Application of machine learning to prediction model of nervous system prognosis in out-of-hospital cardiac arrest patients: A systematic review_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHENG Ping ,  LIU Ning

Department of Nursing, Zunyi Medical University, Zhuhai, 519041, Guangdong, P. R. China;

Corresponding author：

LIU Ning, Email: 761066906@qq.com

Keywords：

Machine learning; out-of-hospital cardiac arrest; prediction model; nervous system; systematic review

DOI：

10.7507/1007-4848.202201044

Video：

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Objective To systematically evaluate the clinical value of machine learning (ML) for predicting the neurological outcome of out-of-hospital cardiac arrest (OHCA), and to develop a prediction model. Methods We searched the PubMed, Web of Science, EMbase, CNKI, Wanfang database from January 1, 2011 to November 24, 2021. Studies on ML for predicting neurological outcomes in OHCA pateints were collected. Two researchers independently screened the literature, extracted the data and evaluated the bias of the included literature, evaluated the accuracy of different models and compared the area under the receiver operating characteristic curve (AUC). Results A total of 20 studies were included. Eleven of the studies were from open source databases and nine were from retrospective studies. Sixteen studies directly predicted OHCA neurological outcomes, and four predicted OHCA neurological outcomes after target temperature management. A total of seven ML algorithms were used, among which neural network was the ML algorithm with the highest frequency (n=5), followed by support vector machine and random forest (n=4). Three papers used multiple algorithms. The most frequently used input characteristic was age (n=19), followed by heart rate (n=17) and gender (n=13). A total of 4 studies compared the predictive value of ML with other classical statistical models, and the AUC value of ML model was higher than that of classical statistical models. Conclusion Existing evidence suggests that ML can more accurately predict OHCA nervous system outcomes, and the predictive performance of ML is superior to traditional statistical models in certain situations.

Citation： ZHENG Ping, LIU Ning. Application of machine learning to prediction model of nervous system prognosis in out-of-hospital cardiac arrest patients: A systematic review. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(9): 1172-1180. doi: 10.7507/1007-4848.202201044 Copy

