• Stephen S Johnston, John M Morton, Iftekhar Kalsekar, Eric M Ammann, Chia-Wen Hsiao, and Jenna Reps.
• Epidemiology, Medical Devices, Johnson & Johnson, New Brunswick, NJ, USA. Electronic address: sjohn147@its.jnj.com.
• Value Health. 2019 May 1; 22 (5): 580-586.

ObjectivesLaparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning techniques to a common data model; we develop and validate a predictive model of antihyperglycemic medication cessation (validated proxy for A1c control) in patients with treated T2D who underwent MxS.MethodsWe selected patients meeting the following criteria in 2 large US healthcare claims databases (Truven Health MarketScan Commercial [CCAE]; Optum Clinformatics [Optum]): underwent MxS between January 1, 2007, to October 1, 2013 (first = index); aged ≥18 years; continuous enrollment 180 days pre-index (baseline) to 730 days postindex; baseline T2D diagnosis and treatment. The outcome was no antihyperglycemic medication treatment from 365 to 730 days after MxS. A regularized logistic regression model was trained using the following candidate predictor categories measured at baseline: demographics, conditions, medications, measurements, and procedures. A 75% to 25% split of the CCAE group was used for model training and testing; the Optum group was used for external validation.Results13 050 (CCAE) and 3477 (Optum) patients met the study inclusion criteria. Antihyperglycemic medication cessation rates were 72.9% (CCAE) and 70.8% (Optum). The model possessed good internal discriminative accuracy (area under the curve [AUC] = 0.778 [95% CI = 0.761-0.795] in CCAE test set N = 3527) and transportability (external AUC = 0.759 [95% CI = 0.741-0.777] in Optum N = 3477).ConclusionThe application of machine learning techniques to real-world healthcare data can yield useful predictive models to assist patient selection. In future practice, establishment of prerequisite technological infrastructure will be needed to implement such models for real-world decision support.Copyright © 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.

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