切换至 "中华医学电子期刊资源库"

中华危重症医学杂志(电子版) ›› 2023, Vol. 16 ›› Issue (02) : 116 -122. doi: 10.3877/cma.j.issn.1674-6880.2023.02.005

论著

急性心肌梗死患者住院期间死亡风险模型的构建
欧阳建, 厉锦巧, 徐淑英(), 王斌, 陈剑平   
  1. 322100 浙江东阳,温州医科大学附属东阳医院急诊科
  • 收稿日期:2022-08-02 出版日期:2023-04-30
  • 通信作者: 徐淑英

Establishment of a prediction model for death during hospitalization in patients with acute myocardial infarction

Jian Ouyang, Jinqiao Li, Shuying Xu(), Bin Wang, Jianping Chen   

  1. Department of Emergency Medicine, the Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang 322100, China
  • Received:2022-08-02 Published:2023-04-30
  • Corresponding author: Shuying Xu
引用本文:

欧阳建, 厉锦巧, 徐淑英, 王斌, 陈剑平. 急性心肌梗死患者住院期间死亡风险模型的构建[J]. 中华危重症医学杂志(电子版), 2023, 16(02): 116-122.

Jian Ouyang, Jinqiao Li, Shuying Xu, Bin Wang, Jianping Chen. Establishment of a prediction model for death during hospitalization in patients with acute myocardial infarction[J]. Chinese Journal of Critical Care Medicine(Electronic Edition), 2023, 16(02): 116-122.

目的

识别住院期间高死亡风险的急性心肌梗死患者,尽早进行干预,减少病死率。

方法

收集2013年6月1日至2022年5月31日期间在温州医科大学附属东阳医院住院的1 251例急性心肌梗死患者的临床资料,分析该类患者住院期间死亡的独立危险因素,绘制列线图。采用受试者工作特征(ROC)曲线评估模型的判别能力,同时用校准图评估模型的校准度和决策曲线分析法(DCA)评估预测模型的临床有效性,并应用bootstrap法进行内验证。

结果

逐步回归分析显示,年龄[比值比(OR)= 1.060,95%置信区间(CI)(1.035,1.087),P < 0.001]及入院后24 h内肌酐[OR = 1.003,95%CI(1.001,1.005),P = 0.012]、乳酸[OR = 1.190,95%CI(1.064,1.332),P = 0.002]、肌酸激酶同工酶[OR = 1.003,95%CI(1.001,1.004),P < 0.001]、B型脑钠肽前体[OR = 1.006,95%CI(1.004,1.008),P < 0.001]及白细胞计数[OR = 1.055,95%CI(1.006,1.105),P = 0.024]的最大值为急性心肌梗死患者住院期间死亡的独立危险因素。建模后ROC曲线下面积为0.851(P < 0.001),校准图的P值为0.896,Brier scaled为0.055,calibration slope为1.000,R2为0.328,DCA曲线在两条极端曲线之上。内验证曲线提示校正曲线与理想曲线之间重合度高。

结论

入院后24 h内肌酐、乳酸、肌酸激酶同工酶、B型脑钠肽前体、白细胞计数的最大值及发病的年龄为急性心肌梗死患者住院期间死亡的独立危险因素,由上述指标建立的模型在预测急性心肌梗死患者住院期间死亡风险中有较好的意义。

Objective

To identify the acute myocardial infarction patients with bad outcomes during hospitalization and to help clinical physicians to take intervention measures to reduce the mortality.

Methods

Clinical data were collected from 1 251 patients with acute myocardial infarction who were hospitalized in the Affiliated Dongyang Hospital of Wenzhou Medical University between 1st Jan 2013 and 31th May 2022. Independent risk factors of death during hospitalization were screened. Based on the screened variables, a nomogram prediction model was established. Then the model was evaluated for its prediction power by a receiver operating characteristic (ROC) curve, calibration accuracy by a GiViTI calibration curve and clinical effectiveness by decision curve analysis (DCA). Finally, the established prediction model was validated by a bootstrap assay.

Results

Stepwise regression analysis showed that age [odds ratio (OR) = 1.060, 95% confidence interval (CI) (1.035, 1.087), P < 0.001] and the maximum values of creatinine [OR = 1.003, 95%CI (1.001, 1.005), P = 0.012], lactic acid [OR = 1.190, 95%CI (1.064, 1.332), P = 0.002], creatine kinase isoenzymes [OR = 1.003, 95%CI (1.001, 1.004), P < 0.001], B-type natriuretic peptide precursor [OR = 1.006, 95%CI (1.004, 1.008), P < 0.001] and white blood cell [OR = 1.055, 95%CI (1.006, 1.105), P = 0.024] within 24 hours after hospitalization were significantly associated with death in hospital among patients with acute myocardial infarction. The area under the ROC curve of the prediction model was 0.851 (P < 0.001), with P value of 0.896, calibration slope of 1.000, R2 of 0.328 and Brier scaled value of 0.055 in the calibration curve and with the DCA curve above the two extreme curves. In validation using bootstrap, the bias-corrected curve was closed to the idea curve.

