, abnormal haemostasis develops before

impaired global tissue oxygenation [7].

Thrombelastography was developed as a research tool

[8] but has been developed into a clinically useful

coagulation monitor [9]. Thrombelastography can be

used at the bedside and can detect impaired coagulation as

well as hypercoagulable states. By using thrombelasto-

graphy, Tuman et al. found increased coagulability with

progressive blood loss, even when losses were replaced by

crystalloids and packed red cells [3].

Therefore the aim of the present investigation was to

use thrombelastography in vitro to find the level of

dilution required to cause hypercoagulation with com-

monly used crystalloids and colloids. We also wanted to

determine whether the degree of dilution influenced

coagulation.

Methods

The thrombelastograph (Haemoscope Corp., 5693 West

Howard Street ⁄Niles, IL 60714, USA) consists of a

heated (37 �C) cuvette containing 0.36 ml of whole

blood. A pin suspended by a torsion wire is lowered into

the blood. The cuvette oscillates on its vertical axis

through 4.0–4.5 degrees. While the blood remains liquid,

Anaesthesia, 2002, 57, pages 1102–1133.....................................................................................................................................................................................................................

1102 � 2002 Blackwell Publishing Ltd

the motion of the cup does not affect the pin. However,

when the clot starts to form, the motion of the cup is

transmitted to the pin and then amplified to give the

thrombelastography trace. The technique evaluates both

clot initiation and the structural characteristics of the clot

formed together with its stability [9].

The reaction time (r: normal range 40–55) measures

the time from when the blood is placed in the cuvette

until an amplitude of 2 mm is reached on the thromb-

elastography trace (Fig. 1). The reaction time represents

the time necessary for initial fibrin formation. The

coagulation time (k: normal value 19–24 mm) is the

time between 2 and 20 mm divergence on the

thrombelastography tracing. The k-value is a measure

of the rapidity of fibrin build-up and fibrin cross-linking.

The maximum amplitude (normal value 46–60 mm) is

the width of the thrombelastography trace at its maxi-

mum divergence and reflects the absolute strength of the

fibrin clot and depends on platelet number and function

as well as fibrinogen levels. The a-angle (normal value

18–25 degrees) is the slope of the outside divergence of

the tracing from the r to k-values. It denotes the speed at

which clot is formed and cross-linked [3].

Twelve healthy volunteers who were not taking

salicylates or non-steroid anti-inflammatory drugs were

enrolled. Approval was obtained from the local ethics

committee, and all the participants gave informed con-

sent. Blood samples (20 ml) were taken from a large

forearm vein and divided into seven 2 ml portions. One

portion was undiluted and acted as control. The other six

2 ml portions were diluted with normal saline, Ringer

Acetate, 4% albumin, Dextran 70, 6% or 10% hydroxy-

ethylstarch (Fresenius Kabi Norge AS, Svinesundv 80,

1750 Halden, Norway). New samples from successive

vein punctures were diluted to 10%, 20%, 40%, 50% and

60% into each of the different crystalloid or colloid

solutions. After obtaining the appropriate dilution,

0.36 ml of solution was placed in parallel-connected

thrombelastography channels. Before analysing samples

from a new individual all channels were calibrated and

the first blood sample in each individual was used in all

channels to check calibration. After dilution all samples

were inverted in the same manner to ensure an equal

activation of the clotting system. Each control and diluted

sample was checked for accuracy of dilution by measuring

the haemoglobin concentration.

The r and k-values, a-angle and maximal amplitude

were obtained from the thromobelastograph. Each dilu-

ted sample was compared to the corresponding control

sample. Analysis of differences between the test solutions

was performed using one- and two-way analysis of

variance (ANOVA) with multiple measurement correction.

The means and standard deviations (SDs) are presented as

summary statistics.

Results

Reaction time

The r-values for the different solutions and different

dilutions are presented in Table 1 and Fig. 2. The analysis

of variance p-value for the solutions (F ¼ 24.0), dilution

(F ¼ 55.3) and their interaction (F ¼ 3.9) was < 0.0001.

Ringer Acetate, normal saline and 4% albumin had a

reduced r-value until 50% dilution after which further

dilution caused an increased r-value for normal saline and

4% albumin, while the reaction time remained reduced

for Ringer Acetate at 60% dilution.

Six percent and 10% hydroxyethylstarch initially also

produced a slightly reduced r-value, but from 40%

dilution the r-value was increased. At 60% dilution 6%

and 10% hydroxyethylstarch had much increased r-values;

however, Dextran 70 seems to have an intermediate

effect, turning from hypercoagulation to hypocoagulation

at 50% dilution.

K-value

The k-values for the different solutions and different

dilutions are presented in Table 2 and Fig. 3.

Figure 1 The thromboelastographtracing is composed of four principalparameters, reaction time (r), a – angle,k-value and maximum amplitude.

Anaesthesia, 2002, 57, pages 1102–1133 Forum......................................................................................................................................................................................................................

� 2002 Blackwell Publishing Ltd 1103

The analysis of variance p-value for the solutions

(F ¼ 31.095), dilution (F ¼ 43.811) and their interaction

(F ¼ 4.375) was < 0.0001. Both crystalloids and albumin

caused hypercoagulation at low dilution. The synthetic

colloids initially showed only minor changes but changed

into hypocoagulation at 40% dilution. The k-value record-

ing of 6% hydroxyethylstarch, however, needed 50%

dilution until hypocoagulation was seen. For crystalloids

and albumin, dilution had to be carried until 60% before

any sign of hypocoagulation was seen. For Ringer Acetate

at 60% dilution, the k-value was still reduced.

a-angle

The a-angle values for the different solutions and

different dilutions are presented in Table 3 and Fig. 4.

The analysis of variance p-value for the solutions

(F ¼ 35.863), dilution (F ¼ 37.02) was < 0,0001. The

p-value for their interaction (F ¼ 1.82) was 0.0178.

All diluents initially increase the a-angle as a sign of

hypercoagulation. For the synthetic colloids this is

returned to baseline at 40% dilution and present hypo-

coagulative values with increasing dilution. Crystalloids

and albumin on the other hand show 50% increase in

the a-angle value between 20% and 40% dilution and

maintain hypercoagulation until 60% dilution.

Maximal amplitude

The maximal amplitude values for the different solutions

and different dilutions are presented in Table 4 and

Fig. 5. The analysis of variance p-value for the solutions

(F ¼ 63.165), dilution (F ¼ 75.125) and their interaction

(F ¼ 2.197) was < 0.0001. Maximal amplitude was the

thrombelastography parameter that was least affected by

dilution. The crystalloids and albumin showed signs of

increased clotting with increased maximal amplitude

values from 10% to 40% dilution, but from 50% dilution

a slightly impaired coagulation was found. The maximal

amplitude values for 10% hydroxyethylstarch and dextran

were unaffected by 10% and 20% dilution, but from 40%

dilution hypocoagulation was present. Ringer Acetate

and albumin showed signs of increased clotting until 50%

dilution. The maximal amplitude value was most affected

Table 1 The r-values (means and (standard deviations)) for the various test solutions and dilutions. The superscripted numerals showwhich solutions differed significantly between each other. Comparisons in this table are not significant unless the correspondingp-value is less than 0.0033.

Dilution (1) NaCl 4,5,6 (2) Ringer4,5,6 (3) Albumin4,5 (4) 6% HES1,2,3 (5) 10% HES1,2,3,6 (6) Dextran1,2,5

10% 39.5 (6.9) 40.8 (7.1) 42.3 (9.8) 42.1 (7.6) 45.1 (6.6) 46.5 (10.9)20% 36.6 (6.5) 37.8 (10.4) 36.4 (7.5) 40.6 (12.5) 44.8 (9.8) 42.8 (8.0)40% 36.3 (12.0) 36.0 (9.8) 37.0 (11.5) 48.7 (18.7) 47.5 (15.1) 40.5 (10.5)50% 41.9 (12.0) 35.4 (4.1) 42.0 (8.4) 62.4 (25.9) 59.6 (16.9) 47.1 (16.4)60% 52.4 (15.7) 40.0 (6.6) 64.4 (20.6) 74.6 (19.8) 75.4 (32.0) 70.4 (18.5)

Figure 2 Mean r-values of the solutions,and their reaction to increasing haemo-dilution; shortening of r suggests hyper-coagulation. Albumin, shaded diamond;10% hydroxyethylstartch, open square;Dextran 70, solid triangle; Normalsaline, open circle; Ringer Acetate, star;6% hydroxyethylstartch, solid circle.

Forum Anaesthesia, 2002, 57, pages 1102–1133......................................................................................................................................................................................................................


by normal saline showing hypercoagulative effect until

60% dilution.

Discussion

Dilution of blood reduces its viscosity, but the assumption

that this also causes reduced coagulative ability is not

necessarily true. Haemodilution causes a dilution of

coagulation factors and has generally been thought to

attenuate clot formation. However, Tocantins reported

that �regardless of the activating agent used, the rate of

coagulation of haemophilic plasma can be made equal to

that of normal plasma by appropriate dilution.� [2].

Furthermore, he showed that the nature of the crystalloid

was not crucial, since all crystalloids had a similar effect.

Recently these findings have been re-evaluated and

several studies have reported hypercoagulation following

haemodilution. In 1979 Tsunehiro performed an in vitro

study using thrombelastography with colloid dilution of

blood and found an initial hypercoagulation changing

Table 2 The k-values (means and (standard deviations)) for the various test solutions and dilutions. The superscripted numerals showwhich solutions differed significantly between each other. Comparisons in this table are not significant unless the correspondingp-value is less than 0.0033.


10% 16.5 (3.9) 17.3 (4.3) 19.0 (5.7) 18.3 (4.8) 19.3 (5.3) 21.1 (5.9)20% 13.2 (3.5) 14.8 (4.2) 15.2 (5.0) 16.7 (5.9) 19.5 (6.3) 20.0 (6.7)40% 13.9 (4.9) 14.8 (8.3) 13.4 (4.5) 19.7 (7.1) 22.6 (6.0) 26.3 (10.3)50% 14.9 (5.6) 14.8 (3.7) 16.8 (6.0) 26.2 (7.7) 31.6 (8.9) 30.5 (15.1)60% 23.3 (8.1) 17.8 (4.3) 24.0 (9.1) 36.0 (13.0) 66.7 (59.4) 55.3 (32.6)

Figure 3 Mean k-values of the solu-tions, and their reaction to increasinghaemodilution; shortening of k suggestshypercoagulation. Albumin, shadeddiamond; 10% hydroxyethylstarch,open square; Dextran 70, solid triangle;Normal saline, open circle; RingerAcetate, star; 6% hydroxyethylstarch,solid circle.

Table 3 The a-angle values in degrees (means and (standard deviations)) for the various test solutions and dilutions. The superscriptednumerals show which solutions differed significantly between each other. Comparisons in this table are not significant unless thecorresponding p-value is less than 0.0033.


10% 28.0 (7.4) 26.3 (8.1) 24.5 (9.1) 23.7 (6.4) 23.7 (6.8) 21.9 (7.6)20% 32.9 (6.4) 32.6 (11.2) 29.5 (10.3) 27.3 (10.1) 24.4 (8.0) 23.5 (8.5)40% 32.0 (9.4) 33.2 (8.0) 32.4 (7.3) 23.3 (6.8) 22.6 (8.5) 20.8 (7.1)50% 30.9 (10.6) 29.6 (7.4) 28.2 (9.4) 17.1 (5.6) 16.2 (4.6) 18.2 (7.4)60% 21.0 (7.5) 24.6 (5.6) 19.6 (5.6) 13.2 (5.0) 10.5 (4.5) 11.5 (5.0)



Figure 4 Mean a-angle values of thesolutions, and their reaction to increas-ing haemodilution; increasing a-anglesuggests hypercoagulation. Albumin,shaded diamond; 10% hydroxy-ethylstarch, open square; Dextran 70,solid triangle; Normal saline, open circle;Ringer Acetate, star; 6% hydroxy-ethylstarch, solid circle.

Table 4 The maximum amplitude values in mm (means and (standard deviations)) for the various test solutions and dilutions. Thesuperscripted numerals show which solutions differed significantly between each other. Comparisons in this table are not significantunless the corresponding p-value is less than 0.0033.


