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CARDIOTHORACIC ANESTHESIOLOGY:

The Annals of Thoracic Surgery CME Program is located online at http://cme.ctsnetjournals.org.To take the CME activity related to this article, you must have either an STS member or anindividual non-member subscription to the journal.

evelopment of a Hybrid Decision Support Modelor Optimal Ventricular Assist Device Weaninginda C. Santelices, MSE, Yajuan Wang, MS, Don Severyn, MS,arek J. Druzdzel, PhD, Robert L. Kormos, MD, and James F. Antaki, PhD

epartment of Bioengineering and School of Information Science, University of Pittsburgh, Pittsburgh; Department of Biomedicalngineering, Carnegie Mellon University, Pittsburgh; and Artificial Heart Program, University of Pittsburgh Medical Center,

ittsburgh, Pennsylvania

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Background. Despite the small but promising body ofvidence for cardiac recovery in patients that have re-eived ventricular assist device (VAD) support, the crite-ia for identifying and selecting candidates who might beeaned from a VAD have not been established.Methods. A clinical decision support system was devel-

ped based on a Bayesian Belief Network that combinedxpert knowledge with multivariate statistical analy-is. Expert knowledge was derived from interviews of 11embers of the Artificial Heart Program at the University

f Pittsburgh Medical Center. This was supplemented byetrospective clinical data from the 19 VAD patients con-idered for weaning between 1996 and 2004. Artificialeural Networks and Natural Language Processing weresed to mine these data and extract sensitive variables.Results. Three decision support models were com-

ared. The model exclusively based on expert-derived

nowledge was the least accurate and most conservative.

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eering, 700 Technology Dr, Carnegie Mellon University, Pittsburgh,A 15219; e-mail: antaki@andrew.cmu.edu.

2010 by The Society of Thoracic Surgeonsublished by Elsevier Inc

t underestimated the incidence of heart recovery, incor-ectly identifying 4 of the successfully weaned patientss transplant candidates. The model derived exclusivelyrom clinical data performed better but misidentified 2atients: 1 weaned successfully, and 1 that needed aardiac transplant ultimately. An expert-data hybridodel performed best, with 94.74% accuracy and 75.37%

o 99.07% confidence interval, misidentifying only 1atient weaned from support.Conclusions. A clinical decision support system may

acilitate and improve the identification of VAD patientsho are candidates for cardiac recovery and may benefit

rom VAD removal. It could be potentially used toranslate success of active centers to those less estab-ished and thereby expand use of VAD therapy.

(Ann Thorac Surg 2010;90:713–21)

© 2010 by The Society of Thoracic Surgeons

he use of ventricular assist devices (VADs) for treat-ment of end-stage heart failure has steadily in-

reased during the past 20 years [1, 2]. For a growingumber of patients with advanced or refractory cardiacisease, VAD therapy has demonstrated the potential toxtend life, improve the quality of remaining life [3–6],nd even lead to cardiac recovery [7, 8]. After the firsteport of VAD weaning in 1995 [9], numerous centersave demonstrated the possibility of cardiac recovery forsubset of VAD patients, including the University of

ittsburgh Medical Center (UPMC), Texas Heart Insti-ute, Berlin Heart Center, Columbia Presbyterian, To-onto General Hospital, and others. Nevertheless, thencidence of VAD weaning remains relatively low com-ared with the volume of patients treated with VAD

herapy [1, 10–13].Although studies of myocardial function of VAD pa-

ients suggest that chronic unloading of the native heartan lead to reverse remodeling [5, 14–16], the underlyingellular, biochemical, and biomechanical mechanisms

ccepted for publication March 26, 2010.

ddress correspondence to Dr Antaki, Department of Biomedical Engi-

emain uncertain and are topics of active research. It isherefore not surprising that different sets of criteria haveeen used for attempting to wean patients from VADupport [4, 17–20]. Lack of a definitive marker in turnimits the confidence to screen patients for recovery and

ay be partly responsible for the scarcity of VADeaning.The decision to wean a patient from VAD support is

urther complicated by the distributed expertise involvedn postoperative management. It also entails competitivebjectives, such as survival rate, quality of life, patientreference, and alternative treatment strategies. Thisomplexity confounds efforts to articulate a definitivelgorithm for identifying and facilitating cardiac recov-ry. Consequently, it also hinders the translation of theuccess of experienced centers to centers that are lessstablished.The complexity and uncertainty of this decision pro-

ess makes it an excellent candidate for a clinical decisionupport system. Motivated by the success of such sys-ems in numerous fields of medicine [21–27], we under-ook this study to develop a clinical decision supportystem specifically customized to the management of