1.	Luc G, Baert V, Escutnaire J, et al. Epidemiology of out-of-hospital cardiac arrest: A French national incidence and mid-term survival rate study. Anaesth Crit Care Pain Med, 2019, 38(2): 131-135.
2.	张建阁, 秦历杰, 程艳伟. 骨髓腔通路在院外心脏骤停中的应用进展及思考. 中国急救医学, 2021, 41(9): 817-820.
3.	Tian S, Niu S, Zhang L, et al. National survey of do not attempt resuscitation decisions on out-of-hospital cardiac arrest in China. BMC Emerg Med, 2022, 22(1): 25.
4.	李春林, 赵翠, 司迁, 等. 智慧医疗的发展现状与未来. 生命科学仪器, 2021, 19(2): 4-13.
5.	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.
6.	Lam K, Chen J, Wang Z, et al. Machine learning for technical skill assessment in surgery: A systematic review. NPJ Digit Med, 2022, 5(1): 24.
7.	Heo J, Yoon JG, Park H, et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke, 2019, 50(5): 1263-1265.
8.	中华医学会, 中华医学会杂志社, 中华医学会全科医学分会, 等. 心脏骤停基层诊疗指南(2019年). 中华全科医师杂志, 2019, 18(11): 1034-1041.
9.	张丹妮, 沈理, 张俊, 等. 胃超声检查对胃癌诊断价值的Meta分析. 中华医学超声杂志(电子版), 2021, 18(4): 344-354.
10.	Shih HM, Chen YC, Chen CY, et al. Derivation and validation of the SWAP score for very early prediction of neurologic outcome in patients with out-of-hospital cardiac arrest. Ann Emerg Med, 2019, 73(6): 578-588.
11.	Seki T, Tamura T, Suzuki M, et al. Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique. Resuscitation, 2019, 141: 128-135.
12.	Pérez-Castellanos A, Martínez-Sellés M, Uribarri A, et al. Development and external validation of an early prognostic model for survivors of out-of-hospital cardiac arrest. Rev Esp Cardiol (Engl Ed), 2019, 72(7): 535-542.
13.	Pätz T, Stelzig K, Pfeifer R, et al. Age-associated outcomes after survived out-of-hospital cardiac arrest and subsequent target temperature management. Acta Anaesthesiol Scand, 2019, 63(8): 1079-1088.
14.	Park JH, Shin SD, Song KJ, et al. Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis. Resuscitation, 2019, 142: 127-135.
15.	Pareek N, Kordis P, Beckley-Hoelscher N, et al. A practical risk score for early prediction of neurological outcome after out-of-hospital cardiac arrest: MIRACLE2. Eur Heart J, 2020, 41(47): 4508-4517.
16.	Okada Y, Kiguchi T, Irisawa T, et al. Development and validation of a clinical score to predict neurological outcomes in patients with out-of-hospital cardiac arrest treated with extracorporeal cardiopulmonary resuscitation. JAMA Netw Open, 2020, 3(11): e2022920.
17.	Okada K, Ohde S, Otani N, et al. Prediction protocol for neurological outcome for survivors of out-of-hospital cardiac arrest treated with targeted temperature management. Resuscitation, 2012, 83(6): 734-739.
18.	Kwon JM, Jeon KH, Kim HM, et al. Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes. Resuscitation, 2019, 139: 84-91.
19.	Kim JH, Kim MJ, You JS, et al. Multimodal approach for neurologic prognostication of out-of-hospital cardiac arrest patients undergoing targeted temperature management. Resuscitation, 2019, 134: 33-40.
20.	Heo JH, Kim T, Shin J, et al. Prediction of neurological outcomes in out-of-hospital cardiac arrest survivors immediately after return of spontaneous circulation: Ensemble technique with four machine learning models. J Korean Med Sci, 2021, 36(28): e187.
21.	Harford S, Darabi H, Del Rios M, et al. A machine learning based model for out of hospital cardiac arrest outcome classification and sensitivity analysis. Resuscitation, 2019, 138: 134-140.
22.	Goto Y, Maeda T, Nakatsu-Goto Y. Decision tree model for predicting long-term outcomes in children with out-of-hospital cardiac arrest: A nationwide, population-based observational study. Crit Care, 2014, 18(3): R133.
23.	Einav S, Kaufman N, Algur N, et al. Brain biomarkers and management of uncertainty in predicting outcome of cardiopulmonary resuscitation: A nomogram paints a thousand words. Resuscitation, 2013, 84(8): 1083-1088.
24.	Eertmans W, Tran TMP, Genbrugge C, et al. A prediction model for good neurological outcome in successfully resuscitated out-of-hospital cardiac arrest patients. Scand J Trauma Resusc Emerg Med, 2018, 26(1): 93.
25.	Dutta A, Alirhayim Z, Masmoudi Y, et al. Brain natriuretic peptide as a marker of adverse neurological outcomes among survivors of cardiac arrest. J Intensive Care Med, 2022, 37(6): 803-809.
26.	Cheong RW, Li H, Doctor NE, et al. Termination of resuscitation rules to predict neurological outcomes in out-of-hospital cardiac arrest for an intermediate life support prehospital system. Prehosp Emerg Care, 2016, 20(5): 623-629.
27.	Cheng CY, Chiu IM, Zeng WH, et al. Machine learning models for survival and neurological outcome prediction of out-of-hospital cardiac arrest patients. Biomed Res Int, 2021, 2021: 9590131.
28.	Andersson P, Johnsson J, Björnsson O, et al. Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm. Crit Care, 2021, 25(1): 83.
29.	Ahn S, Lee BK, Youn CS, et al. Predictors of good neurologic outcome after resuscitation beyond 30 min in out-of-hospital cardiac arrest patients undergoing therapeutic hypothermia. Intern Emerg Med, 2018, 13(3): 413-419.
30.	Ryoo SM, Jeon SB, Sohn CH, et al. Predicting outcome with diffusion-weighted imaging in cardiac arrest patients receiving hypothermia therapy: Multicenter retrospective cohort study. Crit Care Med, 2015, 43(11): 2370-2377.
31.	Kim HS, Park KN, Kim SH, et al. Prognostic value of OHCA, C-GRApH and CAHP scores with initial neurologic examinations to predict neurologic outcomes in cardiac arrest patients treated with targeted temperature management. PLoS One, 2020, 15(4): e0232227.
32.	李发挥, 李雁浩, 桂逢烯, 等. 声空化对巨噬细胞损伤效应的人工神经网络自适应模型辨识. 中国超声医学杂志, 2020, 36(3): 269-272.
33.	刘雨安, 杨小文, 李乐之. 机器学习在疾病预测的应用研究进展. 护理学报, 2021, 28(7): 30-34.
34.	崔建伟, 赵哲, 杜小勇. 支撑机器学习的数据管理技术综述. 软件学报, 2021, 32(3): 604-621.
35.	Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol, 2020, 9(2): 14.
36.	Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology, 2019, 131(6): 1346-1359.
37.	Arshaghi A, Ashourian M, Ghabeli L. Denoising medical images using machine learning, deep learning approaches: A survey. Curr Med Imaging, 2021, 17(5): 578-594.
38.	李郅琴, 杜建强, 聂斌, 等. 特征选择方法综述. 计算机工程与应用, 2019, 55(24): 10-19.
39.	李舵, 董超群, 司品超, 等. 神经网络验证和测试技术研究综述. 计算机工程与应用, 2021, 57(22): 53-67.
40.	夏佳志, 李杰, 陈思明, 等. 可视化与人工智能交叉研究综述. 中国科学:信息科学, 2021, 51(11): 1777-1801.
41.	纪守领, 杜天宇, 李进锋, 等. 机器学习模型安全与隐私研究综述. 软件学报, 2021, 32(1): 41-67.
42.	Chen S, Lachance BB, Gao L, et al. Targeted temperature management and early neuro-prognostication after cardiac arrest. J Cereb Blood Flow Metab, 2021, 41(6): 1193-1209.

