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.