european urology 52 (2007)

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1 european urology 52 (2007) available at journal homepage: Kidney Cancer Platelet Count and Preoperative Haemoglobin Do Not Significantly Increase the Performance of Established Predictors of Renal Cell Carcinoma-Specific Mortality Pierre I. Karakiewicz a, *,1, Quoc-Dien Trinh a,1, John S. Lam b, Jacques Tostain c, Allan J. Pantuck b, Arie S. Belldegrun b, Jean-Jacques Patard d a Cancer Prognostics and Health Outcome Unit, University of Montreal Health Center, Montreal, Quebec, Canada b Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, California, United States c Department of Urology, North Hospital, CHU of Saint-Etienne, Saint-Etienne, France d Department of Urology, Rennes University Hospital, Rennes, France Article info Article history: Accepted March 15, 2007 Published online ahead of print on March 28, 2007 Keywords: Renal cell carcinoma Survival Anaemia Thrombocytosis Prognostic Abstract Objective: Anaemia and/or thrombocytosis were identified as independent predictors of poor survival in renal cell carcinoma (RCC). We tested the extent to which these markers worsen the prognosis in these patients. Methods: Analyses targeted 1828 patients with renal cell carcinoma. Univariable, multivariable, and predictive accuracy analyses addressed RCC-specific mortality (RCC-SM). Results: In univariable and multivariable analyses, both platelet count and preoperative haemoglobin level were statistically significant predictors of RCC-SM. However, neither platelet count nor preoperative haemoglobin level increased the combined multivariable accuracy of established RCC-SM (predictive accuracy gain = 0.3%) predictors. Conclusions: Patients who present with severe anaemia or elevated platelets are at no higher risk of RCC-SM than that related to their stage, grade, histologic subtype, and Eastern Cooperative Oncology Group- Performance Status. # 2007 European Association of Urology. Published by Elsevier B.V. All rights reserved. * Corresponding author. Cancer Prognostics and Health Outcome Unit, University of Montreal Health Center (CHUM), 1058, Rue St-Denis, Montreal, Quebec, Canada, H2X 3J4. Tel ; Fax: address: pierre.karakiewicz@umontreal.ca (P.I. Karakiewicz). 1 Equally contributing authors. 1. Introduction Renal cell carcinoma (RCC) accounts for 3% of cancers in adults as well as 85% of all primary malignant kidney tumours [1]. Unfortunately, nearly 25% of contemporary patients are diagnosed with either nodal or distant metastases and have poor prognosis [2,3]. Stage at presentation represents a /$ see back matter # 2007 European Association of Urology. Published by Elsevier B.V. All rights reserved. doi: /j.eururo

2 european urology 52 (2007) valuable prognostic variable and is closely associated with renal cell carcinoma-specific mortality (RCC-SM). Nonetheless, some patients with localized RCC succumb to their disease. Unfavourable histologic subtypes, such as collecting duct or sarcomatoid variants, account for worse survival. However, in other patients, obvious variables, such as grade, stage, and histology, cannot account for the unexpectedly poor survival. Patients demonstrating systemic disease manifestations, such as anaemia and/or thrombocytosis, may represent a subgroup with particularly poor prognosis. Indeed, thrombocytosis was shown to portend a particularly poor survival in five contemporary RCC cohorts [4 8]. Anaemia was associated with an equally poor prognosis [9 12]. Despite this evidence, the added value of these markers in prediction of RCC-SM has never been tested. Thus, it is presently unknown whether anaemia or thrombocytosis should be routinely considered when survival is considered. On the basis of these considerations, we tested the ability of thrombocytosis and anaemia to improve the predictive accuracy of established RCC-SM predictors. If thrombocytosis and/or anaemia prove to add to the prognostic ability of established markers, then they should be added to standard prognostic variables. If not, then it can be safely assumed that patients with thrombocytosis or anaemia are at no different risk than that caused by the established risk factors. 