Journal Article Page
European UrologyVolume 61, issue 4, pages e23-e40, April 2012
A Non–Cancer-Related Survival Benefit Is Associated With Partial Nephrectomy
Accepted 24 November 2011, Published online 3 December 2011, pages 725 - 731
Partial nephrectomy (PN) may better protect against other-cause mortality (OCM) when compared with radical nephrectomy (RN) in patients with localized renal cell carcinoma (RCC).
Test the effect of treatment type on OCM.
Design, setting, and participants
Using the Surveillance Epidemiology and End Results–Medicare-linked database, 4956 RN patients (82%) and 1068 PN patients (18%) with T1a RCC were identified (1988–2005).
To adjust for inherent differences between treatment types, we relied on propensity-matched analyses. One-to-one matching was performed according to age, sex, race, baseline Charlson comorbidity index (CCI), baseline diagnosis of hypercalcemia and hyperlipidemia, socioeconomic status (SES), population density, tumor size, and year of surgery. The 2- and 5-yr OCM rates were computed using cumulative incidence. Univariable and multivariable competing-risks regression analyses for prediction of OCM were performed according to treatment type. Adjustment was made for cancer-specific mortality (CSM), patient age, CCI, sex, race, SES, tumor grade, and year of surgery.
Results and limitations
Following propensity-based matching, 1068 RN patients were matched with 1068 PN patients. The 2- and 5-yr OCM rates after nephrectomy were 5.0% and 16.0% for PN versus 6.9% and 18.1% for RN, respectively. In the postpropensity multivariable analyses, patients who underwent PN were significantly less likely to die of OCM compared with their RN-treated counterparts (hazard ratio [HR]: 0.83; 95% confidence interval, 0.69–0.98; p = 0.04). Increasing age (HR: 1.08, p < 0.001), higher CCI (HR: 1.14, p < 0.001), female gender (HR: 0.79, p = 0.02), baseline hypercalcemia (HR: 2.05, p = 0.03), baseline hyperlipidemia (HR: 0.73, p = 0.003), and year of surgery (HR: 0.95, p = 0.003) were independent predictors of OCM.
Compared with PN-treated patients, RN-treated patients are more likely to die of OCM after surgery, even after adjusting for CSM, as well as baseline CCI. Consequently, PN should be offered whenever technically feasible.
Because of the increased incidence of localized renal cell carcinoma (RCC) and downward stage migration  and , utilization rates of partial nephrectomy (PN) have risen significantly  and . Compared with radical nephrectomy (RN), PN has been shown to provide improved renal function , a lower risk of cardiovascular events , and improved survival  and . Despite the established benefits, utilization of PN remains low in the community setting (45% in 2006) . In contrast, RN remains the most common procedure performed for patients with small renal masses worldwide. Its widespread use may be detrimental in patients with multiple baseline conditions and competing causes of mortality, given that only a small minority of patients with localized RCC will die of cancer-specific mortality (CSM).
Previous studies that have reported improved mortality in PN-treated patients compared with RN-treated patients in the community setting failed to account for baseline comorbidities , as well as CSM events  and . Consequently, we sought to compare the effect of treatment type—namely, PN compared with RN—on other-cause mortality (OCM), using a larger and more contemporary population-based cohort and with the consideration of baseline conditions and means of competing-risks mortality, which represents CSM in this context. The current study represents an update of T1a RCC patients treated with PN or RN between 1988 and 2005, extracted from the Surveillance Epidemiology and End Results (SEER)–Medicare-linked database.
2. Patients and methods
2.1. Data source
The current study relied on the SEER–Medicare-linked database. The SEER regions represented approximately 14% of the US population prior to the year 2000 and 26% of the US population thereafter . The Medicare-linked database is 98% complete for case ascertainment. The database encompasses approximately 97% of persons ≥65 yr in the United Sates. Linkage to the SEER database is complete for roughly 93% of the patients.
2.2. Study population
Patients with a primary diagnosis of nonmetastatic T1a RCC (C64.0) between 1988 and 2005 were analyzed. Moreover, patients were included only if they had both Medicare Part A and Part B claims available 12 mo before the first recorded diagnosis and 6 mo after diagnosis and if they were not enrolled in a health maintenance organization throughout the duration of the study period. This step allows computation of a noncancer comorbidity score and relevant treatment information following diagnosis. RCC diagnoses based on autopsy were removed from the study, as were RCC diagnoses whose original or current reason for Medicare entitlement was listed as “disability” or “Medicare status code including disability.” These exclusion criteria resulted in an overall study population of 6024 patients.