AD patients, with particular emphasis on ventricular

0003-4975/$36.00doi:10.1016/j.athoracsur.2010.03.073

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ecovery. The clinical experience at UPMC with 19 VADatients who were considered for weaning between 1996nd 2004 [28, 29] was used as the basis for evaluation ofhis model.

aterial and Methods

he protocol for this study was approved by Institutionaleview Board at University of Pittsburgh. Two primaryources of procedural knowledge were collected for theurrent study: retrospective statistical analysis of patientata and expert knowledge.

ata-Derived Knowledgen accordance with the Health Insurance Portability andccountability Act of 1996, de-identified patient dataere obtained from the UPMC VAD registry through anonest broker. The study included 19 patients who wereupported by a left ventricular assist device (LVAD) or aiventricular assist device (BiVAD) and originally iden-

ified as bridge-to-transplant but later considered forecovery between 1996 and 2004. Thoratec (Pleasanton,A) pneumatic paracorporeal systems were used to sup-

Abbreviations and Acronyms

ANN � artificial neural networkAPsys � systolic arterial pressureAST � aspartate amino transferaseBBN � Bayesian belief networkBiVAD � biventricular assist deviceBUN � blood urea nitrogenCI � cardiac indexCREAT � creatinine clearanceDCM � dilated cardiomyopathyECHO � echocardiographyEF � ejection fractionFAC � fractional area changeFU � follow-upHR � heart rateHTx � heart transplantationLDH � lactate dehydrogenaseLV � left ventricleLVAD � left ventricular assist deviceLVEF � left ventricular ejection fractionMG � magnesiumMETs � metabolic equivalentsMPAP � mean pulmonary artery pressureNLP � natural language processingPCWP � pulmonary capillary wedge

pressurePVR � pulmonary vascular resistancePWR � ventricular powerRER � respiratory exchange ratioRET � reticulocyte countSA � stroke areaTPG � transpulmonary gradientWU � wood unitsVAD � ventricular assist deviceVO2% � peak oxygen consumption

ort 18 patients and the Thoratec implantable VAD was

sed in 1. Of the 19 patients who were considered foreaning, 10 were eventually weaned and 9 received a

ardiac transplant. Patient details are provided inable 1.A total of 250 numeric variables from 6 categories were

nalyzed using commercially available artificial neuraletwork (ANN) software (Clementine 7.0, SPSS, Chicago,

L) to identify the most predictive variables and theirssociated thresholds. The variables were decimated us-ng the prune algorithm to eliminate those that were

eakly correlated with weaning. To avoid overtraining,nly 50% of the data sets were analyzed at a time.dditional analysis was performed on the written shiftotes recorded by the clinical staff responsible for routineonitoring of these patients. Language patterns within

he textual data contained in the shift notes were identi-ed by natural language processing (NLP) using theoftware program Concordance 3.2 (R. J. C. Watt,undee, UK). Word patterns were tabulated in order of

requency and context and compared between weanednd transplanted patients.

xpert Knowledgenowledge derived from retrospective experience waslicited through a series of structured interviews anduestionnaires of 11 members of the multidisciplinaryrtificial Heart Program at UPMC, including surgery,

linical bioengineering, nursing, and psychiatry. Thenterviews were conducted individually and in smallroups to derive a binary decision flowchart for selectingAD weaning candidates. The flowchart was reviewednd revised in a second interview. The individual flow-harts were combined into a final version and presentedo the full panel for approval. The resulting decisionowchart consisted of a five-tier health status screening,

ollowed by a three-tier evaluation of cardiac recoveryFig 1).

The flowchart defines an optimal weaning candidate asnonischemic patient who has been supported by theAD for more than 4 weeks, with normal cardiac rhythm,ositive nutritional status, and normal end-organ func-

ion. Indices of cardiac recovery, gathered through echo-ardiographic measurements [29], are considered opti-al if the patient is able to maintain an ejection fraction

xceeding 40%, ventricular power exceeding 4 (mW/m4), and positive change in stroke area with temporaryuspension of VAD support. Patients who pass this initialcreening are referred for right heart catheterization.