2. Material and methods 2.1. Patient population Data were retrieved from three combined institutional review board-approved institutional databases, which totalled 1828 patients treated with partial or radical nephrectomy between 1984 and 2001 (Table 1) Clinical and pathologic evaluation Platelet count and preoperative haemoglobin level were available for all patients in our cohort. Tumours were classified according to the 2002 TNM staging system and the Fuhrman grade. Tumour size was based on pathologic specimens and was defined as the greatest diameter in centimetres. Prior to study inclusion, histologic subtypes were stratified according to the 2002 American Joint Committee on Cancer/Union Internationale Contre le Cancer classifications [13]. Symptoms were prospectively recorded at each of the participating institutions. The symptom classification was defined as previously described [14]. Patients were staged preoperatively with computed tomography (CT) of the abdomen and pelvis, chest CT or chest x-ray, serum electrolytes, and liver function tests. Presence of nodal metastases was defined according to lymphadenectomy findings. In all cases a hilar lymphadenectomy was performed, which included all lymph nodes on the ipsilateral side of the great vessels. In select cases, on the basis of surgeon preference, more extensive lymphadenectomies that included inter-aorto-caval lymph nodes were performed. In all cases presence of nodal metastases was confirmed pathologically. Presence of distant metastases was confirmed on the basis of radiographic and/or histologic findings. Follow-up consisted of one postoperative baseline visit and was then performed every 6 mo for a minimum of 2 yr. Subsequently, minimum follow-up consisted of annual visits. At each visit, a CT of the chest or a chest x-ray accompanied a CT of the abdomen. The cause of death was either obtained from the medical chart and recorded prospectively or was obtained from the death certificate in a retrospective fashion. RCC-specific mortality included deaths that were directly attributable to kidney cancer Statistical analyses Kaplan-Meier plots were used to graphically illustrate the RCC-specific survival (RCC-SS) for the entire cohort, as well as the effect of dichotomised platelet count and haemoglobin level on RCC-SS. Univariable and multivariable Cox regression models addressed the effect of all predictors on RCC-SM. These predictors included age, TNM stages, tumour size, Fuhrman grade, histologic type, Eastern Cooperative Oncology Group-Performance Status (ECOG-PS), as well as the platelet count and haemoglobin level [15 18]. Univariable predictive accuracy was determined for each predictor, including platelet count and haemoglobin level. For platelet count and haemoglobin level, we tested whether one or several cut-offs could result in an increase in predictive accuracy. We also included the previously suggested platelet count cut-off of per litre in univariable as well as all multivariable analyses [4]. Anaemia was defined as preoperative haemoglobin level 11.9 g/dl. In all analyses, predictive accuracy was defined as the ability of the model to discriminate between those who succumbed to RCC from those who did not. Predictive accuracy was expressed as a percentage with the use of the Harrell s concordance index, in which 50% represents a flip of a coin and 100% represents ideal prediction. The combined predictive accuracy of all standard RCC-SM predictors was quantified in the base multivariable model, which included neither the platelet count nor the haemoglobin level. The change in the multivariable predictive accuracy related to the inclusion of either one or both