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. DESIGN: Cohort study. SETTING: The Second Longitudinal Study of Aging (LSOA II). PARTICIPANTS: 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. MAIN MEASURES: 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.
RCT Entities:
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. DESIGN: Cohort study. SETTING: The Second Longitudinal Study of Aging (LSOA II). PARTICIPANTS: 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. MAIN MEASURES: 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.
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