Prediction of Healing Trajectory of Chronic Wounds Using a Machine Learning Approach

Objective: New treatment options are emerging for chronic wounds, which represent a growing problem because of population ageing and increasing burden of chronic disease. While promising, the existing evidence for advanced modalities is commonly derived from small and/or poorly controlled studies and clear criteria for selecting patients, who are likely to benefit from these expensive options are lacking. In this study, we develop and validate a machine learning model to predict if a chronic wound, independent of etiology, is expected to heal within 12 weeks to identify cases in potential need of advanced treatment options. Approach: Retrospective analysis of electronic health record data from 2014 to 2018 covering 532 wound care clinics in the United States and 261,398 patients with 620,356 unique wounds. Prediction of 12-week healing trajectories with a machine learning model. Results: The best-performing model in a training dataset of a randomly drawn 75% subset of wounds contained variables for patient demographics, comorbidities, wound characteristics at initial presentation, and changes in wound dimensions over time, with the latter group being the most influential predictors. The final machine learning model had a high predictive accuracy with area under the receiver operating characteristic curves of 0.9 and 0.92 after 4 and 5 weeks of treatment, respectively. Innovation: A machine learning model can identify chronic wounds at risk of not healing by week 12 with high accuracy in the early weeks of treatment. Conclusions: If embedded in real-world care, the generated information could be able to guide effective and efficient treatment decisions.