variables was then established in extended models. The effect of inclusion of either continuously coded or categorized platelet count and haemoglobin level was tested. To reduce overfit bias, all univariable and multivariable models were subjected to 200 bootstrap resamples. All analyses were repeated after stratification of the cohort into three subcohorts (Table 1): localized RCC (T 1 2 N 0 M 0 ), locally advanced RCC (T 3 N 0 1 M 0 ), and metastatic RCC (T any N any M 1 ). The intent of these subanalyses was to explore the potential contribution of platelet count and haemoglobin levelling patients with significantly different

3 1430 european urology 52 (2007) Table 1 Descriptive characteristics of 1828 patients treated with nephrectomy for renal cell carcinoma Variable Overall cohort Localized RCC (T1 2N0M0) Locally advanced RCC (T3N0 1M0) Metastatic RCC (TanyNanyM1) Total 1828 (100.0%) 827 (100.0%) 449 (100.0%) 508 (100.0%) Centre Rennes University Hospital, Rennes, France 483 (26.4%) 230 (27.8%) 147 (32.7%) 92 (18.1%) CHU of Saint-Etienne, Saint-Etienne, France 431 (23.6%) 195 (23.6%) 165 (36.7%) 64 (12.6%) UCLA 914 (50.0%) 402 (48.6%) 137 (30.5%) 352 (69.3%) Age (yr) Mean (median) 61.4 (63.0) 61.0 (63.0) 63.3 (65.0) 60.1 (61.0) Range Gender F 617 (33.8%) 302 (36.5%) 157 (35.0%) 141 (27.8%) M 1211 (66.2%) 525 (63.5%) 292 (65.0%) 367 (72.2%) T stage T (40.2%) 656 (79.3%) 72 (14.2%) T (12.8%) 171 (20.7%) 55 (10.8%) T (43.9%) 449 (100.0%) 341 (67.1%) T 4 56 (3.1%) 40 (7.9%) Tumor size (cm) Mean (median) 7.1 (6.4) 5.2 (4.5) 8.1 (8.0) 8.9 (8.5) Range Histologic type Conventional clear cell 1538 (84.1%) 666 (80.5%) 392 (87.3%) 448 (88.2%) Papillary 196 (10.7%) 121 (14.6%) 37 (8.2%) 32 (6.3%) Chromophobe 66 (3.6%) 38 (4.6%) 18 (4.0%) 8 (1.6%) Collecting duct and unclassified 28 (1.5%) 2 (0.2%) 2 (0.4%) 20 (3.9%) Fuhrman grade I 210 (11.5%) 175 (21.2%) 26 (5.8%) 8 (1.6%) II 747 (40.9%) 430 (52.0%) 165 (36.7%) 145 (28.5%) III 634 (34.7%) 201 (24.3%) 193 (43.0%) 219 (43.1%) IV 237 (13.0%) 21 (2.5%) 65 (14.5%) 136 (26.8%) Presence of nodal metastases (N 1 2 ) 249 (13.6%) 60 (13.4%) 156 (30.7%) N (9.0%) 60 (13.4%) 93 (18.3%) N 2 84 (4.6%) 63 (12.4%) Presence of distant metastases (M 1 ) 508 (27.8%) 508 (100.0%) ECOG-PS (42.8%) 197 (23.8%) 193 (43.0%) 364 (71.7%) Platelet count (10 9 /l) Mean (median) (271.0) (253.0) (271.0) (310.0) Range Platelet count (10 9 /l) (dichotomized) < (90.6%) 793 (95.9%) 406 (90.4%) 424 (83.5%) (9.4%) 34 (4.1%) 43 (9.6%) 84 (16.5%) Platelet count (10 9 /l) (most informative cut-offs) (27.3%) 274 (33.1%) 127 (28.3%) 90 (17.7%) (33.5%) 308 (37.2%) 148 (33.0%) 148 (29.1%) (17.1%) 134 (16.2%) 67 (14.9%) 103 (20.3%) (22.1%) 111 (13.4%) 107 (23.8%) 167 (32.9%) Preoperative haemoglobin (g/dl) Mean (median) 13.0 (13.1) 13.5 (13.7) 12.8 (12.9) 12.4 (12.4) Range Preoperative haemoglobin (g/dl) (dichotomized) < (29.3%) 149 (18.0%) 149 (33.2%) 217 (42.7%) (70.7%) 678 (82.0%) 300 (66.8%) 291 (57.3%) Preoperative haemoglobin (g/dl) (most informative cut-offs) (15.4%) 67 (8.1%) 79 (17.6%) 122 (24.0%) (13.9%) 82 (9.9%) 70 (15.6%) 95 (18.7%) (20.7%) 167 (20.2%) 90 (20.0%) 113 (22.2%) (17.5%) 171 (20.7%) 67 (14.9%) 75 (14.8%)

4 european urology 52 (2007) Table 1 (Continued ) Variable Overall cohort Localized RCC (T1 2N0M0) Locally advanced RCC (T3N0 1M0) Metastatic RCC (TanyNanyM1) (10.2%) 107 (12.9%) 43 (9.6%) 34 (6.7%) (21.2%) 226 (27.3%) 94 (20.9%) 63 (12.4%) (1.1%) 7 (0.8%) 6 (1.3%) 6 (1.2%) RCC-specific mortality 489 (26.8%) 54 (6.5%) 119 (26.5%) 296 (58.3%) Follow-up (yr) Mean (median) 3.6 (2.1) 4.4 (3.3) 4.3 (2.9) 1.6 (0.8) Range Time to death for those that died (yr) Mean (median) 2.0 (1.2) 3.9 (3.5) 2.9 (2.0) 1.4 (0.8) Range Actuarial median (mean) survival (yr) 13.6 (17.3) 18.9 (not reached) 13.4 (14.7) 3.4 (1.7) RCC = renal cell carcinoma; UCLA = University of California, Los Angeles; ECOG-PS = Eastern Cooperative Oncology Group-Performance Status. prognostic profiles. All statistical