Classification of anaemia on the basis of ferrokinetic parameters

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<ul><li><p>British Journal ofHaernatology. 1985, 61, 357-370 </p><p>Classification of anaemia on the basis of ferrokinetic parameters </p><p>G . BAROSI, M. CAZZOLA, C. BERZUINI,* S. QUAGLINI' A N D M. STEFANELLI* Dipartimento di Medicina Interna e Terapia Medica, Policlinico S. Matteo, and Dipartimento di lnformatica e Sistemistica, Strada Nuova 106/C, 271 00 Pavia, ltaly </p><p>Received 3 August 1 9 8 4 ; accepted for publication 9 January 1 9 8 5 </p><p>SUMMARY. Quantitative information on abnormalities of erythropoiesis and mechanisms of anaemia has been obtained in 1 3 6 anaemic patients by means of ferrokinetic studies. To derive a functional classification of anaemia based on ferrokinetic parameters, agglomerative hierarchical cluster analysis and princi- pal coordinate analysis were utilized as techniques for unsupervised classifica- tion. Two main clusters were found and named anaemia with low potential erythropoiesis and with high potential erythropoiesis, since the most discri- minant parameter between them was total erythroid iron turnover, a measure of total erythropoietic activity. A value of total erythropoiesis equal to 4 times the normal was found to discriminate these two types of anaemia in 94% of cases. Within the group with low potential erythropoiesis, three clusters showing different qualitative disturbances of erythropoiesis were singled out. Among patients with high potential erythropoiesis, two clusters were found. A value of effective erythropoiesis equal to 2 . 5 times the normal was shown to have a high discriminant power between these clusters. This threshold level distinguished between patients having ineffective erythropoiesis or peripheral haemolysis as the major mechanism of anaemia. The present functional classification of anaemia provides a complete picture of the different pathogene- tic mechanisms and may represent the basis for a more rational diagnostic approach to erythroid disorders. </p><p>Anaemia may be functionally classified as hypoproliferative or hyperproliferative on the basis of the bone marrow picture (Finch, 1982; Dancey et al, 1976). Reticulocyte count or index (Hillman &amp; Finch, 1969), in addition, provides a simple estimation of the efficiency of red cell production. To derive a more accurate quantitation of erythroid proliferation and its efficiency a variety of kinetic studies such as bilirubin turnover (Engstedt et al, 1967; Berk et </p><p>Correspondence: Dr G. Barosi, Dipartimento di Medicina Interna e Terapia Medica, Policlinico S. Matteo, 27100 Pavia, Italy. </p><p>357 </p></li><li><p>3 5 8 G. Barosi et al </p><p>al, 1976; Samson et al, 1976), carbon monoxide production (Coburn et al, 1968; Lundh et al, 1975) and fecal urobilin excretion (Crosby, 1950). have been employed. </p><p>A valuable improvement in our understanding of the pathogenesis of anaemia has been provided by ferrokinetic studies (Finch et al, 1970; Cavil1 &amp; Ricketts, 1980; Stefanelli et ul, 1982), which allow the quantitation of total, effective and ineffective erythropoiesis and mean red cell lifespan. These studies have shown that, first, the rate of erythroid proliferation can range from a negligible level to as much as 15 times the normal (Pootrakul et al, 1981) according to the severity of the anaemia and the proliferative capacity of the erythroid marrow. Second, a mild degree of ineffective erythropoiesis is present in healthy bone marrow and it may reach 90% of total erythropoiesis in some diseases (Stefanelli