Mortality Risk Prediction: Can Comorbidity Indices Be Improved With Psychosocial Data?

ABSTRACT:

Background

Predicting risk of premature death is one of the most basic tasks in medicine and public health, but has proven to be difficult over the long term even with the best prognostic models. One popular strategy has been to improve prognostic models with candidate genes and other novel biomarkers. However, the gains in predictive power have been modest and the costs have been high, leading to a demand for cost-effective alternatives. We conducted a proof-of-principle investigation to examine whether simple, cheap, and noninvasive paper-and-pencil measures of social class and personality phenotype could improve the performance of one of the most widely used prediction models for all-cause mortality, the Charlson Comorbidity Index (CCI).

Methods

We used data from baseline

SUBMITTER: Chapman BP 

PROVIDER: S-EPMC4658312 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature