Using machine learning in the prediction of symptomatic venous thromboembolism following ankle fracture

Abstract

Background

Venous thromboembolism (VTE) is a major cause of morbidity and mortality in the trauma setting, and both prediction and prevention of VTE have long been a concern for healthcare providers in orthopedic surgery. The purpose of this study was to evaluate the use of novel statistical analysis and machine-learning in predicting the risk of VTE and the usefulness of prophylaxis following ankle fractures.

Methods

The medical profiles of 16,421 patients with ankle fractures were screened retrospectively for symptomatic VTE. In total, 238 patients sustaining either surgical or nonsurgical treatment for ankle fracture with subsequently confirmed VTE within 180 days following the injury were placed in the case group. Alternatively, 937 patients who sustained ankle fractures managed similarly but had no documented evidence of VTE were randomly chosen as the control group. Individuals from both the case and control populations were also divided into those who had received VTE prophylaxis and those who had not. Over 110 variables were included. Conventional statistics and machine learning methods were used for data analysis.

Results

Patients who had a motor vehicle accident, surgical treatment, increased hospital stay, and were on warfarin were shown to have a higher incidence of VTE, whereas patients who were on statins had a lower incidence of VTE. The highest Area Under the Receiver Operating Characteristic Curves (AUROC) showing the performance of our machine learning approach was 0.88 with 0.94 sensitivity and 0.36 specificity. The most balanced performance was seen in a model that was trained using selected variables with 0.86 AUROC, 0.75 sensitivity, and 0.85 specificity.

Conclusion

By using machine learning, this study successfully pinpointed several predictive factors linked to the occurrence or absence of VTE in patients who experienced an ankle fracture. Training these algorithms using larger, more granular, and multicentric data will further increase their validity and reliability and should be considered the standard for the development of such algorithms.

Level of evidence

Case-Control study – 3