et al, 1982; Barosi et al, 1981; Cazzola et al, 1982). Third, the major mechanism producing anaemia may be different across patients with the same disorder, and ferrokinetic studies allow it to be identified in individual patients (Barosi et al, 1979). </p><p>In the present work, we have analysed ferrokinetic results obtained in 136 anaemic patients with the aim of defining a functional classification of anaemia based on the quantitative evaluation of erythropoiesis. Then, we have tested the feasibility of using simple haematological parameters for discriminating groups of patients according to the classifica- tion obtained. </p><p>MATERIALS A N D METHODS </p><p>Patients </p><p>In the last 10 years more than 300 patients referred to our Clinical Department underwent an erythrokinetic study, i.e. ferrokinetics combined with red cell survival study. Due to the aims of the present work, only 136 cases were utilized. The selection criteria were the following: (a) anaemia was present at the time of study, that is haemoglobin concentration was lower than 14 g/dl in males and 12 g/dl in females; (b) a definite clinical diagnosis had been established; (c) patients were not strictly transfusion dependent. In no case had the patient been transfused in the 2 months before the study: (d) no treatment capable of modifying erythropoiesis had been given before the study; (e) none of the patients had a multisystem disorder likely to influence the degree of anaemia. </p><p>Haematological parameters </p><p>Haemoglobin concentration (Hb), mean corpuscular haemoglobin concentration (MCHC) and mean cell volume (MVC) were measured by Coulter S Counter. Reticulocyte count was measured by standard technique (Dacie &amp; Lewis, 1975). Reticulocyte index was then computed according to Hillman &amp; Finch (1969). Serum iron (SI) and transferrin saturation (TS) were determined by colorimetric analyses (ICSH Panel, 1 978a, b). Bilirubin concentra- tion was obtained by automated technique. In each case bone marrow aspirate and/or trephine biopsy were used to establish diagnosis. Summary statistics of haematological data are reported in Table I, where clinical classes were defined according to Bothwell et a1 (1 9 79). </p></li><li><p>Classijfication of Anaemia 359 </p><p>Table I. Summary statistics for haematological parameters within clinical classes </p><p>Haematological parameters No. of Age Hb MCV MCHC Ketics Serum iron Transf. sat. Bilir. </p><p>Disease cases (yr) (g/dl) (fl) (g/dl) (10'/1) (pmol/l) (%) (ms/l) - </p><p>AA </p><p>RA </p><p>CMML </p><p>RAEB </p><p>HCL </p><p>IDA </p><p>CDA </p><p>PASA </p><p>THAL </p><p>HS </p><p>AIHA </p><p>MMM </p><p>~ </p><p>1 3 </p><p>8 </p><p>2 </p><p>1 7 </p><p>11 </p><p>10 </p><p>8 </p><p>11 </p><p>9 </p><p>8 </p><p>4 </p><p>35 </p><p>48.0 7.9 101.0 31.8 37.5 38.8 73.0 26-71 5'2-11'4 88-121 30.2-34.3 2-326, 25.0-62'7 53-95 59.1 8.1 101.4 31.1 67.0 25.1 50.0 </p><p>40-77 4.9-12.2 82-130 30.0-32.9 10-300 1 0 * 0 4 4 * 0 21-81 </p><p>66.5 6.7 89.5 31.8 36.5 25.5 45.5 61-72 6.5-6.9 89-90 28.8-34.8 23-50 11.0-40.0 21-70 </p><p>59.2 8.9 99.4 32.4 50.6 24.1 47.9 14-77 6.2-12.3 86-123 28.8-34.8 16-96 5.4-54.1 13-86 </p><p>54.0 10.5 94.6 32.4 62.0 19.6 ma. </p><p>34.5 8.1 71.5 28.8 67.0 4.1 5.6 7-63 5'7-10'9 59-90 27.1-32'6 7-166 2.0-8'0 3-11 19.0 10.0 96.0 32.9 39.6 30.0 57.0 8-40 7.2-12.0 85-1 12 29'3-34'3 8-76 10'7-38.9 22'8-75 66.0 7.4 104.0 31.2 36.5 27.8 56.0 </p><p>47-69 8.2-12.8 67-105 29.6-35.0 9-341 10'7-35'4 </p><p>50-80 6'0-8'1 96-117 29'5-34'0 7-127 1 0 ' 4 4 4 . 