Email Alert RSS
Volume 14 Issue 3
May  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
LIU Shuai, ZHU Huadong. Application of Artificial Intelligence in Cardiopulmonary Resuscitation[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(3): 453-458. DOI: 10.12290/xhyxzz.2022-0711
Citation: LIU Shuai, ZHU Huadong. Application of Artificial Intelligence in Cardiopulmonary Resuscitation[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(3): 453-458. DOI: 10.12290/xhyxzz.2022-0711
shu

Application of Artificial Intelligence in Cardiopulmonary Resuscitation

  • 1.

    Emergency Department, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

  • 2.

    State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China

Funds: 

Scientific and Technological Innovation 2030-New Generation Artificial Intelligence Project 2020AAA0109600

More Information
  • Corresponding author:

    ZHU Huadong, E-mail: huadongzhu@hotmail.com

  • Received Date: December 10, 2022
  • Accepted Date: January 28, 2023
  • Available Online: February 15, 2023
  • Issue Publish Date: May 29, 2023
    Graphical Abstract
    • Abstract
  • With the development of artificial intelligence, computer hardware and big data, more and more artificial intelligent devices and algorithm are springing up. Based on the analysis of these data, great achievements has been made by artificial intelligent devices to predict the risk of the cardiac arrest, identify early period of cardiac arrest, instruct the process to improve the quality of cardiopulmonary resuscitation and resuscitation prediction. Meanwhile these tools not only provide new insight for clinical research, but play significant roles in clinical decision making and medical resources distribution. This paper focuses on the application of artificial intelligence in cardiopulmonary resuscitation, in order to provide reference for clinical practice.
    • FullText(HTML)
    • References (56)
  • [1]
    王敏, 周满红. 输出生理参数反馈指导下的心肺复苏[J]. 中国急救医学, 2020, 40: 768-772. https://www.cnki.com.cn/Article/CJFDTOTAL-ZJJY202008015.htm
    [2]
    Shah A, Ahirrao S, Pandya S, et al. Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk[J]. Front Public Health, 2021, 9: 762303. DOI: 10.3389/fpubh.2021.762303
    [3]
    Sim I. Mobile Devices and Health[J]. N Engl J Med, 2019, 381: 956-968. DOI: 10.1056/NEJMra1806949
    [4]
    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[J]. Resuscitation, 2019, 138: 134-140. DOI: 10.1016/j.resuscitation.2019.03.012
    [5]
    Lo YH, Siu YCA. Predicting Survived Events in Nontraumatic Out-of-Hospital Cardiac Arrest: A Comparison Study on Machine Learning and Regression Models[J]. J Emerg Med, 2021, 61: 683-694. DOI: 10.1016/j.jemermed.2021.07.058
    [6]
    Ha ACT, Doumouras BS, Wang CN, et al. Prediction of Sudden Cardiac Arrest in the General Population: Review of Traditional and Emerging Risk Factors[J]. Can J Cardiol, 2022, 38: 465-478. DOI: 10.1016/j.cjca.2022.01.007
    [7]
    Layeghian Javan S, Sepehri MM, Layeghian Javan M, et al. An intelligent warning model for early prediction of cardiac arrest in sepsis patients[J]. Comput Methods Programs Biomed, 2019, 178: 47-58. DOI: 10.1016/j.cmpb.2019.06.010
    [8]
    Li YJ, Ye WY, Yang K, et al. Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring[J]. Comput Methods Programs Biomed, 2022, 214: 106568. DOI: 10.1016/j.cmpb.2021.106568
