In-hospital cardiac arrest risk prediction models for patients with cardiovascular disease: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

CAI Zhenzhen ,  CHEN Yuan , LIN Bixia

Department of Nursing, Xiamen Cardiovascular Hospital, Xiamen University, Xiamen 361006, P. R. China;

Corresponding author：

CHEN Yuan, Email: 28837445@qq.com

Keywords：

Cardiac arrest; In-hospital; Cardiovascular; Risk prediction; Model; Systematic review

DOI：

10.7507/1672-2531.202206065

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To systematically review risk prediction models of in-hospital cardiac arrest in patients with cardiovascular disease, and to provide references for related clinical practice and scientific research for medical professionals in China. Methods Databases including CBM, CNKI, WanFang Data, PubMed, ScienceDirect, Web of Science, The Cochrane Library, Wiley Online Journals and Scopus were searched to collect studies on risk prediction models for in-hospital cardiac arrest in patients with cardiovascular disease from January 2010 to July 2022. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias of the included studies. Results A total of 5 studies (4 of which were retrospective studies) were included. Study populations encompassed mainly patients with acute coronary syndrome. Two models were modeled using decision trees. The area under the receiver operating characteristic curve or C statistic of the five models ranged from 0.720 to 0.896, and only one model was verified externally and for time. The most common risk factors and immediate onset factors of in-hospital cardiac arrest in patients with cardiovascular disease included in the prediction model were age, diabetes, Killip class, and cardiac troponin. There were many problems in analysis fields, such as insufficient sample size (n=4), improper handling of variables (n=4), no methodology for dealing with missing data (n=3), and incomplete evaluation of model performance (n=5). Conclusion The prediction efficiency of risk prediction models for in-hospital cardiac arrest in patients with cardiovascular disease was good; however, the model quality could be improved. Additionally, the methodology needs to be improved in terms of data sources, selection and measurement of predictors, handling of missing data, and model evaluations. External validation of existing models is required to better guide clinical practice.

Citation： CAI Zhenzhen, CHEN Yuan, LIN Bixia. In-hospital cardiac arrest risk prediction models for patients with cardiovascular disease: a systematic review. Chinese Journal of Evidence-Based Medicine, 2022, 22(10): 1175-1181. doi: 10.7507/1672-2531.202206065 Copy