2.3. Treatment type
Treatment type was identified by searching Part A and Part B Medicare files and the outpatient claims file using the codes of the International Classification of Diseases, ninth revision (ICD–9), or the Current Procedural Terminology coding system, fourth edition (CPT–4), within 6 mo of primary diagnosis of RCC. Patients who underwent RN were identified using ICD–9 procedure code 55.5x or CPT–4 codes 50220, 50225, 50230, 50545, and 50546. Patients who underwent PN were identified by the presence of ICD–9 procedure code 55.4 or CPT–4 codes 50240, 50280, 50290, and 50543. Patients who had a claim for PN followed by a claim for RN within 30 d were defined as RN and vice versa.
2.4. Patient and sociodemographic characteristics
Patient age at diagnosis, residence (rural, urban), marital status (married, single, unmarried, unknown), and year of surgery were obtained from the Medicare file. Information on sex and race (white, black, other) was obtained using the SEER demographic data file. Subject comorbidity was quantified using the Klabunde modification  of the Charlson comorbidity index (CCI) , which gives a weighted score based on diagnosis in which a higher score indicates a subject with a greater burden of comorbid conditions (0, 1, 2, ≥3). Additional comorbidities associated with RCC outcomes were considered: hypercalcemia (ICD–9: 275.42) and hyperlipidemia (ICD–9: 272.4, 272.0, 272.2) .
Socioeconomic status (SES) was defined according to three county-attribute variables: education level as the percentage of persons who did not complete high school, poverty as the percentage of persons living below the poverty line at the time, and the median family income. These county-attribute variables were measured using the 2000 census documentation files available at http://www.census.gov/main/www/cen2000.html. We created a composite variable of SES using the three variables previously mentioned based on the previous methodology . First, we recoded the variables individually to ensure that low values represent low SES, and vice versa. Second, we transformed all values into standardized scores. Third, we computed the sum of these scores and categorized the total scores into two groups according to the median, which resulted in our low and high SES.
2.5. Tumor characteristics
No patients had any known presence of nodal metastases or distant metastases at surgery. Tumor size was assigned using the variables named e10sz1 for patients diagnosed between 1991 and 2003 and cstum1 for patients diagnosed between 2004 and 2005.
2.6. Statistical analyses
Frequencies and proportions were assessed for categorical variables, while means, medians, and interquartile ranges were computed for continuously coded variables. The chi-square test and independent Mann-Whitney test were used to test the statistical significance of proportions and medians, respectively.
First, because of inherent differences between patients undergoing PN and RN in terms of baseline patient and hospital characteristics, we used 1:1 nearest-neighbor propensity-score matched analyses to adjust for these differences. The use of the propensity score method eliminates the customary bias associated with the conventional multivariable modeling approach  and . Propensity scores were computed by modeling a logistic regression with the dependent variable as the odds of undergoing a PN and the independent variables as patient age, sex, race, baseline CCI, year of surgery, marital status, SES, population density, baseline hypercalcemia, baseline hyperlipidemia, and tumor size. Subsequently, covariate balance between the matched groups was examined.
Second, univariable and multivariable competing-risks regression analyses for prediction of OCM were performed to assess the effect of treatment type. Covariates comprised CSM, patient age, baseline CCI, sex, race, SES, marital status, population density, tumor size, baseline hypercalcemia, baseline hyperlipidemia, Fuhrman grade, and year of surgery.
All tests were two-sided, with a statistical significance set at p < 0.05. Analyses were conducted using the statistical package for R v.2.13.1 (R Foundation for Statistical Computing, Vienna, Austria).
