TY - JOUR
T1 - Performance of models for predicting one to three year mortality in older adults
T2 - a systematic review of externally validated models
AU - Ho, Leonard
AU - Pugh, Carys
AU - Seth, Sohan
AU - Arakelyan, Stella
AU - Lone, Nazir I
AU - J Lyall, Marcus
AU - Anand, Atul
AU - Fleuriot, Jacques D
AU - Galdi, Paola
AU - Guthrie, Bruce
N1 - Funding Information:
The systematic review was funded by the National Institute for Health Research (NIHR) Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (NIHR202639) and the Advanced Care Research Centre at the University of Edinburgh (established by a research grant from Legal and General Group). The funders had no role in conduct of the study, interpretation, or the decision to submit for publication. The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or Legal and General.
Publisher Copyright:
© 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2024/2/5
Y1 - 2024/2/5
N2 - Mortality prediction models support identifying older adults with short life expectancy for whom clinical care may need modifications. We systematically reviewed validations of mortality prediction models in older adults with up to three years of follow-up. We included 36 studies reporting 74 validations of 64 unique models. Model applicability was fair but validation risk of bias was mostly high, with 67·7% not reporting calibration. Morbidities were used as predictors by 70·0% of models, most commonly cardiovascular diseases. For 1-year prediction, 31/46 models had acceptable discrimination, but only one had excellent performance. Models with >20 predictors were more likely to have acceptable discrimination (risk ratio (RR) versus <10 predictors 1·68, 95%CI 1·06–2·66), as were models including sex (RR 1·75, 95%CI 1·12–2·73) or predicting risk during comprehensive geriatric assessment (RR 1·86, 95%CI 1·12–3·07). There is a need for derivation and validation of better-performing mortality prediction models in older people.Keywords: Aged; Mortality; Risk; Validation Study; Systematic Review
AB - Mortality prediction models support identifying older adults with short life expectancy for whom clinical care may need modifications. We systematically reviewed validations of mortality prediction models in older adults with up to three years of follow-up. We included 36 studies reporting 74 validations of 64 unique models. Model applicability was fair but validation risk of bias was mostly high, with 67·7% not reporting calibration. Morbidities were used as predictors by 70·0% of models, most commonly cardiovascular diseases. For 1-year prediction, 31/46 models had acceptable discrimination, but only one had excellent performance. Models with >20 predictors were more likely to have acceptable discrimination (risk ratio (RR) versus <10 predictors 1·68, 95%CI 1·06–2·66), as were models including sex (RR 1·75, 95%CI 1·12–2·73) or predicting risk during comprehensive geriatric assessment (RR 1·86, 95%CI 1·12–3·07). There is a need for derivation and validation of better-performing mortality prediction models in older people.Keywords: Aged; Mortality; Risk; Validation Study; Systematic Review
U2 - 10.1016/S2666-7568(23)00264-7
DO - 10.1016/S2666-7568(23)00264-7
M3 - Article
SN - 2666-7568
JO - The Lancet Healthy Longevity
JF - The Lancet Healthy Longevity
ER -