Machine learning prediction model for respiratory failure in patients with asthma

Background Asthma is one of the most common chronic respiratory diseases worldwide, and its global burden continues to increase. Although most patients can achieve good disease control with standardized treatment, some still experience severe acute exacerbations triggered by infection, environmental exposure, poor treatment adherence, or comorbid COPD, which may further progress to respiratory failure. Respiratory failure is one of the most serious adverse outcomes during hospitalization for asthma and is often associated with a greater need for mechanical ventilation, longer intensive care stays, and poorer clinical prognosis. Therefore, early identification of high-risk patients and the development of reliable prediction tools are of substantial clinical importance. Objective To develop and validate a machine learning-based risk prediction model using routinely available clinical data from hospitalized patients with asthma to identify those at high risk of developing respiratory failure during hospitalization, screen key predictive factors, and evaluate the model’s discrimination, calibration, clinical net benefit, and interpretability. Methods This was a single-center retrospective cohort study. A total of 2,098 patients hospitalized for asthma at Zhongnan Hospital of Wuhan University between November 2016 and December 2024 were retrospectively screened. After applying predefined inclusion and exclusion criteria, 1,405 patients were included in the final analysis, among whom 233 developed respiratory failure during hospitalization (respiratory failure group) and 1,172 did not (control group). Patients were randomly assigned by stratified sampling in an 8:2 ratio to a training set ( n = 1,124) and an internal validation set ( n = 281). In addition, 403 patients hospitalized for asthma at the same institution between January 2025 and June 2026 were screened using the same criteria, and 371 patients were ultimately included as an temporal validation cohort from an independent time period (72 with respiratory failure and 299 controls). A total of 62 candidate variables were collected, including demographic characteristics, smoking history, COPD history, and laboratory indicators such as complete blood count, coagulation function, liver and renal function, electrolytes, and blood lipids. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression combined with the Boruta algorithm. Eleven machine learning models were then developed based on the selected variables, including logistic regression, random forest, naive Bayes, multilayer perceptron, support vector machine, k-nearest neighbors, decision tree, gradient boosting decision tree (GBDT), LightGBM, CatBoost, and XGBoost. Model performance was comprehensively evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), precision-recall (PR) curves, and calibration curves. Shapley additive explanations (SHAP) and a nomogram were further applied to interpret and visualize the optimal model. Results LASSO regression combined with the Boruta algorithm ultimately identified five core predictive variables from the 62 candidate features: D-dimer, bicarbonate concentration (HCO 3 − ), neutrophil percentage, direct bilirubin, and lymphocyte percentage. Among the 11 candidate models, CatBoost showed the best overall performance. In the training set, the model achieved an AUC of 0.945 (95% CI: 0.928–0.962), accuracy of 0.923 (95% CI: 0.889–0.962), precision of 0.943 (95% CI: 0.896–0.989), specificity of 0.994 (95% CI: 0.989–0.996), recall of 0.556 (95% CI: 0.403–0.712), F1 score of 0.699 (95% CI: 0.678–0.714), and Matthews correlation coefficient (MCC) of 0.689. In the internal validation set, the model achieved an AUC of 0.796 (95% CI: 0.723–0.868), accuracy of 0.858 (95% CI: 0.812–0.899), precision of 0.842 (95% CI: 0.758–0.911), specificity of 0.987 (95% CI: 0.973–0.994), recall of 0.302 (95% CI: 0.217–0.381), and F1 score of 0.444 (95% CI: 0.369–0.513). In the temporal validation set, the model achieved an AUC of 0.878 (95% CI: 0.829–0.926), accuracy of 0.879 (95% CI: 0.832–0.924), precision of 0.909 (95% CI: 0.857–0.962), specificity of 0.989 (95% CI: 0.982–0.995), recall of 0.417 (95% CI: 0.333–0.485), and F1 score of 0.571 (95% CI: 0.549–0.602). Conclusion The CatBoost machine learning model developed using routinely available clinical data can accurately predict the risk of respiratory failure in hospitalized patients with asthma. The model demonstrated good discrimination, acceptable temporal generalizability, and strong interpretability, and may provide useful support for early identification of severe progression and individualized management in patients with asthma.