Introduction: Predicting risk of care home admission could identify older adults for early intervention to support independent living but require external validation in a different dataset before clinical use. We systematically reviewed external validations of care home admission risk prediction models in older adults.
Methods: We searched Medline, Embase and Cochrane Library until 14 August 2023 for external validations of prediction models for care home admission risk in adults aged ≥65 years with up to 3 years of follow-up. We extracted and narratively synthesised data on study design, model characteristics, and model discrimination and calibration (accuracy of predictions). We assessed risk of bias and applicability using Prediction model Risk Of Bias Assessment Tool.
Results: Five studies reporting validations of nine unique models were included. Model applicability was fair but risk of bias was mostly high due to not reporting model calibration. Morbidities were used as predictors in four models, most commonly neurological or psychiatric diseases. Physical function was also included in four models. For 1-year prediction, three of the six models had acceptable discrimination (area under the receiver operating characteristic curve (AUC)/c statistic 0.70-0.79) and the remaining three had poor discrimination (AUC < 0.70). No model accounted for competing mortality risk. The only study examining model calibration (but ignoring competing mortality) concluded that it was excellent.
Conclusions: The reporting of models was incomplete. Model discrimination was at best acceptable, and calibration was rarely examined (and ignored competing mortality risk when examined). There is a need to derive better models that account for competing mortality risk and report calibration as well as discrimination.
Keywords: aged; long-term care; older people; risk; systematic review; validation study.
© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.