{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T03:20:53Z","timestamp":1783567253288,"version":"3.55.0"},"reference-count":16,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:p><jats:bold>Introduction:<\/jats:bold> To predict acute kidney injury (AKI) risk in patients with type 2 diabetes (T2D) prescribed sodium-glucose cotransporter two inhibitors (SGLT2i).<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> Using a 5% random sample of Medicare claims data, we identified 17,694 patients who filled \u22651 prescriptions for canagliflozin, dapagliflozin and empagliflozin in 2013\u20132016. The cohort was split randomly and equally into training and testing sets. We measured 65 predictor candidates using claims data from the year prior to SGLT2i initiation. We then applied three machine learning models, including random forests (RF), elastic net and least absolute shrinkage and selection operator (LASSO) for risk prediction.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The incidence rate of AKI was 1.1% over a median 1.5\u00a0year follow up. Among three machine learning methods, RF produced the best prediction (C-statistic = 0.72), followed by LASSO and elastic net (both C-statistics = 0.69). Among individuals classified in the top 10% of the RF risk score (i.e., high risk group), the actual incidence rate of AKI was as high as 3.7%. In the logistic regression model including 14 important risk factors selected by LASSO, use of loop diuretics [adjusted odds ratio (95% confidence interval): 3.72 (2.44\u20135.76)] had the strongest association with AKI incidence.<\/jats:p><jats:p><jats:bold>Disscusion:<\/jats:bold> Our machine learning model efficiently identified patients at risk of AKI among Medicare beneficiaries with T2D undergoing SGLT2i treatment.<\/jats:p>","DOI":"10.3389\/fphar.2022.834743","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T04:31:11Z","timestamp":1646973071000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Identifying Patients at Risk of Acute Kidney Injury Among Medicare Beneficiaries With Type 2 Diabetes Initiating SGLT2 Inhibitors: A Machine Learning Approach"],"prefix":"10.3389","volume":"13","author":[{"given":"Lanting","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nico","family":"Gabriel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inmaculada","family":"Hernandez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott M.","family":"Vouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen E.","family":"Kimmel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchuan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e0214904","DOI":"10.1371\/journal.pone.0214904","article-title":"Improved Predictive Models for Acute Kidney Injury with IDEA: Intraoperative Data Embedded Analytics","volume":"14","author":"Adhikari","year":"2019","journal-title":"PloS one"},{"key":"B2","doi-asserted-by":"publisher","first-page":"506","DOI":"10.2215\/cjn.04100318","article-title":"A Prediction Model for Severe AKI in Critically Ill Adults that Incorporates Clinical and Biomarker Data","volume":"14","author":"Bhatraju","year":"2019","journal-title":"Clin. 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