Of 6024 T1a RCC patients, 1068 (18%) and 4956 (82%) were treated with PN and RN, respectively (Table 1). Prior to propensity-based adjustment, PN-treated patients were slightly younger than RN-treated patients (median: 72 yr compared with 74 yr, respectively; p < 0.001) and were more likely to have multiple baseline comorbidities than RN-treated patients (CCI ≥3: 33% compared with 30%, respectively; p < 0.001). Moreover, PN-treated patients compared with RN-treated patients were more likely to be male (59% compared with 52%, respectively; p < 0.001), to be married (66% compared with 61%, respectively; p < 0.001), to reside in a county described as high SES (51% compared with 37%, respectively; p < 0.001), and to harbor smaller tumors (median: 2.5 cm compared with 3.0 cm, respectively; p < 0.001).
|PN||RN||Std. mean diff.||PN||RN||Std. mean diff.|
|Patients, no. (%)||6024 (100)||1068 (17.7)||4956 (82.3)||–||1068 (50.0)||1068 (50.0)||–|
|Mean (median)||74 (73)||72 (72)||74 (74)||72 (72)||72 (71)|
|Male, no. (%)||3197 (53.1)||629 (58.9)||2568 (51.8)||629 (58.9)||643 (60.2)|
|Female, no. (%)||2827 (46.9)||439 (41.1)||2388 (48.2)||439 (41.1)||425 (39.8)|
|White, no. (%)||5164 (85.7)||901 (84.4)||4263 (86.0)||901 (84.4)||912 (85.4)|
|Black, no. (%)||441 (7.3)||95 (8.9)||346 (7.0)||95 (8.9)||79 (7.4)|
|Other, no. (%)||419 (7.0)||72 (6.7)||347 (7.0)||72 (6.7)||77 (7.2)|
|Married, no. (%)||3721 (61.8)||703 (65.8)||3018 (60.9)||703 (65.8)||716 (67.0)|
|Single, no. (%)||388 (6.4)||70 (6.6)||318 (6.4)||70 (6.6)||66 (6.2)|
|Previously married, no. (%)||1734 (28.8)||259 (24.3)||1475 (29.8)||259 (24.3)||261 (24.4)|
|Unknown, no. (%)||181 (3.0)||36 (3.4)||145 (2.9)||36 (3.4)||25 (2.3)|
|High, no. (%)||2397 (39.8)||543 (50.8)||1854 (37.4)||543 (50.8)||511 (47.8)|
|Low, no. (%)||2388 (39.6)||457 (42.8)||1931 (39.0)||457 (42.8)||481 (45.0)|
|Unknown, no. (%)||1239 (20.6)||68 (6.4)||1171 (23.6)||68 (6.4)||76 (7.1)|
|Urban, no. (%)||5512 (91.5)||989 (92.6)||4523 (91.3)||989 (92.6)||981 (91.9)|
|Rural, no. (%)||512 (8.5)||79 (7.4)||433 (8.7)||79 (7.4)||87 (8.1)|
|0, no. (%)||2204 (36.6)||366 (34.3)||1838 (37.1)||366 (34.3)||373 (34.9)|
|1, no. (%)||991 (16.5)||184 (17.2)||807 (16.3)||184 (17.2)||189 (17.7)|
|2, no. (%)||1016 (16.9)||169 (15.8)||847 (17.1)||169 (15.8)||174 (16.3)|
|≥3, no. (%)||1813 (30.1)||349 (32.7)||1464 (29.5)||349 (32.7)||332 (31.1)|
|Baseline hypercalcemia, no. (%)||100 (1.7)||22 (2.1)||78 (1.6)||0.034||22 (2.1)||23 (2.2)||−0.007|
|Baseline hyperlipidemia, no. (%)||3532 (58.6)||755 (70.7)||2777 (56.0)||0.322||755 (70.7)||743 (69.6)||0.025|
|Tumor size, cm||−0.140||−0.011|
|Mean (median)||2.9 (3.0)||2.6 (2.5)||3.0 (3.0)||2.6 (2.5)||2.6 (2.5)|
|Year of surgery||0.794||0.000|
|1988–1996, no. (%)||1626 (27.0)||109 (10.2)||1517 (30.6)||109 (10.2)||101 (9.5)|
|1997–2001, no. (%)||1816 (30.1)||270 (25.3)||1546 (31.2)||270 (25.3)||277 (25.9)|
|2002–2003, no. (%)||1316 (21.8)||303 (28.4)||1013 (20.4)||303 (28.4)||309 (28.9)|
|2004–2005, no. (%)||1266 (21.0)||386 (36.1)||880 (17.8)||386 (36.1)||381 (35.7)|
* Surveillance Epidemiology and End Results–Medicare-linked database.