tests were performed with S-PLUS Professional, version 1 (MathSoft Inc, Seattle, WA, USA). Moreover, all tests were two-sided with a significance level set at Results Patient characteristics are shown in Table 1. The majority (n = 1211, 66.2%) were men whose age ranged from 10 to 94 yr (mean: 61.4; median: 63.0). Of 1828 RCC patients treated with nephrectomy, pt1, pt2, pt3, and pt4 respectively accounted for 710 (38.8%), 259 (14.2%), 803 (43.9%), and 56 (3.1%) cases. The mean tumour size was 7.1 cm (range: ; median: 6.4 cm). Clear-cell histology was present in 1583 cases (84.1%). Sarcomatoid and unclassified RCC tumours accounted for 1.5% of all cases (n = 28). Fuhrman II (40.9%) and III (34.7%) represented the most frequent tumour grades. Node-positive disease was diagnosed in 13.6% of cases, whilst 27.8% had systemic metastases. ECOG- PS 1 was present in 42.8% of patients. Platelet count ranged from 44 to per litre. Of the entire cohort, 171 patients (9.4%) demonstrated a platelet count per litre [4]. The mean and median preoperative haemoglobin levels were respectively 13.0 and 13.1 g/dl (range: ). Most patients (n = 1292, 70.7%) had a preoperative haemoglobin level 11.9 g/dl, whilst 29.3% were anaemic at the time of diagnosis. The overall follow-up time ranged from 0.1 to 22.8 yr (mean: 3.6; median: 2.1). Of all patients, 489 (26.8%) died of RCC, and an additional 149 (8.1%) died of other causes. For those who died of RCC, the mean and median times to RCC-specific death were respectively 2.0 and 1.2 yr (range: ). For all patients at risk, the actuarial mean and median survival values were respectively 17.3 and 13.6 yr. Fig. 1 shows RCC-SS for the entire cohort, whilst Fig. 2 shows the survival dichotomised according to the previously suggested platelet count ( per litre). The mean and median values for survival of patients with platelet count per litre were 5.6 and 2.4 yr. Fig. 3 shows the survival dichotomised according to the previously suggested preoperative haemoglobin level (11.9 g/dl). The mean and median values for survival of patients with preoperative haemoglobin level <11.9 g/dl were 10.0 and 4.7 yr. Table 2 shows the univariable analyses addressing RCC-SM, for which tumour size, TNM stages, ECOG-PS, histologic subtype, platelet count, and preoperative haemoglobin level represented statistically significant predictors of RCC-SM. When univariable predictive accuracy was analysed, M stage represented the most informative predictor of RCC-SM (74.3%), followed by T stage (71.4%), Fuhrman grade (70.5%), tumour size (70.2%), and Fig. 1 Renal cell carcinoma-specific survival in the study cohort of 1828 patients treated with nephrectomy.

5 1432 european urology 52 (2007) Fig. 2 Renal cell carcinoma-specific survival stratified according to dichotomously coded platelet count (cut-off of 450 T 10 9 per litre). ECOG-PS (68.6%), with 50% accuracy representing a random event. The accuracy of platelet count ranged from 55.8% to 65.2%, depending on its coding. The lowest predictive accuracy was recorded when the previously reported cut-off of per litre was used. Continuously coded platelet count yielded 65.2% predictive accuracy; when the most informative cut-offs were identified, predictive accuracy was 65.2%. Preoperative haemoglobin level was 65.2% accurate in predicting RCC-SM; its value increased to 65.3% when the most informative cut-offs were identified. Table 2 also shows the multivariable analyses and the predictive accuracy of multivariable models. All variables achieved independent predictor status in the base model (all p values 0.05). The base multivariable model, which excluded platelet count and preoperative haemoglobin, was 85.3% accurate in predicting RCC-SM. Inclusion of the platelet count, using its most informative categoric coding, Fig. 3 Renal cell carcinoma-specific survival stratified according to dichotomously coded preoperative haemoglobin (cut-off of 11.9 g/dl).