10% 51.9 (8.0) 49.0 (6.5) 48.5 (5.9) 48.9 (7.5) 45.5 ( ⁄ .6) 43.3 (6.7)20% 52.5 (6.2) 48.5 (6.2) 52.6 (9.0) 46.4 (8.4) 43.4 (6.5) 42.9 (6.6)40% 51.8 (5.9) 47.6 (6.2) 49.5 (6.3) 45.4 (4.5) 36.1 (5.9) 36.7 (5.9)50% 48.9 (7.2) 43.0 (6.8) 45.2 (7.2) 39.9 (3.6) 31.5 (5.2) 34.0 (6.4)60% 42.3 (6.8) 37.6 (5.8) 41.8 (5.9) 35.9 (3.8) 27.1 (5.5) 29.1 (4.0)

Figure 5 Mean maximal amplitude val-ues of the solutions, and their reaction toincreasing haemodilution. An increasingmaximal amplitude suggests hypercoag-ulation. Albumin, shaded diamond; 10%hydroxyethylstarch, open square; Dex-tran 70, solid triangle; Normal saline,open circle; Ringer Acetate, star; 6%hydroxyethylstarch, solid circle.



into hypocoagulation when the dilution exceeded 20%

[10]. The fact that small to moderate amounts of

intravenous normal saline can cause hypercoagulation

was also reported by Janvrin et al. [11]. They found an

increased frequency of deep venous thrombosis in patients

receiving intravenous normal saline during surgery com-

pared to patients who were not administered intravenous

fluids. Others have also observed that a hypercoagulable

rather than a hypocoagulable state developing in associ-

ation with mild to moderate degrees of haemodilution

[12] and with a moderate degree of blood loss and

haemodilution in vivo [3]. Ruttman also concluded that

dilution of blood with both saline and Hemaccel caused

hypercoagulation measured by thrombelastography [6].

Clotting is a dynamic process, and measurements using

static end-points provide no information about the

quality of the clot or the dynamics of its formation [9].

Thrombelastography in contrast to conventional coagu-

lation laboratory tests is capable of comparing different

levels of activity throughout the integrated coagulation

process and is especially helpful in demonstrating hyper-

coagulability. We have shown that mild to moderate

haemodilution using different solutions causes increased

coagulability. The speed of clot formation was increased

significantly for all solutions at mild to moderate

dilution. The clot strength, represented by the maximal

amplitude, was moderately, but significantly increased,

indicating enhanced fibrin and platelet activity. Acceler-

ated clotting was seen until approximately 40% dilution

for the synthetic colloids and until 50% dilution for the

crystalloids. As blood becomes increasingly diluted the

initial hypercoagulation changes into normo- or hypo-

coagulation. However, with the crystalloids and albumin,

dilution has to be extreme before this change is reached.

Many hospitals have adopted protocols for substitution

of blood loss with plasma when intra-operative blood

loss exceeds 50% of the estimated blood volume. Some

authors using thrombelastography to assess coagulation

have challenged this notion and found that blood loss in

many instances can exceed 50% before administration of

plasma or platelets is required [3]. In our study the level of

dilution that transforms hypercoagulation into normo- or

hypocoagulation with crystalloids or albumin would

represent a blood loss of 70–90% of the circulating blood

volume in vivo. Tuman reported four patients with an

estimated blood volume loss of 80% who had trombo-

elastographic signs of increased coagulant activity [3]. If

these results represent the normal effects of haemodilution

with crystalloids or albumin in patients undergoing

surgery, the protocol for substituting blood loss with

plasma or coagulation factors may need re-evaluation.

Synthetic colloids have been implicated as a cause of

coagulopathy [13]. Experimental evidence indicates that

dextran decreases platelet function and the activity of

clotting factors [14]. According to our results, the effect of

dextran as anticoagulant appears to be dose-dependent.

Dextran in low concentrations activates the coagulation

cascade. Haemodilution above 20% was required to

achieve a hypocoagulative effect in-vitro.

Hydroxyethylstarch is considered to have haemostatic

profile similar to that of dextran [15, 16] although

conflicting evidence have been reported [17–20]. Based

on the results from the present investigation, hydroxy-

ethylstarch causes less hypercoagulation than the crystal-

loids and Albumin. Initially, we tested only the 10%

hydroxyethylstarch solution. This hydroxyethylstarch is a

hyperoncotic solution that acts by extracting fluid from

the interstitium diluting itself to iso-oncotic levels. The

6% solution may be better suited in these experiments to

represent the effects of hydroxyethylstarch on coagulation

in vivo. The importance of the molecular weight of

hydroxyethylstarch is often neglected. The high molecu-

lar weight starches interfere more with the factor VIII

complex than do the medium molecular weight startch

[20, 21]. Trumble et al. [18] reported coagulopathy with

hydroxyethylstarch of 480 kDalton while Davies found

that a higher molecular weight decelerated clotting [13].

A study on the effect of hydroxyethylstarch 40 (molecular

weight 56–61 kDalton) using conventional coagulation

tests discovered no changes beyond simple dilution [22].

We used hydroxyethylstarch with an average weight of

200 kDalton, but nevertheless found significantly attenu-

ated coagulation with dilution above 40% for 10%

hydroxyethylstarch and above 50% for 6% hydroxyeth-

ylstarch. At lower degrees of dilution normal or even

accelerated clotting was observed.

The mechanism by which haemodilution causes

enhanced coagulation has not been elucidated. Several

authors have attributed their observation of hypercoag-

ulation to surgical stress, tissue trauma and elevation in

serum catecholamine levels [3, 9, 23, 24]. Platelet adhesion

increases significantly after catecholamine and angiotensin

administration [23, 25]. The in vitro setting of our

experiments has eliminated the impact of drugs, endo-

thelium, tissue damage and stress, and the described

reaction to dilution is thus predominantly initiated

exclusively by the intrinsic coagulation system. Several

other factors including other drugs or ASA status and

physical factors such as hypothermia may modify the

effect of dilution and so alter the coagulation effects of

intravenous solutions in clinical practice.

In summary this study demonstrates an increased

coagulative activity measured by thrombelastography

in vitro occurring at low and medium levels of dilution

with all tested crystalloids and colloids. At more than 40%

dilution the hypercoagulation decreases and in the case of



dextran and hydroxyethylstarch turns into hypocoagula-

tion. Dilution with crystalloids and albumin has to be

more than 50%; this represents a substituted blood loss of

approximately 70%. If these findings can be reproduced

in vivo, they may have implications on transfusion practice

and prophylaxis against thrombosis.

References

1 Consensus conference: perioperative red blood cell transfu-

sion. Journal of American Medical Association 1988; 260: 2700–3.

2 Tocantins L, Carrel T, Holburn RH. The clot accelerating

effect of dilution on blood and plasma. Relation to the

mechanism of coagulation of normal and hemophilic blood.

Blood 1951; 6: 720–39.

3 Tuman KJ, Speiss BD, McCarthy RJ, Ivankovich AD.

Effects of progressive blood loss on coagulation as measured

by thrombelastography. Anesthesia and Analgesia 1987; 66:

856–63.

4 Howland WS. A comparison of intraoperative measurements

of coagulation. Anesthesia and Analgesia 1974; 53: 657–63.

5 Ng KF, Lo JW. The development of hypercoagulability

state, as measured by thrombelastography, associated with

intraoperative surgical blood loss. Anaesthesia and Intensive

Care 1996; 24: 20–5.

6 Ruttmann TG. Haemodilution induces a hypercoagulable

state. British Journal of Anaesthesia 1996; 76: 412–14.

7 McLoughlin TM. Profound normovolemic hemodilution:

hemostatic effects in patients and in a porcine model.

Anesthesia and Analgesia 1996; 83: 459–65.

8 Hartert H. Blutgerinnugsstudien mit der Thrombelasto-

graphie. Einen neuen Untersuchungsverfahren. Klinische

Wochenschrift 1948; 16: 257.

9 Mallett S, Cox D. Thrombelastography. British Journal of

Anaesthesia 1992; 69: 307–13.

10 Tsunehiro N, Isao M, Teruo K, Etsutaro I. An experimental

study on the effect of plasma expanders on blood coagula-

bility. Bulletin Tokyo Medical Dental University 1979; 26:

25–32.

11 Janvrin S, Davies G, Greenhalgh R. Postoperative deep vein

thrombosis caused by intravenous fluids during surgery.

British Journal of Surgery 1980; 67: 690–3.

12 Heather B, Jennings S, Greenhalgh M. The saline dilution

test – a preoperative predictor of DVT. British Journal of

Surgery 1980; 67: 63–5.

13 Davies M. The role of colloids in blood conservation.

International Anesthesiology Clinics 1990; 28: 205–9.

14 Aberg M, Bergentz S, Hedner U. Effect of dextran on factor

VIII (antihemophilic factor) and platelet function. Annals of

Surgery 1979; 189: 243–7.

15 Mischler JM. Synthetic plasma volume expanders: their

pharmacology, safety and clinical efficacy. Clinical Haema-

tology 1984; 13: 75–92.

16 London MJ, Ho JS, Triedman JK, et al. A randomized

clinical trial of 10% pentastarch (low molecular weight

hydroxyethyl starch) versus 5% albumin for plasma Volume

expansion after cardiac operations. Journal of Thoracic

Cardiovascular Surgery 1989; 97: 785–97.

17 Damon L, Adams M, Stricker RB, Ries C. Intracranial

bleeding during treatment with hydroxyethylstarch. New

England Journal of Medicine 1987; 317: 964–5.

18 Trumble ER, Muizelaar JP, Myseros JS, Choi SC, Warren

BB. Coagulopathy with the use of hetastarch in the treat-

ment of vasospasm. Journal of Neurosurgery 1995; 82: 44–7.

19 Treib J, Haass A, Pindur G, et al. HES, 200 ⁄ 0.5 is not HES

200 ⁄ 0.5. Influence of the C2 ⁄ C6 hydroxyethylation ratio of

hydroxyethyl starch (HES) on haemorrheology, coagulation

and elimination kinetics. Thrombosis and Haemostasis 1995;

74: 1452–6.

20 Mortier E, Ongenae M, De BL, et al. In vitro evaluation of

the effect of profound haemodilution with hydroxyethyl

starch 6%, modified fluid gelatin 4% and dextran 40, 10% on

coagulation profile measured by thromboelastography.

Anaesthesia 1997, 52: 1061–4.

21 Strauss RG. Pentastarch may cause fewer effects on coagu-

lation than hetastarch. Transfusion 1988; 28: 257–60.

22 Treib J. All medium starches are not the same. Influence of

the degree of hydroxyethyl substitution of hydroxyethyl

starch on plasma volume, haemorrheologic conditions, and

coagulation. Transfusion 1996; 36: 450–5.

23 Barrer MJ. Platelet function. Anesthesiology 1977; 46: 202–

11.

24 Gibbs NM, Crawford GP, Michalopoulos N. Postoperative

changes in coagulant and anticoagulant factors after

abdominal aortic surgery. Journal of Cardiothoracic Vascular

Anesthesia 1992; 6: 680–5.

25 Uza G, Crisnic I. Effect of angiotensin II upon platelet

adhesiveness and the thrombelastogram in patients with

essential arterial hypertension. Pathology Europe 1975; 10:

327–32.



FORUM

Estimating unmeasured anions in critically ill patients:

Anion-gap, base-deficit, and strong-ion-gap

D. A. Story,1 S. Poustie2 and R. Bellomo3

1 Joint Coordinator of Research, 2 Trial Coordinator and 3 Director of Research, Department of Intensive Care, Austin

and Repatriation Medical Centre, Studley Road, Heidelberg, Victoria 3084, Australia

Summary

We used 100 routine blood samples from critically ill patients to establish whether correcting the

anion-gap and base-deficit for decreased plasma albumin improves agreement with the strong-ion-

gap for estimating unmeasured anions and whether the modifications increase the proportion of

samples with levels of anion-gap or base-deficit above the reference ranges. We used Bland)Altman analyses to compare the methods of estimating unmeasured ions. Compared with the

strong-ion-gap, modification reduced the limits of agreement for both the anion-gap and the base-

deficit. The bias for the base-deficit was also reduced but the bias for the anion-gap was increased.

The proportion of samples with an anion-gap > 22 meq.l)1 increased from 4 to 29% (p < 0.001),

and the proportion with a base-deficit > 5 meq.l)1 in creased from 8 to 42% (p < 0.001). Con-

sequently, metabolic acidosis from unmeasured ions in critically ill patients maybe more frequent

than often recognised.

Keywords Acid)base measurement.

........................................................................................................

Correspondence to: Dr D. A. Story

Accepted: 29 June 2002

We have previously examined the acid-base status of

routine blood samples from critically ill patients. We

found decreased plasma albumin concentration in all the

samples and an overall metabolic alkalosis [1]. Several

groups have suggested that decreased plasma albumin can

conceal other acid)base disturbances [2–4]. One such

disturbance is increased unmeasured ions. In the Stewart

approach to acid)base disorders [5], the total concentra-

tion of unmeasured anions can be estimated using the

strong-ion-gap equation [3, 6]. Fencl and colleagues [3]

suggested improving the accuracy of the anion-gap

approach for detecting unmeasured ions in the critically

ill by modifying for decreased plasma albumin.

