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摘要: 随着人工智能技术、计算机硬件以及大数据的发展, 生命科学领域涌现出越来越多的人工智能设备及算法。大量数据分析结果显示, 人工智能设备能够对心脏骤停进行风险预测和早期识别, 还可指导心肺复苏实施及复苏后临床预后的预测和个性化诊疗。人工智能不仅可为科研提供新思路, 且在临床决策与医疗资源配置中扮演重要角色, 本文将围绕人工智能在心肺复苏中的应用情况进行阐述, 以期为临床提供参考。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.
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Keywords:
- artificial intelligence /
- deep learn /
- neural network /
- cardiopulmonary resuscitation /
- prediction /
- prognosis
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我国每年因心脏骤停而死亡的人数达50万以上，位居全球之首[1]。而院前心脏骤停猝死率较高的原因之一是难以对患者进行及时抢救。心脏骤停的及时识别、启动胸外按压以及电除颤是心肺复苏(cardiopulmonary resuscitation，CPR)的关键。随着人工智能技术的日益发展，CPR领域也涌现出越来越多的智能化工具及辅助机器，指导CPR的实施，提升复苏质量，改善患者预后。
人工智能是计算机学科的一个分支，是研发模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术学科。机器学习是人工智能领域的一个重要组成部分，采用计算机算法对庞大数据库中的特征值进行分析，并不断优化算法，从而实现对某些临床结局的预测。随着计算机技术的不断发展，越来越多的算法不断涌现，如近邻法、Logistic回归、Apriori、XG-Boosting、支持向量机、随机森林以及决策树等。深度学习是近几年不断发展的一项能够模拟人脑进行分析的人工智能神经网络算法[2]。目前，一些人工智能技术及算法已被Google、NetFlix以及亚马逊等科技公司开发，用于改善和提升人类的行为。在移动医疗领域，包括远程感应、可穿戴设备等在内的多种人工智能技术已参与到医疗决策中[3]。同时，越来越多的研究者开始应用计算机算法模型，对涉及多个变量的、复杂的、非线性数据进行统计学分析以建立预测模型，并应用其在不同临床场景中进行患者结局的预测[4]。本文针对人工智能在心脏骤停风险预测、快速识别、应急反应以及预后预测中的应用进行阐述，以期为临床提供参考。
1. 心脏骤停风险预测
研究证实，对电子病历数据库中的数据进行算法分析后，可应用机器学习结果对院前、院内及普遍人群的心脏骤停风险进行预测[5-6]。Layeghian Javan等[7]采用MIMIC(Medical Information Mart for Inten-sive Care)-Ⅲ数据库，借助传统的支持向量机、决策树、Logistic回归以及集合算法等，对脓毒症患者的人口学特征、格拉斯哥昏迷评分(glasgow coma score，GCS)、急性生理与慢性健康(acute physiology and chronic health evaluation，APACHE)-Ⅱ评分系统、MEWS(modified early warning score)评分、生命体征以及相关实验室检查等多个数据变量进行算法分析，预测脓毒症患者心脏骤停的发生风险，较单独使用APACHE-Ⅱ评分系统或MEWS评分更具优势，并能够应用患者的生命体征、实验室检查结果等进行实时动态的病情评估[7-8]。