Prediction of in-hospital adverse clinical outcomes in patients with pulmonary thromboembolism, machine learning based models

BackgroundPulmonary thromboembolism (PE) is the third leading cause of cardiovascular events. The conventional modeling methods and severity risk scores lack multiple laboratories, paraclinical and imaging data. Data science and machine learning (ML) based prediction models may help better predict outcomes. Materials and methodsIn this retrospective registry-based design, all consecutive hospitalized patients diagnosed with pulmonary thromboembolism (based on pulmonary CT angiography) from 2011 to 2019 were recruited. ML based algorithms [Gradient Boosting (GB) and Deep Learning (DL)] were applied and compared with logistic regression (LR) to predict hemodynamic instability and/or all-cause mortality. ResultsA total number of 1,017 patients were finally enrolled in the study, including 465 women and 552 men. Overall incidence of study main endpoint was 9.6%, (7.2% in men and 12.4% in women; p-value = 0.05). The overall performance of the GB model is better than the other two models (AUC: 0.94 for GB vs. 0.88 and 0.90 for DL and LR models respectively). Based on GB model, lower O-2 saturation and right ventricle dilation and dysfunction were among the strongest adverse event predictors. ConclusionML-based models have notable prediction ability in PE patients. These algorithms may help physicians to detect high-risk patients earlier and take appropriate preventive measures.

Automated summary

In this study, data science and machine learning models were used to predict the prognosis of pulmonary thromboembolism (PE), and the results showed that the GB model had the best predictive performance. Lower O-2 saturation and right ventricle dilation and dysfunction were identified as strong predictors of adverse events.