Predicting the Development of Surgery-Related Pressure Injury Using a Machine Learning Algorithm Model

Surgery-related pressure injury (SRPI) is a serious problem in patients who undergo cardiovascular surgery. Identifying patients at a high risk of SRPI is important for clinicians to recognize and prevent it expeditiously. Machine learning (ML) has been widely used in the field of healthcare and is well suited to predictive analysis.

The aim of this study was to develop an ML-based predictive model for SRPI in patients undergoing cardiovascular surgery.

This secondary analysis of data was based on a single-center, prospective cohort analysis of 149 patients who underwent cardiovascular surgery. Data were collected from a 1,000-bed university-affiliated hospital. We developed the ML model using the XGBoost algorithm for SRPI prediction in patients undergoing cardiovascular surgery based on major potential risk factors. Model performance was tested using a receiver operating characteristic curve and the C-index.

Of the sample of 149 patients, SRPI developed in 37, an incidence rate of 24.8%. The five most important predictors included duration of surgery, patient weight, duration of the cardiopulmonary bypass procedure, patient age, and disease category. The ML model had an area under the receiver operating characteristic curve of 0.806, which indicates that the ML model has a moderate prediction value for SRPI.

Applying ML to clinical data may be a reliable approach to the assessment of the risk of SRPI in patients undergoing cardiovascular surgical procedures. Future studies may deploy the ML model in the clinic and focus on applying targeted interventions for SRPI and related diseases.