PN = partial nephrectomy; RN = radical nephrectomy; std. mean diff. = standardized mean differences; CCI = Charlson comorbidity index.
Relying on propensity-score matched analyses, 1068 RN patients were matched to another 1068 PN patients (Table 1). Baseline patient and sociodemographic differences, as well as tumor characteristic differences, between PN and RN were now comparable between the two groups, as demonstrated by standardized mean differences of <10%, which signifies a high degree of similarity in the distribution of both populations.
Within the matched cohort, the 2- and 5-yr OCM rates were 5.0% and 16.0% for PN, compared with 6.9% and 18.1% for RN, respectively (hazard ratio [HR]: 0.85; 95% confidence interval [CI], 0.71–1.03; p = 0.096). In multivariable analyses that accounted for other covariates, including CSM (Table 2), PN was associated with a decreased risk of OCM compared with RN (HR: 0.83; 95% CI, 0.69–0.98; p = 0.04). Of note, female gender (HR: 0.79; 95% CI, 0.65–0.96), baseline hyperlipidemia (HR: 0.73; 95% CI, 0.59–0.90), and more contemporary treated patients (HR: 0.95; 95% CI, 0.91–0.98) were also independently associated with a lower risk of OCM.
|HR (95% CI)||p value||HR (95% CI)||p value|
|Partial nephrectomy||0.85 (0.71–1.03)||0.1||0.83 (0.69–0.98)||0.04|
|Age||1.08 (1.06–1.10)||<0.001||1.08 (1.06–1.10)||<0.001|
|CCI||1.14 (1.10–1.18)||<0.001||1.14 (1.10–1.19)||<0.001|
|Female||0.85 (0.70–1.03)||0.1||0.79 (0.65–0.96)||0.02|
|Black||1.46 (1.08–1.98)||0.02||1.33 (0.98–1.82)||0.07|
|Other||0.75 (0.51–1.11)||0.2||0.76 (0.50–1.44)||0.2|
|Low||1.07 (0.89–1.30)||0.5||1.16 (0.93–1.44)||0.2|
|Unknown||1.41 (1.10–1.79)||0.005||1.07 (0.74–1.56)||0.7|
|Single||1.34 (0.95–1.88)||0.09||1.28 (0.88–1.85)||0.2|
|Previously married||1.13 (0.91–1.41)||0.3||1.08 (0.86–1.37)||0.5|
|Unknown||1.21 (0.71–2.05)||0.5||1.20 (0.69–2.09)||0.5|
|Rural||0.97 (0.69–1.35)||0.8||1.00 (0.71–1.40)||1.0|
|Tumor size||1.00 (0.99–1.00)||0.9||0.99 (0.98–1.00)||0.6|
|Baseline hypercalcemia||1.64 (0.85–3.13)||0.1||2.05 (1.09–3.87)||0.03|
|Baseline hyperlipidemia||0.76 (0.62–0.92)||0.005||0.73 (0.59–0.90)||0.003|
|III||1.03 (0.77–1.39)||0.8||1.15 (0.85–1.56)||0.4|
|IV||1.30 (0.66–2.57)||0.4||1.37 (0.63–2.97)||0.4|
|Unknown||1.02 (0.84–1.25)||0.8||0.99 (0.80–1.21)||0.9|
|Year of surgery||0.96 (0.94–0.98)||<0.001||0.95 (0.91–0.98)||0.003|
* Surveillance Epidemiology and End Results–Medicare-linked database.
HR = hazard ratio; CI = confidence interval; CCI = Charlson comorbidity index; Ref. = referent category.
In the last two decades, PN has become the gold standard for treatment of T1a RCC, even in the context of a normal contralateral kidney . Existing data show equal CSM rates between PN and RN, as well as superiority of preservation of renal function, prevention of chronic kidney disease, improved long-term cardiac morbidity, and improved overall survival , , , , and . Given the established benefits, PN should be performed whenever technically feasible. Unfortunately, the adoption of PN has been predominantly concentrated at select hospitals and has been substantially less gradual in the community setting than in centers of excellence  and . Moreover, given the increased popularity of laparoscopy in recent years , laparoscopic RN may have further overshadowed PN.
