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

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• 作者：David Margel (1) (2)
  David R Urbach (3) (4) (5) (6) (7)
  Lorraine L Lipscombe (5) (7) (8)
  Chaim M Bell (5) (7) (9)
  Girish Kulkarni (10) (5)
  Jack Baniel (1)
  Neil Fleshner (10)
  Peter C Austin (5) (7)
  
  1. Division of Urology ; Rabin Medical Center ; Beilinson Campus ; 39 Jabotinsky ; Petah Tikva ; 4941492 ; Israel
  2. Davidoff Cancer Center ; Rabin Medical Center ; Beilinson Campus ; Petah Tikva ; Israel
  3. Departments of Surgery and Health Policy Management and Evaluation ; University of Toronto ; Toronto ; Canada
  4. Division of Clinical Decision Making and Health Care ; Toronto General Hospital Research Institute ; Toronto ; Canada
  5. Institute for Clinical Evaluative Sciences (ICES) ; Toronto ; Canada
  6. Cancer Care Ontario ; Ontario ; Canada
  7. Institute for Health Policy Management and Evaluation ; University of Toronto ; Toronto ; Canada
  8. Department of Medicine ; Women鈥檚 College Hospital and Research Institute ; University of Toronto ; Toronto ; Canada
  9. Department of Medicine and Keenan Research Centre in the Li Ka Shing Knowledge Institute ; St. Michael鈥檚 Hospital ; Toronto ; Canada
  10. Division of Urology ; Department of Surgical Oncology ; Princess Margaret Hospital ; University Health Network ; Toronto ; Ontario ; Canada
  
• 关键词：Prostate cancer ; Survival ; Prediction models ; Population ; based study
• 刊名：BMC Medical Informatics and Decision Making
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：14
• 期：1
• 全文大小：210 KB
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• 刊物主题：Health Informatics; Information Systems and Communication Service; Management of Computing and Information Systems;
• 出版者：BioMed Central
• ISSN：1472-6947

文摘

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.