Balasubramanyan et al. [2] used a similar modification

of the base-deficit calculation for altered plasma albumin

concentration. This modification is another way of

incorporating the Stewart approach [4, 7]. Neither Fencl

et al. [3] nor Balasubramanyan et al. [2] compared the

modified anion-gap and modified base-deficit measure-

ments to the strong-ion-gap.

Extending our previous data [1], we examined wheth-

er: (i) modifying the anion-gap and base-deficit improves

agreement with the strong-ion-gap for estimating

unmeasured anions, and (ii) whether such modifications

increase the proportion of samples with levels of anion-

gap or base-deficit well above the reference ranges.

Methods

Data were prospectively collected from intensive care unit

records at the Austin and Repatriation Medical Centre, a

tertiary referral hospital affiliated to the University of

Melbourne. All samples were routine morning samples

taken from arterial lines in patients requiring intensive

care management. No additional sampling was required

and collected data did not identify patients. The Austin

and Repatriation Medical Centre Human Research

Ethics Committee waived the need for informed consent.

Arterial blood samples were collected in heparinised

blood-gas syringes (Rapidlyte, Chiron Diagnostics, East

Walpole, MA, USA) and analysed in the intensive care

unit (ICU) blood gas analyser (Ciba Corning 865, Ciba

Corning Diagnostics, Medfield, MA, USA). The analyser

measured the samples at 37 �C and ICU nursing staff who



had been taught to use the machine performed the

analyses. Samples were not stored on ice. We recorded

lactate and ionised calcium concentrations and the

calculated base-deficit and bicarbonate concentrations.

For each data set, another routine sample was drawn at

the same time from the same arterial sampling point using

a vacuum technique with clot activating tubes for serum or

lithium heparin tubes for plasma (Vacuette, Greiner

labourtechnik, Kremsmunster, A-4550, Austria). Our

laboratory gives an identical reference range for serum

and plasma levels for the variables measured in this study.

We included the data if the nurse recorded that the two

samples were taken simultaneously. These samples were

sent to the hospital core laboratory in the Division of

Laboratory Medicine. Plasma underwent a multicompo-

nent analysis (Hitachi 747, Roche Diagnostics, Sydney,

Australia). Scientific staff from the hospital clinical chem-

istry department analysed samples. Samples were not

stored on ice. We recorded sodium, potassium, magne-

sium, chloride, phosphate and albumin concentrations.

We calculated the anion-gap, modified-anion-gap, and

the modified-base-deficit.

Anion-gap

The anion-gap was estimated as

Anion-gap (meq.l-1) ¼ ½Naþ� þ ½Kþ� � ½HCO�3 �

� ½Cl��

Figge et al. [8] used regression analysis to modify the

anion-gap for changes in plasma albumin concentration.

We used this correction factor to modify the anion-gap

for changes in albumin in which 42 g.l)1 was the median

value of the reference range for albumin in our hospital

and 0.25 the correction factor:

Modified anion-gap (meq.l�1Þ ¼ anion gap

þ 0:25 � ½42 � measured albumin concentration�:

Base-deficit

The base-deficit was calculated as the negative of the

standard base-excess reported from the blood gas ma-

chine. The contribution to the measured base-deficit by

unmeasured ions was calculated by taking the negative

values of base-excess corrected for the effects of changes

in the strong-ion-difference and total weak acid concen-

tration [2, 4, 7].

We corrected the base-deficit (base excess) using the

approach developed by Fencl and modified by others

[2, 4, 7].

The major determinants of the strong-ion-difference

are the sodium and chloride concentrations. The base-

excess is modified for the effect of changes in free water

on the sodium concentration and for changes in the

chloride concentration [2]:

sodium modification (meq.l�1Þ ¼ 0:3� ð½Naþ� � 140Þ;

chloride modification (meq.l�1Þ¼ 102�ð½Cl�� 140=½Naþ�Þ:

Albumin is the major determinant of total weak-acid

concentration. Gilfix et al. [4] used regression analysis

from Figge’s group [9] to calculate the base-excess effect

of changes in plasma albumin. This calculation was

simplified by Balasubramanyan et al. [2]:

albumin modification (meq.l�1Þ¼ 0:34 � ð45 � ½albumin�Þ:

Therefore, the base-excess due to unmeasured ions

equals calculated base-excess from blood gas analysis

minus sodium modification minus chloride modification

minus albumin modification [2].

For comparison with the strong-ion-gap we used the

negative of the base-excess, the base-deficit. Both the

base-deficit and the strong-ion-gap become more positive

as the concentration of unmeasured-anions increases.

Strong-ion-gap

We calculated the strong-ion-gap with the all the ions

currently measured at our hospital [10]

strong-ion-gap (meq.l�1Þ¼ ½Naþ� þ ½Kþ� þ ½Mg2þ� þ ½Ca2þ� � ½Cl�� ½lactate anions� � ½albumin anions�� ½phosphate anions� � ½bicarbonate�:

Hydrogen, hydroxyl and carbonate ions were excluded

because of their micromolar concentrations [9].

The anions formed from the weak acids albumin and

phosphate were calculated from Figge et al.’s equations [9]:

albumin anions (meq.l�1Þ¼ ½albumin� g.l�1 � ð0:123 � pH � 0:631Þ

phosphate anions (meq.l�1Þ¼ ½phosphate� mmol.l�1 � ð0:309 � pH � 0:469Þ:

Statistical analysis

Data were collected from patient charts and the hospital

computer system and were stored on a computer

spreadsheet (Excel, Microsoft, Seattle, WA, USA). All

statistical calculations were performed using STATVIEW

software (Abacus Concepts, Berkeley, CA, USA). We

used the limits of agreement method of Bland & Altman

[11, 12] to determine the agreement between the strong-



ion-gap and each of the anion-gap, modified-anion-gap,

base-deficit and modified-base-deficit. We proposed that

acceptable agreement was a bias of up to ± 1 meq.l)1 and

limits of agreement less than bias ± 2 meq.l)1 so making a

total difference of up to 3 meq.l)1.

At our hospital the reference range for the anion gap is

10–20 meq.l)1 and for the base-deficit is )3 to 3 meq.l)1.

We examined the number of samples that, after

modification, exceeded thresholds that were above the

reference ranges for anion-gap (22 meq.l)1) [3] and base-

deficit (5 meq.l)1) [2]. Chi-squared analysis was used to

analyse the number of patients who exceeded these

thresholds for anion-gap and base-deficit before and after

modification. A p-value < 0.05 was considered statistically

significant.

Results

One hundred pairs of data were collected from 100

patients. Data collection was limited by sample pairs not

being drawn at the same time or failure to measure the

required variables.

Overall, the sodium and chloride concentrations were

within the normal range, which led to the strong-ion-

difference having little effect on the modified base-deficit

(Table 1). All samples had a plasma albumin below the

lower limit of our normal range (36 g.l)1) (Table 1).

These low plasma albumin levels had considerable effects

on the modified anion-gap and modified base-deficit.

Modification reduced the bias between the strong-ion-

gap and the base-deficit but increased the bias between

the strong-ion-gap and the anion-gap (Figs 1–4). Mod-

ification improved the precision of the agreement

between the strong-ion-gap and both the anion-gap and

the base-deficit (Figs 2 and 4). All four estimates had

limits of agreement well outside our proposed acceptable

limits of a bias of up to ± 1 meq.l)1 and limits of

agreement within ± 2 meq.l)1.

Fewer than 10% of samples had an anion-gap

> 22 meq.l)1 or a base-deficit > 5 meq.l)1 before mod-

ification (Table 2). Modification revealed an anion-gap

> 22 meq.l)1 in a quarter of the samples and base-deficit

> 5 meq.l)1 in a third of the samples (Table 2).

Discussion

Main findings

We found that modifying the anion-gap and base-deficit

for decreased plasma albumin improved agreement with

the strong-ion-gap. Both estimates, however, were out-

side our proposed acceptable limits of a bias (± 1 meq.l)1)

and limits of agreement (± 2 meq.l)1). The agreement

was better with the base-deficit than the anion-gap. The

unmeasured ions did not include lactate because lactate

was measured and used in the strong-ion-gap calculation.

Figure 1 Bland)Altman plot of thedifferences in unmeasured anion con-centration between the strong-ion-gapand the base deficit and the average ofthe two measures [(strong-ion-gap +anion-gap) ⁄ 2]. The full lines are thelimits of agreement and the dashed lineis the bias. In meq.l)1 the summarystatistics are bias )6.7, lower limit )11.4and upper limit )2.0.

Table 1 Acid)base variables for 100 samples.

Variable Median Interquartile range

pH 7.43 7.38 to 7.48CO2; kPa 5.5 4.8 to 6.3HCO3; mmol.l)1 25.4 23.7 to 30.6Sodium; mmol.l)1 140 138 to 144Chloride; mmol.l)1 103 99 to 106Lactate; mmol.l)1 1.6 1.1 to 2.4Albumin; g.l)1 23 19 to 27Strong-ion-gap; meq.l)1 8.5 6.0 to 11.4Anion-gap; meq l)1 14.9 12.4 to 17.5Modified anion-gap; meq.l)1 19.2 16.3 to 22.8Base-deficit; meq.l)1 ) 1.7 ) 6.6 to 1.3Modified base-deficit; meq.l)1 4.4 2.0 to 8.3



Further, in a quarter of the samples, modification

increased levels of anion-gap or base-deficit to above

the normal range.

Comparison with other studies

As in previous studies, we found that modifying the

anion-gap [6] or base-deficit [4] for decreased plasma

albumin improved the agreement with the strong-ion-

gap. We used Bland)Altman analyses rather than corre-

lation analyses that were used in the other studies [4, 6].

Correlation statistics are likely to overestimate the

relationship between estimates and cannot quantify

agreement [11].

In quantifying the effect of falling plasma albumin

concentration Figge et al. found that a 10 g.l)1 decrease in

plasma albumin required a 2.5 meq.l)1 correction in the

anion-gap [8]. In response to criticism from Reilly &

Anderson [13] and as part of a further study of their data,

Fencl et al. [3] examined a subgroup with base-deficit and

anion-gap within the reference range. After adjusting for

decreased plasma albumin the proportion of patients with

an anion-gap of > 22 meq.l)1 increased from 3 of 20 to

13 of 20. In Balasubramyan et al.’s study of 255 critically

ill children [2], after modifying for both decreases in

plasma albumin and changes in strong-ion-difference, the

proportion of patients with a base-deficit > 5 meq.l)1

increased from 27 to 54%. We also found important

increases in the number of samples that exceeded an anion

gap of 22 meq.l)1 [3] or a base-deficit of > 5 meq.l)1 [2]

or both after modification.

Figure 2 Bland)Altman plot of thedifferences in unmeasured anion con-centration between the strong-ion-gapand the modified anion-gap, and theaverage of the two measures [(strong-ion-gap + modified anion-gap) ⁄ 2]. Thefull lines are the limits of agreement andthe dashed line is the bias. In meq.l)1

the summary statistics are bias )11.3,lower limit )14.7 and upper limit )7.9.

Figure 3 Bland)Altman plot of thedifferences in unmeasured anion con-centration between the strong-ion-gapand the base deficit, and the average ofthe two measures [(strong-ion-gap +base-deficit) ⁄ 2]. The full lines are thelimits of agreement and the dashed lineis the bias. In meq.l)1 the summarystatistics are bias 11.0, lower limit 2.0,and upper limit 20.0.



Limitations

There is currently no ‘gold standard’ for detecting the

presence or concentration of unmeasured ions [3]. One

limitation of using the strong-ion-gap is that the

measurement errors for each of the components of the

calculation are added. We found, even with this limita-

tion, improved agreement particularly between the

strong-ion-gap and the base-deficit.

Clinical importance

Low plasma albumin is almost universal in critically ill

patients. Modifying the anion-gap for decreased plasma

albumin increases the proportion of patients with anion-

gap concentration well above the reference range. Even

with modification, however, the anion-gap agrees poorly

with the strong-ion-gap, particularly due to a large bias.

Modifying the base-deficit for decreased plasma albu-

min increases the number of samples with metabolic

acidoses. Further, modifying the base-deficit improves

agreement with the strong-ion-gap. The strong-ion-gap

is one method to estimate unmeasured ions but is limited

by having many variables and therefore many combined

measurement errors. The measurement error of the

strong-ion-gap maybe up to 10 meq.l)1 [14]. Although

the agreement between the strong-ion-gap and the

modified base-deficit was greater than our defined

acceptable limits (3 meq.l)1), measurement error in the

strong-ion-gap may have contributed substantially.

