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

David Margel, David R. Urbach, Lorraine L. Lipscombe, Chaim M. Bell, Girish Kulkarni, Jack Baniel, Neil Fleshner, Peter C. Austin

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number114
JournalBMC Medical Informatics and Decision Making
Volume14
Issue number1
DOIs
StatePublished - 12 Dec 2014
Externally publishedYes

Keywords

  • Population-based study
  • Prediction models
  • Prostate cancer
  • Survival

Fingerprint

Dive into the research topics of 'Is pathology necessary to predict mortality among men with prostate-cancer?'. Together they form a unique fingerprint.

Cite this