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

David Margel1,2, David R Urbach3,4,5,6,7, Lorraine L Lipscombe8,9,10

  • 1Division of Urology, Rabin Medical Center, Beilinson Campus, 39 Jabotinsky, Petah Tikva, 4941492, Israel. sdmargel@gmail.com.

BMC Medical Informatics and Decision Making
|December 16, 2014
PubMed

Insights

Adding pathology data to administrative databases significantly improves survival prediction models for prostate cancer (PC) patients. This enhances accuracy for both all-cause and PC-specific mortality, especially for the latter.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Oncology

Background:

  • Administrative databases offer cost-effective survival prediction models.
  • Chart review for pathological data is time-consuming and expensive.
  • The study aimed to assess the added value of pathology data to administrative data for mortality prediction.

Purpose of the Study:

  • To determine the incremental value of pathological data from chart abstraction.
  • To enhance prediction models for all-cause and prostate cancer (PC)-specific mortality.
  • To compare models using administrative data alone versus administrative data plus pathology information.

Main Methods:

  • A cohort of 4001 men with diabetes and PC was identified using population-based data.
  • Two Cox-proportional hazard models were compared using c-statistic and net reclassification improvement (NRI).
  • Model 1: Administrative data covariates (age, co-morbidity, year, SES, rurality). Model 2: Model 1 + Gleason grade and cancer volume.

Main Results:

  • The extended model (including pathology) improved 5-year all-cause mortality prediction accuracy (c-statistic 0.74 vs 0.70) with a 14.8% NRI.
  • The extended model significantly improved 5-year PC-specific mortality prediction accuracy (c-statistic 0.85 vs 0.76) with a 28% NRI.
  • Pathology data provided a substantial improvement, particularly for PC-specific mortality.

Conclusions:

  • Pathology chart abstraction enhances the accuracy of mortality prediction models.
  • The improvement is more pronounced for prostate cancer (PC)-specific mortality than for all-cause mortality.
  • Integrating pathology data offers significant value for refining survival predictions in PC patients.
Abstract

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