Because the modified base-deficit requires fewer mea-

surements and has less measurement error, we believe that

modifying the base-deficit to estimate the acid-base effect

of unmeasured ions is a reasonable alternative to using the

strong-ion-gap. This is particularly so if the clinically

important threshold for the unmeasured ion effect on

base-deficit is 5 meq.l)1 [2].

We support combining the base-deficit with a ‘Stewart’

style analysis of a patient’s acid)base status [2, 4, 7]. The

base-deficit can be used to examine the overall metabolic

component [15] as well as the effects of the strong-ion-

difference, weak acids (mainly albumin) and unmeasured

ions. Therefore, one variable, base-deficit, can be used to

analyse the components of a patient’s acid)base status

with simple calculations. The analysis of these base-deficit

effects, particularly the effects of the strong-ion-differ-

ence, will probably need further work.

We conclude that of the four estimates we tested the

modified base-deficit agrees most closely with the strong-

ion-gap. Further, metabolic acidosis due to unmeasured

ions in critically ill patients maybe more frequent than

often recognised. The identities of the unmeasured ions

and the importance of these findings to physiological

Figure 4 Bland)Altman plot of thedifferences in unmeasured anion con-centration between the strong-ion-gapand the modified base-deficit, and theaverage of the two measures [(strong-ion-gap + modified base-deficit) ⁄ 2].The full lines are the limits of agreementand the dashed line is the bias.In meq.l)1 the summary statistics arebias 3.5, lower limit )1.6 and upperlimit 8.5.

Table 2 Effect of modification on the anion-gap and base-deficit.

VariableAnion-gap> 22 meq.l)1 (%)

Base-deficit> 5 meq.l)1 (%)

Before modification 4 8After modification 29 42Absolute increase 25 3495% confidence interval

of increase18)34 26)44

p-Value (before vs. aftermodification)

< 0.001 < 0.001



homeostasis and outcome for patients need to be explored

further.

Acknowledgements

We wish to thank the ICU nursing staff; and Mr Sam

Tobgui, Senior Scientist from the Division of Laboratory

Medicine, for advice on the assays.

References

1 Story DA, Poustie S, Bellomo R. Quantitative physical

chemistry analysis of acid)base disorders in critically ill

patients. Anaesthesia 2001; 56: 530–3.

2 Balasubramanyan N, Havens PL, Hoffman GM. Unmea-

sured anions identified by the Fencl)Stewart method predict

mortality better than base excess, anion gap, and lactate in

patients in the pediatric intensive care unit. Critical Care

Medicine 1999; 27: 1577–81.

3 Fencl V, Jabor A, Kazda A, Figge J. Diagnosis of metabolic

acid)base disturbances in critically ill patients. American

Journal of Respiratory and Critical Care Medicine 2000; 162:

2246–51.

4 Gilfix BM, Bique M, Magder S. A physical chemical

approach to the analysis of acid)base balance in the clinical

setting. Journal of Critical Care 1993; 8: 187–97.

5 Stewart PA. Modern quantitative acid)base chemistry.

Canadian Journal of Physiology and Pharmacology 1983; 61:

1444–61.

6 Kellum JA, Kramer DJ, Pinsky MR. Strong ion gap: a

methodology for exploring unexplained anions. Journal of

Critical Care 1995; 10: 51–5.

7 Fencl V, Leith DE. Stewart’s quantitative acid)base chem-

istry: applications in biology and medicine. Respiratory

Physiology 1993; 91: 1–16.

8 Figge J, Jabor A, Kazda A, Fencl V. Anion gap and hypo-

albuminemia. Critical Care Medicine 1998; 26: 1807–10.

9 Figge J, Mydosh T, Fencl V. Serum proteins and acid)base

equilibria: a follow-up. Journal of Laboratory and Clinical

Medicine 1992; 120: 713–9.

10 Hayhoe M, Bellomo R, Liu G, McNicol L, Buxton B. The

aetiology and pathogenesis of cardiopulmonary bypass-as-

sociated metabolic acidosis using polygeline pump prime.

Intensive Care Medicine 1999; 25: 680–5.

11 Bland JM, Altman DG. Statistical methods for assessing

agreement between two methods of clinical measurement.

Lancet 1986; 1: 307–10.

12 Bland J, Altman D. Measuring methods in medical research.

Statistical Methods in Medical Research 1999; 8: 135–60.

13 Reilly R, Anderson R. Interpreting the anion gap. Critical

Care Medicine 1998; 26: 1771.

14 Story D, Poustie S, Bellomo R. Comparison of three

methods to estimate plasma bicarbonate in critically ill pa-

tients. Henderson)Hasselbach, enzymatic, and strong-ion-

gap. Anaesthesia and Intensive Care 2001; 29: 585–90.

15 Siggard-Anderson O. The Acid-Base Status of the Blood, 4th

edn. Copenhagen: Munksgaard, 1974.

FORUM

Anaesthesia for cardioversion: a comparison

of sevoflurane and propofol*

S. Karthikeyan,1� S. Balachandran,1� J. Cort,2 M. H. Cross3 and M. Parsloe4

1 Clinical Fellow in Cardiothoracic Anaesthesia, 2 Specialist Registrar in Anaesthesia, and 3 Consultant Anaesthetist,

Yorkshire Heart Centre, Leeds General Infirmary, Leeds, UK, 4 Consultant Anaesthetist, Conquest Hospital, The Ridge,

St Leonards-on-Sea TN37 7RD, UK

Summary

This study compared the induction time, haemodynamic changes, recovery characteristics and patient

satisfaction for sevoflurane and propofol when used as the main anaesthetic agents for cardioversion.

Sixty-one unpremedicated patients scheduled for elective cardioversion were anaesthetised with

either inhaled sevoflurane 8% or an intravenous propofol target-controlled infusion set at 6 lg.ml)1.

There was no significant difference in induction time between the two groups: mean

(SD) ¼ 90.1(40) s in the sevoflurane group vs. 83.7(35) s in the propofol group. Mean (SD) time to

recovery was significantly shorter in the sevoflurane group than in the propofol group: 318 (127) s vs.



738 (355) s, respectively, p < 0.001. At recovery, the patients in the propofol group had significantly

lower systolic and diastolic blood pressures than those in the sevoflurane group, p < 0.001. The

incidence of complications was low in both groups, with similar patient satisfaction expressed after the

procedure. We conclude that sevoflurane is a suitable choice for anaesthesia for cardioversion and may

provide greater haemodynamic stability than a target-controlled infusion of propofol.

Keywords Anaesthetics, inhalational; sevoflurane. Anaesthetics, intravenous; propofol. Heart;

cardioversion.

........................................................................................................

Correspondence to: Dr M. Parsloe

*This study was presented as a poster presentation with oral discussions

in the 9th ESA Annual Meeting, Gothenburg, Sweden 7–10 April

2001.

�Present address: Consultant Anaesthetists, University Hospital of

Wales, Cardiff CF14 4XW, UK.

Accepted: 3 July 2002

Cardioversion has been used in clinical practice to

convert abnormal cardiac rhythms to sinus rhythm since

1962 [1]. Patients undergoing cardioversion require a

short anaesthetic for which various agents have been

used. The ideal agent should produce rapid loss of

consciousness, cardiovascular stability and prompt recov-

ery with few side-effects. Thiopental, etomidate and

benzodiazepines have been widely used for cardiover-

sion, although they can be associated with prolonged

recovery [2], haemodynamic instability [3] and myo-

clonic activity [4]. Studies by Valtonen et al. [2] and

Hullander et al. [4] considered propofol to be an

ideal agent for anaesthesia for cardioversion. The use

of inhalational anaesthetic agents in anaesthesia for

cardioversion has not previously been studied. Sevoflu-

rane and propofol both have properties such as rapid

induction and quick recovery that are suitable for day-

case anaesthesia [5, 6]. The purpose of our study was to

compare sevoflurane and a target-controlled infusion

(TCI) of propofol for induction and maintenance of

anaesthesia for cardioversion.

Methods

After local research ethics committee approval and writ-

ten, informed consent, 61 patients who were scheduled

for elective cardioversion were included in this rando-

mised trial. All were given an information sheet one

week before the procedure. Patient were not studied if

they were aged < 18 years, ASA physical status IV,

considered to be at risk of regurgitation or with a

potentially difficult airway or a body mass index

(BMI) > 35 kg.m)2. Patients were randomly allocated to

either of the treatment groups using computer-generated

random numbers.

No premedication was given. All patients had an

intravenous cannula sited in their non-dominant hand

and were given intravenous glycopyrronium 200 lg

during a 3 min pre-oxygenation period. On induction

of anaesthesia, both groups breathed 50% oxygen in

nitrous oxide from a Mapleson D breathing system that

had a 5 l reservoir bag. Patients in the propofol group

received a propofol TCI with the target concentration set

at 6 lg.ml)1. This concentration was continued through-

out the procedure. The total amount of propofol used

was recorded. The sevoflurane group received 8%

sevoflurane in 50% oxygen in nitrous oxide, with the

patient being asked to breathe normally during induction.

Anaesthesia was maintained with sevoflurane during the

procedure.

Just before induction, patients were asked to tap the

index finger of their dominant hand repeatedly on their

chest. Cessation of finger tapping was taken as the time

of loss of consciousness. One minute after this, the DC

cardioversion shock was applied. Induction time, dura-

tion of anaesthesia, number of shocks, energy used in

joules and successful conversion to sinus rhythm were

recorded. At the end of the procedure, the patient was

transferred to the recovery area. A recovery nurse, who

was unaware of the anaesthetic technique used, called the

patient’s name every 30 s, and the time of eye opening

was taken as the time to wakening.

Heart rate, non-invasive blood pressure, SpO2 and

respiratory rate were recorded before and after induction,

just after the application of the DC shock and on

awakening. Complications such as apnoea, cough,



abnormal movements and postoperative nausea and

vomiting (PONV) were recorded. According to local

guidelines, patients were discharged when their systolic

blood pressure was within 20% of baseline values, when

they were steady in gait and had minimal nausea and

vomiting. Before discharge, patients completed a ques-

tionnaire regarding their satisfaction with the anaesthetic.

Data were analysed using a computer statistical package

(STATVIEW, Version 5.0.1, SAS Institute Inc, Cary, NC,

USA). Student’s t-test was used for continuous variables

and the Chi-squared test was used for nominal data. One

way analysis of variance with Fisher’s post-hoc least

significant difference test was applied to the haemodya-

namic variables. A p-value of < 0.05 was considered

significant.

Results

The two groups were comparable with regard to age,

gender, BMI, as well as initial systolic and diastolic blood

pressure (Table 1). Atrial fibrillation was the most com-

mon arrhythmia in both groups (93% of the sevoflurane

group, 87% of the propofol group), with atrial flutter in

the remaining patients.

Induction and recovery characteristics are shown in

Table 2. Induction time was similar in the two groups.

Time to eye opening was quicker in the sevoflurane

group than in the propofol group, mean (SD) ¼ 318

(127) s vs. 738 (355) s, respectively, p < 0.001. The

mean (SD) propofol dose used for the procedure was

2.7 (0.49) mg.kg)1. There were no significant differ-

ences in heart rate (Fig. 1) or return to sinus rhythm,

which occurred in 94% of the propofol group and 84%

of the sevoflurane patients. Both groups showed a

decrease in systolic and diastolic blood pressure after

induction and in the period after cardioversion (Fig. 2).

The decrease in blood pressure was not significantly

different between the groups, except at the time of

wakening, when it was significantly lower in the

propofol group (p < 0.0001). No patient was consid-

ered to require haemodynamic support to treat this

decrease in blood pressure.

Respiratory rate and SpO2 are shown in Table 3. One

patient in the sevoflurane group desaturated to 88% after

cardioversion. Neither laryngospasm nor bronchospasm

were observed in any patient and there were no

significant differences in the incidence of other compli-

cations (Fig. 3). Patient responses to the postoperative

questionnaire are shown in Table 4. In both groups,

50% of patients thought the anaesthetic was pleasant,

with only 5% of each group finding it unpleasant. The

majority of patients would opt for the same anaesthetic for

future procedures.

Discussion

There have been no studies comparing propofol with

an inhalational agent for anaesthesia for cardioversion. In

contrast to comparative studies in day-case anaesthesia [5,

7], there was no significant difference in time to loss of

consciousness as assessed by finger tapping. We believe

this is because we used a propofol infusion rather than a

bolus technique [7]. Recovery times in our study suggest

that sevoflurane produces significantly quicker recovery

than propofol TCI at a target concentration of 6 lg.ml)1.

