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Type: Journal article
Title: Using elastic nets to estimate frailty burden from routinely collected national aged care data
Author: Moldovan, M.
Khadka, J.
Visvanathan, R.
Wesselingh, S.
Inacio, M.C.
Citation: Journal of the American Medical Informatics Association, 2020; 27(3):419-428
Publisher: Oxford University Press (OUP)
Issue Date: 2020
ISSN: 1067-5027
Statement of
Max Moldovan, Jyoti Khadka, Renuka Visvanathan, Steve Wesselingh, and Maria C. Inacio
Abstract: OBJECTIVES: To (1) use an elastic net (EN) algorithm to derive a frailty measure from a national aged care eligibility assessment program; (2) compare the ability of EN-based and a traditional cumulative deficit (CD) based frailty measures to predict mortality and entry into permanent residential care; (3) assess if the predictive ability can be improved by using weighted frailty measures. MATERIALS AND METHODS: A Cox proportional hazard model based EN algorithm was applied to the 2003-2013 cohort of 903 996 participants for selecting items to enter an EN based frailty measure. The out-of-sample predictive accuracy was measured by the area under the curve (AUC) from Cox models fitted to 80% training and validated on 20% testing samples. RESULTS: The EN approach resulted in a 178-item frailty measure including items excluded from the 44-item CD-based measure. The EN based measure was not statistically significantly different from the CD-based approach in terms of predicting mortality (AUC 0.641, 95% CI: 0.637-0.644 vs AUC 0.637, 95% CI: 0.634-0.641) and permanent care entry (AUC 0.626, 95% CI: 0.624-0.629 vs AUC 0.627, 95% CI: 0.625-0.63). However, the weighted EN based measure statistically outperforms the weighted CD measure for predicting mortality (AUC 0.774, 95% CI: 0.771-0.777 vs AUC 0.757, 95% CI: 0.754-0.760) and permanent care entry (AUC 0.676, 95% CI: 0.673-0.678 vs AUC 0.671, 95% CI: 0.668-0.674). CONCLUSIONS: The weighted EN and CD-based measures demonstrated similar prediction performance. The CD-based measure items are relevant to frailty measurement and easier to interpret. We recommend using the weighted and unweighted CD-based frailty measures.
Keywords: frailty
penalized regression
statistical learning
Description: Advance Access Publication Date: 17 January 2020
Rights: © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
DOI: 10.1093/jamia/ocz210
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