Exploring the potential effects of acute respiratory failure on patients with early-stage bronchopulmonary cancer.

Acute respiratory failure is a common disease, impacting various cancer survivors and reflecting post-surgery complications. However, little evidence based on respiratory failure has been developed for prognostic assessment and prediction in patients with bronchopulmonary cancer. Data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV v2.2) database were analyzed to identify risk factors associated with acute respiratory failure in patients with bronchopulmonary cancer. The primary outcome was the occurrence of acute respiratory failure. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression model, and significant predictors were incorporated into a nomogram for individualized risk prediction. Among the 1,318 included patients with bronchopulmonary cancer, 35.6% developed acute respiratory failure. Ten independent risk factors were identified and used to construct the nomogram: activated partial thromboplastin time (PTT), glucose, blood urea nitrogen (BUN), neutrophil percentage, sodium, respiratory rate (RR), heart rate (HR), chronic obstructive pulmonary disease (COPD), shock, and race. The model exhibited good discrimination, with an area under the ROC curve of 0.741. Internal validation using 1000 bootstrap replicates demonstrated strong agreement between the calibration curve and the ideal reference, indicating satisfactory model performance. We developed and validated nomograms for patients with early-stage bronchopulmonary cancer, which could provide individual prediction of acute respiratory failure.