{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T17:26:05Z","timestamp":1763141165409,"version":"3.40.5"},"reference-count":55,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2019,3,19]],"date-time":"2019-03-19T00:00:00Z","timestamp":1552953600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["K12 CA120780","R21 CA191454"],"award-info":[{"award-number":["K12 CA120780","R21 CA191454"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2019,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>De\u2010implementation of low\u2010value services among patients with limited life expectancy is challenging. Robust mortality prediction models using routinely collected health care data can enhance health care stakeholders' ability to identify populations with limited life expectancy. We developed and validated a claims\u2010based prediction model for 5\u2010year mortality using regularized regression methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Medicare beneficiaries age 66 or older with an office visit and at least 12\u00a0months of pre\u2010visit continuous Medicare A\/B enrollment were identified in 2008. Five\u2010year mortality was assessed through 2013. Secondary outcomes included 30\u2010, 90\u2010, and 180\u2010day and 1\u2010year mortality. Claims\u2010based predictors, including comorbidities and indicators of disability, frailty, and functional impairment, were selected using regularized logistic regression, applying the least absolute shrinkage and selection operator (LASSO) in a random 80% training sample. Model performance was assessed and compared with the Gagne comorbidity score in the 20% validation sample.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Overall, 183\u00a0204 (24%) individuals died. In addition to demographics, 161 indicators of comorbidity and function were included in the final model. In the validation sample, the c\u2010statistic was 0.825 (0.823\u20100.828). Median\u2010predicted probability of 5\u2010year mortality was 14%; almost 4% of the cohort had a predicted probability greater than 80%. Compared with the Gagne score, the LASSO model led to improved 5\u2010year mortality classification (net reclassification index\u00a0=\u00a09.9%; integrated discrimination index\u00a0=\u00a05.2%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our claims\u2010based model predicting 5\u2010year mortality showed excellent discrimination and calibration, similar to the Gagne score model, but resulted in improved mortality classification. Regularized regression is a feasible approach for developing prediction tools that could enhance health care research and evaluation of care quality.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pds.4769","type":"journal-article","created":{"date-parts":[[2019,3,20]],"date-time":"2019-03-20T05:33:33Z","timestamp":1553060013000},"page":"584-592","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Development and validation of a 5\u2010year mortality prediction model using regularized regression and Medicare data"],"prefix":"10.1002","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1108-0689","authenticated-orcid":false,"given":"Jennifer L.","family":"Lund","sequence":"first","affiliation":[{"name":"Department of Epidemiology University of North Carolina Chapel Hill NC USA"},{"name":"Lineberger Comprehensive Cancer Center University of North Carolina Chapel Hill NC USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tzy\u2010Mey","family":"Kuo","sequence":"additional","affiliation":[{"name":"Lineberger Comprehensive Cancer Center University of North Carolina Chapel Hill NC USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. 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