Abstract
PURPOSE: The present study aimed to develop a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit to facilitate risk stratification and treatment planning. METHODS: We used the MIMIC IV 2.0 (the Medical Information Mart for Intensive Care IV) to retrieve patients with secondary bone tumors as a study cohort. To evaluate the predictive ability of each characteristic on patient mortality, stepwise Cox regression was used to screen variables, and the selected variables were included in the final Cox proportional hazard model. Finally, the performance of the model was tested using the decision curve, calibration curve, and receiver operating characteristic (ROC) curve. RESULTS: A total of 1028 patients were enrolled after excluding cases with missing information. In the training cohort, albumin, APSIII (Acute Physiology Score III), chemotherapy, lactate, chloride, hepatic metastases, respiratory failure, SAPSII (Simplified Acute Physiology Score II), and total protein were identified as independent risk factors for patient death and then incorporated into the final model. The model showed good and robust prediction performance. CONCLUSION: We developed a nomogram prognostic model for patients with secondary bone tumors in the intensive care unit, which provides effective survival prediction information.