1. Luc G, Baert V, Escutnaire J, et al. Epidemiology of out-of-hospital cardiac arrest: A French national incidence and mid-term survival rate study. Anaesth Crit Care Pain Med, 2019, 38(2): 131-135.
2. 张建阁, 秦历杰, 程艳伟. 骨髓腔通路在院外心脏骤停中的应用进展及思考. 中国急救医学, 2021, 41(9): 817-820.
3. Tian S, Niu S, Zhang L, et al. National survey of do not attempt resuscitation decisions on out-of-hospital cardiac arrest in China. BMC Emerg Med, 2022, 22(1): 25.
4. 李春林, 赵翠, 司迁, 等. 智慧医疗的发展现状与未来. 生命科学仪器, 2021, 19(2): 4-13.
5. 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.
6. Lam K, Chen J, Wang Z, et al. Machine learning for technical skill assessment in surgery: A systematic review. NPJ Digit Med, 2022, 5(1): 24.
7. Heo J, Yoon JG, Park H, et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke, 2019, 50(5): 1263-1265.
8. 中华医学会, 中华医学会杂志社, 中华医学会全科医学分会, 等. 心脏骤停基层诊疗指南(2019年). 中华全科医师杂志, 2019, 18(11): 1034-1041.
9. 张丹妮, 沈理, 张俊, 等. 胃超声检查对胃癌诊断价值的Meta分析. 中华医学超声杂志(电子版), 2021, 18(4): 344-354.
10. Shih HM, Chen YC, Chen CY, et al. Derivation and validation of the SWAP score for very early prediction of neurologic outcome in patients with out-of-hospital cardiac arrest. Ann Emerg Med, 2019, 73(6): 578-588.
11. Seki T, Tamura T, Suzuki M, et al. Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique. Resuscitation, 2019, 141: 128-135.
12. Pérez-Castellanos A, Martínez-Sellés M, Uribarri A, et al. Development and external validation of an early prognostic model for survivors of out-of-hospital cardiac arrest. Rev Esp Cardiol (Engl Ed), 2019, 72(7): 535-542.
13. Pätz T, Stelzig K, Pfeifer R, et al. Age-associated outcomes after survived out-of-hospital cardiac arrest and subsequent target temperature management. Acta Anaesthesiol Scand, 2019, 63(8): 1079-1088.
14. Park JH, Shin SD, Song KJ, et al. Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis. Resuscitation, 2019, 142: 127-135.
15. Pareek N, Kordis P, Beckley-Hoelscher N, et al. A practical risk score for early prediction of neurological outcome after out-of-hospital cardiac arrest: MIRACLE2. Eur Heart J, 2020, 41(47): 4508-4517.
16. Okada Y, Kiguchi T, Irisawa T, et al. Development and validation of a clinical score to predict neurological outcomes in patients with out-of-hospital cardiac arrest treated with extracorporeal cardiopulmonary resuscitation. JAMA Netw Open, 2020, 3(11): e2022920.
17. Okada K, Ohde S, Otani N, et al. Prediction protocol for neurological outcome for survivors of out-of-hospital cardiac arrest treated with targeted temperature management. Resuscitation, 2012, 83(6): 734-739.
18. Kwon JM, Jeon KH, Kim HM, et al. Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes. Resuscitation, 2019, 139: 84-91.
19. Kim JH, Kim MJ, You JS, et al. Multimodal approach for neurologic prognostication of out-of-hospital cardiac arrest patients undergoing targeted temperature management. Resuscitation, 2019, 134: 33-40.
20. Heo JH, Kim T, Shin J, et al. Prediction of neurological outcomes in out-of-hospital cardiac arrest survivors immediately after return of spontaneous circulation: Ensemble technique with four machine learning models. J Korean Med Sci, 2021, 36(28): e187.
21. Harford S, Darabi H, Del Rios M, et al. A machine learning based model for out of hospital cardiac arrest outcome classification and sensitivity analysis. Resuscitation, 2019, 138: 134-140.