6 european urology 52 (2007) Table 2 Univariable and multivariable analyses predicting renal cell carcinoma-specific mortality Predictor Univariable Multivariable Rate ratio; p value Predictive accuracy Baseline Model 1 Model 2 Model 3 Rate ratio; p value Rate ratio; p value Rate ratio; p value Rate ratio; p value Age 1.0; % 1.0; ; ; ; 0.01 Tumour size 1.2; < % 1.0; ; ; ; 0.1 T stage ; < % ; <0.001 ; <0.001 ; <0.001 ; <0.001 T 2 vs. T 1 4.0; < ; ; ; ; T 3 vs. T 1 6.7; < ; < ; < ; < ; <0.001 T 4 vs. T ; < ; < ; < ; < ; <0.001 Presence of nodal metastases (N 1 2 ) ; < % ; <0.001 ; <0.001 ; <0.001 ; <0.001 N 1 vs. N 0 4.8; < ; < ; < ; < ; <0.001 N 2 vs. N 0 4.2; < ; ; ; ; 0.4 Presence of distant metastases (M 1 ) 8.5; < % 4.1; < ; < ; < ; <0.001 ECOG-PS (0 vs. 1) 4.1; < % 1.9; < ; < ; < ; <0.001 Fuhrman grade ; < % ; <0.001 ; <0.001 ; <0.001 ; <0.001 II vs. I 4.1; < ; ; ; ; 0.02 III vs. I 8.7; < ; ; ; ; IV vs. I 22.7; < ; < ; < ; < ; <0.001 Histologic type ; < % ; ; ; <0.001 ; Papillary vs. clear 0.6; ; ; ; ; 0.7 Chromophobe vs. clear 0.5; ; ; ; ; 0.9 Sarcomatoid and undifferentiated vs. clear 9.6; < ; < ; ; < ; <0.001 Platelet count (10 9 /l) (continuously coded) 1.0; < % Platelet count (450 vs. < /l) 2.8; < % Platelet count (10 9 /l) (most informative) ; < % ; <0.001 ; <0.001 > vs ; ; ; 0.02 > vs ; < ; ; > vs ; < ; < ; <0.001 Preoperative haemoglobin (g/dl) (continously coded) 0.8; < % Preoperative haemoglobin (g/dl) ; < % ; 0.02 ; 0.2 > vs ; ; ; 0.1 > vs ; < ; ; 0.1 > vs ; < ; ; 0.2 > vs ; < ; ; 0.02 > vs ; < ; ; 0.3 > vs ; ; ; 0.8 Predictive accuracy 85.3% 85.6% 85.4% 85.6% ECOG-PS = Eastern Cooperative Oncology Group-Performance Status. Model 1: platelet count coded using most informative cut-offs. Model 2: preoperative haemoglobin coded using most informative cut-offs. Model 3: platelet count and preoperative haemoglobin coded using most informative cut-offs. The rate ratio indicates the increase in the rate of renal cell carcinoma-specific mortality related to each predictor. resulted in predictive accuracy of 85.6% (gain of 0.3%). When the most informative variant of preoperative haemoglobin level was added to the base model, predictive accuracy was 85.4% (gain of 0.1% from base). Finally, when both variables were added by using their most informative univariable coding, predictive accuracy reached 85.6% (gain of 0.3%). The three subgroup analyses showed no benefit related to the addition of platelet count or haemoglobin level or both variables, as evidenced by predictive accuracy gains that ranged from 0.6 to +1.2% ( p 0.5). 4. Discussion Several studies have shown that anaemia and thrombocytosis correlated with poor outcomes in patients with RCC [4 12]. Because of their potential value and the availability of platelet count and

7 1434 european urology 52 (2007) preoperative haemoglobin level for invariably all patients with RCC, we hypothesized that these variables could improve the prognostic ability of other established markers. To address this hypothesis, we used the most stringent methodologic criteria suggested by Kattan [19], in which, besides demonstrating the independent predictor status of a novel marker, the candidate marker should enhance the accuracy of established predictors. We added this methodology to the standard univariable and multivariable regression analyses that all previous studies relied on. Our Kaplan-Meier analyses demonstrated that platelet count as well as preoperative haemoglobin level can accurately stratify between those with poor prognosis and those with better prognosis (both log-rank p 0.001). Moreover, univariable analyses predicting RCC-SM also confirmed that both platelet count and preoperative haemoglobin level represented statistically significant predictors ( p < 0.001). In univariable predictive accuracy tests, accuracy of a platelet count coded as a dichotomous variable (cut-off of per litre), continuously coded, or coded using its most informative categoric coding (Table 1) was respectively 55.8%, 65.2%, or 65.2%. Similarly, accuracy value of preoperative haemoglobin level coded as a continuous variable or using its most informative categoric coding (Table 1) was respectively 65.2% or 65.3%. In multivariable analyses, platelet count ( p < 0.001) and preoperative haemoglobin level ( p = 0.02), in their most informative formats, achieved independent