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.	張建閣, 秦歷杰, 程艷偉. 骨髓腔通路在院外心臟驟停中的應用進展及思考. 中國急救醫學, 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, 趙翠, 司遷, 等. 智慧醫療的發展現狀與未來. 生命科學儀器, 2021, 19(2): 4-13.
5.	賀冰潔, 陳暐燁, 劉立立, 等. 宮頸癌發病風險預測模型的系統綜述. 中華流行病學雜志, 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.	中華醫學會, 中華醫學會雜志社, 中華醫學會全科醫學分會, 等. 心臟驟停基層診療指南(2019年). 中華全科醫師雜志, 2019, 18(11): 1034-1041.
9.	張丹妮, 沈理, 張俊, 等. 胃超聲檢查對胃癌診斷價值的Meta分析. 中華醫學超聲雜志(電子版), 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.	P?tz 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, Bj?rnsson 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.	李發揮, 李雁浩, 桂逢烯, 等. 聲空化對巨噬細胞損傷效應的人工神經網絡自適應模型辨識. 中國超聲醫學雜志, 2020, 36(3): 269-272.
33.	劉雨安, 楊小文, 李樂之. 機器學習在疾病預測的應用研究進展. 護理學報, 2021, 28(7): 30-34.
34.	崔建偉, 趙哲, 杜小勇. 支撐機器學習的數據管理技術綜述. 軟件學報, 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, 杜建強, 聶斌, 等. 特征選擇方法綜述. 計算機工程與應用, 2019, 55(24): 10-19.
39.	李舵, 董超群, 司品超, 等. 神經網絡驗證和測試技術研究綜述. 計算機工程與應用, 2021, 57(22): 53-67.
40.	夏佳志, 李杰, 陳思明, 等. 可視化與人工智能交叉研究綜述. 中國科學:信息科學, 2021, 51(11): 1777-1801.
41.	紀守領, 杜天宇, 李進鋒, 等. 機器學習模型安全與隱私研究綜述. 軟件學報, 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. 張建閣, 秦歷杰, 程艷偉. 骨髓腔通路在院外心臟驟停中的應用進展及思考. 中國急救醫學, 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, 趙翠, 司遷, 等. 智慧醫療的發展現狀與未來. 生命科學儀器, 2021, 19(2): 4-13.
5. 賀冰潔, 陳暐燁, 劉立立, 等. 宮頸癌發病風險預測模型的系統綜述. 中華流行病學雜志, 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. 中華醫學會, 中華醫學會雜志社, 中華醫學會全科醫學分會, 等. 心臟驟停基層診療指南(2019年). 中華全科醫師雜志, 2019, 18(11): 1034-1041.
9. 張丹妮, 沈理, 張俊, 等. 胃超聲檢查對胃癌診斷價值的Meta分析. 中華醫學超聲雜志(電子版), 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. P?tz 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, Bj?rnsson 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. 李發揮, 李雁浩, 桂逢烯, 等. 聲空化對巨噬細胞損傷效應的人工神經網絡自適應模型辨識. 中國超聲醫學雜志, 2020, 36(3): 269-272.
33. 劉雨安, 楊小文, 李樂之. 機器學習在疾病預測的應用研究進展. 護理學報, 2021, 28(7): 30-34.
34. 崔建偉, 趙哲, 杜小勇. 支撐機器學習的數據管理技術綜述. 軟件學報, 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, 杜建強, 聶斌, 等. 特征選擇方法綜述. 計算機工程與應用, 2019, 55(24): 10-19.
39. 李舵, 董超群, 司品超, 等. 神經網絡驗證和測試技術研究綜述. 計算機工程與應用, 2021, 57(22): 53-67.
40. 夏佳志, 李杰, 陳思明, 等. 可視化與人工智能交叉研究綜述. 中國科學:信息科學, 2021, 51(11): 1777-1801.
41. 紀守領, 杜天宇, 李進鋒, 等. 機器學習模型安全與隱私研究綜述. 軟件學報, 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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