Is pathology necessary to predict mortality among men with prostate-cancer? - Abstract

Background: Statistical models developed using administrative databases are powerful and inexpensive tools for predicting survival.

Conversely, data abstraction from chart review is time-consuming and costly. Our aim was to determine the incremental value of pathological data obtained from chart abstraction in addition to information acquired from administrative databases in predicting all-cause and prostate cancer (PC)-specific mortality.

Methods: We identified a cohort of men with diabetes and PC utilizing population-based data from Ontario. We used the c-statistic and net-reclassification improvement (NRI) to compare two Cox- proportional hazard models to predict all-cause and PC-specific mortality. The first model consisted of covariates from administrative databases: age, co-morbidity, year of cohort entry, socioeconomic status and rural residence. The second model included Gleason grade and cancer volume in addition to all aforementioned variables.

Results: The cohort consisted of 4001 patients. The accuracy of the admin-data only model (c-statistic) to predict 5-year all-cause mortality was 0.7 (95% CI 0.69-0.71). For the extended model (including pathology information) it was 0.74 (95% CI 0.73-0.75). This corresponded to a change in category of predicted probability of survival among 14.8% in the NRI analysis.The accuracy of the admin-data model to predict 5-year PC specific mortality was 0.76 (95%CI 0.74-0.78). The accuracy of the extended model was 0.85 (95%CI 0.83-0.87). Corresponding to a 28% change in the NRI analysis.

Conclusions: Pathology chart abstraction, improved the accuracy in predicting all-cause and PC-specific mortality. The benefit is smaller for all-cause mortality, and larger for PC-specific mortality.

Written by:
Margel D, Urbach DR, Lipscombe LL, Bell CM, Kulkarni G, Baniel J, Fleshner N, Austin PC.   Are you the author?
Division of Urology, Rabin Medical Center, Beilinson Campus, 39 Jabotinsky, Petah Tikva 4941492, Israel.

Reference: BMC Med Inform Decis Mak. 2014 Dec 12;14(1):114.
doi: 10.1186/s12911-014-0114-6

PubMed Abstract
PMID: 25495664 Prostate Cancer Section


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