Although we did not perform sophisticated tests to assess

recovery and �street fitness�, our results confirmed the

findings of previous studies that recovery time is more

rapid with sevoflurane in day-case anaesthesia patients [5,

7]. Studies comparing sevoflurane and propofol in day-

case anaesthesia suggest that the two agents have similar

characteristics [6, 8]. A quicker return to consciousness

could be an advantage in decreasing the time spent in the

recovery ward, but this was not found to be statistically

significant in our study.

In order to standardise the injection technique, we

chose TCI for the propofol group. Target induction

plasma concentration with propofol TCI is usually

4–6 lg.ml)1 [9]. Standard clinical practice with TCI is

to induce the patient with 6 lg.ml)1 and then to reduce

to 4 lg.ml)1 after loss of consciousness. In our study, we

chose to maintain a TCI concentration of 6 lg.ml)1

because of the significant stimulus of cardioversion in an

opioid-free anaesthetic technique. The mean (SD) prop-

ofol dose used for the procedure was 2.7 (0.49) mg.kg)1,

Table 1 Demographic data. Values are mean (SD) or number.

Propofol group(n = 31)

Sevoflurane group(n = 30)

Age; years 67 (11) 68 (8.7)Gender; male: female 21 : 10 22 : 8Body mass index; kg.m)2 28 (3.9) 29 (3.4)

Table 2 Induction and recovery characteristics of patientsreceiving sevoflurane or propofol. Values are mean (SD).



Induction time; s 83.7 (35) 90.9 (40)Time to eye opening; s 738 (355) 318 (127)*Duration of procedures; s 253 (85) 232 (83)Hospital stay; h: min 2 : 43 (0.55) 2 : 08 (0.43)

*Significantly different from propofol group, p < 0.001.



which is similar to that given in a previous study that used

an intermittent bolus technique [2].

Both agents were associated with decreased blood

pressure after induction, but the patients in the propofol

group had significantly lower systolic and diastolic

pressures in the recovery area. Decreased blood pressure

after cardioversion may be a disadvantage in patients who

have a compromised myocardium [10] and may also delay

discharge to the ward. Some volatile anaesthetic agents

such as halothane sensitise the ventricle to catecholamines

[11], but others may have a stabilising effect similar to that

of calcium channel-blocking drugs [12]. Volatile agents

may also depress the sinus node leading to an increased

atrial refractory period or decreased atrioventricular

conduction [13]. In this respect, sevoflurane seems to

have properties similar to isoflurane and similar dose-

dependent effects on blood pressure, but a lower

Table 3 Peripheral oxygen saturation and respiratory rate.Values are mean (SD).



SpO2 before induction; % 98 (2) 98 (1.7)SpO2 after induction; % 99 (1) 99 (1)SpO2 after cardioversion; % 98 (1.7) 98 (3)SpO2 in recovery area; % 98 (1.4) 98 (2)Respiratory rate before

induction; breath.min)121 (5) 20 (6)

Respiratory rate afterinduction; breath.min)1

19 (10) 23 (8)

Respiratory rate aftercardioversion; breath.min)1

26 (7) 27 (7)

Respiratory rate in recoveryarea; breath.min)1

24 (4) 25 (4)

Figure 2 Changes in blood pressure.Error bars indicate SD. *Significantlydifferent from sevoflurane group,p < 0.001.

Figure 1 Changes in heart rate. Errorbars indicate SD.



incidence of tachycardia and coronary vasodilatation [14].

In our study, there was no difference between the groups

in the success rate of cardioversion, and new arrhythmias

were not observed in either group. Propofol is not known

to have any myocardial stabilising properties but can

produce more hypotension after bolus dosing when

compared with sevoflurane inhalation induction [7]. It

has been proposed that this is due to a peripheral

vasodilator effect rather than direct cardiac depression

[15]. Other workers have suggested that hypotension can

be minimised with a slow induction by infusion [16], but

even a slow propofol induction may be associated with a

lower mean arterial pressure compared with sevoflurane

inhaled induction [17]. Our study appears to demonstrate

an increased haemodyanamic effect of propofol TCI that

extends into the period after awakening when compared

with sevoflurane for induction and maintenance.

In contrast to previous studies [5, 7], the incidence of

PONV was similar but low in both groups. This may be

due to the opioid-free anaesthetic technique. Patient

satisfaction was similar for both agents, with pain on

injection being the main reason for complaint in 16% of

the propofol group and the unpleasant smell being the

main complaint in 18% of the sevoflurane patients.

Our findings suggest that induction and maintenance

of anaesthesia with sevoflurane has advantages for

cardioversion when compared with propofol TCI at

6 lg.ml)1. There is more rapid awakening and better

maintenance of blood pressure in recovery. We believe

that sevoflurane is a suitable agent for induction and

maintenance of anaesthesia for elective cardioversion.

References

1 Lown B, Amarsingham R, Newman J. New method for

terminating cardiac arrhythmias. Journal of the American

Medical Association 1962; 182: 548–55.

2 Valtonen M, Kanto J, Klossner J. Anaesthesia for cardio-

version: a comparison of propofol and thiopentone. Cana-

dian Journal of Anaesthesia 1988; 35: 479–83.

3 Canessa R, Lerma G, Urzua J. Anesthesia for elective

cardioversion: a comparison of four anesthetic agents. Journal

of Cardiothoracic and Vascular Anesthesia 1991; 5: 566–8.

4 Hullander RM, Leivers D, Wingler K. A comparison of

propofol and etomidate for cardioversion. Anesthesia and

Analgesia 1993; 77: 690–4.

5 Smith I, Thwaites AJ. Target-controlled propofol vs sevo-

flurane: a double-blind, randomised comparison in day case

anaesthesia. Anaesthesia 1999; 54: 745–52.

Figure 3 Incidence of complications.PONV ¼ postoperative nausea andvomiting.

Table 4 Results of patient questionnaire. Values are number (%)of patients.



Mask – unpleasant 2 (8) 2 (9)Smell – unpleasant 0 4 (18)Pain on injection 4 (16) 0

Opinion of anaestheticPleasant 11 (44) 10 (45)Indifferent 13 (52) 11 (50)Unpleasant 1 (4) 1 (5)

Choice of anaesthetic for similar procedure in the futureSame 16 (64) 15 (68)Different 2 (8) 1 (5)No preference 7 (28) 6 (27)



6 Fish WH, Hobbs AJ, Daniels MV. Comparison of sevoflu-

rane and total intravenous anaesthesia for daycase urological

surgery. Anaesthesia 1999; 54: 999–1006.

7 Thwaites A, Edmends S, Smith I. Inhalation induction

with sevoflurane: a double-blind comparison with

propofol. British Journal of Anaesthesia 1997; 78:

356–61.

8 Dashfield AK, Birt DJ, Thurlow J, Kestin IG, Langton JA.

Recovery characteristics using single-breath 8% sevoflurane

or propofol for induction of anaesthesia in day-case

arthroscopy patients. Anaesthesia 1998; 53: 1062–6.

9 Struys M, Versichen L, Rolly G. Influence of pre-

anaesthetic medication on target propofol concentration

using a �Diprifusor� TCI system during ambulatory surgery.

Anaesthesia 1998; 53 (Suppl. 1): 68–71.

10 Gupta A, Lennmarken C, Vegfors M, Tyden H. Anaesthesia

for cardioversion. Anaesthesia 1990; 45: 872–5.

11 Johnston RR, Eger EI, Wilson C. A comparative interaction

of epinephrine with enflurane, isoflurane and halothane in

man. Anesthesia and Analgesia 1976; 55: 709–12.

12 Kroll DA, Knight PR. Antifibrillatory effects of volatile

anesthetics in acute occlusion ⁄ reperfusion arrhythmias.

Anesthesiology 1984; 61: 657–61.

13 Bosnjak ZJ, Kampine JP. Effects of halothane, enflurane and

isoflurane on the SA node. Anesthesiology 1983; 58: 314–21.

14 Ebert TJ, Harkin CP, Muzi M. Cardiovascular responses to

sevoflurane: a review. Anesthesia and Analgesia 1995; 81:

S11–S22.

15 Schmidt C, Roosens C, Struys M et al. Contractility in

humans after coronary artery surgery. Anesthesiology 1999;

91: 58–70.

16 Peacock JE, Lewis RP, Reilly CS, Nimmo WS. Effect of

different rates of infusion of propofol for induction of

anaesthesia in elderly patients. British Journal of Anaesthesia

1990; 65: 346–52.

17 Kirkbride DA, Parker JL, Williams GD, Buggy DJ. Induc-

tion of anesthesia in the elderly ambulatory patient: a

comparison of propofol and sevoflurane. Anesthesia and

Analgesia 2001; 93: 1185–7.

FORUM

The value of risk scores for predicting postoperative

nausea and vomiting when used to compare patient

groups in a randomised controlled trial

R. Thomas1, N. A. Jones1 and P. Strike2

1 Specialist Registrar in Anaesthetics, Department of Anaesthetics, North Hampshire Hospital, Aldermaston Road,

Basingstoke, RG24 9NA, UK

2 Medical Statistician, Research and Development Support Unit, Salisbury District Hospital, Salisbury, UK

Summary

Whilst conducting a randomised controlled trial into the effects of combination anti-emetics, we

endeavoured to confirm that our patient groups were matched using the predictive scoring systems

for postoperative nausea and vomiting (PONV) and postoperative vomiting (POV) reported in the

literature. One hundred and seventy-seven female patients attending for day case gynaecological

surgery were studied and their individual risks of PONV and POV were calculated using four

predictive models for PONV and two predictive models for POV. The scoring systems were then

evaluated to see if agreement existed between them using the method described by Bland and

Altman. Bias and 95% limits of agreement were calculated for each combination. Agreement

between scoring systems was poor. As the scoring systems gave widely divergent predictions, we

concluded that the predictive risk for PONV or POV would be dependent upon the scoring

system chosen, thus limiting their usefulness in this role.

Keywords Postoperative period: nausea and vomiting. Scoring systems.

........................................................................................................

Correspondence to: R. Thomas




Over the last 10–15 years there has been little progress in

reducing the incidence of postoperative nausea and

vomiting (PONV) in spite of research in the area and

the introduction of new classes of anti-emetics [1]. The

idea that PONV has a multifactorial aetiology has led to

an increased interest in the use of combination anti-

emesis. Research in this field is burgeoning in the hope of

elucidating the most efficacious anti-emetic drugs and

dose combinations [2].

The use of combination anti-emesis has potential

implications for cost of treatment, both in financial terms

and in terms of clinical risk)benefit ratios. Local policy

and pharmacy committee decisions are based on such cost

analyses, and recommendations exist for the use of both

monotherapy and combination anti-emesis in those

patients considered at high risk of PONV [3, 4]. It is

therefore of great advantage to the clinician to be able to

identify those patients most at risk of PONV in order to

make clinical judgements about the use of anti-emetic

therapy.

The prediction of PONV also has important implica-

tions for research. Numerous patient, surgical and anaes-

thetic technique-related factors have been proposed in the

aetiology of PONV [5, 6] and controlling these factors

makes clinical trials difficult to conduct. Apfel et al. [7]

recommend the use of validated PONV risk scores for

group comparisons in randomised controlled trials of anti-

emetic strategies.

Several scoring systems exist to predict the risk of PONV

or postoperative vomiting (POV). These are developed

using logistic regression modelling (a model used for

relating binary outcome variables to multiple independent

explanatory variables) and all have been validated for their

discriminatory power by calculating the area under the

receiver operating characteristics (ROC) curve for each

model [8–12]. The scoring systems described by Apfel et al.

[7], Koivuranta et al. [8] and Palazzo & Evans [9] have been

subject to external evaluation and comparison and each is

reported as having moderate but acceptable accuracy as

measured by the area under the curve (AUC) [13].

Because the various scoring systems have been validated

for their discriminating power and are based on similar

risk factors (Tables 1 and 2), the researcher or clinician

should be able to use any number of the scoring systems

and expect reasonable agreement among the scores.

Whilst conducting a randomised controlled trial into

the effects of combination anti-emesis [14], we endeav-

oured to confirm that our patient groups were matched,

as recommended by Apfel et al. [7], by using predictive

scoring systems. We chose to use two scoring systems [10,

11] to ensure the validity of the result and as a quality

control measure. We were surprised that the two scoring

systems chosen did not agree. We conducted a literature

search and identified a total of four models for the

prediction of PONV and two for the prediction of POV.

