AUTHOR=Liu Yaxing , Gao Muyu , Li Bin , Liu Long , Liu Yao , Feng Ying , Wang Xiaojing , Wang Xianbo , Zhou Guiqin 

TITLE=Risk factors for mortality in patients with primary biliary cholangitis: a nomogram to predict 5-year survival

JOURNAL=Frontiers in Gastroenterology

VOLUME=Volume 4 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/gastroenterology/articles/10.3389/fgstr.2025.1534145

DOI=10.3389/fgstr.2025.1534145

ISSN=2813-1169

ABSTRACT=AimThe issue of transplant-free survival rate (OS) among patients with primary biliary cholangitis (PBC) remains a persistent concern. In predicting the long-term OS of PBC patients, given the complexity and population specificity of models such as the GLOBE and UK-PBC, our objective is to calculate and assess the risk factors for mortality and 5-year OS among PBC patients based on routine clinical data, ultimately facilitating its clinical application.MethodsThis study enrolled 315 patients with PBC from Beijing Ditan Hospital and randomly divided them into a training cohort (n = 189) and a validation cohort (n = 126). Through Cox regression analyses, we identified risk predictors of mortality to develop a 5-year survival nomogram for PBC. The model was evaluated with Receiver Operating Characteristic (ROC) curves, calibration curves, Decision Curve Analysis (DCA).Kaplan-Meier (KM) curves compared OS across risk groups. Additionally, correlations among the indicators were analyzed.ResultsUltimately, we established a nomogram incorporating Age, NLR, and TBIL. The Area Under the ROC Curve(AUC-ROC) values for the training and validation groups were 0.7251 and 0.7721, respectively, indicating solid consistency and outperforming the GLOBE model. Calibration and DCA curves further underscored the clinical utility of our model.KM curves revealed the model could differentiate OS across risk levels in subgroup. Additionally, a significant correlation between NLR and TBIL (P=0.0021) was observed, potentially impacting patient prognosis.ConclusionWe have constructed a well-performing prognostic model based on Age, NLR, and TBIL. This model shows good discrimination, consistency, and clinical use. It helps identifying high-risk patients, enabling more frequent follow-ups and tailored interventions, potentially enhancing prognosis and clinical outcomes.