Performance of the Simplified Pulmonary Embolism Severity Index in predicting 30-day mortality after acute pulmonary embolism: Validation from a large-scale cohort

Machine Learning-Based Prediction of Pulmonary Embolism Prognosis Using Nutritional and Inflammatory Indices

PURPOSE

This study aimed to create and assess machine learning (ML) models that utilize nutritional and inflammatory indices, focusing on the advanced lung cancer inflammation index (ALI) and neutrophil-to-albumin ratio (NAR), to improve the prediction accuracy of PE prognosis.

PATIENTS AND METHODS

We conducted a retrospective analysis of 312 patients, comprising 254 survivors and 58 non-survivors. The Boruta algorithm was used to identify significant variables, and four ML models (XGBoost, Random Forest, Logistic Regression, and SVM) were employed to analyze the clinical data and assess the performance of the models.

RESULTS

The XGBoost model, with optimal hyperparameters, achieved the best performance, with an accuracy of 0.882, an F1-score of 0.889, a precision of 0.917, a sensitivity of 0.863, a specificity of 0.905, and an AUC of 0.873. Analysis of feature importance indicated that the most critical predictors across models were respiratory failure, log-transformed ALI, albumin level, age, diastolic blood pressure, and NAR.

CONCLUSION

The ML-based prediction models effectively predicted the prognosis of PE, with the XGBoost model exhibiting good performance. Respiratory failure, ALI, albumin level, age, diastolic blood pressure, and NAR were correlated with PE prognosis.