Derivation and validation of an algorithm to predict transitions from community to residential long-term care among persons with dementia-A retrospective cohort study. - Wenshan Li; Luke Turcotte; Amy T Hsu; Robert Talarico; Danial Qureshi; Colleen Webber; Steven Hawken; Peter Tanuseputro; Douglas G Manuel; Greg Huyer (PLOS Digital Health)
Objectives To develop and validate a model to predict time-to-LTC admissions among individuals with dementia. Design Population-based retrospective cohort study using health administrative data. Setting and participants Community-dwelling older adults (65+) in Ontario living with dementia and assessed with the Resident Assessment Instrument for Home Care (RAI-HC) between April 1, 2010 and March 31, 2017. Methods Individuals in the derivation cohort (n = 95,813; assessed before March 31, 2015) were followed for up to 360 days after the index RAI-HC assessment for admission into LTC. We used a multivariable Fine Gray sub-distribution hazard model to predict the cumulative incidence of LTC entry while accounting for all-cause mortality as a competing risk. The model was validated in 34,038 older adults with dementia with an index RAI-HC assessment between April 1, 2015 and March 31, 2017. Results Within one year of a RAI-HC assessment, 35,513 (37.1%) individuals in the derivation cohort and 10,735 (31.5%) in the validation cohort entered LTC. Our algorithm was well-calibrated (Emax = 0.119, ICIavg = 0.057) and achieved a c-statistic of 0.707 (95% confidence interval: 0.703-0.712) in the validation cohort. Conclusions and implications We developed an algorithm to predict time to LTC entry among individuals living with dementia. This tool can inform care planning for individuals with dementia and their family caregivers.