基于血清SERCA2a水平构建慢性心力衰竭患者短期不良终点事件预测模型  

Construction of short-term adverse end-point event prediction model for patients with chronic heart fail‐ure based on serum SERCA2a level

作　　者：陈锦萍 陈庞何 赖金霞 CHEN Jinping;CHEN Panghe;LAI Jinxia(Department of Public Health,2.Department of Cardiovascular Medicine,the Second Affiliated Hospital of Guang-dong Medical University,Zhanjiang 524000,Guangdong,China)

机构地区：[1]广东医科大学附属第二医院公共卫生科,湛江524000 [2]广东医科大学附属第二医院心血管内科,湛江524000

出　　处：《医学研究与战创伤救治》2025年第1期44-49,共6页Journal of Medical Research & Combat Trauma Care

基　　金：湛江市科技发展专项资金竞争性分配项目(2022A01132)。

摘　　要：目的构建基于血清肌浆网钙离子ATP酶2a(SERCA2a)水平的慢性心力衰竭(CHF)患者短期不良终点事件预测模型。方法选择2020年1月至2023年1月广东医科大学附属第二医院收治的230例CHF患者为研究对象,其中建模组145例,验证组85例。出院后随访3个月,根据是否发生短期不良终点事件分为事件组、无事件组。收集患者基础资料、实验室指标及检测血清SERCA2a水平,通过多因素Logistic回归分析筛选建模组CHF患者短期不良终点事件的独立预测因子,建立列线图模型并进行评估。结果230例CHF患者中,有78例患者发生短期不良终点事件(33.91%),其中建模组50例,验证组28例。事件组与无事件组在年龄、BMI、合并高血压、LVEF、NT-proBNP、CRP、SERCA2a方面差异具有统计学意义(P<0.05)。多因素Logistic回归分析结果显示,年龄、合并高血压、NT-proBNP、CRP、SERCA2a均是CHF患者短期不良终点事件发生的影响因素(P<0.05)。建立的预测模型拟合度较好(χ2=7.652,P=0.213)。ROC曲线显示,AUC分别为0.901、0.897;决策曲线显示,当预测风险阈值>0.01时,模型有良好的临床净收益。结论基于血清SERCA2a、年龄、合并高血压、NT-proBNP、CRP建立的CHF患者短期不良终点事件列线图预测模型具有良好的准确度、区分度,临床应用价值较高。[Abstract]Objective To establish a short-term adverse end-point event prediction model for patients with chronic heart failure(CHF)based on serum sarco reticulum Ca2+-ATPase type 2a(SERCA2a)level.Methods A total of 230 CHF patients admitted to the Second Affiliated Hospital of Guangdong Medical University from January 2020 to January 2023 were selected for the study,including 145 in the modeling group and 85 in the validation group.They were followed up for 3 months after discharge from the hospital and were categorized into event group and no event group according to whether short-term adverse endpoint events occurred.Laboratory indicators of patients´basic data and serum SERCA2a levels were collected.Independent predictors of short-term adverse endpoint events in CHF patients in the modeling group were screened by multivariate Logistic regression analysis,and a nomogram model was established and evaluated.Results Short-term adverse endpoint events occurred in 78 of 230 CHF patients(33.91%),with 50 in the modeling group and 28 in the validation group.There were significant differences in age,body mass index,combined hypertension,LVEF,NT-proBNP,CRP and SERCA2a between the event group and the non-event group(P<0.05).Multivariate Logistic regression analysis showed that age,combined hypertension,NT-proBNP,CRP and SERCA2a were all factors affecting the occurrence of short-term adverse endpoint events in CHF patients(P<0.05).The established prediction model had a good fit(χ2=7.652,P=0.213).ROC curve showed that AUC was 0.901 and 0.897 respectively;The decision curve showed that when the predicted risk threshold is>0.01,the model has a good clinical net benefit.Conclusion Based on SERCA2a,age,hypertension,NT-proBNP and CRP,the short-term adverse endpoint event prediction model of CHF patients has good accuracy and differentiation,and has high clinical application value.

关 键 词：慢性心力衰竭 肌浆网钙离子ATP酶2a 列线图 短期不良终点事件 

分 类 号：R541.6[医药卫生—心血管疾病]