A prediction model for in-hospital mortality in intensive care unit patients with metastatic cancer - Wu, Meizhen; Gao, Haijin (Frontiers in Surgery, 2023)
Aim: To identify the predictors of in-hospital mortality in patients with metastatic cancer in intensive care units (ICUs) and established a prediction model for in-hospital mortality in those patients. Methods: In this cohort study, the data of 2,462 patients with metastatic cancer in ICUs were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Logistic regression analysis identified the predictors for in-hospital mortality in metastatic cancer patients. Participants were randomly divided into the training set (n=1,723) and the testing set (n=739). The prediction model was constructed in the training set. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were employed for measuring the predictive performance of the model. The predictive performance of the model was validated in the testing set Results: In total, 656 (26.65%) metastatic cancer patients were dead in hospital. Respiratory failure [odds ratio (OR)=3.384, 95% confidence interval (CI): 2.576-4.446], partial arterial oxygen pressure (PaO2)/the fraction of inspired oxygen (FiO2) [OR=0.998, 95% confidence interval (CI): 0.997-0.999), asepticaemia (OR=2.297, 95%CI: 1.707-3.091), SOFA score (OR=1.169, 95%CI: 1.122-1.217), white blood cell (WBC) (OR=1.031, 95%CI: 1.017-1.046) and lactate (OR=1.148, 95%CI: 1.078-1.223) were associated with the risk of death in patients with metastatic cancer. The AUCs of the prediction model was 0.800 (95%CI: 0.776-0.825) in the training set and 0.847 (95%CI: 0.815-0.879) in the testing set. The predictive values of the model lymphoma, myeloma, brain/spinal cord, lung, liver, peritoneum/pleura, enteroncus and other cancer populations were also assessed. Conclusion: The prediction model for in-hospital mortality in ICU patients with metastatic cancer exhibited good predictive ability, which might help identify patients with high risk of in-hospital death in ICU patients with metastatic cancer and provide timely interventions on those patients.