Mortality Risk Prediction

Can Comorbidity Indices Be Improved With Psychosocial Data

Bibliographic Data

ID9103428
AuthorsBenjamin P Chapman (0000-0002-5170-3131, University of Rochester Medicine, corresponding author), Alexander Weiss (0000-0002-9125-1555, University of Edinburgh), Kevin Fiscella (0000-0003-3613-8012, University of Rochester Medicine), Peter Muennig (0000-0002-4234-0498, Columbia University), Ichiro Kawachi (0000-0003-3579-4456, Harvard University), Paul R Duberstein (0000-0001-6882-0898, University of Rochester Medicine, corresponding author), Paul Duberstein
Year2015
Volume53
Issue11
Pages909-915
Publication date2015-11-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000428
PMID26421372
OpenAlexW2409514923
LanguageEN
Citations received1
References cited37

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 and 25-year mortality follow-up of the UK Health and Lifestyle Study cohort. In a subset of the cohort, we first identified 5 psychosocial factors highly predictive of mortality: income, education, type A personality, communalism (preference for the company of others), and "lie" scale (a measure of denial, putatively associated with ill health). We then examined the predictive performance of the CCI with and without these measures in a validation subsample. RESULTS: Across 5-, 10-, 15-, 20-, and 25-year time horizons, the psychosocially augmented CCI showed substantially better discrimination [area under the receiver-operating curves (95% confidence interval) from 0.83 (0.81-0.85) to 0.84 (0.83-0.86)] than the CCI [area under the receiver-operating curves from 0.74 (0.71-0.76) to 0.77 (0.76-0.79)]. These translated into net reclassification improvements from 27% (23%-31%) to 35% (32%-38%) of survivors and from 23% (17%-30%) to 34% (17%-30%) of decedents; and 23%-42% reductions in the Number Needed to Screen. Calibration improved at all time horizons except 25 years, where it was decreased. CONCLUSION: Widespread attempts to improve prognostic models might consider not only novel biomarkers, but also psychosocial questionnaire measures

Artificial Intelligence in Healthcare and Education · Chronic Disease Management Strategies · Health disparities and outcomes · Medicine

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