Background: Obesity is associated with an increased risk of multiple conditions, ranging from heart disease to cancer. However, there are few predictive models for these outcomes that have been developed specifically for people with overweight/obesity.
Objective: To develop predictive models for obesity-related complications in patients with overweight and obesity.
Methods: Electronic health record data of adults with body mass index 25-80 kg/m2 treated in primary care practices between 2000 and 2019 were utilized to develop and evaluate predictive models for nine long-term clinical outcomes using a) Lasso-Cox models and b) a machine-learning method random survival forests (RSF). Models were trained on a training dataset and evaluated on a test dataset over 100 replicates. Parsimonious models of <10 variables were also developed using Lasso-Cox.
Results: Over a median follow-up of 5.6 years, study outcome incidence in the cohort of 433,272 patients ranged from 1.8% for knee replacement to 11.7% for atherosclerotic cardiovascular disease. Harrell C-index averaged over replicates ranged from 0.702 for liver outcomes to 0.896 for death for RSF, and from 0.694 for liver outcomes to 0.891 for death for Lasso-Cox. The Harrell C-index for parsimonious models ranged from 0.675 for liver outcomes to 0.850 for knee replacement.
Conclusions: Predictive modeling can identify patients at high risk of obesity-related complications. Interpretable Cox models achieve results close to those of machine learning methods and could be helpful for population health management and clinical treatment decisions.
Keywords: machine learning; obesity; outcomes; risk prediction model.
© 2023 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.