The hemodynamics required to pass the secondarycreening include pulmonary capillary wedge pressureess than 20 mm Hg, cardiac index exceeding 2.2 L/min/

2, and heart rate less than 100 beats/min. Satisfactoryesults of right heart catheterization allow patients tondergo treadmill ergometry according to a modifiedaughton protocol. Patients capable of achieving peak

xygen consumption exceeding 15 mg/kg/min, whileaintaining a respiratory exchange ratio that exceeds 1.0

t maximal exercise, are referred to cardiac surgery foremoval of the VAD.

Owing to the binary nature of the final decision flow-

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hart, weaning is only recommended if all variables aren their positive state; whereas in reality, experts mayonsider less than ideal situations, such as patients whoave a combination of variables in their positive andegative states. The experts were therefore asked to takeart in two 12-item questionnaires in which they wereresented hypothetical case reports in which each of the

ndices of health and cardiac status were toggled andsked to express their confidence of successful weaningnder those conditions. These questionnaires were com-

able 1. Summary of Patients’ Outcome, Device Configuratio

atient ID Outcome DeviceFU After Explant

(1 yr) Demographi

560 HTx BiVAD Alive ✓

075 HTx BiVAD Alive ✓

869 HTx BiVAD Alive ✓

411 HTx BiVAD Alive ✓

883 HTx BiVAD Alive ✓

118 HTx BiVAD Died ✓

284 HTx BiVAD Died ✓

682 HTx LVAD Alive ✓

794 HTx LVAD Died ✓

297 Weaned BiVAD Alive ✓

854 Weaned BiVAD Alive ✓

061 Weaned BiVAD Alive ✓

714 Weaned BiVAD Alive ✓

838 Weaned BiVAD Htx, alive ✓

496 Weaned LVAD Alive ✓

747 Weaned LVAD Alive ✓

822 Weaned LVAD Alive ✓

264 Weaned LVAD Alive ✓

823 Weaned LVAD HTx, died ✓

iVAD � biventricular assist device; FU � follow-up; HTx � heaeart catheterization.

leted in two separate sessions. To ensure consistency,uestions were repeated in reverse order. If the proba-ilities did not directly compliment each other, the ex-erts were asked to reevaluate their estimates. A final 8 �matrix of probabilities was derived from averaging the

onfidence estimates elicited from all of the experts.

ecision Modelinghe relationships between variables extracted from dataining and expert interviews were modeled using a

year After Explant Status, and Available Data Categories

omplications Laboratory RHC Echo Exercise Shift Notes

✓ ✓ ✓ ✓ ✓ . . .✓ ✓ ✓ ✓ ✓ . . .✓ ✓ ✓ ✓ . . . ✓

✓ ✓ ✓ ✓ . . . ✓

✓ ✓ . . . ✓ . . . ✓

✓ ✓ ✓ ✓ . . . ✓

✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ . . . ✓ . . . ✓

✓ ✓ . . . ✓ . . . ✓

✓ ✓ ✓ ✓ . . . . . .✓ ✓ . . . ✓ ✓ ✓

✓ ✓ ✓ ✓ . . . ✓

✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ . . . . . .✓ ✓ ✓ ✓ ✓ . . .✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓

splantation; LVAD � left ventricular assist device; RHC � right

Fig 1. Flowchart shows theknowledge-derived model forassessment of a patient’s readi-ness for weaning from left ven-tricular assist device (VAD) sup-port, based on expert interviews.(CI � cardiac index; ECHO �echocardiogram weaning study;EF � ejection fraction; HR �heart rate; LV � left ventricle;MVO2 � peak oxygen consump-tion; PCWP � pulmonary capil-lary wedge pressure; RER � re-spiratory exchange ratio; RHCATH � right heart catheteriza-tion; SA � stroke area.)

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ayesian Belief Network (BBN) using a custom-writtenoftware, GeNIe 2.0, developed at the Decision Supportaboratory at the University of Pittsburgh [30]. Theecision structure was represented graphically by depict-ng causal influences with arrows from parent variablesnodes) to children (nodes) [24]. By applying a probabilityistribution to each node, the joint probability is ex-ressed as:

P(X1, X2, . . . , Xn) � �i�1

nP(Xi | parents(Xi))

here P(Xi | parents(Xi)) represents the conditional prob-bility of variable Xi, given the occurrence of the parentsf this variable.For each variable, mutually exclusive and cumulatively

xhausted states were defined. Figure 2 shows an exam-le of two of the nodes of the present model, illustrating

he relationship between primary device and outcometransplanted/weaned), and demonstrating their states,rior and conditional probabilities. Assuming all nodesere conditionally independent, all relevant variables

dentified by data mining were combined in a naïve BBN,ach having a direct relationship with outcome predic-ion. Three such BBNs were developed: (1) a data-driven

odel, consisting of 33 variables, (2) an expert modelonsisting of 15 variables, and (3) a hybrid model con-isting of the combined set of 48 variables. The modelsere evaluated for each of the 19 patients in the studyroup by introducing the subset of available variables athe time of transplant or weaning to calculate the prob-bility of weaning. These probability results were con-erted into a binary decision (wean or transplant) based onthreshold of 50%.

esults

he decision structure of the BBN models is shown inigure 3. The variables (nodes) associated with the expertnd data-driven models are separated by the dashedutline. The full set of nodes comprises the hybrid model.

ata-Driven Modelata mining of the 250 numeric variables from 6 catego-

ies (demographics, complications, laboratory tests, exer-ise tests, right heart catheterization, and echocardio-raphic tests) using an ANN that yielded 28 variableshat most closely correlated with outcome, representingn 89% reduction in the raw data (Table 2A–F).Demographically, the ANN analysis identifies an ideal

ig 2. Simple example of theelationship between variablesrimary device (P[D]) and out-ome (O) illustrating nodes, states,nd probabilities. BiVAD �iventricular assist device; VAD �entricular assist device)

andidate for weaning as one who is supported by anVAD rather than a BiVAD, implanted for less than 100ays, younger than 38 years old, white, female, andonischemic (Table 2A).As noted in the analysis of the complications variables

Table 2B), an ideal candidate for weaning is one with noistory of renal complications or reoperation and is free

rom tamponade or other complications associated withleeding.In terms of laboratory tests (Table 2C), the ANN

nalysis associates a greater chance of weaning withatients whose values for aspartate amino transferase,reatinine clearance, blood urea nitrogen, reticulocyteount, magnesium, and lactate dehydrogenase are withinormal references ranges.On the basis of the exercise test (Table 2D), optimal

andidates include those who are able to exercise for 5inutes or more, with peak oxygen consumption exceed-

ng 45%, metabolic equivalents exceeding 4, and canerform at greater than 80% of the maximum predictedeart rate.Optimal right heart catheterization variables (Table 2E)

nclude pulmonary capillary wedge pressure of less than4 mm Hg, pulmonary vascular resistance of less than 1.1

U, mean pulmonary artery pressure of less than 25 mmg, and a transpulmonary gradient of less than 10mHg.Finally, the optimal echocardiographic measurements

ssociated with successful weaning (Table 2F) includeentricular power exceeding 4 mW/cm4, a positive in-rease in stroke area, stable systolic arterial pressure, andtable fractional area change.

This set of data with 28 elements was augmentedith the frequency of keywords identified by NLPithin the free text of the shift notes of the clinical staff.hese were clustered according to five contextual cat-gories: (1) VAD malfunction, (2) socialization, (3)mbulation, (4) positive descriptors, and (5) nutritionTable 3). When compared with the transplant recipi-nts, weaned patients were associated with fewer re-orts of VAD malfunction, better nutritional status,reater activity level, greater prevalence of positiveescriptors, and received more visits from families and

riends (Fig 4).The predictions by this data-driven model, which are

ummarized in Table 4A, misidentified 2 of the 19 pa-ients: classifying 1 patient who was successfully weaneds a transplant candidate and 1 who underwent trans-lantation as a candidate for weaning.

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xpert Modelhe predictive accuracy of the expert model is presented

n Table 4B. This model was less accurate than theata-driven model. It identified 6 of the 10 patients thatere successfully weaned from VAD support; whereas

he other 4 weaned patients were incorrectly identified asransplant candidates.

ybrid Modelhe hybrid model inherited the structure and numericariables from the previous two models. This modelerformed the best. It produced only one incorrect pre-iction, recommending that a patient who had beeneaned should have received a cardiac transplant (Table

C). This represented a prediction accuracy of 94% (95%onfidence interval, 75% to 99%). It is worthy of note thathe latter patient eventually required a cardiac transplantithin 1 year of weaning from VAD support.