The aim of this study was to investigate whether

agreement existed between scoring systems cited in the

literature and therefore whether scoring systems in their

current form are a reliable utility for the comparison of

study groups in randomised controlled trials.

Methods

Having obtained local ethics research committee approval

and written informed consent we investigated 177 ASA I

or II female patients, aged 19–53 years, attending for day

case gynaecological surgery.

The patients were questioned individually, by one of

the two investigators, prior to surgery using a series of

closed questions such that the answers solely represented

the subjective experience of the patient and were not

open to interpretation by the interviewer.

Patients were asked whether they smoked, whether

they had had an episode of nausea or vomiting after a

previous anaesthetic, and whether they suffered from

travel or motion sickness.

Table 1 Risk factors for postoperative nausea and vomiting (PONV) for each scoring system.

Risk factor Sinclair et al. [10] Palazzo & Evans [9] Koivuranta et al. [8] Apfel et al. [11]

Gender * * * *Previous PONV * * *Travel sickness (TS) * *PONV or TS *Non smoker ⁄ smoker * * *Post-operative opioids * *Female and PONV *Duration * *Age *General anaesthesia *Type of surgery *



The age of the patient and duration of surgery were

recorded for each patient.

All patients were prescribed postoperative opioid

analgesia, either in the form of paracetamol 325 mg

and dextropropoxyphene 32.5 mg, or paracetamol

500 mg and codeine phosphate 30 mg. The patients

were then interviewed by telephone 24 h postoperatively

to ascertain whether they had taken the prescribed

medication.

The data were analysed using a standard spreadsheet

(EXCEL 2000, Microsoft USA). From the collated data we

calculated the predictive risk of postoperative nausea and

vomiting for each patient using each one of four scoring

systems: Koivuranta [8], Palazzo [9], Sinclair [10] and

Apfel (Simplified Risk Score) [11]. We also scored the

patients individually for their risk of postoperative

vomiting using two scoring systems: Koivuranta [8] and

Apfel [12]. (See Appendix for details.)

The scoring systems were evaluated to see if agreement

existed between them using the method for testing the

agreement of measurement methods as described by

Bland & Altman [15].

Scatter diagrams including the line of equality and

Bland)Altman plots were constructed to assess the level

of agreement for each combination of scoring systems.

The bias (mean of the difference between scores) and the

95% limits of agreement [mean difference (1.96 SD)]

were calculated for each combination of scoring system

using EXCEL 2000 (Microsoft) and MINITAB v12 (Minitab

Inc., State College, PA, USA).

Results

Of the 177 patients studied, 68 (38.4%) had experienced

previous PONV associated with general anaesthesia, 58

(32.8%) suffered with travel sickness, 113 (63.8%) were

non-smokers and 141 (79.7%) received postoperative

opioids. All had gynaecological surgery that did not

involve dilatation and curettage. The mean (SD) age was

35.7 (7.3) years.

Bland–Altman plots (Fig. 1) show the difference in

predicted risk for each individual patient plotted against

the average risk for each patient using each pair of

scoring systems. Summary statistics from the Bland–

Altman plots (Table 3) show the average difference

between scoring systems (bias), together with the stand-

ard deviation of the difference (precision) and the 95%

limits of agreement.

The best agreement existed when the scores of Sinclair

were compared with those of Palazzo & Evans. Other

pairs of scoring systems showed less agreement.

The results of the anti-emetic study are reported

elsewhere [14].

Discussion

Agreement existed when the scores of Sinclair and

Palazzo were compared (bias )2.5%, precision 5.6%).

However, both scoring systems greatly underestimated

our observed prevalence of PONV in spite of the fact that

all our patients received anti-emetics. Previous studies

have demonstrated an offset in the calibration curves

(correlation between predicted and actual incidences of

PONV) for both Sinclair and Palazzo & Evans of 30%

such that both scores underestimate the true incidence [7,

16, 17]. Our results corroborate these findings [14]. Apfel

et al. suggested that the difference might be explained by

the high odds ratio of 53.0 for a positive history of PONV

in the scoring system proposed by Palazzo & Evans and

that the odds ratios ascribed by Sinclair et al. to different

surgical specialties may differ significantly between centres

[7].

Evaluation of the risk factors for the prediction of

PONV and POV is complex owing to the multifactorial

aetiology of the condition [5, 6]. Nausea is also a highly

subjective experience making it difficult to evaluate as an

endpoint [12]. The scoring systems that we analysed are

designed to give a quantitative risk of PONV or POV,

using complex and precise equations. However, the

data used for the development of many of these

scoring systems rely on subjective qualitative information

obtained from the patient. How the patient actually

reports is therefore dependent upon what an individual

considers significant. This has profound implications for

the reliability of the derived risk (by reliability we mean

the extent to which the instrument delivers the same

results in different hands or at different locations).

Sinclair et al. [10], Koivuranta et al. [8] and Apfel et al.

[11] all identified non-smoking as an independent risk

factor for PONV. However, the extent to which a patient

smokes may have a bearing on the way they report,

Table 2 Risk factors for postoperative vomiting (POV) for eachscoring system.

Risk factor Koivuranta et al. [8] Apfel et al. [12]

Gender * *Previous PONV *Travel Sickness (TS) *PONV or TS *Non-smoker ⁄ smoker * *Post-operative opioidsFemale and PONVDuration * *Age *General anaesthesiaType of surgery



Figure 1b Sinclair v Koivuranta for postoperative nausea and vomiting.

Figure 1a Sinclair v Palazzo for postoperative nausea and vomiting.

Figure 1a–g Bland–Altman plots showing the difference in predicated risk for each individual patient plotted against the average riskfor each patient, using each pair of scoring systems. The bias and 95% limits of agreement are also shown.



especially to health care personnel since occasional smokers

may report as non-smokers if they consider the amount

which they smoke to be insignificant. Indeed, even regular

smokers may report as non-smokers either so as not to be

stigmatised or because they have been issued health or life

insurance on non-smoking risk terms.

Figure 1d Palazzo v Koivuranta for postoperative nausea and vomiting. Data points represent identical results from many subjects.

Figure 1c Sinclair v Apfel for postoperative nausea and vomiting.



Similarly, Palazzo & Evans [9], Koivuranta et al. [8] and

Apfel et al. [11] found a previous history of motion

sickness to be a predictor for PONV. This, too, relies on

subjective interpretation by the patient, and is neither

precise nor reliable. On applying their score to a different

population, Palazzo & Evans found that travel sickness no

Figure 1e Palazzo v Apfel for postoperative nausea and vomiting. Data points represent identical results from many subjects.

Figure 1f Koivuranta v Apfel postoperative nausea and vomiting. Data points represent identical results from many subjects.



longer became important and that only female sex,

previous PONV and postoperative opioids were sig-

nificant predictors of PONV [16].

Sinclair et al. [10], Palazzo & Evans [9], Apfel et al. [11]

and Koivuranta et al. [8] all identified a previous history of

PONV as being a major contributor to the likelihood of

future PONV. Patients who have not previously had an

operation cannot have a history of PONV. The propor-

tion of patients in each of their studies who have never

previously had an operation will therefore influence the

weighting of each score, as they will be considered as

being at low risk, when in fact their true risk of PONV is

not known.

We also question the utility of a predictive test that is

dependent upon prospective data, such as duration of

operation and the use of postoperative opioids. Apfel et al.

[11] defend this, suggesting that, with many patients, it is

possible to predict whether opioids will be required, and

in most circumstances the duration of surgery can also be

predicted.

Such subjective data limit the usefulness of scoring

systems designed to give accurate quantitative informa-

tion, as it is hard to extrapolate binary outcomes from

continuous data. This may help to explain why the scoring

systems only had moderate accuracy when evaluated by

the area under the receiver operator characteristic curves.

Indeed Apfel et al. [11] and Koivuranta et al. [8] both

simplified their scores, making the weighting for each risk

factor unity, and found that this did not significantly

reduce the discriminatory power of the score.

Table 3 Results of Bland)Altman plots for each pair of scoring systems.

Comparativetests

Mean differencein risk (bias)

SD of differencein risk (precision)

95% Limits ofagreement (1.96SD)

PONVSinclair v Palazzo ) 2.5 5.6 ) 13.8 to 8.4Sinclair v Koivuranta ) 35.1 12.5 ) 59.7 to ) 10.5Sinclair v Apfel ) 50.3 13.0 ) 75.8 to ) 24.8Palazzo v Koivuranta ) 32.6 11.3 ) 54.7 to )10.5Palazzo v Apfel ) 47.8 13.5 ) 74.3 to ) 21.3Koivuranta v Apfel ) 15.2 10.5 ) 35.8 to 5.4

POVApfel v Koivuranta POV 19.8 7.4 5.2 to 34.4

Figure 1g Apfel v Koivuranta postoperative vomiting.



It is important to remember that failure of agreement

does not imply that all the systems are poor at predicting

PONV or POV. Our type of analysis could be criticised

as being limited by the fact that all our patients received

anti-emetics on ethical grounds. Hence, we are unable to

specify which of the reported scoring systems most

closely reflected the truth. This, however, should be

incidental when such risk scores are used in demographic

tables for group comparisons in randomised control trials.

For this purpose, an investigator should be able to use

any one of the risk scores at random and be confident

that the prediction obtained will be similar to any of the

others.

The true test of these scoring systems is in the clinical

or research field when applied to the role for which they

have been developed. We were perplexed that different

nausea and vomiting scoring systems gave very different

predictions of risk. As a result, we decided not to use

scoring systems in our demographic table for the com-

parison of our study groups, as recommended by Apfel

et al. [7], as we believed the accuracy of such a test could

not be guaranteed. It is of interest that, in a subsequent

publication, Apfel et al. [18] have used their simplified

predictive risk score to identify those patients who

meet the inclusion criteria for their study but have

not used them in their demographic tables for group

comparisons.

Although outside our study remit, we question the

application of these scoring systems in order to make

clinical risk)benefit decisions. We are unaware of any

randomised clinical trial comparing the incidence of

PONV in a population prescribed prophylactic anti-

emetics on the basis of identified risk using the above

scoring systems with a population prescribed anti-emetic

prophylaxis using any other criteria or at random. Because

the numbers needed to treat, with the currently available

prophylactic anti-emetics, remains at between five and six

[19] and because the best predictive models give an

accuracy AUC of 0.7, we feel that it is unlikely that a

clinically significant difference would exist.

Apfel accepts that AUCs of only 0.7 are not ideal.

However, from work carried out on a virtual model [20],

he believes that the current scoring systems cannot be

improved by the addition or subtraction of further

independent explanatory variables and therefore the

current scoring systems should act as an acceptable model.

Whilst the principle of a model for the prediction of

PONV remains an attractive concept we do not believe

that the above systems currently fulfil the role for which

they have been designed.

We conclude that it would not be possible to use any

one of these risk scores in isolation for demographic

purposes in research, as the predictive risk obtained

would be dependent upon the scoring system chosen.

We accept, however, that such a demographic table

could be constructed using all available risk scores and

allowing the reader to compare all scores (purely as a

similarity of the numbers obtained) between groups

without necessarily assigning predictive risk. In addition,

the �number� that a PONV risk score generates will

invariably lead readers to make judgements about the

effectiveness (or otherwise) of the investigated anti-

emetic strategy that may not necessarily be relevant to the

method analysis. We believe this would produce a

complicated and possibly misleading demographic analy-

sis that may well detract from the main thesis of the anti-

emetic strategy under investigation. Indeed, this is what

we discovered to be the case in our parallel study and

ultimately we decided to restrict our demographic table

to raw data.

References

1 Scholz J, Steinfath M, Tonner PH. Postoperative nausea

and vomiting. Current Opinion in Anesthesiology 1999; 12:

657–61.

2 Hefferman AM, Rowebottom DJ. Postoperative nausea and

vomiting ) time for balanced antiemesis? British Journal of

Anaesthesia 2000; 85: 675–7.

3 Mehernoor F, Watcha MD. The cost effective management

of postoperative nausea and vomiting. Anesthesiology 2000;

92: 931–3.

4 Scuderi PE, James RL, Harris L, et al. Multimodal antiemetic

management prevents early postoperative vomiting after

outpatient laparoscopy. Anesthesia and Analgesia 2000; 91:

1408–14.

5 Palazzo MGA, Strunin L. Anaesthesia and emesis I. Etiology.

Canadian Society of Anaesthetists Journal 1984; 31: 178–87.

6 Watcha M, White P. Postoperative nausea and vomiting. Its

etiology, treatment and prevention. Anesthesiology 1992; 77:

162–84.

7 Apfel CC, Kranke P, Eberhart LHJ, Roos A, Roewer N.

Comparison of predictive models for postoperative nausea

and vomiting. British Journal of Anaesthesia 2002; 88: 234–40.