    [9]
    Wu TT, Lin XQ, Mu Y, et al. Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes[J]. Clin Cardiol, 2021, 44: 349-356. DOI: 10.1002/clc.23541
    [10]
    Fernandes M, Mendes R, Vieira SM, et al. Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing[J]. PLoS One, 2020, 15: e0230876. DOI: 10.1371/journal.pone.0230876
    [11]
    Jang DH, Kim J, Jo YH, et al. Developing neural network models for early detection of cardiac arrest in emergency department[J]. Am J Emerg Med, 2020, 38: 43-49. DOI: 10.1016/j.ajem.2019.04.006
    [12]
    Trayanova NA, Topol EJ. Deep learning a person's risk of sudden cardiac death[J]. Lancet, 2022, 399: 1933. DOI: 10.1016/S0140-6736(22)00881-9
    [13]
    Martinez-Alanis M, Bojorges-Valdez E, Wessel N, et al. Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices[J]. Sensors (Basel), 2020, 20: 5483. DOI: 10.3390/s20195483
    [14]
    Lee YJ, Cho KJ, Kwon O, et al. A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards[J]. Resuscitation, 2021, 163: 78-85. DOI: 10.1016/j.resuscitation.2021.04.013
    [15]
    Kwon JM, Kim KH, Jeon KH, et al. Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography[J]. Scand J Trauma Resusc Emerg Med, 2020, 28: 98. DOI: 10.1186/s13049-020-00791-0
    [16]
    Shashikumar SP, Wardi G, Paul P, et al. Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation[J]. Chest, 2021, 159: 2264-2273. DOI: 10.1016/j.chest.2020.12.009
    [17]
    Ueno R, Xu L, Uegami W, et al. Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study[J]. PLoS One, 2020, 15: e0235835. DOI: 10.1371/journal.pone.0235835
    [18]
    Park JH, Choi J, Lee S, et al. Use of Time-to-Event Analysis to Develop On-Scene Return of Spontaneous Circulation Prediction for Out-of-Hospital Cardiac Arrest Patients[J]. Ann Emerg Med, 2022, 79: 132-144. DOI: 10.1016/j.annemergmed.2021.07.121
    [19]
    Blomberg SN, Folke F, Ersbøll AK, et al. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls[J]. Resuscitation, 2019, 138: 322-329. DOI: 10.1016/j.resuscitation.2019.01.015
    [20]
    Byrsell F, Claesson A, Ringh M, et al. Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: A retrospective study[J]. Resuscitation, 2021, 162: 218-226. DOI: 10.1016/j.resuscitation.2021.02.041
    [21]
    Blomberg SN, Christensen HC, Lippert F, et al. Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial[J]. JAMA Netw open, 2021, 4: e2032320. DOI: 10.1001/jamanetworkopen.2020.32320
    [22]
    Chu J, Leung KHB, Snobelen P, et al. Machine learning-based dispatch of drone-delivered defibrillators for out-of-hospital cardiac arrest[J]. Resuscitation, 2021, 162: 120-127. DOI: 10.1016/j.resuscitation.2021.02.028
    [23]
    Chin KC, Hsieh TC, Chiang WC, et al. Early recognition of a caller's emotion in out-of-hospital cardiac arrest dispatch-ing: An artificial intelligence approach[J]. Resuscitation, 2021, 167: 144-150. DOI: 10.1016/j.resuscitation.2021.08.032
    [24]
    Seki T, Tamura T, Suzuki M. Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique[J]. Resuscitation, 2019, 141: 128-135. DOI: 10.1016/j.resuscitation.2019.06.006
    [25]
    Zhou H, Lin C, Liu J, et al. Continuous monitoring of brain perfusion by cerebral oximetry after spontaneous return of circulation in cardiac arrest: a case report[J]. BMC Neurol, 2022, 22: 365. DOI: 10.1186/s12883-022-02880-2
