Evidence map›Paper›PMID 31951002›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

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

Max Moldovan, Jyoti Khadka, Renuka Visvanathan, Steve Wesselingh, Maria C Inacio

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Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Max MoldovanSouth Australian Health and Medical Research Institute (SAHMRI), Registry of Senior Australians (ROSA), Adelaide, Australia.
Jyoti KhadkaHealthy Ageing Research Consortium and Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, Australia.
Renuka VisvanathanUniversity of Adelaide, The Queen Elizabeth Hospital, National Health and Medical Research Council of Australia Centre of Research Excellence in Frailty and Healthy Ageing, Registry of Senior Australians (ROSA), Adelaide, Australia.
Steve WesselinghSouth Australian Health and Medical Research Institute (SAHMRI), Registry of Senior Australians (ROSA), Adelaide, Australia.
Maria C InacioSouth Australian Health and Medical Research Institute (SAHMRI), Registry of Senior Australians (ROSA), Adelaide, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo (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

methodsA 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.

resultsThe 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).

conclusionsThe 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.

Indexed as

AgedAged, 80 and overAustraliaCause of DeathFemaleFrailtyGeriatric AssessmentHumansMalePrognosisProportional Hazards ModelsResidential FacilitiesSensitivity and Specificityfrailtygeriatricspenalized regressionstatistical learningsurvival

Identifiers

PMID31951002
PMCPMC7647260

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.