An Algorithm for Predicting Death Among Older Adults in the Home Care Setting: Study Protocol for the Risk Evaluation for Support: Predicting Elder Life in the Community Tool (RESPECT)
An Algorithm for Predicting Death Among Older Adults in the Home Care Setting: Study Protocol for the Risk Evaluation for Support: Predicting Elder Life in the Community Tool (RESPECT)
To develop a mortality risk prediction model that can be applied to the wide spectrum of risks that are seen in the home care setting.
ABSTRACT
Introduction: Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning.
Objective: To develop a mortality risk prediction model for older adults in the home care setting. The final algorithm will be implemented as a web-based calculator that can be used by older adults needing care, as well as their informal and formal caregivers.
Design: Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from January 1, 2007, to December 31, 2013.
Participants: The derivation cohort will consist of approximately 437 000 home care recipients from January 1, 2007, to December 31, 2012. A split sample validation cohort will include approximately 122 000 recipients from January 1 to December 31, 2013.
Main outcome measures: Predicted survival from the time of an RAI-HC assessment. All deaths (N ≈ 245 000) will be ascertained through linkage to the provincial vital statistics records.
Statistical analysis: Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic characteristics, social support, health conditions, functional status, cognition, symptoms of decline, and prior healthcare use. Model performance will be evaluated for 6- and 12-month predicted risks, including measures of calibration (e.g., calibration plots) and discrimination (e.g., c-statistics). The final algorithm will be generated by combining development and validation data.
Inclusion Criteria:
Exclusion Criteria: