Prediction of Venous Thromboembolism after Ankle Fractures using Machine Learning: To Give Prophylaxis or Not to Give, That is the Question
Authors: Nassour Nour , Akhbari Bardiya , Ranganathan Noopur , Shin David , Waryasz Gregory R. , Lubberts Bart , DiGiovanni Christopher W. , Schwab Joseph , Ghaednia Hamid , Ashkani-Esfahani Soheil , Guss Daniel 
Published: November 2022

Category:

Ankle; Other

Introduction/Purpose:

The risk of venous thromboembolism (VTE) after foot and ankle surgery is significantly lower than rates after hip/knee arthroplasty, but it isn't zero. Specific subgroups of patients may be at higher risk, forcing patients and clinicians to navigate the risks and benefits of chemoprophylaxis with insufficient data. Efforts have been made to add clarity to such decision making using conventional data-analysis and risk-scoring methods, but none of these methods were patient-specific or built on robust models of a given patient's individual characteristics. In this study we used machine-learning to determine the potential risk factors for VTE after ankle fracture. We aimed to develop a patient-specific predictive model to assist clinicians and patients in deciding upon the use of postoperative chemoprophylaxis after foot and ankle surgery.

Methods:

In this preliminary machine-learning-based case-control study, 16,421 patients with ankle fractures were recruited retrospectively. We used an automated-string search method to find patients who were clinically suspected to have developed VTE. Among 1176 such patients, 239 had confirmed VTE within 180 days of sustaining an ankle fracture (cases) and 937 did not (controls). Groups were further sub-divided in patients who had been receiving chemoprophylaxis and those who hadn't. Over 110 factors and variables including patient demographics, past-medical and surgical history, fracture characteristics, treatment, medications, and laboratory values were included in our machine-learning dataset. Three analytical algorithms were used in our machine-learning methods including backward-logistic-regression, decision-tree-classifier (depth=5), and neural networks (two dense layers (n=16 and 4), two drop-out layers, and a sigmoid classifier). Conventional statistics were also used to compare the case and control groups (chi-squared, t-test, p<0.05 considered significant), and the odds-ratio (OR) was calculated for significant parameters.

Results:

Based on overall performance scores including specificity, sensitivity, area under the ROC curve, accuracy, PPV, NPV, F- 1 score, among the 3 machine-learning methods, the Backward-Logistic-Regression model was superior in predicting VTE post ankle fracture and in determining whether administering prophylaxis can be beneficial for the patient or not (Tables 1 and 2). Other than the previously suggested risk factors, our algorithms showed a positive correlation between the incidence of VTE and smoking (OR=2.09, p<0.001), age <55 y/o (p=0.001), open fracture (OR=2.49, p<0.001), male sex (OR=1.98, p<0.001), surgical versus nonoperative treatment (OR=1.88, p=0.001), and multiple fractures at the time of trauma (OR=1.9, p=0.001). Factors that showed negative correlation with VTE include statins use (OR=0.36, p<0.001), hypertension (OR=0.53, p=0.001), vitamin D (OR=0.43, 0.002), calcium supplementation (OR= 0.43, p= 0.01), hyperlipidemia (OR=0.55, p=0.006), cataract (OR=0.19, p=0.01), osteoporosis (OR=0.36, p=0.02), cardiovascular diseases (OR=0.54, p=0.02), hypokalemia (OR=0.26, p=0.03), and proton pump inhibitor use (OR=0.5, p=0.03).

Conclusion:

Our machine learning algorithms showed that factors such as tobacco use, younger age, open fracture, multi-trauma, operative treatment, as well as male sex heightened the risk of VTE. In contrast, certain factors such as vitamin D supplementation had negative correlation with VTE. Machine learning algorithms acted in a more complex manner and incorporated more factors in decision-making compared to conventional methods. External validation using larger and more granular datasets as well as using the algorithms in trial modes (shadow modes) are needed to build trust in this algorithm to assist clinicians in predicting/preventing VTE after foot and ankle surgeries.