Development of a nomogram model to predict 30-day mortality in ICU cancer patients with acute pulmonary embolism.

Cancer patients with acute pulmonary embolism (APE) admitted to the intensive care unit (ICU) face a high short-term mortality rate. The simplified pulmonary embolism severity index (sPESI) is tool for predicting adverse outcomes. However, its effectiveness in ICU cancer patients with APE remains unclear. This study aimed to validate the sPESI score and develop a predictive model for 30-day mortality in this specific patient group. We conducted a retrospective analysis using data from the MIMIC-IV database, focusing on ICU patients with cancer and APE. The primary outcome of interest was 30-day mortality. Predictors were initially selected using Least Absolute Shrinkage and Selection Operator (LASSO) analysis. A multivariable logistic regression model was then developed. The performance of the nomogram was assessed using calibration, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve analysis to evaluate accuracy, clinical utility, and discrimination, respectively. A total of 286 cancer patients with APE were included in the study, with an average age of 68.9 years; the cohort comprised 137 males (47.9%) and 149 females (52.1%), and the 30-day mortality rate was 32.2%. Multivariable logistic regression analysis identified SOFA score, tumor metastasis, hemoglobin level, anion gap, weight and the prevalence of liver disease as independent predictors of 30-day mortality. The area under the curves (AUCs) of ROC for sPESI and the nomogram model were 0.568 (95% CI, 0.500-0.637) and 0.761 (95% CI, 0.701-0.821). The nomogram model had a higher predictive value for 30-day mortality in patients with acute pulmonary embolism and cancer compared to the sPESI score (P < 0.05). We developed a nomogram to predict the probability of 30-day mortality for ICU patients with acute pulmonary embolism and cancer. This nomogram demonstrated robust performance and serves as a valuable tool for clinicians to identify patients at high risk of 30-day mortality.