1.	Andersen LW, Holmberg MJ, Berg KM, et al. In-hospital cardiac arrest: a review. JAMA, 2019, 321(12): 1200-1210.
2.	Merchant RM, Yang L, Becker LB, et al. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med, 2011, 39(11): 2401-2406.
3.	Perman SM, Stanton E, Soar J, et al. Location of in-hospital cardiac arrest in the United States-variability in event rate and outcomes. J Am Heart Assoc, 2016, 5(10): e003638.
4.	Hodgetts TJ, Kenward G, Vlackonikolis I, et al. Incidence, location and reasons for avoidable in-hospital cardiac arrest in a district general hospital. Resuscitation, 2002, 54(2): 115-123.
5.	陈俊杉, 余金甜, 张爱琴. ICU患者谵妄风险预测模型的系统评价. 中国循证医学杂志, 2019, 19(9): 1093-1100.
6.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
7.	陈茹, 王胜锋, 周家琛, 等. 预测模型研究的偏倚风险和适用性评估工具解读. 中华流行病学杂志, 2020, 41(5): 776-781.
8.	贺冰洁, 陈暐烨, 刘立立, 等. 宫颈癌发病风险预测模型的系统综述. 中华流行病学杂志, 2021, 42(10): 1855-1862.
9.	Faxén J, Hall M, Gale CP, et al. A user-friendly risk-score for predicting in-hospital cardiac arrest among patients admitted with suspected non ST-elevation acute coronary syndrome - The SAFER-score. Resuscitation, 2017, 121: 41-48.
10.	Li H, Wu TT, Yang DL, et al. Decision tree model for predicting in-hospital cardiac arrest among patients admitted with acute coronary syndrome. Clin Cardiol, 2019, 42(11): 1087-1093.
11.	Shang LX, Zhou XH, Zhang JH, et al. Establishment of a predictive model for inpatient sudden cardiac death in a Chinese cardiac department population: a retrospective study. Chin Med J (Engl), 2019, 132(1): 17-24.
12.	龚海蓉, 吴婷婷, 郑若菲, 等. 基于决策树构建胸痛患者院内心脏骤停早期预警模型. 护理学杂志, 2021, 36(14): 23-28.
13.	Wu TT, Yang DL, Li H, et al. Development and validation of a nomogram to predict in-hospital cardiac arrest among patients admitted with acute coronary syndrome. Am J Emerg Med, 2021, 49: 240-248.
14.	丁舒, 吴瑛, 陶香君, 等. 院内患者谵妄风险预测模型的系统评价. 中华护理杂志, 2015, 50(5): 613-619.
15.	Jernberg T, Attebring MF, Hambraeus K, et al. The Swedish web-system for enhancement and development of evidence-based care in heart disease evaluated according to recommended therapies (SWEDEHEART). Heart, 2010, 96(20): 1617-1621.
16.	Herrett E, Smeeth L, Walker L, et al. The myocardial ischaemia national audit project (MINAP). Heart, 2010, 96(16): 1264-1267.
17.	Sullivan LM, Massaro JM, D'Agostino RB. Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Stat Med, 2004, 23(10): 1631-1660.
18.	Churpek MM, Yuen TC, Winslow C, et al. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med, 2016, 44(2): 368-374.
19.	Pirracchio R, Petersen ML, Carone M, et al. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Lancet Respir Med, 2015, 3(1): 42-52.
20.	Mao Y, Chen Y, Hackmann G, et al. Medical data mining for early deterioration warning in general hospital wards. 2011 IEEE 11th International Conference on Data Mining Workshops, 2011, 19: 1042-1049.
21.	Deo R, Albert CM. Epidemiology and genetics of sudden cardiac death. Circulation, 2012, 125(4): 620-637.
22.	Shamsuzzaman AS, Somers VK, Knilans TK, et al. Obstructive sleep apnea in patients with congenital long QT syndrome: implications for increased risk of sudden cardiac death. Sleep, 2015, 38(7): 1113-1119.
23.	韩彬, 权瑛. 心脏性猝死危险因素分析预测. 现代仪器与医疗, 2019, 25(3): 73-76.
24.	Lahousse L, Niemeijer MN, van den Berg ME, et al. Chronic obstructive pulmonary disease and sudden cardiac death: the Rotterdam study. Eur Heart J, 2015, 36(27): 1754-1761.
25.	郑刚. 心力衰竭患者心源性猝死风险预测和干预的临床研究最新进展. 中华老年心脑血管病杂志, 2020, 22(9): 997-1000.
26.	潘碧云, 陈仕银, 温达, 等. 心力衰竭和贫血史是急性ST段抬高型心肌梗死患者直接再灌注后Killip≥Ⅱ级的危险因素. 内科急危重症杂志, 2021, 27(5): 389-392.
27.	梁燕芳, 李钦, 陈亚栋. 急性心肌梗死患者cTnI, D-D, BNP和心肌酶谱变化及临床意义. 海南医学, 2022, 33(6): 766-768.
28.	贺旭东, 孙明辉, 王志超, 等. 老年急性心肌梗死患者中血清白细胞介素-6, 心肌肌钙蛋白I, 超敏C反应蛋白与肿瘤坏死因子-α水平的变化及临床意义. 中国老年学杂志, 2018, 38(6): 1326-1328.
29.	李秋萍, 谷鸿秋, 王俊峰. 临床预测模型: TRIPOD报告规范解读—以心血管疾病预测模型QRISK3为例(上). 中国循证心血管医学杂志, 2020, 12(7): 778-782, 793.
30.	李秋萍, 谷鸿秋, 王俊峰. 临床预测模型: TRIPOD报告规范解读—以心血管疾病预测模型QRISK3为例(下). 中国循证心血管医学杂志, 2020, 12(8): 904-907.