    [26]
    Teng R, Ding Y, See KC. Use of Robots in Critical Care: Systematic Review[J]. J Med Internet Res, 2022, 24: e33380. DOI: 10.2196/33380
    [27]
    Lesimple A, Fritz C, Hutin A, et al. A novel capnogram analysis to guide ventilation during cardiopulmonary resuscitation: clinical and experimental observations[J]. Crit Care, 2022, 26: 287. DOI: 10.1186/s13054-022-04156-0
    [28]
    Kim H, Kim KH, Hong KJ, et al. EEG-Based Prediction of the Recovery of Carotid Blood Flow during Cardiopulmonary Resuscitation in a Swine Model[J]. Sensors (Basel), 2021, 21: 3650. DOI: 10.3390/s21113650
    [29]
    Suh GJ, Park J, Lee JC, et al. End-tidal CO(2)-guided automated robot CPR system in the pig. Preliminary communication[J]. Resuscitation, 2018, 127: 119-124. DOI: 10.1016/j.resuscitation.2018.04.011
    [30]
    Chapman JD, Geneslaw AS, Babineau J, et al. Improving Ventilation Rates During Pediatric Cardiopulmonary Resuscitation[J]. Pediatrics, 2022, 150: e2021053030. DOI: 10.1542/peds.2021-053030
    [31]
    Picard C, Drew R, Norris CM, et al. Cardiac Arrest Quality Improvement: A Single-Center Evaluation of Resuscitations Using Defibrillator, Feedback Device, and Survey Data[J]. J Emerg Nurs, 2022, 48: 224-232. e228. DOI: 10.1016/j.jen.2021.11.005
    [32]
    Huang HK, Chen HH, Chen YL, et al. A Novel Assessment Using a Panoramic Video Camera of Resuscitation Quality in Patients following Out-of-Hospital Cardiac Arrest[J]. Prehosp Emerg Care, 2023, 27: 90-93. DOI: 10.1080/10903127.2021.2015025
    [33]
    Bielski K, Böttiger BW, Pruc M, et al. Outcomes of audio-instructed and video-instructed dispatcher-assisted cardiopulmonary resuscitation: a systematic review and meta-analysis[J]. Ann Med, 2022, 54: 464-471. DOI: 10.1080/07853890.2022.2032314
    [34]
    Latimer AJ, McCoy AM, Sayre MR. Emerging and Future Technologies in Out-of-Hospital Cardiac Arrest Care[J]. Cardiol Clin, 2018, 36: 429-441. DOI: 10.1016/j.ccl.2018.03.010
    [35]
    Kwok H, Coult J, Blackwood J, et al. A method for continuous rhythm classification and early detection of ventricular fibrillation during CPR[J]. Resuscitation, 2022, 176: 90-97. DOI: 10.1016/j.resuscitation.2022.05.019
    [36]
    Isasi I, Irusta U, Aramendi E, et al. Shock decision algorithm for use during load distributing band cardiopulmonary resuscitation[J]. Resuscitation, 2021, 165: 93-100. DOI: 10.1016/j.resuscitation.2021.05.028
    [37]
    Jekova I, Krasteva V. Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation[J]. Sensors (Basel), 2021, 21: 4105. DOI: 10.3390/s21124105
    [38]
    Hajeb MS, Cascella A, Valentine M, et al. Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation[J]. J Am Heart Assoc, 2021, 10: e019065. DOI: 10.1161/JAHA.120.019065
    [39]
    de Graaf C, Beesems SG, Oud S, et al. Analyzing the heart rhythm during chest compressions: Performance and clinical value of a new AED algorithm[J]. Resuscitation, 2021, 162: 320-328. DOI: 10.1016/j.resuscitation.2021.01.003
    [40]
    Orlob S, Kern WJ, Alpers B, et al. Chest compression fraction calculation: A new, automated, robust method to identify periods of chest compressions from defibrillator data- Tested in Zoll X Series[J]. Resuscitation, 2022, 172: 162-169. DOI: 10.1016/j.resuscitation.2021.12.028