7 19-80 34.5 8.3 71.1 28.5 237.8 32.3 65.7 6-54 5'1-12'0 57-78 24.8-31.9 46-514 23'2-48.6 49-85 36.5 12.0 91.0 35.4 237.4 20.8 38.3 </p><p>1 4 4 9 10'6-13.0 83-106 25'5-36'9 81-368 10'3-32'2 30-51 46.7 8.5 107.5 32.2 249.7 14.3 n.a. </p><p>31-58 6'4-9'7 93-130 29.0-35.5 181-300 10.3-21.6 55.0 10.0 88.8 32.1 114.0 19.9 28.0 </p><p>28-77 5'2-13'6 74-102 29'1-34'8 2 0 4 5 0 6 . 7 4 4 ' 7 1 5 4 6 </p><p>9.9 6.3-1 7.0 </p><p>13.8 4'9-3 8 9 </p><p>7.0 5 4-9 .0 </p><p>8.7 3.1-1 7.8 </p><p>8.3 2.7-1 3.1 </p><p>. 5.8 3.8-7.8 </p><p>30.5 22.1-3 8.7 </p><p>11.9 6'3-21'0 </p><p>31.8 11.3-56'1 </p><p>43.1 18.4-57.8 </p><p>13.6 8.8-1 8.4 </p><p>8.8 4.3-2 2 ' 0 </p><p>n.a., not available. </p><p>Ferrokinetic parameters </p><p>The experimental protocol and methods for the analysis of erythrokinetic data have been extensively presented elsewhere (Stefanelli et ul, 19 82) . Subject's transferrin, specifically labelled with 59Fe (ferric citrate), was injected intravenously and blood samples were taken according to a suitable sampling schedule for 14 d. The plasma volume was measured by dilution of 59Fe transferrin and red cell volume (RCV) was measured by "Cr method of ICSH (1 9 71). The plasma 59Fe disappearance and red cell utilization curves were used to estimate the parameters of a multicompartment model of iron kinetics. From these estimates, the following ferrokinetic parameters were calculated: total erythroid iron turnover (TEIT), a measure of total erythropoiesis; ineffective iron turnover (IIT), a measure of ineffective </p></li><li><p>3 60 G. Barosi et a1 </p><p>erythropoiesis. It may be more usefully expressed as a percentage of TEIT and indicated as IW%; red cell iron turnover (KCTT), a measure of effective erythropoiesis: mean red cell life span (MKCI,), the inverse of which provides an estimate of the rate of peripheral haemolysis. </p><p>Statistical analgsis </p><p>In this paper, classification is considered to be the process of allocating patients of the studied sample to initially undefined classes, called clusters, so that patients in the same cluster are, from the point of view of the functional behaviour of erythropoiesis, similar to one another. Five logical steps can be distinguished in this process: </p><p>1. Selection of parameters for the classiJication. 2 . Preliminary tests of rridtiriorrrialitg or1 the global data set. They indicate presence of </p><p>3. Deterniination of clusters by partitioning of the patients sample. 4. Characterization of clusters obtained. 5. Cluster rhoire arid validation. </p><p>Steps 1-5 are described in the following. Step 1. After gaining experience via several cluster analyses based on different subsets of </p><p>ferrokinetic and haematological variables, the final classification was based only 011 estimated ferrokinetic parameters TETT, RCIT, IITX and MRCL. </p><p>Step 2. Simple tests of multinormality and data exploration techniques (Gnanadesikan 1977) were at first carried out on the data. It was found that data contained strong non-normal features, allowing the rejection of the hypothesis of multinormality. </p><p>Step 3. Agglomerative hierarchical cluster analysis (Everlitt, 19 74) and principal components analysis (Gower, 1966) were utilized to determine the clusters. </p><p>The agglomerative hierarchical clustering algorithm can be described using a diagram- matic representation, as that shown in Fig 1, called a dendrogram. Initially, each patient is considered a separate cluster. The algorithm starts by grouping the two nearest patients. This process is repeated by fusing iteratively the two closest clusters, thus reducing at each step the number of clusters by one, until all patients are clustered