Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach

Abstract

To construct and internally validate prediction models to estimate the risk of longterm end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery. RESEARCH DESIGN AND METHODSA total of 2,287 patients with type 2 diabetes who underwent metabolic surgery between 1998 and 2017 in the Cleveland Clinic Health System were propensitymatched 1:5 to 11,435 nonsurgical patients with BMI ‡30 kg/m 2 and type 2 diabetes who received usual care with follow-up through December 2018. Multivariable time-to-event regression and random forest machine learning models were built and internally validated using fivefold cross-validation to predict the 10-year risk for four outcomes of interest. The prediction models were programmed to construct user-friendly web-based and smartphone applications of Individualized Diabetes Complications (IDC) Risk Scores for clinical use. RESULTSThe prediction tools demonstrated the following discrimination ability based on the area under the receiver operating characteristic curve (1 5 perfect discrimination and 0.5 5 chance) at 10 years in the surgical and nonsurgical groups, respectively: all-cause mortality (0.79 and 0.81), coronary artery events (0.66 and 0.67), heart failure (0.73 and 0.75), and nephropathy (0.73 and 0.76). When a patient's data are entered into the IDC application, it estimates the individualized 10-year morbidity and mortality risks with and without undergoing metabolic surgery. CONCLUSIONSThe IDC Risk Scores can provide personalized evidence-based risk information for patients with type 2 diabetes and obesity about future cardiovascular outcomes and mortality with and without metabolic surgery based on their current status of obesity, diabetes, and related cardiometabolic conditions.

Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data

“…It was not our goal for this model to be used for individual level patient care. Our model was trained on data from over 1.5 million patients from Ontario, which is among one of the most diverse populations in the world and, to our knowledge, one of the largest prediction modelling studies that takes into account multiple types of diabetes complications [22][23][24][25][26][27][28][29][30][31][32][33][34][51][52][53] . Our model was also wellcalibrated and showed good discrimination.…”

“…Many prognostic models have been developed for diabetes complications in the clinical setting [22][23][24] , including more recent applications of machine learning approaches [25][26][27][28][29][30][31][32][33][34] . These models generally have made use of rich suites of features (e.g., body mass index, smoking status, biomarkers ranging from commonly ordered lipids to extensive genetic panels) extracted from electronic medical records (EMRs) 25,27,[31][32][33] or clinical trials 28,30 . However, while these models are important for clinical level risk prediction, they are not easily deployed by governments or private health insurance providers at the population level-which is precisely what is needed for addressing the aforementioned systemic barriers to diabetes complications care 35,36 .…”

Metabolic surgery: A clinical update

“…Risk prediction and prevention models for CVD have been developed and refined over time to include patient characteristics, co‐morbidities and precise markers such as age, sex, smoking status, systolic blood pressure, HbA1c, triglyceride levels and HDL 80–82 . The individualized diabetes complications risk score calculates the 10‐year risk of mortality and cardiovascular outcomes in people with diabetes and obesity with and without metabolic surgery.…”

“…One recently developed risk prediction tool developed predicts the 10‐year risk of cardiovascular outcomes (coronary artery events, heart failure and nephropathy) and mortality with and without metabolic surgery 82 . To develop this model, 2278 patients with T2D and obesity who underwent metabolic surgery were propensity‐matched 1:5 with 11 435 non‐surgical controls.…”

Moving Toward the Implementation of Precision Medicine Needs Highly Discriminatory, Validated, Inexpensive, and Easy-to-Use Prediction Models

“…The study of Aminian et al (5), published in this issue of Diabetes Care, presents several models (called Individualized Diabetes Complications [IDC] Risk Scores) able to estimate in obese patients with type 2 diabetes the risk of mortality and of long-term vascular complications, including coronary artery events, heart failure, and estimated glomerular filtration rate (eGFR) ,60 mL/min/1.73 m 2 . Twenty-six baseline variables as potential predictors were modeled by time-toevent regression and random forest machine learning, an ensemble of survival regression trees grown on bootstrap resampling of the observations.…”