ArticleJournal of cancer research and clinical oncology2024
Development of a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit: a retrospective analysis based on the MIMIC IV database.
Article in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 2 citations in OpenAlex.
- A Rho GTPase-related gene signature predicts prognosis and reveals an immunosuppressive microenvironment in lung adenocarcinoma: an integrated analysis of bulk and single-cell RNA sequencing data.Scientific reports · 2026Article
- Prognostic biomarkers for overall survival in patients undergoing surgery for bone metastases: a pan-cancer study.Frontiers in medicine · 2025Article
- Tumor hypoxia in immune infiltration and prognosis of bladder cancer.Translational cancer research · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
purposeThe 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.
methodsWe 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.
resultsA 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.
conclusionWe developed a nomogram prognostic model for patients with secondary bone tumors in the intensive care unit, which provides effective survival prediction information.
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