Conclusions

Age and the maximum values of creatinine, lactic acid, creatine kinase isoenzymes, B-type natriuretic peptide precursor and white blood cell within 24 hours after hospitalization are independent risk factors of death in hospital among patients with acute myocardial infarction. The established model owns good prediction power for their death during hospitalization.

表1 急性心肌梗死患者住院期间死亡风险单因素分析[MP25P75)]
表2 急性心肌梗死患者住院期间死亡风险的多因素Logistic回归分析
表3 急性心肌梗死患者住院期间死亡风险的逐步回归分析
图1 列线图注:B型脑钠肽前体为真实值/100
图2 模型的受试者工作特征曲线(a)、校准图(b)及决策曲线分析法曲线(c)
图3 内验证曲线
1
Piepoli MF, Hoes AW, Agewall S, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice: the Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR)[J]. Eur Heart J, 2016, 37 (29): 2315-2381.
2
Lacey B, Herrington WG, Preiss D, et al. The role of emerging risk dactors in cardiovascular outcomes[J]. Curr Atheroscler Rep, 2017, 19 (6): 28.
3
Sanchis-Gomar F, Perez-Quilis C, Leischik R, et al. Epidemiology of coronary heart disease and acute coronary syndrome[J]. Ann Transl Med, 2016, 4 (13): 256.
4
《心肺血管病杂志》编辑部.中国心血管健康与疾病报告2019[J].心肺血管病杂志202039(9):145-1156.
5
Amsterdam EA, Wenger NK, Brindis RG, et al. 2014 AHA/ACC guideline for the management of patients with non-ST-elevation acute coronary syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines[J]. J Am Coll Cardiol, 2014, 64 (24): e139-e228.
6
Ibanez B, James S, Agewall S, et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the task force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC)[J]. Eur Heart J, 2018, 39 (2): 119-177.
7
Antman EM, Cohen M, Bernink PJ, et al. The TIMI risk score for unstable angina/non-ST elevation MI: a method for prognostication and therapeutic decision making[J]. JAMA, 2000, 284 (7): 835-842.
8
Morrow DA, Antman EM, Charlesworth A, et al. TIMI risk score for ST-elevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation: an intravenous nPA for treatment of infarcting myocardium early Ⅱ trial substudy[J]. Circulation, 2000, 102 (17): 2031-2037.
9
Granger CB, Goldberg RJ, Dabbous O, et al. Predctors of hospital mortality in the global registry of acute coronary events[J]. Arch Intern Med, 2003, 163 (19): 2345-2353.
10
Al'Aref SJ, Singh G, van Rosendael AR, et al. Determinants of in-hospital mortality after percutaneous coronary intervention: a machine learning approach[J]. J Am Heart Assoc, 2019, 8 (5): e011160.
11
Gao N, Qi X, Dang Y, et al. Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI[J]. BMC Cardiovasc Disord, 2020, 20 (1): 513.
12
Hou X, Du X, Wang G, et al. Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients[J]. BMJ Open, 2021, 11 (7): e044518.
13
王斌,陈剑平,欧阳建.脓毒症患者30天死亡风险预测模型的建立[J].中华急诊医学杂志202130(10):1240-1247.
14
Zhang Z, Zheng B, Liu N, et al. Mechanical power normalized to predicted body weight as a predictor of mortality in patients with acute respiratory distress syndrome[J]. Intensive Care Med, 2019, 45 (6): 856-864.
15
Hsieh FY, Lavori PW. Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates[J]. Control Clin Trials, 2000, 21 (6): 552-560.
16
Harrell FE Jr, Califf RM, Pryor DB, et al. Evaluating the yield of medical tests[J]. JAMA, 1982, 247 (18): 2543-2546.
17
Silva TB, Oliveira CZ, Faria EF, et al. Development and validation of a nomogram to estimate the risk of prostate cancer in Brazil[J]. Anticancer Res, 2015, 35 (5): 2881-2886.