predictor status. However, little change in predictive accuracy was recorded when platelet count and preoperative haemoglobin level were added to the base model that contained all standard predictors (age, tumour size, TNM stage, ECOG-PS, Fuhrman grade, and histologic subtype). Specifically, when the most informative coding of the platelet count was added to the base model, predictive accuracy increased by 0.3% (from 85.3% to 85.6%). Similarly, when the base model was complemented with the most informative coding of preoperative haemoglobin, predictive accuracy increased only by 0.1% (from 85.3% to 85.4%). When both platelet count and preoperative haemoglobin level were added, the predictive accuracy increased by 0.3%, from 85.3% to 85.6%. From a practical perspective, a gain of 0.3% corresponds to correctly classifying 3 additional patients out of 1000, relative to a prognostic scheme that does not include these two variables. These predictive accuracy changes were not statistically significant. Moreover, when we repeated our analyses in the three subgroups (localized vs. locally advanced vs. metastatic), we found no benefit from adding platelet count or haemoglobin level or both variables. This finding implies that no specific patient subgroup appears to benefit from the consideration of these variables. These observations emphasize that anaemia and thrombocytosis may represent statistically significant predictors of RCC-SM in univariable and multivariable models. However, their addition to established variables does not improve the ability to predict RCC-SM. These findings indicate that anaemia and thrombocytosis do not fulfil the characteristics of informative markers of RCC-SM. From a clinical perspective, our results demonstrate that patients who present with thrombocytosis and/or anaemia do not have worse prognosis than their counterparts who do not exhibit these apparently unfavourable characteristics, as long as the effect of TNM stage, histology, tumour grade, and ECOG-PS is considered. In consequence, treatment decisions, such as nephrectomy or delivery of systemic therapy should not be based on the consideration of either thrombocytosis or anaemia. This finding is important since many novel markers may be aggressively promoted on the basis of their independent predictor status. To address the issue of potentially exaggerated effect of such candidate markers, Kattan [19] recommends that a novel marker not only should be judged according to its multivariable statistical significance but also should increase the combined predictive accuracy of established base predictors. Consequently, a novel marker should be judged by its added value. Our work has indicated that neither thrombocytosis nor anaemia add any value to established predictors in a large cohort of 1828 patients. Our findings are consistent with previous analyses of independent predictor status and predictive accuracy, in which independent predictor status did not invariably translate into increase of predictive accuracy [17,20,21]. Therefore, it may be postulated that independent predictor status of several predictors might have been overrated, as was the case in this analysis. In the case of thrombocytosis, several investigators demonstrated that it represents an independent predictor of RCC-SM. These include Symbas et al [7] (n = 259), O Keefe et al [5] (n = 204), Inoue et al [6] (n = 196), Gogus et al [8] (n = 151), and Bensalah et al [4] (n = 804). Similarly, a number of studies have shown that anaemia represents a significant predictor of RCC-SM. These include Citterio et al [10] (n = 109) as well as Motzer et al in 1999 [12] (n = 670) and 2004 [11] (n = 251), who respectively assessed the effect of anaemia prior to nephrectomy and in the