together </p><p>Since hierarchical cluster analysis is likely to yield artefacts when clusters are not well isolated or unusually shaped, principal components analysis (PCA) was used for checking by eye inspection clusters found and for local corrections of the classification. </p><p>Step 4. Linear discriminant anaiysis (Jennrich, 19 77) on the basis of the same parameters used for the classification has been applied to clusters obtained, as an informal indicator of which parameters have contributed most to cluster formation. Hence a characterization of clusters has been obtained. </p><p>All results of analyses included in step 4 provided a tool for the interpretation of dusters from the patho-physiological point of view. </p><p>Step 5 . The number of clusters to be retained, i.e. the cut level of the dendrogram, was chosen. This was made judging the clusters on the basis of results of step 4 and external contextual knowledge, to see whether they corresponded to a rational set of different functional patterns of erythropoiesis. </p><p>potentially interesting pattern in the data, and motivate subsequent steps of the analysis. </p></li><li><p>Classification of Anaemia 361 </p><p>~ </p><p>RESULTS </p><p>Based on ferrokinetic parameters, the classification represented by the dendrogram shown in Fig 1 was obtained. </p><p>100 </p><p>a, u c m </p><p>'0 </p><p>(u </p><p>In 3 </p><p>4- </p><p>._ 75 </p><p>L </p><p>c </p><p>50 - sl L </p><p>c a, G </p><p>I - A1 </p><p>9/11 HCL </p><p>A </p><p>13/13 A A 17/17 RAE6 2/2 CMML 10/10 IDA 2/11 HCL 7/35 MMM </p><p>a / a R A </p><p>6/35 MMM </p><p>8/a HS 218 CDA </p><p>4/4 AIHA 1 11/35 MMM 3/11 PASA 4 /9 THAL 6/8 CDA 5 /9 THAL 11/35 MMM 8/11 PASA Fig 1. Dendrogram for the classification of anaemias based on ferrokinetic parameters. A denotes anaemias with low-potential erythropoiesis and B anaemias with high-potential erythropoiesis. Boxes represent clusters a t lowest level considered meaningful. Inside boxes, cluster compositions are reported in terms of clinical diagnoses (n/m means n out of m patients). Labels of clinical diagnoses are explained in Table I. </p><p>Separation into anuemiu with low potential and high potentiul erythropoiesis </p><p>The two biggest and farthest apart clusters joining at the root of the dendrogram, denoted as A and B, contained 74 and 62 patients, respectively. Table I1 shows summary statistics for haematological and ferrokinetic parameters in these clusters. TEIT was found to be the most discriminant parameter between the two clusters: it ranged from 59 to 3 78 pmol/l blood/d in A and from 292 to 1474 pmol/I blood/d in B. A threshold value of 350 pmol/l blood/d for TEIT allowed 126 out of 136 patients (94%) to be correctly allocated. </p><p>Discriminant analysis was applied between cluster A and B using only haematological parameters. The most discriminant parameter was found to be bilirubin concentration, which had a mean value of 8.8 f 5.3 mg/l in cluster A and 23.4 =t 15.9 mg/l in cluster B and allowed 77% of cases to be correctly allocated. The remaining haematological parameters were unable to improve significantly the separation. </p></li><li><p>Tab</p><p>le 11</p><p>. Sum</p><p>mar</p><p>y st</p><p>atis</p><p>tics </p><p>for h</p><p>aem</p><p>atol</p><p>ogic</p><p>al a</p><p>nd fe</p><p>rrok</p><p>inet</p><p>ic p</p><p>aram</p><p>eter</p><p>s w</p><p>ithin</p><p> clu</p><p>ster</p><p>s of</p><p> pat</p><p>ient</p><p>s w</p><p>ith lo</p><p>w p</p><p>oten</p><p>tial a</p><p>nd </p><p>high</p><p> pot</p><p>entia</p><p>l er</p><p>ythr</p><p>opoi</p><p>esis</p><p>Hae</p><p>mat</p><p>olog</p><p>ical</p><p> par</p><p>amet</p><p>ers </p><p>Ferr</p><p>okin</p><p>etic</p><p> par</p><p>amet</p><p>ers </p><p>9 </p><p>F a 16</p><p>0 97</p><p> 35</p><p> 58</p><p> Y. </p><p>A </p><p>8.7 </p><p>94.1</p><p> 31</p><p>.6 </p><p>53.6</p><p> 23</p><p>.8 </p><p>47.4</p><p> 8.