Wu等[9]应用急性冠脉综合征(acute coronary syndrome，ACS)患者的多种临床特征进行算法分析，实现了对ACS患者心脏骤停发生风险的预测。Fernandes等[10]对2011—2016年葡萄牙某急诊科就诊的235 826例患者进行分析，采用Logistic回归、随机森林以及XG-boosting进行建模，结果发现GCS、年龄以及脉搏血氧饱和度等对预测危重症患者心脏骤停具有统计学意义，该数据模型可辅助预检分诊对患者病情进行评估，从而预测患者的病情严重程度及猝死风险。除上述经典算法模型外，神经网络模型在心脏骤停风险预测中的优势也日益凸显[11]。研究显示，纳入患者的人口学特征、血流动力学参数、心电图甚至心肌MRI等影像学资料，通过人工智能神经网络，可辅助临床预测恶性心律失常、心源性猝死的发生风险[12-13]，从而对院内心脏骤停的发生进行早期识别、预警及快速反应[14]。Kwon等[15]采用深度学习算法对住院患者的心电图进行分析，建立了能够预测住院患者24 h内发生心脏骤停风险的预测模型，并将该算法置入可穿戴设备应用于临床实际场景。此外，有研究采用深度学习模型对住院患者的心率、脉搏氧饱和度、呼吸频率、吸入氧浓度及血液pH值等进行算法分析，预测住院患者后续是否需实施机械通气，并辅助临床提前准备气管插管物品及人员配备等[16]。上述机器算法模型在不同的临床研究中表现出不同的优越性，通过患者的不同临床特征实现对院前、院内发生心脏骤停的风险预测，辅助临床医生作出科学决策及快速反应[17]。
2. 心脏骤停快速识别
无论是目击者还是急救中心调度员，院前心脏骤停的识别均极为关键，这关系到胸外心脏按压的及时启动。Park等[18]对108 607例院前心脏骤停猝死患者的性别及年龄、目击者的性别及年龄、两者间关系等信息进行系统分析，并与急救中心调度员的主观判断进行比较，结果发现机器学习具有较高的识别灵敏度(72.5%比84.1%，P＜0.001)，但特异度较低(98.8%比97.3%，P＜0.001)，且机器学习对心脏骤停的识别时间更短。该研究提示，与急救中心调度员相比，机器学习可更好地对院前心脏骤停患者进行识别，并在急救资源的调度中扮演重要角色[19-21]。应用人工智能算法进行急救电话识别，可在一定程度上缩短对心脏骤停患者早期识别的反应时间[20, 22-23]。此外，有研究采用随机森林模型对急诊患者入院前及入院后的多个变量数据进行分析发现，模型可对心脏骤停患者进行早期识别，还可对院前心脏骤停患者1年后的存活率进行预测，从而识别具有潜在救治价值的患者，为后续临床诊治及患者预后提供策略[24]，以优化医疗资源配置。
3. 提供高质量心肺复苏
胸外心脏按压是CPR的关键环节。目前许多实时反馈装置被应用于临床，通过监测一些物理指标(如按压深度、按压位置、按压频率等)及患者的生理指标(呼气末CO2、脑氧饱和度[25]、按压产生的心电图波形及血氧饱和度波形等)实时评估胸外心脏按压质量，从而指导CPR。相应的远程监控机器人可及时识别CPR阶段，减少反应时间，并可作出及时指导[26]。在动物实验中，可应用实时监测的脑电图反映CPR过程中颈动脉的血流情况，并通过描记CO2波形图反映胸腔内气道的开闭状态，从而指导胸外按压[27-28]。Suh等[29]通过动物实验研究发现，实时反馈呼气末CO2水平，可调整智能CPR机器人的胸外心脏按压位置，比较应用呼气末CO2实时指导CPR的机器人与人工胸外心脏按压的效果，前者虽未改善自主循环恢复(return of spontaneous circula-tion，ROSC)的发生率，但能够改善获得ROSC后48 h内的神经系统预后评分。应用实时反馈装置辅助虽未能改善CPR患者的预后，但可明显改善胸外心脏按压的频率和深度，减少过度通气等，提供更高质量的CPR[30-31]。其中，实时视频反馈装置、语音反馈装置借助按压平板、全景摄像机等设备可实现对按压全过程的监控与指导[32-33]。