Using elastic nets to estimate frailty burden from routinely collected national aged care data

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dc.contributor.authorMoldovan, M.
dc.contributor.authorKhadka, J.
dc.contributor.authorVisvanathan, R.
dc.contributor.authorWesselingh, S.
dc.contributor.authorInacio, M.C.
dc.date.issued2020
dc.descriptionAdvance Access Publication Date: 17 January 2020
dc.description.abstractOBJECTIVES: 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.
dc.description.statementofresponsibilityMax Moldovan, Jyoti Khadka, Renuka Visvanathan, Steve Wesselingh, and Maria C. Inacio
dc.identifier.citationJournal of the American Medical Informatics Association : JAMIA, 2020; 27(3):419-428
dc.identifier.doi10.1093/jamia/ocz210
dc.identifier.issn1067-5027
dc.identifier.issn1527-974X
dc.identifier.orcidMoldovan, M. [0000-0001-9680-8474]
dc.identifier.orcidVisvanathan, R. [0000-0002-1303-9479]
dc.identifier.urihttp://hdl.handle.net/2440/130748
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.rights© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
dc.source.urihttps://doi.org/10.1093/jamia/ocz210
dc.subjectfrailty
dc.subjectpenalized regression
dc.subjectstatistical learning
dc.subjectsurvival
dc.subjectgeriatrics
dc.titleUsing elastic nets to estimate frailty burden from routinely collected national aged care data
dc.typeJournal article
pubs.publication-statusPublished

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