Predictors of mortality shortly after entering a long-term care facility.

ABSTRACT

OBJECTIVE: Moving into a long-term care facility (LTCF) requires substantial personal, societal and financial investment. Identifying those at high risk of short-term mortality after LTCF entry can help with care planning and risk factor management. This study aimed to (i) examine individual-, facility-, medication-, system- and healthcare-related predictors for 90-day mortality at entry into an LTCF and (ii) create risk profiles for this outcome. DESIGN: Retrospective cohort study using data from the Registry of Senior Australians. SUBJECTS: Individuals aged ≥ 65 years old with first-time permanent entry into an LTCF in three Australian states between 01 January 2013 and 31 December 2016. METHODS: A prediction model for 90-day mortality was developed using Cox regression with the purposeful variable selection approach. Individual-, medication-, system- and healthcare-related factors known at entry into an LTCF were examined as predictors. Harrell's C-index assessed the predictive ability of our risk models. RESULTS: 116,192 individuals who entered 1,967 facilities, of which 9.4% (N = 10,910) died within 90 days, were studied. We identified 51 predictors of mortality, five of which were effect modifiers. The strongest predictors included activities of daily living category (hazard ratio [HR] = 5.41, 95% confidence interval [CI] = 4.99-5.88 for high vs low), high level of complex health conditions (HR = 1.67, 95% CI = 1.58-1.77 for high vs low), several medication classes and male sex (HR = 1.59, 95% CI = 1.53-1.65). The model out-of-sample Harrell's C-index was 0.773. CONCLUSIONS: Our mortality prediction model, which includes several strongly associated factors, can moderately well identify individuals at high risk of mortality upon LTCF entry.