1. Andersen LW, Holmberg MJ, Berg KM, et al. In-hospital cardiac arrest: a review. JAMA, 2019, 321(12): 1200-1210.
2. Merchant RM, Yang L, Becker LB, et al. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med, 2011, 39(11): 2401-2406.
3. Perman SM, Stanton E, Soar J, et al. Location of in-hospital cardiac arrest in the United States-variability in event rate and outcomes. J Am Heart Assoc, 2016, 5(10): e003638.
4. Hodgetts TJ, Kenward G, Vlackonikolis I, et al. Incidence, location and reasons for avoidable in-hospital cardiac arrest in a district general hospital. Resuscitation, 2002, 54(2): 115-123.
5. 陈俊杉, 余金甜, 张爱琴. ICU患者谵妄风险预测模型的系统评价. 中国循证医学杂志, 2019, 19(9): 1093-1100.
6. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
7. 陈茹, 王胜锋, 周家琛, 等. 预测模型研究的偏倚风险和适用性评估工具解读. 中华流行病学杂志, 2020, 41(5): 776-781.
8. 贺冰洁, 陈暐烨, 刘立立, 等. 宫颈癌发病风险预测模型的系统综述. 中华流行病学杂志, 2021, 42(10): 1855-1862.
9. Faxén J, Hall M, Gale CP, et al. A user-friendly risk-score for predicting in-hospital cardiac arrest among patients admitted with suspected non ST-elevation acute coronary syndrome - The SAFER-score. Resuscitation, 2017, 121: 41-48.
10. Li H, Wu TT, Yang DL, et al. Decision tree model for predicting in-hospital cardiac arrest among patients admitted with acute coronary syndrome. Clin Cardiol, 2019, 42(11): 1087-1093.
11. Shang LX, Zhou XH, Zhang JH, et al. Establishment of a predictive model for inpatient sudden cardiac death in a Chinese cardiac department population: a retrospective study. Chin Med J (Engl), 2019, 132(1): 17-24.
12. 龚海蓉, 吴婷婷, 郑若菲, 等. 基于决策树构建胸痛患者院内心脏骤停早期预警模型. 护理学杂志, 2021, 36(14): 23-28.
13. Wu TT, Yang DL, Li H, et al. Development and validation of a nomogram to predict in-hospital cardiac arrest among patients admitted with acute coronary syndrome. Am J Emerg Med, 2021, 49: 240-248.
14. 丁舒, 吴瑛, 陶香君, 等. 院内患者谵妄风险预测模型的系统评价. 中华护理杂志, 2015, 50(5): 613-619.
15. Jernberg T, Attebring MF, Hambraeus K, et al. The Swedish web-system for enhancement and development of evidence-based care in heart disease evaluated according to recommended therapies (SWEDEHEART). Heart, 2010, 96(20): 1617-1621.
16. Herrett E, Smeeth L, Walker L, et al. The myocardial ischaemia national audit project (MINAP). Heart, 2010, 96(16): 1264-1267.
17. Sullivan LM, Massaro JM, D'Agostino RB. Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Stat Med, 2004, 23(10): 1631-1660.
18. Churpek MM, Yuen TC, Winslow C, et al. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med, 2016, 44(2): 368-374.
19. Pirracchio R, Petersen ML, Carone M, et al. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Lancet Respir Med, 2015, 3(1): 42-52.
20. Mao Y, Chen Y, Hackmann G, et al. Medical data mining for early deterioration warning in general hospital wards. 2011 IEEE 11th International Conference on Data Mining Workshops, 2011, 19: 1042-1049.
21. Deo R, Albert CM. Epidemiology and genetics of sudden cardiac death. Circulation, 2012, 125(4): 620-637.
22. Shamsuzzaman AS, Somers VK, Knilans TK, et al. Obstructive sleep apnea in patients with congenital long QT syndrome: implications for increased risk of sudden cardiac death. Sleep, 2015, 38(7): 1113-1119.
23. 韩彬, 权瑛. 心脏性猝死危险因素分析预测. 现代仪器与医疗, 2019, 25(3): 73-76.
24. Lahousse L, Niemeijer MN, van den Berg ME, et al. Chronic obstructive pulmonary disease and sudden cardiac death: the Rotterdam study. Eur Heart J, 2015, 36(27): 1754-1761.
25. 郑刚. 心力衰竭患者心源性猝死风险预测和干预的临床研究最新进展. 中华老年心脑血管病杂志, 2020, 22(9): 997-1000.
26. 潘碧云, 陈仕银, 温达, 等. 心力衰竭和贫血史是急性ST段抬高型心肌梗死患者直接再灌注后Killip≥Ⅱ级的危险因素. 内科急危重症杂志, 2021, 27(5): 389-392.
27. 梁燕芳, 李钦, 陈亚栋. 急性心肌梗死患者cTnI, D-D, BNP和心肌酶谱变化及临床意义. 海南医学, 2022, 33(6): 766-768.
28. 贺旭东, 孙明辉, 王志超, 等. 老年急性心肌梗死患者中血清白细胞介素-6, 心肌肌钙蛋白I, 超敏C反应蛋白与肿瘤坏死因子-α水平的变化及临床意义. 中国老年学杂志, 2018, 38(6): 1326-1328.
29. 李秋萍, 谷鸿秋, 王俊峰. 临床预测模型: TRIPOD报告规范解读—以心血管疾病预测模型QRISK3为例(上). 中国循证心血管医学杂志, 2020, 12(7): 778-782, 793.
30. 李秋萍, 谷鸿秋, 王俊峰. 临床预测模型: TRIPOD报告规范解读—以心血管疾病预测模型QRISK3为例(下). 中国循证心血管医学杂志, 2020, 12(8): 904-907.

Chinese Journal of Evidence-Based Medicine

In-hospital cardiac arrest risk prediction models for patients with cardiovascular disease: a systematic review

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