应用智能手机或智能手表等电子设备，可对附近的自动体外除颤器(automated external defibrigator, AED) 设备进行定位，帮助施救者及时获取AED并尽早启动电除颤[34]，同时此类电子设备可记录患者发生心脏骤停时的生命体征、甚至心电图等数据，有助于识别猝死及为预后决策提供指导。研究指出，在不终止胸外按压的情况下，利用内置特殊算法的AED设备对胸外心脏按压期间的可除颤或不可除颤心律进行分析，可为除颤提供指导[35-38]。研究证实，应用除颤仪进行心律分析可使胸外心脏按压的停止时间缩短，从而在一定程度上提升胸外心脏按压的分数[39-40]。基于心电图波形特征建立的神经网络模型，可实现对CPR过程中胸外心脏按压干扰下的心电波形进行准确识别[41]，从而实时指导CPR过程中除颤时机的选择。
4. 预测心肺复苏结局
绝大多数机器学习模型主要用于心脏骤停后CPR结局的预测，包括患者的生存率、死亡率、多种器官功能的恢复等[15, 42]，并能够指导临床医生作出恰当的医疗决策[43-44]，进行合理的医疗资源配置，以及必要时终止CPR[45]。研究显示，对于获得ROSC但意识障碍的患者进行早期头颅CT深度学习，可对CPR患者早期缺血缺氧性脑病进行及时鉴别[46]。Hirano等[47]应用支持向量机、随机森林等多种算法对具有可除颤心律的院前心脏骤停患者特征进行学习，其均可在不同程度上预测患者发生心脏骤停1个月后的死亡率以及神经功能预后情况。而Harford等[43]研发的EFCN机器学习模型，除可在一定程度上对心脏骤停患者的神经功能预后及死亡率进行预测外，还能识别部分院前心脏骤停患者，以尽早启动CPR及早期实施冠状动脉造影恢复再灌注。因此，该模型可在一定程度上指导临床医生作出是否继续胸外按压、是否行冠状动脉造影以及是否进行体温控制等复苏后的临床决策[4, 48]。在此基础上，联合患者猝死时的脑电图，亦可实现对神经功能预后的预测和评价[49]。有研究通过机器算法进行分析发现，影响院外心脏骤停患者预后的主要因素为初始心律、年龄、开始CPR的时间、应急反应系统的反应时间以及发生院外心脏骤停的地点等[50]。亦有研究采用深度学习方法构建模型，对院内心脏骤停患者的死亡率及再入院率进行预测，显示出机器学习模型潜在的预测能力[51]。Park等[52-53]对线性回归、XG-Boosting、支持向量机、随机森林、神经网络等多个机器算法模型进行对比研究发现，在院外心脏骤停患者神经功能预后预测方面，XG-Boosting和线性回归算法的检验效能相对更占优势；而在机器学习算法中增加来自社区水平的变量，如犯罪水平、医疗保健水平以及经济条件等因素，可优化心脏骤停预后预测模型[54]。
5. 开展心肺复苏培训
随着人工智能技术的不断发展，智能化设备参与CPR培训与教学活动亦日益频繁，如教学反馈装置、模拟人及实时评分系统等，可帮助受训者高效掌握CPR技能，从而为实时高质量的CPR提供帮助[55]。应用虚拟现实技术进行CPR技能培训，可显著提升受训学生的CPR技能掌握程度[56]。此外，应用CPR语音助手，可帮助施救者准确拨打救援电话，并提供详细的按压指导，从而提高胸外心脏按压质量。
6. 小结与展望
随着人工智能技术及大数据的快速发展，生命科学领域涌现出越来越多的智能化设备，通过对大数据的算法分析，产生多种数据模型。这些智能化工具，一方面能够辅助临床医师及院前急救人员早期识别高危心脏骤停患者，提前进行急救预案；另一方面通过胸外心脏按压、机械通气等多维度智能辅助指导CPR实施，提高复苏质量。此外，还可通过对海量数据的系统化分析，预测复苏后患者的临床预后，为其提供个体化病情分析和医疗决策。虽然智能算法系统及工具对临床工作具有较大的指导作用，但仍需前瞻性的随机对照试验对其进行验证。
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