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

doi:10.1371/journal.pdig.0000441

Derivation and validation of an algorithm to predict transitions from community to residential long-term care among persons with dementia-A retrospective cohort study

PLOS Digit Health. 2024 Oct 18;3(10):e0000441. doi: 10.1371/journal.pdig.0000441. eCollection 2024 Oct.

Authors

Wenshan Li¹, Luke Turcotte², Amy T Hsu^{3

4}, Robert Talarico⁵, Danial Qureshi⁶, Colleen Webber^{1

3}, Steven Hawken^{1

3

7}, Peter Tanuseputro⁸, Douglas G Manuel^{1

5

9}, Greg Huyer¹⁰

Affiliations

¹ Ottawa Hospital Research Institute, Ottawa, Ontario.
² Broke University, Department of Health Sciences, St. Catherines, Ontario, Canada.
³ Bruyère Research Institute, Ottawa, Canada.
⁴ University of Ottawa, Brain and Mind Institute, Ottawa, Canada.
⁵ ICES uOttawa, Ottawa, Canada.
⁶ University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom.
⁷ University of Ottawa, School of Epidemiology and Public Health, Ottawa, Canada.
⁸ University of Hong Kong, Department of Family Medicine and Primary Care, Hong Kong.
⁹ University of Ottawa, Department of Family Medicine, Ottawa, Canada.
¹⁰ Health Canada, Ottawa, Canada.

Abstract

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.

Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Grants and funding

This study was funded by the Canadian Institutes of Health Research (CIHR), grant No. PJT-173346. This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and the Ministry of Long-Term Care. WL is supported by CIHR and Associated Medical Services (AMS) postdoctoral fellowships. The analyses, conclusions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred.