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

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2014
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Title
Is pathology necessary to predict mortality among men with prostate-cancer?
Published in
BMC Medical Informatics and Decision Making, December 2014
DOI 10.1186/s12911-014-0114-6
Pubmed ID
Authors

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

Abstract

BackgroundStatistical 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.MethodsWe 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.ResultsThe 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.ConclusionsPathology 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.

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Mendeley readers

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The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 8%
Unknown 22 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 33%
Researcher 4 17%
Lecturer 1 4%
Student > Doctoral Student 1 4%
Other 1 4%
Other 4 17%
Unknown 5 21%
Readers by discipline Count As %
Medicine and Dentistry 8 33%
Mathematics 2 8%
Nursing and Health Professions 2 8%
Economics, Econometrics and Finance 2 8%
Social Sciences 2 8%
Other 1 4%
Unknown 7 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 December 2014.
All research outputs
#18,386,678
of 22,774,233 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,567
of 1,984 outputs
Outputs of similar age
#258,274
of 356,557 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#29
of 33 outputs
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