8 Koivuranta M, Laara L, Alahuhta S. A survey of postoper-

ative nausea and vomiting. Anaesthesia 1997; 52: 443–9.

9 Palazzo M, Evans R. Logistic regression analysis of fixed

patient factors for postoperative sickness: a model for

risk assessment. British Journal of Anaesthesia 1993; 70:

135–40.

10 Sinclair DR, Chung F, Mezei G. Can postoperative nausea

and vomiting be predicted? Anesthesiology 1999; 91: 109–18.

11 Apfel CC, Laara E, Koivuranta M, Greim CA, Roewer N.

A simplified risk score for predicting postoperative nausea

and vomiting. Anesthesiology 1999; 91: 693–700.

12 Apfel CC, Greim CA, Haubitz I, et al. A risk score to predict

the probability of postoperative vomiting in adults. Acta

Anaesthesiologica Scandinavica 1998; 42: 495–501.



13 Eberhart LHJ, Hogel J, Seeling W, Staack AM, Geldner G,

Georgieff M. Evaluation of three risk scores to predict

postoperative nausea and vomiting. Acta Anaesthesiologica

Scandinavica 2000; 44: 480–8.

14 Thomas R, Jones N. Prospective randomised double-blind

comparative study of dexamethasone, ondansetron and

ondansetron plus dexamethasone as prophylactic anti-emetic

therapy in patients undergoing day-case gynaecological

surgery. British Journal of Anaesthesia 2001; 87: 588–92.

15 Bland JM, Altman DG. Statistical methods for assessing

agreement between two methods of clinical measurement.

Lancet 1986; 8 (8476): 307–10.

16 Toner CC, Broomhead CJ, Littlejohn IH, et al. Prediction of

postoperative nausea and vomiting using a logistic regression

model. British Journal of Anaesthesia 1996; 76: 347–51.

17 Pierre S, Bernais H, Pouymayou J. A comparison of two risk

scores for predicting postoperative nausea and vomiting.

European Journal of Anaesthesiology 2001; 18: 8–28.

18 Apfel CC, Kranke P, Katz MH, et al. Volatile anaesthetics

may be the main cause of early but not delayed postoperative

vomiting: a randomised controlled trail of factorial design.

British Journal of Anaesthesia 2002; 88: 659–68.

19 Tramer MR, Reynolds DJ, Moore RA, McQuay HJ.

Efficacy, dose)response, and safety of ondansetron in pre-

vention of postoperative nausea and vomiting. A quantita-

tive systematic review of randomized placebo-controlled

trials. Anesthesiology 1997; 87: 1277–89.

20 Apfel CC, Kranke P, Greim C-A, Roewer N. What can

be expected from risk scores for predicting postoperative

nausea and vomiting? British Journal of Anaesthesia 2001; 86:

822–7.

Appendix

The scoring systems proposed by Palazzo & Evans,

Koivuranta, Apfel, Sinclair and their colleagues were

produced by logistic regression analysis (a model for

relating independent explanatory variables to a binary

outcome variable) using the following equation:

Loge ðp=1 � pÞ ¼ z

where p ¼ proportion of patients with sickness (PONV

or POV) and (1 ) p) ¼ proportion of patients without

sickness (PONV or POV) and

z ¼ b0 þ b1X1 þ b2v2 þ bnXn

where b represents the parameter estimates for the

variable (i.e. the weighting) and Xn ¼ the value (presence

or absence) of the nth independent variable.

The z-value for each of the predictive tests is given by

the equations that are listed below.

The probability of an event (postoperative nausea and

vomiting or postoperative vomiting) is related to the odds

ratio such that:

Probability ¼ Odds ratio=ð1 þ Odds ratioÞ

The odds ratio ¼ (p ⁄ 1 ) p), and as Loge(p ⁄ 1 ) p) ¼ z

and it can be seen that the odds ratio ¼ ez.

Therefore the probability of PONV or POV is

estimated from the equation

p ¼ ez=ð1 þ ezÞ

which simplified equals 1 ⁄ (1 + e–z).

Sinclair score for PONV [10]

Z ¼� 5:97 þ ð�0:14 � age=10Þþ ð�1:03 � SexÞþ ð�0:42 � SmokeÞþ ð1:14 � PONV historyÞþ ð0:46 � DurationÞþ ð2:36 � GAÞþ ð1:20 � GynNon DCÞ:

(Sex ¼ 0 for female, smoke ¼ 1 if smoker, PONV history

¼ 1 if previous history of PONV, duration ¼ duration of

surgery in 30-min increments, general anaesthetic ¼ 1,

GynNon DC ¼ 1 if gynaecological surgery and non-

dilatation and curettage procedure).

Palazzo & Evans score for PONV [9]

Z¼�5:03þð2:24�postoperative opioidsÞþð3:97�previous sickness historyÞþð2:4�genderÞþð0:78�history of motion sicknessÞ�ð3:2�gender�previous sickness historyÞ:

[Postoperative opioids ¼ 1, previous sickness history

(PONV) ¼ 1, gender ¼ 1 if female, history of motion

sickness ¼ 1].

Apfel et al. score for POV [12]

Z ¼ ð1:28 � female genderÞ � ð0:029 � ageÞ� ð0:74 � smokingÞþ ð0:63 � history of motion sickness or PONVÞþ ð0:26 � durationÞ � 0:92

(Female ¼ 1, age ¼ in years, smoking ¼ 1 if smoker,

history of motion sickness and ⁄ or PONV ¼ 1, duration

of anaesthesia ¼ hours).

Simplified Apfel Score for PONV [11]

Using a logistic regression model, Apfel et al. identified

four risk factors for the prediction of PONV. These were

female gender, prior history of PONV or motion

sickness, non-smoking and the use of postoperative



opioids. These had equal weighting. The probability of

PONV was 10, 21%, 39, 61 and 78 if no, one, two, three

or four risk factors were present.

Simplified Koivuranta scores for PONV

and POV [8]

Based on logistic regression analysis Koivuranta identified

the five strongest predictors for PONV and POV, as

female sex, previous PONV, duration of operation

> 60 min, history of motion sickness and non-smoking.

The score was simplified by giving each predictor equal

weight. The risk for PONV was related to the number of

factors present such that the probability of nausea was 17%

with no factors present, increasing to 18, 42, 54, 74 and

87 for one to five factors present. Correspondingly the

risk for vomiting was 7, 7, 17, 25, 38 and 61 for no factors

to all five present.

The probability of PONV and POV was calculated for

each patient using the above equations such that for each

patient we generated four predictive values for PONV

and two for POV.

FORUM

Measurement of liver tissue oxygenation after orthotopic

liver transplantation using a multiparameter sensor

A pilot study

T. S. Leary,1 J. R. Klinck,2 G. Hayman,3 P. Friend,4 N. V. Jamieson5 and A. K. Gupta2

1 Consultant, Intensive Care, Norfolk and Norwich University Hospital, UK

2 Consultant, Department of Anaesthesia, 3 Operating Department Practitioner, and 5 Consultant, Department

of Surgery, Addenbrooke’s Hospital, Cambridge CB2 2QQ, UK

4 Professor of Transplant Surgery and Oxford Transplant Centre, Churchill Hospital, Oxford, UK

Summary

The currently used methods of monitoring liver perfusion and oxygenation after liver transplan-

tation have major limitations in clinical use. We describe the use of a multiparameter sensor to

enable continuous monitoring of liver tissue oxygen tension, carbon dioxide tension and hydrogen

ion concentration in the early postoperative period in 12 patients after liver transplantation. The

sensor was inserted under direct vision via the falciform ligament into the liver before skin closure.

Tissue oxygen tension values decreased in the first 24 h and subsequently increased to a mean

(SD) ¼ 7.3 (2.8) kPa at 48 h after surgery. This was associated with a decrease in the degree of

acidosis. There were no complications attributable to the sensor. This study demonstrates that

continuous measurement of liver oxygen tension, carbon dioxide tension and pH is possible. This

technique may be useful as a continuous monitor to help identify grafts at risk of ischaemia.

Keywords Liver transplantation. Monitoring: physiologic.

........................................................................................................

Correspondence to: Dr A. K. Gupta

E-mail: [email protected]


The use of orthotopic liver transplantation as a treatment

for end-stage liver disease and fulminant hepatic failure is

increasing. Whilst 5-year survival rates continue to

improve [1], catastrophic primary non-function as a result

of ischaemia still has a reported incidence of 6% [2].

Injury during organ preservation and transfer, or pro-



longed systemic hypotension after transplantation, may be

aetiological factors in hepatic infarction. However, such

injury is usually caused by hepatic artery or portal vein

thrombosis (incidences 3–12% and 1–7%, respectively

[3]). Continuous and reliable monitoring of graft oxy-

genation might provide early detection of ischaemia and

allow timely surgical re-exploration and correction of

vascular insufficiencies. Without a functioning graft,

patient mortality approaches 100% [1].

Methods of monitoring liver organ perfusion during

the early postoperative period include Doppler ultr-

asonography [3], laser flowmetry [4] and near-infrared

spectroscopy [5]. These methods all have significant

limitations that decrease their usefulness as clinical

monitors of perfusion and oxygenation. In our unit,

we currently rely on biochemical markers or meas-

urement of prothrombin time to assess hepatic func-

tion, from which adequacy of perfusion may be

inferred.

The ‘Paratrend 7’ (Diametrics Medical, High

Wycombe, UK) is a multiparameter sensor that was

originally designed for the continuous intra-arterial mon-

itoring of PaO2, PaCO2, hydrogen ion concentration and

temperature. This sensor has been used as a monitor of

brain tissue oxygenation in animals [6] and humans [7].

We aimed to evaluate a Paratrend 7 sensor placed within

the liver tissue as a continuous monitor of allograft

oxygenation and metabolism during the first 48 h after

surgery.

Methods

After Local Research Ethics Committee approval and

written, informed consent, 12 adult patients admitted for

liver transplantation were studied. Surgery comprised a

conventional orthotopic approach with caval anasto-

moses. A standardised anaesthetic technique including

fentanyl, atracurium and isoflurane in oxygen and air was

used. A low-dose dopamine infusion was given through-

out the peri-operative period.

Two Paratrend 7 sensors were calibrated against

standard concentrations of gases before insertion. One

sensor was inserted into the patient’s brachial artery for

continuous blood gas monitoring before surgery. After

revascularisation of the donor liver in the abdomen of the

recipient, the second sensor was passed aseptically to the

surgeon who inserted it into liver tissue via the falciform

ligament. The proximal end of the sensor was brought

out from the abdominal wall about 5 cm from the main

incision. Readings were taken for tissue and arterial PO2,

PCO2 and hydrogen ion concentration, and were recor-

ded initially at 30-min intervals for the first 2 h and then

four hourly.

Both sensors were removed from the patient approxi-

mately 48 h after surgery. A simple dressing was applied

to the entry site of the liver probe. Pressure was applied to

the antecubital fossa. A note of any complications

attributable to the catheter or any observed impairment

in graft function was made.

The data obtained from the liver probe were analysed

using repeated measures analysis of variance. Significance

was set at p < 0.05.

Results

Of the 12 patients recruited, complete data were collected

from five patients and partial data from a further four.

Data could not be collected when irreparable damage to

the fibres within the sensor occurred due to kinking

during insertion, at the time of skin closure, or whilst the

patient was nursed on the ward. The oxygen electrode,

which is a Clarke electrode, appeared to be far more

Figure 1 Liver tissue oxygen tension(PlO2) in nine subjects measured withthe Paratrend probe. Different symbolsand lines indicate values in individualpatients. Times are given in hours afterinsertion of the probe.



robust than the fibreoptic hydrogen ion and carbon

dioxide sensors.

The hepatic tissue measurements of oxygen tension,

carbon dioxide tension and hydrogen ion concentration

(PlO2, PlCO2 and [Hl+]) are shown in Figs 1–3. The values

of PlO2, PlCO2 and [Hl+] taken at 6-h intervals are

presented in Table 1 along with arterial values for

comparison. The mean (SD) PlO2 decreased in the first

24 h from 6.1 (5.8) to 4.7 (2.0) kPa, and then increased to

7.3 (2.8) kPa (p ¼ 0.97). These changes were associated

with a decrease in tissue acidosis, as the mean (SD)

hydrogen ion concentration decreased from 66.2 (23.7) to

46.5 (3.2) nmol.l)1 (p ¼ 0.54).

Routine measurements of prothrombin time, alanine

transaminase and serum bilirubin concentrations are

documented in Table 2. There were no cases of hepatic

impairment requiring urgent re-exploration in our series.

