Identifying Patients at Risk of Acute Kidney Injury Among Medicare Beneficiaries With Type 2 Diabetes Initiating SGLT2 Inhibitors: A Machine Learning Approach - Yang, Lanting; Gabriel, Nico; Hernandez, Inmaculada; Vouri, Scott M.; Kimmel, Stephen E.; Bian, Jiang; Guo, Jingchuan (Frontiers in Pharmacology, 2022)
INTRODUCTION To predict acute kidney injury (AKI) risk in patients with type 2 diabetes (T2D) prescribed sodium-glucose cotransporter 2 inhibitors (SGLT2i). METHODS Using a 5% random sample of Medicare claims data, we identified 17,694 patients who filled ≥1 prescriptions for canagliflozin, dapagliflozin, and empagliflozin in 2013-2016. 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 3 machine learning models, including random forests (RF), elastic net, and least absolute shrinkage and selection operator (LASSO) for risk prediction. RESULTS The incidence rate of AKI was 1.1 % over a median 1.5-year 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, the use of loop diuretics [adjusted odds ratio (95% confidence interval): 3.72 (2.44–5.76)] had the strongest association with AKI incidence. DISSCUSSION Our machine learning model efficiently identified patients at risk of AKI among Medicare beneficiaries with T2D undergoing SGLT2i treatment.