omment

he decision to wean a patient from VAD support entailsrocessing complex, uncertain, and incomplete data,hich are dynamically evolving. In lieu of a definitive setf quantitative criteria, the decisions ultimately rely onhe expert intuition and experience of the clinician.onsequently, those centers with a greater patient vol-me are at an advantage compared with those that treatnly a few VAD patients per year. The introduction of alinical decision support system provides the potential toranslate valuable expert knowledge to standardize, per-onalize, and optimize VAD weaning therapy based onultifactorial criteria. This may ultimately lead to a

reater proportion of patients who are considered for F

eaning, which may in turn increase the proportion ofatients initially referred for VAD insertion.The translation of expert knowledge is not necessarily

traightforward. In the present study, the counterintui-ive inaccuracy of the expert model suggests that thexperts are not fully able to articulate the algorithm(s) byhich they themselves formulate their treatment strat-

gy. The data-driven model was also imperfect. It is alsoounterintuitive that the combination of two imperfectodels would yield an improved model. This may sug-

est that incorporation of expert knowledge serves toartially offset the effects of the small sample size of theata-driven model.Conversely, the results of this study may be inter-

reted as suggesting that an otherwise imperfect modelf the expert’s decision process may be improved by the

Fig 3. Hybrid Bayesian beliefnetwork model combining expertand data models. (See the Abbre-viations List for expansions ofthe abbreviations.)

ig 4. Results of natural language processing (NLP) of shift notes.

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able 2. (A–F) Artificial Neural Network Analysis Results of8 Independent Variablesa

ode Name and State Wean No. Transplant No.

A) Minimized Set of Patient Demographics Variables

rimary deviceBiVAD 5 7LVAD 5 2ays implanted�100 8 2�100 2 7ge, y�37 7 3�37 3 6

aceWhite 9 7Asian 1 0Black 0 1Arabic 0 1

exFemale 7 5Male 3 4iagnosisDilated cardiomyopathy

Postpartum 4 2Myocarditis 3 0Idiopathic 1 3Ischemic 1 1

Heart diseaseAcute ischemic 1 2Valvular 0 1

B) Minimized Set of Complication Variables

leedingNo 6 2Yes 4 7

eoperationNo 4 2Yes 6 7

amponadeNo 9 7Yes 1 2

enalNo 10 7Yes 0 2

C) Minimized Set of Laboratory Test Variables

spartate amino transferase0–250 U/L 6 4�250 U/L 4 5

reatinine0–1.9 mg/dL 7 2�1.9 mg/dL 3 6

lood urea nitrogen2.0–47.0 mg/dL 7 2Out of range 3 7

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able 2. Continued

ode Name and State Wean No. Transplant No.

eticulocyte count3.2–10 � 106/�L 5 3Out of range 2 5agnesium1–2.6 mEq/L 7 2Out of range 3 7

actate dehydrogenase167–1022 U/L 5 2Out of range 5 6

D) Minimized Set of Exercise Test Variables

xercise time�5 min 7 1�5 min 0 3

eak O2 consumption, %�45 6 1�45 1 2etabolic equivalents�4 METs 6 0�4 METs 1 3eart rate % target�80 6 1�80 1 2

E) Minimized Set of Right Heart Catheterization Variables

CWP�24 mm Hg 7 3�24 mm Hg 2 4

eripheral vascular resistance�1.1 WU 5 1�1.1 WU 4 4ean pulmonary artery pressure�25 mm Hg 7 3�25 mm Hg 2 4

ranspulmonary gradient�10 mm Hg 7 2�10 mm Hg 1 4

F) Minimized Set of Echocardiographic Variables

entricular power�4 7 1�4 1 7

troke areaIncreased �0.2 cm2 5 0Maintained 2 1Decreased �0.2 cm2 2 6

ystolic arterial pressureChange �40 mm Hg 5 0Change �40 mm Hg 0 4

ractional area changeChange �10% 6 2Change �10% 3 5

Tables are organized by data category; corresponding states for each variable,nd patient numbers of two outcomes under different variable states.

iVAD � biventricular assist device; LVAD � left ventricular assist de-

ice; METs � metabolic equivalents; PCWP � pulmonary capillaryedge pressure; WU � wood units.