Patients numbers 3 and 9 had presumed episodes of acute

rejection with increased biochemical markers that subse-

quently returned to normal over the ensuing 5 days.

Figure 3 Liver tissue hydrogen ionconcentration [Hl

+] in nine subjectsmeasured with the Paratrend probe.

Figure 2 Liver tissue carbon dioxidetension (PlCO2) in nine subjects meas-ured with the Paratrend probe.

Table 1 Mean (SD) values of arterial and liver tissue PO2, PCO2 and hydrogen ion concentration as measured by the Paratrend probeafter liver transplantation.

Time afterprobe insertion; h

Arterial[H+]

Liver[H+]

ArterialPO2; kPa

LiverPO2; kPa

ArterialPCO2; kPa

LiverPCO2; kPa

0 65.2 (37.3) 66.2 (23.7) 20.6 (11.2) 6.1 (5.8) 5.5 (1.0) 7.3 (3.0)6 41.8 (6.1) 75.7 (40.4) 15.6 (5.1) 5.5 (5.1) 5.3 (0.7) 7.1 (1.1)

12 40.4 (6.4) 54.0 (11.5) 16.7 (7.1) 5.0 (3.2) 5.3 (0.5) 6.8 (0.9)18 39.5 (5.9) 55.5 (15.0) 15.2 (3.9) 4.7 (2.0) 5.5 (0.5) 6.5 (0.4)24 38.4 (3.4) 58.7 (10.4) 13.2 (2.87) 4.8 (2.2) 5.1 (0.7) 7.0 (0.8)36 38.3 (2.42) 55.1 (11.0) 16.2 (3.6) 6.1 (4.0) 5.8 (0.6) 7.3 (0.4)48 38.5 (2.4) 46.5 (6.8) 12.8 (4.0) 7.3 (2.8) 5.45 (0.3) 6.3 (0.8)



Patient number 6 developed acute cellular rejection on

the fourth postoperative day and required re-transplanta-

tion at 10 days due to graft failure secondary to non-

thrombotic infarction. Patient number 8 initially showed

poor hepatic function associated with increasing trans-

aminase levels. By day 4, hepatic function was clearly

recovering. However, on day 5, the patient died of acute

respiratory distress syndrome and sepsis. Post-mortem

examination confirmed the biochemical findings, dem-

onstrating vascular anastomotic patency.

No complications occurred in any of the patients that

could be attributed to the sensors.

Discussion

This study attempted to monitor intrahepatic changes in

graft PO2, PCO2 and hydrogen ion concentration con-

tinuously after liver transplantation. The mean PlO2

appears to decrease initially and subsequently to plateau.

This does not appear to be a function of systemic

oxygenation, which did not change significantly. The

initial mean tissue PlO2 of 6.1 kPa is similar to values

measured when samples are taken from the hepatic vein

after liver graft reperfusion [8]. The initial decrease in

PlO2 may be due to a change in the metabolic function of

the new graft in the first 24 h. Hydrogen ion concen-

tration and PlCO2 do not change significantly, confirming

the adequacy of perfusion and excluding this as a cause for

changes in PlO2.

A continuous method of monitoring the new graft is

required so that deteriorating function can be detected

early, allowing rapid intervention to rescue the new

organ. The methods currently used have significant

limitations. Biochemical markers (serum transaminases,

hypoglycaemia, acidosis and hyperkalaemia), prothrom-

bin time, indocyanine green [9] clearance and mono-

ethylglycinxylid production following lidocaine bolus

[10] have been used to monitor hepatic graft function.

The disadvantage of these methods is that there may be a

long lag time between graft ischaemia and the detection

of malfunction. Furthermore, the cause of malfunction

may not be identified, as the results are often non-specific

[11, 12].

A low gastric mucosal pH (pHi) has been associated

with impaired liver function after transplantation. How-

ever, no predictive effects for patient morbidity or

mortality have been shown using this method [13].

Therefore, pHi monitoring as an indicator of gastric

mucosal perfusion either before [14] or after [11] surgery

should not be regarded as a reliable indicator of poor graft

function.

Although its sensitivity remains unknown, the speci-

ficity of transabdominal colour Doppler ultrasonography

in the diagnosis of hepatic artery and portal vein

thrombosis has been shown to be 100% [3], hence

avoiding time-consuming angiography. However, such

examinations require significant operator skill and are

intermittent. Thus, in most cases, manifestation of graft

dysfunction is seen before ultrasonographic diagnosis is

made. Implantable Doppler probes have been successfully

attached to the relevant vessels [15], allowing continuous

monitoring of blood flow. However, risks of vessel

damage and thrombus initiation exist [16]. Laser Doppler

flowmetry may be used to monitor hepatic microcircu-

lation. However, this method is non-quantitative and its

suitability as a postoperative technique remains untested.

The method of tissue monitoring using the Paratrend 7

is similar to that of near-infrared spectroscopy, in that

both methods are estimates of tissue oxygenation in a

small area of tissue. Near-infrared spectroscopy also

measures Caa3, the terminal enzyme in the respiratory

chain, allowing defects in oxygen supply and utilisation to

be distinguished. The Paratrend 7 monitor continuously

measures tissue PCO2 and pH in addition to PO2. The

advantage of measuring PlCO2 and [Hl+] as well as PlO2 is

that an indication of changes in tissue metabolism is

Table 2 Biochemical markers of graft function after liver transplantation in the nine subjects studied.

Time afterprobe insertion; h

Prothrombin time; s Serum bilirubin; lmol.l)1 Serum alanine transaminase; lmol.l)1

0 24 48 0 24 48 0 24 48

Patient no.1 15.0 18.7 17.8 25 90 103 58 726 7522 23.0 20.4 17.2 103 72 133 632 471 3273 32.8 28.5 41.6 54 81 125 1437 706 29554 17.3 18.6 17.3 104 153 156 694 645 7635 23.7 21.4 17.5 65 39 33 320 318 2526 22.8 23.2 22.7 62 77 95 619 701 7377 25.0 23.7 22.6 129 194 160 311 279 1328 14.6 30.5 30.6 9 84 112 41 2924 43419 17.2 18.6 17.4 70 40 51 401 439 1100



obtained. An organ failing because of impaired perfusion

would be expected to show a decrease in PO2 and pH,

and an increase in PCO2. Kitai et al. [5] have demonstrated

the application of near-infrared spectroscopy for monit-

oring hepatic allografts. Graft oxygen saturations as low as

20% were recorded, with a successful outcome. These

were well below that measured in a peripheral artery. At

48 h after surgery, we recorded a mean PaO2 of 12.8 kPa

and PlO2 of 7.3 kPa. However, Kitai also describes a

significant wound infection rate. Although near-infrared

spectroscopy is non-invasive, it does require a large skin

port for the probe, whereas the Paratrend 7 sensor is only

0.5 mm in diameter and can be passed through the skin

through a 20G intravenous cannula.

The ability of the Paratrend 7 probe to monitor tissue

oxygen tensions reliably has recently been validated in

brain tissue using positron emission tomography [17]. We

did not address the issue of catheter calibration drift in our

study. Valedka has recently shown that when used as a

monitor of brain tissue oxygen for at least 40 h, the

Paratrend 7 sensor can be expected to return mean (SD)

values of 0.9 (0.2) kPa in zero oxygen calibration solution

and of 18.4 (2.7) kPa in room air [18]. We felt that this

degree of accuracy was sufficient to identify trends of

deterioration in organ oxygenation.

Although the fibreoptic technology incorporated into

the Paratrend 7 sensor has led to a significant advance in

the ability to measure tissue gas tension continuously,

further modifications are required before this system can

be used as a reliable monitor. Data were lost in four

patients due to the fragility of the fibreoptic CO2 and

pH sensors within the system. These parameters are

particularly useful in detecting deteriorating tissue per-

fusion. A simple modification could stiffen the sensor

system to preserve all the variables so as to ensure

continuous monitoring over a prolonged period of time,

either during surgery or afterwards in the intensive care

unit.

In this study, we have established that the Paratrend 7

multiparameter sensor can be used as a method of

continuously monitoring tissue oxygenation and meta-

bolism in the post-transplantation organ. Furthermore,

baseline values of PlO2, PlCO2 and [Hl+] have been

described in nine patients. Further studies are required

to confirm these values and to determine threshold

levels below which primary non-function can be

predicted.

References

1 Hickman PE, Potter JM, Pesce AJ. Clinical chemistry and

post-liver–transplant monitoring. Clinical Chemistry 1997;

43: 1546–54.

2 Ploeg RJ, D’Alessandro AM, Knechtle SJ, et al. Risk factors

for primary dysfunction after liver transplantation – a mul-

tivariate analysis. Transplantation 1993; 55: 807–13.

3 Hellinger A, Roll C, Stracke A, Erhard J, Eigler FW. Impact

of colour Doppler sonography on the hepatic artery and the

portal vein after liver transplanation. Langenbecks Archiv Fur

Chirurgie 1996; 381: 182–3.

4 Steifalian AM, Mallet SV, Rolles K, Davidson BR. Hepatic

microcirculation during human orthotopic liver transplan-

tation. British Journal of Surgery 1997; 84: 1391–5.

5 Kitai T, Shinohara H, et al. Postoperative monitoring of the

oxygenation state of the graft liver in cases with hepato-

pulmonary syndrome. Transplantation 1996; 62: 1676–8.

6 Zauner A, Bullock MR, Di X, Young HF. Brain oxygen,

CO2, pH and temperature monitoring: evaluation in the

feline brain. Neurosurgery 1995; 37: 1168–77.

7 Gupta AK, Hutchinson PJ, et al. Measuring brain tissue

oxygenation compared with jugular venous oxygen satura-

tion for monitoring cerebral oxygenation after traumatic

brain injury. Anesthesia and Analgesia 1999; 88: 549–53.

8 Tallgren M, Makisalo H, Hockerstedt K, Lindgren L.

Hepatic and splanchnic oxygenation during liver transplan-

tation. Critical Care Medicine 1999; 27: 2383–8.

9 Krenn CG, Schafer B, Berlakovich GA, Steininger R,

Steltzer H, Spiss CK. Detection of graft nonfunction after

liver transplantation by assessment of indocyanine green

kinetics. Anesthesia and Analgesia 1998; 87: 34–6.

10 Schutz E, Luy-Kaltefleiter M, Kaltefleiter M, Burdeslski M,

Ringe B, Armstrong VW, Oellerich M. The value of serial

determination of MEGX and hyaluronic acid early after

orthotopic liver transplantation. European Journal of Clinical

Investigation 1996; 26: 907–16.

11 Muraca M, Kohlhaw K, Vilei MT. Serum bile acids and

esterified biirubin in early detection and differential diagnosis

of hepatic dysfunction following orthotpic liver transplan-

tation. Journal of Hepatology 1993; 17: 141–5.

12 Sankary HN, Williams JW, Foster PF. Can serum liver

function tests differentiate rejection from other causes of

liver dysfunction after hepatic transplantation? Transplantation

Proceedings 1988; 20: 669–70.

13 Maring JK, Klompmaker IJ, Zwaveling JH, Verwer R,

Slooff MJ. Gastric mucosal pH is associated with initial graft

function but is not a predictor of major morbidity after liver

transplantation. Liver Transplant Surgery 1997; 3: 611–6.

14 Welte M, Pichler B, Groh J, Anthuber M, Jauch KW,

Pratschke E, Lenhart FP, Haller M, Frey L, Peter K. Peri-

operative mucosal pH and splanchnic endotoxin concen-

tration in orthotopic liver transplantation. British Journal of

Anaesthesia 1996; 77: 560–1.

15 Yokoyama I, Tabuchi Y, Negita M et al. Measurement of

portal venous flow velocity with an implantable miniature

Doppler probe in pig liver transplantation. Transplantation

International 1997; 10: 116–20.

16 Zurbrugg HRP, Bachmann S, Liebold A, Behr R, Philipp

A, Birnbaum DE, Neuhaus P. Continuous blood flow

measurement after liver transplantation: first clinical experi-

ence. Transplantation Proceedings 1994: 2218–26.



17 Gupta AK, Hutchinson PJ, Fryer T. Measurement of brain

tissue oxygenation performed using Positron Emission To-

mography scanning to validate a novel monitoring method.

Journal of Neurosurgery 2002; 96: 263–8.

18 Valedka AB, Gopinath SP, Constant CF, Uzura M,

Robertson CS. Relationship of brain tissue PO2 to

outcome after severe head injury. Critical Care Medicine

1998; 26: 1576–81.



Documents

The value of risk scores for predicting postoperative nausea and vomiting when used to compare patient groups in a randomised controlled trial