AUTHOR=Yang Lanting , Gabriel Nico , Hernandez Inmaculada , Vouri Scott M. , Kimmel Stephen E. , Bian Jiang , Guo Jingchuan 

TITLE=Identifying Patients at Risk of Acute Kidney Injury Among Medicare Beneficiaries With Type 2 Diabetes Initiating SGLT2 Inhibitors: A Machine Learning Approach

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.834743

DOI=10.3389/fphar.2022.834743

ISSN=1663-9812

ABSTRACT=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.