title: Mortality predictive indexes for the community-dwelling elderly US population.
abstract: BACKGROUND
  Few predictive indexes for long-term mortality have been developed for community-dwelling elderly populations. Parsimonious predictive indexes are important decision-making tools for clinicians, policy makers, and epidemiologists.
  
  
  OBJECTIVE
  To develop 1-, 5-, and 10-year mortality predictive indexes for nationally representative community-dwelling elderly people.
  
  
  METHODS
  Cohort study.
  
  
  METHODS
  The Second Longitudinal Study of Aging (LSOA II).
  
  
  METHODS
  Nationally representative civilian community-dwelling persons at least 70 years old. We randomly selected 60% of the sample for prediction development and used the remaining 40% for validation.
  
  
  METHODS
  Sociodemographics, impairments, and medical diagnoses were collected from the LSOA II baseline interviews. Instrumental activities of daily living (IADLs) stages were derived to measure functional status. All-cause mortality was obtained from the LSOA II Linked Mortality Public-use File.
  
  
  RESULTS
  The analyses included 7,373 sample persons with complete data, among which mortality rates were 3.7%, 23.3%, and 49.8% for 1, 5, and 10 years, respectively. Four, eight, and ten predictors were identified for 1-, 5-, and 10-year mortality, respectively, in multiple logistic regression models to create three predictive indexes. Age, sex, coronary artery disease, and IADL stages were the most essential predictors for all three indexes. C-statistics of the three indexes were 0.72, 0.74, and 0.75 in the development cohort and 0.72, 0.72, and 0.74 in the validation cohort for 1-, 5-, and 10-year mortality, respectively. Five risk groups were defined based on the scores.
  
  
  CONCLUSIONS
  The 1-, 5-, and 10-year mortality indexes include parsimonious predictor sets maximizing ease of mortality prediction in community settings. Thus, they may provide valuable information for prognosis of elderly patients and guide the comparison of alternative interventions. Including IADL stage as a predictor yields simplified mortality prediction when detailed disease information is not available.
authors.affiliation: Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
authors.forename: Zi
authors.initials: Z
authors.lastname: Zhang
authors.forename: Dawei
authors.initials: D
authors.lastname: Xie
authors.forename: Jibby E
authors.initials: JE
authors.lastname: Kurichi
authors.forename: Joel
authors.initials: J
authors.lastname: Streim
authors.forename: Guangyu
authors.initials: G
authors.lastname: Zhang
authors.forename: Margaret G
authors.initials: MG
authors.lastname: Stineman
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D015331:Cohort Studies; D005260:Female; D005544:Forecasting; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D009026:Mortality / Q000639:trends*; D011446:Prospective Studies; D012111:Residence Characteristics; D014481:United States / Q000453:epidemiology