Machine Learning to Predict Outcomes in Patients with Acute Pulmonary Embolism Who Prematurely Discontinued Anticoagulant Therapy

Abstract

Background: Patients with pulmonary embolism (PE) who prematurely discontinue anticoagulant therapy (<90 days) are at an increased risk for death or recurrences.

Methods: We used the data from the RIETE (Registro Informatizado de Pacientes con Enfermedad TromboEmbólica) registry to compare the prognostic ability of five machine-learning (ML) models and logistic regression to identify patients at increased risk for the composite of fatal PE or recurrent venous thromboembolism (VTE) 30 days after discontinuation. ML models included decision tree, k-nearest neighbors algorithm, support vector machine, Ensemble, and neural network [NN]. A "full" model with 70 variables and a "reduced" model with 23 were analyzed. Model performance was assessed by confusion matrix metrics on the testing data for each model and a calibration plot.

Results: Among 34,447 patients with PE, 1,348 (3.9%) discontinued therapy prematurely. Fifty-one (3.8%) developed fatal PE or sudden death and 24 (1.8%) had nonfatal VTE recurrences within 30 days after discontinuation. ML-NN was the best method for identification of patients experiencing the composite endpoint, predicting the composite outcome with an area under receiver operating characteristic (ROC) curve of 0.96 (95% confidence interval [CI]: 0.95-0.98), using either 70 or 23 variables captured before discontinuation. Similar numbers were obtained for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. The discrimination of logistic regression was inferior (area under ROC curve, 0.76 [95% CI: 0.70-0.81]). Calibration plots showed similar deviations from the perfect line for ML-NN and logistic regression.

Conclusion: The ML-NN method very well predicted the composite outcome after premature discontinuation of anticoagulation and outperformed traditional logistic regression.