Given the equivalence of oncologic outcomes, treatment-induced chronic kidney disease, and related cardiovascular events, RN-treated patients may be at higher risk for OCM compared with PN-treated patients. Two previous studies have reported convincing data on this matter. Huang et al. , relying on the SEER–Medicare-linked database, demonstrated that RN was associated with a greater risk of overall mortality compared with PN (HR: 1.38; p < 0.01; n = 2547). Similarly, Zini et al. , using the SEER limited data, demonstrated a 1.23-fold higher risk of overall mortality for RN compared with PN (p = 0.001). However, previous studies either failed to account for inherent patient and sociodemographic differences between PN and RN individuals or the consideration of baseline conditions, or else they did not account for the effect of CSM, looking at overall survival only. In the current study, we sought to overcome all the aforementioned caveats by examining and comparing OCM rates between PN and RN patients relying on propensity-based matching within a competing-risks regression analysis. Baseline conditions, defined as CCI, as well as hypercalcemia and hyperlipidemia, were also considered.
The first principal finding relates to recorded patient and sociodemographic differences between PN- and RN-treated individuals. Similar to previous studies, PN-treated patients were more likely to be male, white, married, and residing in a county of high SES. Moreover, use of PN decreased with increasing age and increasing tumor size. It is noteworthy that PN patients were more likely to harbor multiple baseline comorbidities and had higher rates of hypercalcemia and hyperlipidemia at RCC diagnosis. These observations are consistent with the underlying renal insufficiency that exists in many PN candidates.
Second, our study was able to identify a protective effect of PN on OCM compared with RN only in multivariable analyses. In univariable analyses, in contrast, treatment type by itself was not associated with a lower risk of OCM. These findings may reflect a careful selection of PN candidates and further highlight the importance of individualized treatment decision making. For example, only a minority of patients diagnosed with small renal masses will actually die of CSM. In that regard, surgical intervention can only be considered appropriate if it can be curative and when the cancer is likely to reduce the life expectancy of the individual. In other cases, active treatment may prove to be a futile attempt, especially in some patients with very advanced age and a high prevalence of comorbidities. Authors recently showed that neither PN nor RN was associated with a survival benefit among patients aged ≥75 yr compared with nonsurgical management . In the contemporary management of patients with localized RCC, other treatment options, such as active surveillance and ablative therapies, may also represent viable strategies. Nonetheless, it is noteworthy that in localized RCC, progression to metastatic disease is non-negligible (1–10%); the median survival time is approximately 21 mo  and . Consequently, despite the potential roles of ablative therapies and active surveillance for small renal masses, further prospective studies with larger sample sizes and longer follow-up (≥5 yr) are needed.
In summary, the treatment objectives for small renal masses extend well beyond tumor control. One has to consider not only the biology of the disease but also the effect of the treatment on non–cancer-related survival. However, the selection of surgical candidates cannot be solely based on age and comorbidities; it must include other factors as recommended by the comprehensive geriatric society . If active treatment is considered for localized RCC, the preservation of renal function should remain among the primary targets.
That being said, while the findings of the current study are in line with previous population-based reports, a recent randomized controlled trial comparing PN and RN (n = 541) reported no difference with respect to overall survival (HR: 1.50; 95% CI, 1.03–2.16; p = 0.8) . Furthermore, RN conferred a longer survival compared with PN (p = 0.03). The discrepancy between the findings of the randomized trial and the current report may stem from the inherent selection biases applicable to retrospective population-based data, such as the SEER. It may be postulated that RN-treated patients in the current study were sicker and older at baseline, harbored more aggressive tumors, or both. Despite the attempt to reduce to a minimum the differences between PN and RN patients, propensity-based analysis cannot adjust for differences that are not available within the database. Previous analyses using retrospective observational data were also limited by this factor  and . Nonetheless, well-designed population-based analyses are more generalizable and allow researchers and physicians to extend the results—in this case, efficacy of treatment type—in the large community. As such, while the current results may be different from those reported by Van Poppel et al. , they remain of essential importance.