    [41]
    余明, 袁晶, 张广. 基于机器学习的心肺复苏干扰下心电节律识别算法研究[J]. 中国医疗设备, 2021, 36: 31-34. https://www.cnki.com.cn/Article/CJFDTOTAL-YLSX202106009.htm
    [42]
    Mayampurath A, Hagopian R, Venable L, et al. Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest[J]. Crit Care Med, 2022, 50: e162-e172. DOI: 10.1097/CCM.0000000000005286
    [43]
    Harford S, Del Rios M, Heinert S, et al. A machine learning approach for modeling decisions in the out of hospital cardiac arrest care workflow[J]. BMC Med Inform Decis Mak, 2022, 22: 21. DOI: 10.1186/s12911-021-01730-4
    [44]
    Busl KM, Maciel CB. When the heart comes back but the brain is lost-are we ready to predict brain death after cardiac arrest?[J]. Resuscitation, 2022, 179: 256-258. DOI: 10.1016/j.resuscitation.2022.09.002
    [45]
    Kim JW, Ha J, Kim T, et al. Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea[J]. J Med Internet Res, 2021, 23: e28361. DOI: 10.2196/28361
    [46]
    Mansour A, Fuhrman JD, Ammar FE, et al. Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest[J]. Neurocrit Care, 2022, 36: 974-982. DOI: 10.1007/s12028-021-01405-y
    [47]
    Hirano Y, Kondo Y, Sueyoshi K, et al. Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models[J]. Resuscitation, 2021, 158: 49-56. DOI: 10.1016/j.resuscitation.2020.11.020
    [48]
    Chung CC, Chiu WT, Huang YH, et al. Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks[J]. J Neurol Sci, 2021, 425: 117445. DOI: 10.1016/j.jns.2021.117445
    [49]
    Zheng WL, Amorim E, Jing J, et al. Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks[J]. Resuscitation, 2021, 169: 86-94. DOI: 10.1016/j.resuscitation.2021.10.034
    [50]
    Al-Dury N, Ravn-Fischer A, Hollenberg J, et al. Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study[J]. Scand J Trauma Resusc Emerg Med, 2020, 28: 60. DOI: 10.1186/s13049-020-00742-9
    [51]
    Chi CY, Ao S, Winkler A, et al. Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach[J]. J Med Internet Res, 2021, 23: e27798. DOI: 10.2196/27798
    [52]
    Park JH, Shin SD, Song KJ, et al. Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis[J]. Resuscitation, 2019, 142: 127-135. DOI: 10.1016/j.resuscitation.2019.07.020
    [53]
    Cheng CY, Chiu IM, Zeng WH, et al. Machine Learning Models for Survival and Neurological Outcome Prediction of Out-of-Hospital Cardiac Arrest Patients[J]. Biomed Res Int, 2021, 2021: 9590131. http://www.socolar.com/Article/Index?aid=100090552048&jid=100000009046
    [54]
    Harford S, Darabi H, Heinert S, et al. Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning[J]. Resuscitation, 2022, 178: 78-84. DOI: 10.1016/j.resuscitation.2022.07.006
    [55]
    Vincelette C, Sokoloff C, Nadon N, et al. Efficacy and cost-feasibility of the Timely Chest Compression Training (T-CCT): a contextualized cardiopulmonary resuscitation training for personal support workers participating during in-hospital cardiac arrests[J]. CJEM, 2021, 23: 180-184. DOI: 10.1007/s43678-020-00038-y
    [56]
    Moll-Khosrawi P, Falb A, Pinnschmidt H, et al. Virtual reality as a teaching method for resuscitation training in undergraduate first year medical students during COVID-19 pandemic: a randomised controlled trial[J]. BMC Med Educ, 2022, 22: 483. DOI: 10.1186/s12909-022-03533-1
    • Related Articles

