摘要
目的分析重症心力衰竭患者血清微小核糖核酸-30b-3p(miR-30b-3p)、可溶性致癌抑制因子2(sST2)的表达,并构建重症心力衰竭患者预后不良的列线图模型。方法回顾性分析2020年6月至2023年6月于遂宁市中心医院心血管内科收治的332例重症心力衰竭患者(心力衰竭组)及36例同期来院体检的健康人群(对照组)的临床资料,依照7:3的比例将心力衰竭组分为建模队列232例,模型验证队列100例。对心力衰竭组进行随访6个月,根据患者预后效果,将建模列队分为预后良好组(n=162)和预后不良组(n=70);模型验证队列分为预后良好组(n=70)和预后不良组(n=30)。利用二元Logistic回归分析血清miR-30b-3p、sST2在重症心力衰竭患者的表达差异及预后不良的影响因素,构建预测模型并验证,受试者工作特征(ROC)曲线评价模型效能,校准曲线评估预测事件与实际事件的一致性,决策曲线分析(DCA)评价模型的有效性。结果与对照组相比,心力衰竭组脑钠肽(BNP)、miR-30b-3p及sST2高于对照组(t=18.829、11.282、8.313,P=0.000),而左室射血分数(LVEF)低于对照组(t=23.766,P=0.000),差异均有统计学意义。建模队列中预后不良组BNP、miR-30b-3p、sST2水平较预后良好组更高(t=11.734、t=14.181、t=13.218,P=0.000),而LVEF低于预后良好组(t=13.285,P=0.000)。经Logistic多因素分析,BNP增高(OR=1.244,P=0.005)、LVEF降低(OR=0.432,P=0.001)、miR-30b-3p(OR=44.179,P=0.001)及sST2(OR=10.125,P=0.003)升高是重症心力衰竭患者预后不良的影响因素。基于多因素分析结果构建预测模型方程,为Log(P)=0.218×BNP-0.839×LVEF+3.788×miR-30b-3p+2.315×sST2-55.620。建模队列ROC的曲线下面积(AUC)为0.994(95%CI:0.987~1.000),验证队列利用列线图预测验证组存活概率,模型AUC为0.964(95%CI:0.924~1.000)。建模队列、验证队列所生成的列线图实际结果曲线与校准曲线的偏差较小,预测事件与实际事件的一致性较高(χ^(2)=1.206,P=0.99
Objective To analyze the expressions of serum microRNA-30b-3p(miR-30b-3p)and soluble suppression of tumorigenicity 2(sST2),and to establish a nomogram predictive model for poor prognosis in patients with severe heart failure(HF).Methods The clinical materials of patients with severe HF(n=332,HF group)and healthy controls with physical examination(n=36,control group)were retrospectively analyzed in Department of Cardiovascular Medicine of Central Hospital of Suining City from June 2020 to June 2023.The AF group was divided,in a ratio of 7:3,into modeling cohort(n=232)and model validation cohort(n=100).After followed up for 6 months,modeling cohort was divided,according to prognostic status,into modeling good prognosis group(n=162)and modeling poor prognosis group(n=70),and model validation cohort was divided into validation good prognosis group(n=70)and validation poor prognosis group(n=30).The differences in expressions of miR-30b-3p and sST2 and influence factors for poor prognosis were analyzed by using binary Logistic regression analysis.A predictive通讯作者:黄刚,E-mail:huanggangsn@126.com model was established and its efficacy was verified by using ROC curve.The consistency between predicted events and actual events was reviewed by using calibration curve.The effectiveness of the model was reviewed by using decision curve analysis(DCA).Results The levels of B-type natriuretic peptide(BNP),miR-30b-3p and sST2 were higher(t=18.829,t=11.282,t=8.313,all P=0.000),and left ventricular ejection fraction(LVEF)was lower(t=23.766,P=0.000)in HF group than those in control group.The levels of BNP,miR-30b-3p and sST2 were higher(t=11.734,t=14.181,t=13.218,all P=0.000),and LVEF was lower(t=13.285,P=0.000)in modeling poor prognosis group than those in modeling good prognosis group.The results of multi-factor Logistic analysis showed that increased BNP(OR=1.244,P=0.005),decreased LVEF(OR=0.432,P=0.001)and increased miR-30b-3p(OR=44.179,P=0.001)and sST2(OR=10.125,P=0.003)were influence factors for poor prognosis.The pre
作者
黄刚
戴大银
张永红
黄冬梅
唐锴
Huang Gang;Dai Dayin;Zhang Yonghong;Huang Dongmei;Tang Kai(Department of Cardiovascular Medicine,Central Hospital of Suining City,Suining 629000,China;不详)
出处
《中国循证心血管医学杂志》
2024年第12期1461-1466,共6页
Chinese Journal of Evidence-Based Cardiovascular Medicine
基金
四川省基层卫生事业发展研究中心立项项目(SWFZ20-Q-058)。