22. Goto Y, Maeda T, Nakatsu-Goto Y. Decision tree model for predicting long-term outcomes in children with out-of-hospital cardiac arrest: A nationwide, population-based observational study. Crit Care, 2014, 18(3): R133.
23. Einav S, Kaufman N, Algur N, et al. Brain biomarkers and management of uncertainty in predicting outcome of cardiopulmonary resuscitation: A nomogram paints a thousand words. Resuscitation, 2013, 84(8): 1083-1088.
24. Eertmans W, Tran TMP, Genbrugge C, et al. A prediction model for good neurological outcome in successfully resuscitated out-of-hospital cardiac arrest patients. Scand J Trauma Resusc Emerg Med, 2018, 26(1): 93.
25. Dutta A, Alirhayim Z, Masmoudi Y, et al. Brain natriuretic peptide as a marker of adverse neurological outcomes among survivors of cardiac arrest. J Intensive Care Med, 2022, 37(6): 803-809.
26. Cheong RW, Li H, Doctor NE, et al. Termination of resuscitation rules to predict neurological outcomes in out-of-hospital cardiac arrest for an intermediate life support prehospital system. Prehosp Emerg Care, 2016, 20(5): 623-629.
27. Cheng CY, Chiu IM, Zeng WH, et al. Machine learning models for survival and neurological outcome prediction of out-of-hospital cardiac arrest patients. Biomed Res Int, 2021, 2021: 9590131.
28. Andersson P, Johnsson J, Björnsson O, et al. Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm. Crit Care, 2021, 25(1): 83.
29. Ahn S, Lee BK, Youn CS, et al. Predictors of good neurologic outcome after resuscitation beyond 30 min in out-of-hospital cardiac arrest patients undergoing therapeutic hypothermia. Intern Emerg Med, 2018, 13(3): 413-419.
30. Ryoo SM, Jeon SB, Sohn CH, et al. Predicting outcome with diffusion-weighted imaging in cardiac arrest patients receiving hypothermia therapy: Multicenter retrospective cohort study. Crit Care Med, 2015, 43(11): 2370-2377.
31. Kim HS, Park KN, Kim SH, et al. Prognostic value of OHCA, C-GRApH and CAHP scores with initial neurologic examinations to predict neurologic outcomes in cardiac arrest patients treated with targeted temperature management. PLoS One, 2020, 15(4): e0232227.
32. 李发挥, 李雁浩, 桂逢烯, 等. 声空化对巨噬细胞损伤效应的人工神经网络自适应模型辨识. 中国超声医学杂志, 2020, 36(3): 269-272.
33. 刘雨安, 杨小文, 李乐之. 机器学习在疾病预测的应用研究进展. 护理学报, 2021, 28(7): 30-34.
34. 崔建伟, 赵哲, 杜小勇. 支撑机器学习的数据管理技术综述. 软件学报, 2021, 32(3): 604-621.
35. Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol, 2020, 9(2): 14.
36. Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology, 2019, 131(6): 1346-1359.
37. Arshaghi A, Ashourian M, Ghabeli L. Denoising medical images using machine learning, deep learning approaches: A survey. Curr Med Imaging, 2021, 17(5): 578-594.
38. 李郅琴, 杜建强, 聂斌, 等. 特征选择方法综述. 计算机工程与应用, 2019, 55(24): 10-19.
39. 李舵, 董超群, 司品超, 等. 神经网络验证和测试技术研究综述. 计算机工程与应用, 2021, 57(22): 53-67.
40. 夏佳志, 李杰, 陈思明, 等. 可视化与人工智能交叉研究综述. 中国科学:信息科学, 2021, 51(11): 1777-1801.
41. 纪守领, 杜天宇, 李进锋, 等. 机器学习模型安全与隐私研究综述. 软件学报, 2021, 32(1): 41-67.
42. Chen S, Lachance BB, Gao L, et al. Targeted temperature management and early neuro-prognostication after cardiac arrest. J Cereb Blood Flow Metab, 2021, 41(6): 1193-1209.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Application of machine learning to prediction model of nervous system prognosis in out-of-hospital cardiac arrest patients: A systematic review

Abstract Full text Figures/Tables Video References Cited by

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