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ddition of quantitative, statistical data. Accordingly, theariables that were excluded from the expert model (Fig), yet found to be statistically relevant, can be subdi-ided into two sets: those that are consistent with clinicalxpectations and those that are either counterintuitive oror which there is no clinical benchmark. The formerariables, such as duration of implantation, gender, age,nd race, can be readily introduced into the expertodel—with the approval of the expert—to correct for

missions that were overlooked when the expert wasriginally interrogated. The counterintuitive variablesould be more appropriate for inclusion in the Bayesian

omponent of the hybrid model.A limitation of this study is the apparent bias intro-

uced by the exclusive use of a single-center experience.he decision model would clearly benefit from enlarging

he data set to include multiple centers and enlarging thexpert knowledge base beyond the 11 who were polled inhis study. Implementing this system across multiple

edical centers will provide an opportunity to combinexpert understanding of causal or synergistic relation-hips between variables, which may improve the topol-gy of the Bayesian network, compared with the naïvetructure of the present model. On the other hand, itight be advisable to limit the data to the most experi-

nced or successful centers to translate their accumu-ated knowledge to less experienced centers.

An additional bias was introduced by the preselectionf the 19 patients used for this study. Although therediction accuracy of the hybrid clinical decision sup-ort system was very good (94%), the associated 95%onfidence interval was relatively wide (75% to 99%). To

able 3. Word Examples for Five Contextual Categories inatural Language Processing

Ventricular assist device malfunction● alarm● (poor filling)Socialization● visiting● family● friendsAmbulation● walked● stairs● bike, chair● outside● physical therapyPositive descriptor● happy● good● improving● talkativeNutrition● eating● cafeteria

● (good) appetite

mprove the lower bound of the confidence interval to,ay, 94% would require 1500 patients according to statis-ical power analysis. By virtue of the retrospective treat-

ent of these data, it was not advantageous to includehe 172 VAD patients that were not considered foreaning between 1996 and 2004 at UPMC because there

s no way to discriminate retrospectively between pa-ients that could have been entered into the weaningrotocol. Likewise there is no way of knowing if theistoric clinical decisions of the 19 patients were neces-arily the correct or optimal decisions. Accordingly, anngoing prospective study is currently being conductedherein every consenting patient who receives a VAD is

nrolled. By also evaluating long-term outcomes anddverse events, this ongoing study hopes to provide aore informative and accurate decision support model.

e thank the participants of the expert panel: Richard Schaub,hD, Michael Mathier, MD, Eileen Stanford, RN, Lisa Carozza,N, Margo Holm, PhD, Ketki Desai, Mary Amanda Dew, PhD,

ohn Gorcsan, MD, and Steve Winowich.

his research was partially supported by NSF Grant ECS-0300097,IH contract HHSN268200448192C, NIH Grant 2R44HL61069–02 andIH Grant 1R01HL086918–01.

eferences

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able 4. (A–C) Predictions of Each of Three Modelsompared With Actual Clinical Strategiesa

redicted Actual Wean Transplant

A) Model 1: Data-Derived Knowledge

ean 9 1ransplant 1 8otal 10 9

B) Model 2: Expert-Derived Knowledge

ean 6 0ransplant 4 9otal 10 9

C) Model 3: Hybrid (Expert Plus Data)

ean 9 0ransplant 1b 9otal 10 9

Note: one “falsely” predicted transplant who was actually weanedrom ventricular assist device support ultimately received transplant at

year. b Required transplant at 1-year after weaning.

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NVITED COMMENTARY

s with many modern technological advancements, thepplication of ventricular assist device (VAD) care to aroad spectrum of patients can quickly result in out ofontrol costs for healthcare delivery. Currently, VADs aresed primarily as a method to safely prolong the waitingeriod for patients needing a cardiac transplant. How-ver, with the recent Food and Drug Administrationpproval of a durable nonpulsatile, long-term VAD foratients who are not transplant candidates, it will be-ome important to be able to identify patients that willenefit most from this therapy. Ideally, the existence of aredictive rule which can be widely applied and that willesult in an accurate prediction of the outcome of therapyn any given individual would be extremely useful. Tra-itional risk-outcome prediction models (based primarilyn logistic regression analyses) are dependent on havingany patients and many events. In the case of VAD

nd events, even though there may be a great deal ofnformation available on any given patient. Thus, newer

ethods of predicting outcomes become important inhis population.

In the current report, Santelices and colleauges [1] usehybrid decision support model (combining a data-

ervied model with an expert consensus model) to arrivet a decision regarding the weanability of VAD support.hey demonstrate that the hybrid model performedetter than either the expert-derived model or the theata-derived model in identifiying patients who wereuccessfully weaned from VAD therapy. The data used toerive the hybrid-model consists of information from thereoperative, perioperative, and postoperative periods.Unfortunately, the use of such a rule may be limited

ecause it cannot be used to determine the eligibility of aatient before the therapy is offered. However, if the

rospective use of this rule results in avoiding heart

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