Objective: To explore the risk factors of short-term prognosis of severe BCS patients,established and verified the nomogram prediction model for these BCS patients and evaluated its clinical application value. Methods: This study is a retrospective cohort study. The clinical data of 171 patients with severe BCS diagnosed were retrospectively analyzed in the Department of Hepatopancreatobiliary Surgery First Affiliated Hospital of Zhengzhou University from January 2018 to December 2023. There were 105 males and 66 females, aged (52.1±12.8) years (range: 18 to 79 years). The patients were divided into two groups based on whether they died within 28 days: the death group (n=38) and the survival group (n=133). The risk factors for short-term death of patients were analyzed,and independent risk factors were screened by univariate and multivariate analysis. Furthermore,these factors were used to establish the nomogram prediction model. The area under the curve(AUC),the Bootstrap Resampling,the Hosmer-Lemeshow test and the Decision Curve Analysis(DCA) were used to verify the model's differentiation,internal verification,calibration degree and clinical effectiveness,respectively. Results: Univariate and multivariate Logistics regression analysis showed that the history of hepatic encephalopathy,white blood cell,glomerular filtration rate and prothrombin time are independent risk factors (P<0.05). The above factors were used to successfully establish the prediction model with 0.908 of AUC and 0.895 of the internal verification of AUC,indicating that the predictive model was valuable. The 0.663 P-values in the Hosmer-Lemeshow test indicated the high calibration degree of the model. The clinical effectiveness of the model was proved by the 18% clinical benefit population using the DCA curve with the 17% probability threshold. Conclusions: The independent risk factors are the history of hepatic encephalopathy,white blood cell,glomerular filtration rate and prothrombin time. An adequate basis was acquired by establishing a nomogram prediction model of the short-term prognosis of severe BCS,which was helpful for early clinical screening and identification of high-risk patients with severe BCS who could die in the short term and timely providing timely intervention measures for improving the prognosis.
目的： 探讨重症巴德-吉亚利综合征患者的短期预后因素，建立重症巴德-吉亚利综合征患者短期预后的列线图预测模型并评价其临床应用价值。 方法： 本研究为回顾性队列研究。回顾性分析2018年1月1日至2023年12月31日郑州大学第一附属医院肝胆胰外科收治的171例重症巴德-吉亚利综合征患者的临床资料。其中男性105例，女性66例，年龄（52.1±12.8）岁（范围：18~79岁）。根据患者确诊后28 d内是否死亡分为死亡组（38例）和生存组（133例）。分析患者短期死亡的危险因素，通过单因素及多因素Logistic回归分析筛选出重症巴德-吉亚利综合征患者短期死亡的独立危险因素，将这些因素纳入并建立列线图预测模型。通过受试者工作特征曲线下面积（AUC）验证模型区分度，并采用自举重采样法对其进行内部验证；通过Hosmer-Lemeshow检验验证模型的校准度；通过临床决策曲线（DCA）验证模型临床有效性。 结果： 单因素及多因素Logistic回归分析结果显示，肝性脑病史、白细胞计数、肾小球滤过率和凝血酶原时间是患者死亡的独立相关因素（P值均<0.05）。建立患者短期死亡的预测模型AUC为 0.908（内部验证AUC为 0.895），具有良好的区分度和校准度（P=0.663）。DCA曲线中阈概率值设定为17%，人群临床净获益为18%，表明预测模型具有临床有效性。 结论： 肝性脑病史、白细胞计数、肾小球滤过率和凝血酶原时间是重症巴德-吉亚利综合征患者死亡的独立相关因素。重症巴德-吉亚利综合征短期预后列线图预测模型可以在临床早期筛选并识别重症巴德-吉亚利综合征短期死亡的高危患者。.