Abstract
IntroductionRecent trials have emphasized the importance of a precise patient selection for cytoreductive nephrectomy (CN). In 2013, a nomogram was developed for pre- and postoperative prediction of the probability of death (PoD) after CN in patients with metastatic renal cell carcinoma. To date, the single-institutional nomogram which included mostly patients from the cytokine era has not been externally validated. Our objective is to validate the predictive model in contemporary patients in the targeted therapy era.Methods: Multi-institutional European and North American data from patients who underwent CN between 2006 and 2013 were used for external validation. Variables evaluated included preoperative serum albumin and lactate dehydrogenase levels, intraoperative blood transfusions (yes/no) and postoperative pathologic stage (primary tumour and nodes). In addition, patient characteristics and MSKCC risk factors were collected. Using the original calibration indices and quantiles of the distribution of predictions, Kaplan-Meier estimates and calibration plots of observed versus predicted PoD were calculated. For the preoperative model a decision curve analysis (DCA) was performed.Results: Of 1108 patients [median OS of 27months (95% CI 24.6-29.4)], 536 and 469 patients had full data for the validation of the pre- and postoperative models, respectively. The AUC for the pre- and postoperative model was 0.68 (95% CI 0.62-0.74) and 0.73 (95% CI 0.68-0.78), respectively. In the DCA the preoperative model performs well within threshold survival probabilities of 20-50%. Most important limitation was the retrospective collection of this external validation dataset.Conclusion: sIn this external validation, the pre- and postoperative nomograms predicting PoD following CN were well calibrated. Although performance of the preoperative nomogram was lower than in the internal validation, it retains the ability to predict early death after CN.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Medizin |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
ISSN: | 0724-4983 |
Sprache: | Englisch |
Dokumenten ID: | 64347 |
Datum der Veröffentlichung auf Open Access LMU: | 19. Jul. 2019, 12:15 |
Letzte Änderungen: | 04. Nov. 2020, 13:43 |