Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

• Published in: Scientific reports Vol. 14; no. 1; pp. 13938 - 15
• Main Authors: Yao, Rucheng, Zheng, Bowen, Hu, Xueying, Ma, Baohua, Zheng, Jun, Yao, Kecheng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/2324; 692/308/409; 692/4028/67/2324; 692/499; Aged; Aged, 80 and over; Carcinoma, Hepatocellular - mortality; Carcinoma, Hepatocellular - therapy; Chronic infection; Chronic obstructive pulmonary disease; Cirrhosis; Comorbidity; Decision making; Female; Health risks; Hepatic encephalopathy; Hepatitis B; Hepatocellular carcinoma; Hospital Mortality; Humanities and Social Sciences; Humans; Hypertension; In-hospital mortality; Liver cancer; Liver diseases; Liver Neoplasms - mortality; Liver Neoplasms - therapy; Lung diseases; Male; Microvasculature; Middle Aged; Mortality; Mortality risk; multidisciplinary; Multimorbidity; Nomogram; Nomograms; Palliation; Palliative Care - methods; Patients; Predictive model; Retrospective Studies; Risk Assessment; Risk Factors; Science; Science (multidisciplinary); Statistical analysis; Thrombocytopenia; Hepatocellular carcinoma; In-hospital mortality; Nomogram; Predictive model; Multimorbidity
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-64457-y

Patients diagnosed with hepatocellular carcinoma (HCC) often present with multimorbidity, significantly contributing to adverse outcomes, particularly in-hospital mortality. This study aimed to develop a predictive nomogram to assess the impact of comorbidities on in-hospital mortality risk in HCC patients undergoing palliative locoregional therapy. We retrospectively analyzed data from 345 hospitalized HCC patients who underwent palliative locoregional therapy between January 2015 and December 2022. The nomogram was constructed using independent risk factors such as length of stay (LOS), hepatitis B virus (HBV) infection, hypertension, chronic obstructive pulmonary disease (COPD), anemia, thrombocytopenia, liver cirrhosis, hepatic encephalopathy (HE), N stage, and microvascular invasion. The model demonstrated high predictive accuracy with an AUC of 0.908 (95% CI: 0.859–0.956) for the overall dataset, 0.926 (95% CI: 0.883–0.968) for the training set, and 0.862 (95% CI: 0.728–0.994) for the validation set. Calibration curves indicated a strong correlation between predicted and observed outcomes, validated by statistical tests. Decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility in predicting in-hospital mortality. This nomogram offers a practical tool for personalized risk assessment in HCC patients undergoing palliative locoregional therapy, facilitating informed clinical decision-making and improving patient management.