8 european urology 52 (2007) metastatic setting, where nephrectomy was not always performed. Our overall analysis indicated that anaemia has no prognostic relevance. Moreover, our subgroup analyses, in which we assessed the prognostic relevance of anaemia in patients with localized, locally advanced, or finally metastatic RCC confirmed its lack of prognostic relevance in either of the subgroups. The discrepancy between our study and the previous reports, in which either thrombocytosis and/or anaemia represented key predictor variables, may be explained in several ways. First, none of the previous studies used the Kattan approach for testing of the added value of a novel marker. It is possible that, if this approach had been used, anaemia or thrombocytosis or both variables would not have been identified as prognostically relevant variables. Second, important differences exist between our study and, for example, that of Motzer et al [12], in which 670 patients with metastatic RCC were studied and anaemia represented one of the key prognostic variables. One of the main differences relates to the fact that only 65% of patients underwent a nephrectomy in Motzer et al s cohort versus 100% in our series. Thus, one third of Motzer et al s cohort had disease or ECOG-PS that did not allow for a nephrectomy. This important difference might have obliterated the prognostic relevance of anaemia in our cohort, whilst anaemia exerted a significant effect in Motzer et al s cohort. Finally, crucial differences between available predictor variables distinguish our study from others, such as that of Motzer et al [12]. In our series, TNM stages, tumour grade, histology, and ECOG-PS represented the strategic prognostic determinants. In Motzer et al s series, except for ECOG-PS, these variables were not universally available and could not be included in the final multivariable model (Motzer criteria). The recognition of this difference between our model and that of Motzer et al is crucial when the prognostic relevance of anaemia is interpreted and possibly extrapolated to a setting different from a surgical series. As we stated in our results, anaemia has no prognostic relevance when it is considered alongside stage, grade, histology, and ECOG-PS. However, this observation does not imply that anaemia has no bearing on prognosis when the Motzer criteria are used and when the effect of anaemia is considered with entirely different prognostic variables such as albumin, alkaline phosphatase, lactate dehydrogenase, and corrected calcium. These laboratory values certainly exert a different effect on prognosis than TNM stage, histology, and grade. Therefore, we wish to emphasize that the prognostic irrelevance of anaemia applies when it is considered with TNM stage, histology, tumour grade, and ECOG-PS. Conversely, anaemia may have an entirely different effect on prognosis when it is examined with variables that are part of the Motzer criteria. In the latter case, the effect of anaemia should not be discounted on the basis of the current report. Several other limitations may apply to our findings. Detailed information regarding adjuvant and/or salvage treatment regimens of some of our patients was not available. Some received adjuvant immunotherapy, whilst others received immunotherapy at relapse. Finally, some were treated with experimental chemotherapy, whilst others received only the best supportive care. It is unlikely that adjuvant or salvage therapies have contributed to a significantly longer survival, because the majority of historic regimens are associated with dismal effect on survival [22]. Nonetheless, our survival findings might have been contaminated by the effect of immunotherapy in some individuals. Moreover, we addressed only survival. Thrombocytosis and anaemia may have a stronger effect on RCC recurrence. Therefore, separate analyses are needed to elucidate the prognostic significance of these variables in recurrence prediction. Finally, our patients originated from Europe and the United States. Therefore, individuals of different ethnic or racial backgrounds may exhibit different characteristics with respect to the importance of thrombocytosis and anaemia. Despite these and other limitations, our data provide an important insight into RCC-SM of patients with high platelet counts or low preoperative haemoglobin levels. 5. Conclusions Our findings indicate that platelet count and preoperative haemoglobin level are statistically significant and independent predictors of poor RCC-SM. However, platelet count and preoperative haemoglobin level do not add to the predictive accuracy of established predictors of RCC-SM in multivariable analyses. Therefore, clinical decision making should not be affected by either platelet count or preoperative haemoglobin level when TNM stage, tumour histology, tumour grade, and ECOG- PS are known. Conflicts of interest The authors have nothing to disclose.

