title: 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 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.
  
  
  CONCLUSIONS
  Widespread attempts to improve prognostic models might consider not only novel biomarkers, but also psychosocial questionnaire measures.
authors.affiliation: *Department of Psychiatry, University of Rochester Medical Center, Rochester, NY †Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, South Bridge, Edinburgh, UK ‡Departments of Family Medicine and Community and Preventive Medicine, Family Medicine Research Programs, University of Rochester Medical Center, Rochester §Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, NY ∥Harvard School of Public Health, Division of Social Epidemiology, Boston, MA.
authors.forename: Benjamin P
authors.initials: BP
authors.lastname: Chapman
authors.forename: Alexander
authors.initials: A
authors.lastname: Weiss
authors.forename: Kevin
authors.initials: K
authors.lastname: Fiscella
authors.forename: Peter
authors.initials: P
authors.lastname: Muennig
authors.forename: Ichiro
authors.initials: I
authors.lastname: Kawachi
authors.forename: Paul
authors.initials: P
authors.lastname: Duberstein
mesh_terms: D001294:Attitude to Health; D015897:Comorbidity / Q000639:trends*; D006304:Health Status; D006305:Health Status Indicators; D006801:Humans; D009026:Mortality / Q000639:trends*; D011379:Prognosis; D011788:Quality of Life; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods; D012919:Social Behavior