18
Niu XK, He WF, Zhang Y, et al. Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer[J]. Clin Radiol, 2017, 72 (6): 458-464.
19
Nattino G, Finazzi S, Bertolini G. A new test and graphical tool to assess the goodness of fit of logistic regression models[J]. Stat Med, 2016, 35 (5): 709-720.
20
Vickers AJ, Cronin AM, Elkin EB, et al. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers[J]. BMC Med Inform Decis Mak, 2008 (8): 53.
21
James SK, Lindahl B, Siegbahn A, et al. N-terminal pro-brain natriuretic peptide and other risk markers for the separate prediction of mortality and subsequent myocardial infarction in patients with unstable coronary artery disease: a Global Utilization of Strategies To Open occluded arteries (GUSTO)-Ⅳ substudy[J]. Circulation, 2003, 108 (3): 275-281.
22
Lazzeri C, Valente S, Chiostri M, et al. Clinical significance of lactate in acute cardiac patients[J]. World J Cardiol, 2015, 7 (8): 483-489.
23
Zhao XT, Zhang CF, Liu QJ. Meta-analysis of Nicorandil effectiveness on myocardial protection after percutaneous coronary intervention[J]. BMC Cardiovasc Disord, 2019, 19 (1): 144.
24
Auer J, Verbrugge FH, Lamm G. Editor's choice-What do small serum creatinine changes tell us about outcomes after acute myocardial infarction[J]. Eur Heart J Acute Cardiovasc Care, 2018, 7 (8): 739-742.
25
Shen S, Ye J, Wu X, et al. Association of N-terminal pro-brain natriuretic peptide level with adverse outcomes in patients with acute myocardial infarction: a meta-analysis[J]. Heart Lung, 2021, 50 (6): 863-869.
26
Eimonte M, Paulauskas H, Daniuseviciute L, et al. R-esidual effects of short-term whole-body cold-water immersion on the cytokine profile, white blood cell count, and blood markers of stress[J]. Int J Hyperthermia, 2021, 38 (1): 696-707.
[1] 薛艳玲, 马小静, 谢姝瑞, 何俊, 夏娟, 何亚峰. 左心声学造影在急性心肌梗死合并室间隔穿孔中的应用价值[J]. 中华医学超声杂志(电子版), 2023, 20(10): 1036-1039.
[2] 刘欢颜, 华扬, 贾凌云, 赵新宇, 刘蓓蓓. 颈内动脉闭塞病变管腔结构和血流动力学特征分析[J]. 中华医学超声杂志(电子版), 2023, 20(08): 809-815.
[3] 马艳波, 华扬, 刘桂梅, 孟秀峰, 崔立平. 中青年人颈动脉粥样硬化病变的相关危险因素分析[J]. 中华医学超声杂志(电子版), 2023, 20(08): 822-826.
[4] 唐旭, 韩冰, 刘威, 陈茹星. 结直肠癌根治术后隐匿性肝转移危险因素分析及预测模型构建[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 16-20.
[5] 杨倩, 李翠芳, 张婉秋. 原发性肝癌自发性破裂出血急诊TACE术后的近远期预后及影响因素分析[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 33-36.
[6] 吴方园, 孙霞, 林昌锋, 张震生. HBV相关肝硬化合并急性上消化道出血的危险因素分析[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 45-47.
[7] 栗艳松, 冯会敏, 刘明超, 刘泽鹏, 姜秋霞. STIP1在三阴性乳腺癌组织中的表达及临床意义研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 52-56.
[8] 甄子铂, 刘金虎. 基于列线图模型探究静脉全身麻醉腹腔镜胆囊切除术患者术后肠道功能紊乱的影响因素[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 61-65.
[9] 陈旭渊, 罗仕云, 李文忠, 李毅. 腺源性肛瘘经手术治疗后创面愈合困难的危险因素分析[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 82-85.
[10] 马伟强, 马斌林, 吴中语, 张莹. microRNA在三阴性乳腺癌进展中发挥的作用[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 111-114.
[11] 李永胜, 孙家和, 郭书伟, 卢义康, 刘洪洲. 高龄结直肠癌患者根治术后短期并发症及其影响因素[J]. 中华临床医师杂志(电子版), 2023, 17(9): 962-967.
[12] 陆猛桂, 黄斌, 李秋林, 何媛梅. 蜂蛰伤患者发生多器官功能障碍综合征的危险因素分析[J]. 中华临床医师杂志(电子版), 2023, 17(9): 1010-1015.
[13] 张生怀. 急性心肌梗死致心源性猝死救治分析一例[J]. 中华临床医师杂志(电子版), 2023, 17(08): 924-926.
[14] 王军, 刘鲲鹏, 姚兰, 张华, 魏越, 索利斌, 陈骏, 苗成利, 罗成华. 腹膜后肿瘤切除术中大量输血患者的麻醉管理特点与分析[J]. 中华临床医师杂志(电子版), 2023, 17(08): 844-849.
[15] 李达, 张大涯, 陈润祥, 张晓冬, 黄士美, 陈晨, 曾凡, 陈世锔, 白飞虎. 海南省东方市幽门螺杆菌感染现状的调查与相关危险因素分析[J]. 中华临床医师杂志(电子版), 2023, 17(08): 858-864.
阅读次数
全文


摘要