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New York: Springer; [14] Patard J-J, Leray E, Rodriguez A, et al. Correlation between symptom graduation, tumor characteristics and survival in renal cell carcinoma. Eur Urol 2003;44: [15] Taccoen X, Valeri A, Descotes J-L, et al. Renal cell carcinoma in adults 40 years old or less: young age is an independent prognostic factor for cancer-specific survival. Eur Urol 2007;51: [16] Ficarra V, Martignoni G, Galfano A, et al. Prognostic role of the histologic subtypes of renal cell carcinoma after slide revision. Eur Urol 2006;50:786 93, discussion [17] Karakiewicz PI, Lewinshtein DJ, Chun FK-H, et al. Tumor size improves the accuracy of TNM predictions in patients with renal cancer. Eur Urol 2006;50: [18] Lane BR, Kattan MW. Predicting outcomes in renal cell carcinoma. Curr Opin Urol 2005;15: [19] Kattan MW. Judging new markers by their ability to improve predictive accuracy. J Natl Cancer Inst 2003;95: [20] Mallah KN, DiBlasio CJ, Rhee AC, et al. Body mass index is weakly associated with, and not a helpful predictor of, disease progression in men with clinically localized prostate carcinoma treated with radical prostatectomy. Cancer 2005;103: [21] Graefen M, Ohori M, Karakiewicz PI, et al. Assessment of the enhancement in predictive accuracy provided by systematic biopsy in predicting outcome for clinically localized prostate cancer. J Urol 2004;171: [22] Bleumer I, Oosterwijk E, De Mulder P, Mulders PFA. Immunotherapy for renal cell carcinoma. Eur Urol 2003;44: Editorial Comment on: Platelet Count and Preoperative Haemoglobin Do Not Significantly Increase the Performance of Established Predictors of Renal Cell Carcinoma-Specific Mortality Axel Bex The Netherlands Cancer Institute, Division of Surgical Oncology, Department of Urology, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands a.bex@nki.nl Several attempts are underway to find a consensus for a unifying and universal model that can be used to predict prognosis and treatment outcome in the future for patients with renal cell carcinoma (RCC). In this regard, the article by Karakiewicz et al [1] adds valuable information to the discussion. Some of the established predictive factors are part of the Universal Integrated Staging System (UISS) score. In established metastatic RCC the TNM stage is less important with regard to T stage, which is why a metastatic UISS score has been validated with N1 and N2/M1 as a variable together with grade and Eastern Cooperative Oncology Group (ECOG) performance [2]. However, other metastatic risk assessment scores such as the Memorial Sloan-Kettering Cancer Center (MSKCC) score are widely used [3]. Though initially established from analysing patients who were treated with interferon-a, this score is now often applied to assess the risk of a metastatic patient prior to treatment. In France a similar, but slightly different score is used [4]. In the MSKCC score hemoglobin count is one of the five clinical parameters of Karnofsky, time from diagnosis to treatment, serum calcium, lactic dehydrogenase, and hemoglobin that predict survival with a low, intermediate, and poor risk. Thus, having a low hemoglobin and no other parameters (=1 risk factor) would put a patient with an otherwise low risk (0 risk factor)

10 european urology 52 (2007) and a survival of >29.6 mo in the intermediate-risk group with a survival of 13.8 mo. Low hemoglobin may therefore be responsible for a survival difference of a median of almost 16 mo. Karakiewicz et al [1] evaluated the added accuracy of hemoglobin in M1 and N2 patients without being able to demonstrate an increase. This may be a result of a different patient population, as they analysed preoperative hemoglobin values. Additionally, only 27.8% of their patients had distant metastases, whereas the MSKCC score was established in an entirely metastatic patient population. Again, this article underscores the need for a universal model. References [1] Karakiewicz PI, Trinh Q-D, Lam JS, et al. Platelet count and preoperative haemoglobin do not significantly increase the performance of established predictors of renal cell carcinoma-specific mortality. Eur Urol 2007; 52: [2] Patard JJ, Kim HL, Lam JS, et al. Use of the University of California Los Angeles integrated staging system to predict survival in renal cell carcinoma: an international multicenter study. J Clin Oncol 2004;22: [3] Motzer RJ, Bacik J, Murphy BA, Russo P, Mazumdar M. Interferon alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma. J Clin Oncol 2002;20: [4] Negrier S, Douilard JY, Gomez F, Lasset C, Chevreau C, Escudier B. Interleukin-2 and interferon in metastatic kidney cancer. Experience of the French Immunotherapy Group. Prog Urol 2002;12: DOI: /j.eururo DOI of original article: /j.eururo