Finally, it is not surprising that baseline comorbid conditions were associated with an increased risk of OCM, which is a finding that is consistent with previous reports . However, it is noteworthy that in the current study, hyperlipidemia was strongly associated with a protective effect on OCM (HR: 0.73, p = 0.003). A previous study examining survival among Medicare patients diagnosed with RCC also showed a similar effect (HR: 0.78, p < 0.001) . It is possible that hyperlipidemia represents a marker condition that prompts closer follow-up and encourages physicians and patients to consider a broad range of health-promoting steps. These steps usually include dietary modalities and lifestyle changes that attempt to reduce weight, stress, and other detrimental effects. Conversely, exercise, relaxation, and use of pharmacologic agents such as statins  may further potentiate the effects of other measures. The net benefit likely relates to longer overall survival. Given the limitations of the SEER–Medicare data, as well as the retrospective nature of the report, we may only speculate on the recorded observations. Although the reasons for these correlations remain unclear, additional studies using clinical data should investigate the influence of comorbidities on postoperative outcomes in RCC.
Other limitations of the current study include restriction of the study sample to Medicare beneficiaries, which captures patients aged ≥65 yr only. This restriction may have compromised the generalizability of the results and may represent a major difference relative to previous studies that did not have an age restriction applicable to their analyses. Nonetheless, the current limitation may also be seen as a strength of the study, since elderly patients are often underrepresented in clinical trials. The lack of central pathology also represents a limitation. Furthermore, CCI was coded using administrative codes. While the validity of this method has been previously examined and properly established, the method may be associated with a degree of incorrectness . Consequently, the distribution of comorbidities may not have been properly captured, which resulted in an improper consideration of baseline comorbidities and treatment type. The same observation may have also been operational for attribution of cause of death; some authors have voiced concerns about the reliability of death certificates in ascertainment of cause-of-death information. Specifically, several reports have demonstrated that attribution of cause of death is related to knowledge of previous treatment of prostate cancer . While this effect may not have been directly operational in the current study, it remains a possible bias. Nonetheless, previous authors have examined the reliability of the coding and reported a high degree of accuracy for cause of death within the SEER . Finally, the lack of information on baseline kidney functions and serum creatinine levels to identify patients with preexisting and treatment-induced chronic kidney disease may have influenced the rates of OCM events. As such, the observed effect between treatment type and OCM cannot be inferred as a direct causal relationship but only as a mere correlation, which would need to be confirmed in future studies.
Compared with PN-treated patients, RN-treated patients are more likely to die of OCM after surgery, even after adjusting for CSM, as well as baseline CCI. Consequently, PN should be offered whenever technically feasible.
Author contributions: Maxine Sun had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Sun, Trinh, Bianchi, Hansen, Hanna, Karakiewicz.
Acquisition of data: Sun, Karakiewicz.
Analysis and interpretation of data: Sun, Bianchi.
Drafting of the manuscript: Sun, Karakiewicz.
Critical revision of the manuscript for important intellectual content: Trinh, Abdollah, Shariat, Briganti.
Statistical analysis: Sun.
Obtaining funding: Montorsi, Perrotte, Karakiewicz.
Administrative, technical, or material support: Karakiewicz.
Supervision: Montorsi, Perrotte, Karakiewicz.
Other (specify): None.
Financial disclosures: I certify that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: Pierre I. Karakiewicz is partially supported by the University of Montreal Health Centre Urology Specialists, Fonds de la Recherche en Santé du Québec, the University of Montreal Department of Surgery and the University of Montreal Health Centre (CHUM) Foundation.
Funding/Support and role of the sponsor: None.
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a Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Canada
b Vattikuti Urology Institute, Henry Ford Health System, Detroit, MI, USA
c Department of Urology, Vita Salute San Raffaele University, Milan, Italy
d Martini-Klinic, Prostate Cancer Center Hamburg-Eppendorf, Hamburg, Germany
e Department of Urology, Weill Medical College, Cornell University, New York, NY, USA
f Department of Urology, University of Montreal Health Center, Montreal, Canada
Corresponding author. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, 264 Boul. René-Lévesque East, Suite 228, Montreal QC, Canada, H2X 1P1. Tel. +1 514 890 8000 ext. 35335; Fax: +1 514 227 5103.
Please visit www.eu-acme.org/europeanurology to read and answer questions on-line. The EU-ACME credits will then be attributed automatically.
© 2011 Published by Elsevier B.V.
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