  • Related Articles

    [1]ZHOU Gaosheng, WANG Xiaoting, LIU Jingjing, ZHANG Hongmin, ZHANG Qing, LIU Dawei. Relationship between Oxygen Extraction Ratio and Prognosis in Septic Shock Patients During Early Resuscitation[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(6): 1217-1223. DOI: 10.12290/xhyxzz.2023-0008
    [2]LIU Huan, HUANG Xiaoling, DAI Mengying, GUO Jiejie, GAO feng. Clinical Characteristics and Inflammatory Markers of Omicron BA.5.2 Variant Infection in Hospitalized Patients and Their Predictive Role in Disease Prognosis[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(5): 1038-1045. DOI: 10.12290/xhyxzz.2023-0055
    [3]YANG Wenlei, LIU Fangfang, XU Ruiping, YANG Wei, HE Yu, LIU Zhen, ZHOU Fuyou, HENG Fanxiu, HOU Bolin, ZHANG Lixin, CHEN Lei, ZHANG Fan, CAI Fen, XU Huawen, LIN Miaoping, LIU Mengfei, PAN Yaqi, LIU Ying, HU Zhe, CHEN Huanyu, HE Zhonghu, KE Yang. Development and Validation of A Prognosis Prediction Model for Esophageal Squamous Cell Carcinoma Patients Treated with Esophagectomy: A Multicenter Real-world Cohort Study[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 101-113. DOI: 10.12290/xhyxzz.2022-0496
    [4]ZHANG Ning, ZHU Wen-ling, LIU Xiao-hong, CHEN Wei, KANG Jun-ren, ZHU Ming-lei, TIAN Ran. Impact of Frailty on the Short-term Prognosis of Hospitalized Elderly Patients with Coronary Heart Disease:A Prospective Cohort Study[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(1): 59-66. DOI: 10.12290/xhyxzz.20190150
    [5]Jian-hua DENG, Han-zhong LI. Clinical Diagnosis and Prognosis of Metastatic Pheochromocytoma and Paraganglioma[J]. Medical Journal of Peking Union Medical College Hospital, 2019, 10(6): 654-659. DOI: 10.3969/j.issn.1674-9081.2019.06.018
    [6]Jia-qi ZHANG, Lei LIU, Gui-ge WANG, Wen-liang BAI, Shan-qing LI. Clinical Pathological Features and Prognosis of Non-small Cell Lung Cancer with Skip N2 Lymph Node Metastasis[J]. Medical Journal of Peking Union Medical College Hospital, 2019, 10(3): 272-277. DOI: 10.3969/j.issn.1674-9081.2019.03.015
    [7]Ran ZHOU, Tong-juan ZOU, Wan-hong YIN, Yao QIN, Yi LI, Jing YANG, Yan KANG. nfluence of Mitral Regurgitation on the Prognosis of Patients with Septic Shock[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(5): 426-430. DOI: 10.3969/j.issn.1674-9081.2018.05.010
    [8]Cheng HUANG, Ye-ye CHEN, Shan-qing LI, Xiao-yun ZHOU, Hong-sheng LIU, Li LI, Ying-zhi QIN, Jia HE, Dong-jie MA. Ectopic Adrenocorticotropic Hormone Syndrome Caused by Thoracic Neuroendocrine Tumors: Surgical Treatment and Prognosis Factors[J]. Medical Journal of Peking Union Medical College Hospital, 2017, 8(2-3): 147-153. DOI: 10.3969/j.issn.1674-9081.2017.03.012
    [9]Qing-yuan DENG, Yi-cong YE, Ying ZHONG, Miao-miao SHENG, Feng-ming HUANG, Cheng-yu JIANG, Shu-yang ZHANG. Angiotensin Ⅱ in Predicting the In-hospital Prognosis of Patients Undergoing Elective Percutaneous Coronary Intervention[J]. Medical Journal of Peking Union Medical College Hospital, 2016, 7(1): 13-17. DOI: 10.3969/j.issn.1674-9081.2016.01.003
    [10]Yu YANG, Quan-zong MAO, Han-zhong LI, Zhi-gang JI, Wei-gang YAN, Shi RONG, Guang-hua LIU, Wei-feng XU, He XIAO, Hui-jun WANG. Clinical Features and Prognosis of Paraneoplastic Syndromes in Renal Malignancies[J]. Medical Journal of Peking Union Medical College Hospital, 2012, 3(2): 195-199. DOI: 10.3969/j.issn.1674-9081.2012.02.014

  • Cited by

    Periodical cited type(4)

    1. 中华医学会急诊医学分会信息化学组，中华医学会急诊医学分会，中国医师协会急诊医师分会，中国国际医疗保健国际交流促进会急诊医学分会，国家急诊医学专业医疗质量控制中心，天津市急诊医学专业医疗质量控制中心. 中国急诊信息化建设规范专家共识. 中华急诊医学杂志. 2024(09): 1224-1232 .
    2. 刘旭东，李敏香，徐百超，骆丁，侯梦楠，张华. 人工智能在心脏骤停急救应用中的伦理挑战与对策. 中国急救医学. 2024(11): 985-990 .
    3. 徐之殿奎，鲍懿喜. 社会创新视角下设计发展趋向研究. 包装工程. 2023(22): 77-87 .
    4. 宋菲，宁泽惺，陈超，王淳秀，王亚军，费甄甄，杭莺，李瑞瑞，尹春琳. 新的基于双ZED摄像头的心肺复苏按压姿势检测模型. 中华急诊医学杂志. 2023(09): 1189-1194 .

    Other cited types(0)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (2155) PDF downloads (224) Cited by(4)
    Related
    Links: National Health Commission of the People's Republic of China Chinese Academy of Medical Sciences(CAMS) AMLC Check CNKI Database CNKI Term Online Chinese General Practice
    Address：Room 302,Third Floor,Building 7, No. 1 Shuaifuyuan, Wangfujing, Dongcheng District, Beijing 100730, China
    Tel：010-69154261/4262
    E - mail：medj@pumch.cn mjpumch@126.com
    Copyright of website：Editorial Office of Medical Journal of Peking Union Medical College Hospital

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return
    x Close Forever Close