Authors: Leyh-Bannurah SR, Zaffuto E, Dell'Oglio P, Tian Z, Moschini M, Capitanio U, Briganti A, Montorsi F, Fisch M, Chun F, Kachanov M, Budäus L, Graefen M, Huland H, Karakiewicz PI
Title: Prediction of Complications in Radical Prostatectomy Prostate Cancer Patients: Simulated Annealing versus Co-Morbidity Indexes.
Journal: Urol Int 102(1):51-59
PubMed ID: 30481764
Abstract: BACKGROUND: The Deyo/Charlson co-morbidity index (CCI) and Klabunde co-morbidity index (KCI) co-morbidity indexes represent outdated indexes when the endpoint of complications after radical prostatectomy (RP) is considered. A novel group of co-morbidities derived from International Classification of Diseases-9 diagnostic codes in a contemporary RP database could provide better accuracy. Research Design, Subjects and Measures: We relied on 20,484 patients with clinically localized non-metastatic prostate cancer treated with RP between 2000 and 2009 in the Surveillance, Epidemiology, and End Results-Medicare linked database. We examined 2 endpoints, namely, 90-day medical complication rate and 90-day surgical complication rate after RP. Simulated annealing (SA) was used to develop a novel co-morbidity index. Finally, the newly identified groups of co-morbid conditions were compared with the CCI and Klabunde indexes. RESULTS: Our SA identified 10 and 7 individual co-morbid conditions able to predict 90-day medical and surgical complications respectively. This novel model showed improved predictive accuracy over CCI and KCI for the 2 endpoints considered (respectively: 59.4 vs. 58.1 and 58.0% for medical complications, 58.0 vs. 56.8 and 56.7% for surgical complications). CONCLUSIONS: The newly defined groupings of co-morbid conditions resulted in better ability to predict the 2 endpoints of interest compared to CCI and KCI. However, the gain was marginal. This implies that better tools should be defined to more accurately predict these outcomes.