Factors linked to lung cancer in MIMIC-IV database.

Background: Lung cancer (LC) is the most prevalent type of cancer, yet early prediction of hospital mortality and risk stratification remains inadequate. This study aims to develop a predictive model for in-hospital mortality among patients with LC. Methods: This study utilized data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database [2008-2019]. Patients were randomly assigned to training (70%) and validation (30%) cohorts. Baseline characteristics and their associations with LC outcomes were analyzed. Feature selection was performed using Cox regression in the training set. The selected variables were incorporated into a nomogram model, of which its predictive performance and clinical utility were evaluated using calibration plots, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) in both cohorts. Results: A total of 645 LC patients were included, with 451 in the training set and 194 in the validation set. Twenty-two baseline characteristics were significantly associated with LC mortality. Six key variables-alkaline phosphatase (ALP) minimum (min), bilirubin total min, length of stay (LOS), race, respiratory rate min, and Simplified Acute Physiology Score II (SAPS II)-were identified for nomogram development. The final model demonstrated strong predictive accuracy and clinical utility, achieving an area under the curve (AUC) exceeding 0.7 in both cohorts. Conclusions: This study identified key risk factors for in-hospital mortality among critically ill patients with LC and developed a robust predictive model using MIMIC-IV data. The findings provide valuable insights into LC prognosis and may aid in clinical decision-making.