Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes

Abstract

Introduction: To identify predictors of hypoglycemia and five other clinical and economic outcomes among treated patients with type 2 diabetes (T2D) using machine learning and structured data from a large, geographically diverse administrative claims database. Methods: A retrospective cohort study design was applied to Optum Clinformatics claims data indexed on first antidiabetic prescription date. A hypothesis-free, Bayesian machine learning analytics platform (GNS Healthcare REFS TM : Reverse Engineering and Forward Simulation) was used to build ensembles of generalized linear models to predict six outcomes defined in patients' 1-year post-index claims history, including hypoglycemia, antidiabetic class persistence, glycated hemoglobin (HbA1c) target Enhanced Digital Features To view enhanced digital features for this article go to https://doi.org/10.6084/ m9.figshare.11527902.

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

“…Table 1 shows the summary of study characteristics. Of the 33 studies, 19 studies (58%) [ 26 - 31 , 33 , 35 , 36 , 38 - 42 , 44 - 47 , 54 ] predicted hypoglycemia, and the remaining 14 studies (42%) detected hypoglycemia [ 15 , 20 , 25 , 32 , 34 , 37 , 43 , 48 - 53 , 55 ]. As much as 25 of the 33 included studies (76%) [ 15 , 20 , 25 - 27 , 29 , 30 , 32 , 35 , 36 , 38 , 39 , 41 - 44 , 46 - 53 , 55 ] specified type 1 as the type of DM.…”

“…When the analyses were limited to 13 studies that specified type 1 as the DM type [ 26 , 27 , 29 , 30 , 35 , 36 , 38 , 39 , 41 , 42 , 44 , 46 , 47 ], the pooled estimates (95% CI) were 0.77 (0.67-0.85) for sensitivity, 0.92 (0.84-0.96) for specificity, 9.82 (4.58-21.04) for PLR, and 0.25 (0.16-0.38) for NLR. In the analyses of 7 studies that specified night as the time of hypoglycemic events [ 26 , 30 , 31 , 35 , 36 , 41 , 44 ], the predictive ability was low compared with that of the overall analysis—pooled estimate (95% CI): 0.74 (0.65-0.82) for sensitivity, 0.81 (0.72-0.88) for specificity, 3.98 (2.64-6.00) for PLR, and 0.31 (0.23-0.43) for NLR. Relatively high sensitivity and low NLR were observed in the 13 studies that used CGM historical data for predicting hypoglycemia—pooled estimate (95% CI): 0.82 (0.71-0.90) for sensitivity, 0.92 (0.83-0.97) for specificity, 10.41 (4.52-24.01) for PLR, and 0.19 (0.12-0.32) for NLR—compared with 6 studies that did not use CGM—pooled estimate (95% CI): 0.76 (0.66-0.84) for sensitivity, 0.92 (0.88-0.95) for specificity, 10.14 (6.13-16.77) for PLR, and 0.26 (0.17-0.38) for NLR).…”

“…Relatively high sensitivity and low NLR were observed in the 13 studies that used CGM historical data for predicting hypoglycemia—pooled estimate (95% CI): 0.82 (0.71-0.90) for sensitivity, 0.92 (0.83-0.97) for specificity, 10.41 (4.52-24.01) for PLR, and 0.19 (0.12-0.32) for NLR—compared with 6 studies that did not use CGM—pooled estimate (95% CI): 0.76 (0.66-0.84) for sensitivity, 0.92 (0.88-0.95) for specificity, 10.14 (6.13-16.77) for PLR, and 0.26 (0.17-0.38) for NLR). After excluding 3 studies [ 31 , 33 , 54 ] that showed that the supposed hypoglycemic events occurred in-hospital, the pooled estimates (95% CI) of the 16 studies with such events occurring in an out-of-hospital setting were 0.82 (0.74-0.88) for sensitivity, 0.92 (0.85-0.96) for specificity, 10.58 (5.44-20.55) for PLR, and 0.20 (0.13-0.39) for NLR.…”

“…Table 1 shows the summary of study characteristics. Of the 33 studies, 19 studies (58%) [26][27][28][29][30][31]33,35,36,[38][39][40][41][42][44][45][46][47]54] predicted hypoglycemia, and the remaining 14 studies (42%) detected hypoglycemia [15,20,25,32,34,37,43,[48][49][50][51][52][53]55]. As much as 25 of the 33 included studies (76%) [15,20,[25][26][27]29,30,32,35,36,38,39,[41][42][43][44]…”

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses

“…Such statistical tools, including gradient-boosted decision trees, least absolute shrinkage and selection operator (LASSO) regression, random forest, and artificial neural networks (NN), can be applied to raw data sets for the imputation of missing data, replacement of outliers, feature extraction, statistical classification, and optimization of predictive model accuracy [12]. Among other applications, ML techniques have been shown in multiple RWE studies to be useful for model development for the prediction of diagnoses, clinical variables, and disease risk [12][13][14][15][16][17][18][19][20]. The objective of this study was to construct models by implementing ML algorithms to predict BMI classifications (C 30, C 35, and C 40 kg/m 2 ) in administrative healthcare claims databases, and then internally and externally validate them, and thereby expand the utility for RWE generation of administrative healthcare claims database analyses.…”