</p><p>8 P+</p><p> B </p><p>9.6 </p><p>90.6</p><p> 31</p><p>.9 </p><p>136 </p><p>23.3</p><p> 53</p><p>.7 </p><p>23.4</p><p> 63</p><p>4 21</p><p>9 61</p><p> 38</p><p> 2 </p><p>Hb </p><p>MCV</p><p> M</p><p>CHC </p><p>Ret</p><p>ics </p><p>S.I.</p><p> T.</p><p>S. </p><p>Bili</p><p>r. TE</p><p>IT </p><p>RCIT</p><p> II</p><p>T% </p><p>MRC</p><p>I, C</p><p>lust</p><p>er </p><p>(g/d</p><p>l) </p><p>(fl) </p><p>(g/d</p><p>l) </p><p>(1O</p><p>9/U</p><p> (p</p><p>mol</p><p>/l) </p><p>(%I </p><p>(mg/</p><p>U </p><p>(pm</p><p>d/L</p><p> bl</p><p>ood/</p><p>d) </p><p>(%I </p><p>(dl </p><p>4.9-</p><p>12.8</p><p> 59</p><p>-130</p><p> 26</p><p>.8-3</p><p>5.0 </p><p>24</p><p>50</p><p> 2.</p><p>0-62</p><p>.7 </p><p>3-95</p><p> 2.</p><p>7-38</p><p>.9 </p><p>59-3</p><p>78 </p><p>36-2</p><p>00 </p><p>2-87</p><p> 26</p><p>-125</p><p>5.1-</p><p>13.6</p><p> 57</p><p>-130</p><p> 24</p><p>.8-3</p><p>6.9 </p><p>7.5-</p><p>514 </p><p>6.7-</p><p>54.1</p><p> 5-</p><p>85 </p><p>6.3-</p><p>57.8</p><p> 29</p><p>2-14</p><p>74 </p><p>50-5</p><p>71 </p><p>6-92</p><p> 7-</p><p>1 11</p><p>Nor</p><p>mal</p><p> 14</p><p>.9 </p><p>89 </p><p>33.5</p><p> 58</p><p>.4 </p><p>18.6</p><p> 30</p><p>.0 </p><p>6.2 </p><p>89 </p><p>82 </p><p>7 10</p><p>9 su</p><p>bjec</p><p>ts </p><p>13.5</p><p>-15.</p><p>9 86</p><p>-94 </p><p>31.5</p><p>-34.</p><p>2 40</p><p>-71 </p><p>12.5</p><p>-24.</p><p>7 19</p><p>-38 </p><p>4.3-</p><p>1 5.</p><p>1 74</p><p>-105</p><p> 69</p><p>-100</p><p> 1-</p><p>17 </p><p>88-1</p><p>28 </p><p>S.I.</p><p>, ser</p><p>um ir</p><p>on: T</p><p>.S.. </p><p>tran</p><p>sfer</p><p>rin </p><p>satu</p><p>ratio</p><p>n. </p></li><li><p>Tabl</p><p>e 11</p><p>1. Su</p><p>mm</p><p>ary </p><p>stat</p><p>istic</p><p>s fo</p><p>r ha</p><p>emat</p><p>olog</p><p>ical</p><p> and</p><p> fer</p><p>roki</p><p>netic</p><p> par</p><p>amet</p><p>ers </p><p>wit</p><p>hin </p><p>clus</p><p>ters</p><p> of </p><p>patie</p><p>nts </p><p>with</p><p> low</p><p> pot</p><p>entia</p><p>l er</p><p>ythr</p><p>opoi</p><p>esis</p><p> and</p><p> with</p><p>in c</p><p>lust</p><p>ers </p><p>of p</p><p>atie</p><p>nts </p><p>with</p><p> hig</p><p>h po</p><p>tent</p><p>ial e</p><p>ryth</p><p>ropo</p><p>iesi</p><p>s </p><p>Hae</p><p>mat</p><p>olog</p><p>ical</p><p> par</p><p>amet</p><p>ers </p><p>Ferr</p><p>okin</p><p>etic</p><p> par</p><p>amet</p><p>ers </p><p>Hb </p><p>MCV</p><p> M</p><p>CHC </p><p>Clu</p><p>ster</p><p> (g</p><p>/dl)</p><p> (f</p><p>l) (g</p><p>/dl)</p><p>A1 </p><p>10.1</p><p> 94</p><p>.3 </p><p>32.2</p><p> 8.</p><p>2-12</p><p>'7 </p><p>67-1</p><p>05 </p><p>29.6</p><p>-35.</p><p>0 A</p><p>2 8.</p><p>4 93</p><p>.5 </p><p>31.4</p><p> 4.</p><p>9-12</p><p>.8 </p><p>59-1</p><p>30 </p><p>26.8</p><p>-34.</p><p>8 A</p><p>3 9.</p><p>0 96</p><p>.2 </p><p>31.9</p><p> 5'</p><p>2-12</p><p>.2 </p><p>82-1</p><p>23 </p><p>30.0</p><p>-35.</p><p>0 </p><p>29.0</p><p> 9-</p><p>6 1</p><p> 55</p><p>.3 </p><p>24</p><p>50</p><p> 64</p><p>.0 </p><p>10-3</p><p>00 </p><p>18.4</p><p> 10</p><p>.7-2</p><p> 8 '0</p><p>2 5.</p><p>4 2-</p><p>62.7</p><p> 21</p><p>.9 </p><p>10</p><p>44</p><p>.0 </p><p>n.a.</p><p> 7.</p><p>6 2.</p><p>7-1 </p><p>3.1 </p><p>47.8</p><p> 8.</p><p>8 3-</p><p>95 </p><p>3.1-</p><p>38.9</p><p> 45</p><p>.3 </p><p>9.3 </p><p>21-8</p><p>1 4.</p><p>3-16</p><p>'3 </p><p>TEIT</p><p> RC</p><p>IT </p><p>IITX</p><p> M</p><p>RCL </p><p>(pm</p><p>ol/l </p><p>b...</p></li></ul>