Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT). Issue 12 (1st December 2016)

• Record Type: Journal Article
• Authors: Hsu, Amy T; Manuel, Douglas G; Taljaard, Monica; Chalifoux, Mathieu; Bennett, Carol; Costa, Andrew P; Bronskill, Susan; Kobewka, Daniel; Tanuseputro, Peter
• Is Part Of: BMJ open. Volume 6:Issue 12(2016)
• Publication Date: 2016-12-01
• Subjects: Advance care planning -- Survival analysis -- Clinical prediction rule -- Frail older adults -- Mortality -- Home care; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2016-013666
• Languages: English
• ISSNs: 2044-6055

Abstract

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 predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers. Design: Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013. Participants: The derivation cohort will consist of ∼437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ∼122 000 older adults with an RAI-HC assessment between 1 January and 31 December 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 a population-based registry that is maintained by the Ministry of Health in Ontario. Statistical analysis: Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data. Ethics and dissemination: Research ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals. Trial registration number: NCT02779309, Pre-results.