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BACKGROUND: Currently, one-year survival of older people with complex co-morbidities is unpredictable. Identifying older adults with a reduced life expectancy will lead to more targeted care and better healthcare resource allocation. METHODS: Development and validation of one-year and three-month mortality risks in people aged ≥65 years who had completed an International Resident Assessment Instrument-Home Care (interRAI-HC) assessment between July 2012 and March 2018. Data was split into development (90%) and validation data sets (10%). A multivariable logistic regression model using data from 108 interRAI questions across multiple domains was developed and validated using discrimination metrics and calibration curves. Variables each explaining at least 1% of the model were then used to develop and validate a parsimonious model. Subgroups by sex, age, ethnicity, and comorbidities were evaluated. FINDINGS: There were 104,436 persons (60.2% female; mean age 82.1 years) in the study cohort of whom 20,972 (20.1%) died within one year. The full multivariable model had area under the curves (AUCs) of 0.778 to 0.795 in the 5 validation datasets and was well calibrated. After variable reduction a parsimonious model consisted of 16 variables and was well calibrated and the AUC remained high: 0.773 (0.769 to 0.777). The three-month parsimonious model comprised 22 variables and was well calibrated with an AUC of 0.843 (95%CI: 0.839 to 0.848). INTERPRETATION: These community-based risk prediction models accurately predict mortality in older people with complex co-morbidities. They may contribute to both forecasting for policy making and clinical decision making regarding an individual's needs. FUNDING: The New Zealand Health Research Council.
Pickering et al. (Thu,) studied this question.