机器学习对急救医疗服务中心呼叫中外来性心脏骤停调度员识别的影响：一项随机临床试验。

Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial.

机构信息

Copenhagen Emergency Medical Services, Copenhagen, Denmark.

Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

出版信息

JAMA Netw Open. 2021 Jan 4;4(1):e2032320. doi: 10.1001/jamanetworkopen.2020.32320.

DOI:10.1001/jamanetworkopen.2020.32320

PMID:33404620

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7788469/

Abstract

IMPORTANCE

Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.

OBJECTIVE

To examine how a machine learning model trained to identify OHCA and alert dispatchers during emergency calls affected OHCA recognition and response.

DESIGN, SETTING, AND PARTICIPANTS: This double-masked, 2-group, randomized clinical trial analyzed all calls to emergency number 112 (equivalent to 911) in Denmark. Calls were processed by a machine learning model using speech recognition software. The machine learning model assessed ongoing calls, and calls in which the model identified OHCA were randomized. The trial was performed at Copenhagen Emergency Medical Services, Denmark, between September 1, 2018, and December 31, 2019.

INTERVENTION

Dispatchers in the intervention group were alerted when the machine learning model identified out-of-hospital cardiac arrest, and those in the control group followed normal protocols without alert.

MAIN OUTCOMES AND MEASURES

The primary end point was the rate of dispatcher recognition of subsequently confirmed OHCA.

RESULTS

A total of 169 049 emergency calls were examined, of which the machine learning model identified 5242 as suspected OHCA. Calls were randomized to control (2661 [50.8%]) or intervention (2581 [49.2%]) groups. Of these, 336 (12.6%) and 318 (12.3%), respectively, had confirmed OHCA. The mean (SD) age among of these 654 patients was 70 (16.1) years, and 419 of 627 patients (67.8%) with known gender were men. Dispatchers in the intervention group recognized 296 confirmed OHCA cases (93.1%) with machine learning assistance compared with 304 confirmed OHCA cases (90.5%) using standard protocols without machine learning assistance (P = .15). Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA (85.0% vs 77.5%; P < .001) but lower specificity (97.4% vs 99.6%; P < .001) and positive predictive value (17.8% vs 55.8%; P < .001).

CONCLUSIONS AND RELEVANCE

This randomized clinical trial did not find any significant improvement in dispatchers' ability to recognize cardiac arrest when supported by machine learning even though artificial intelligence did surpass human recognition.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04219306.

摘要

重要性

紧急医疗调度员未能识别约 25%的院外心脏骤停 (OHCA) 病例，导致通过启动心肺复苏术来挽救生命的机会丧失。

目的

研究一种经过训练可识别 OHCA 并在紧急呼叫期间提醒调度员的机器学习模型如何影响 OHCA 的识别和反应。

设计、设置和参与者：这是一项双盲、2 组、随机临床试验，分析了丹麦所有拨打紧急号码 112（相当于 911）的电话。这些电话由使用语音识别软件的机器学习模型处理。机器学习模型评估正在进行的呼叫，以及模型识别出 OHCA 的呼叫被随机化。该试验在丹麦哥本哈根紧急医疗服务处进行，时间为 2018 年 9 月 1 日至 2019 年 12 月 31 日。

干预措施

当机器学习模型识别出院外心脏骤停时，干预组的调度员会收到警报，而对照组的调度员则按照正常协议进行操作，不会收到警报。

主要结果和措施

主要终点是调度员识别随后确认的 OHCA 的比率。

结果

共检查了 169049 个紧急电话，其中机器学习模型识别出 5242 个疑似 OHCA。呼叫被随机分配到对照组（2661 [50.8%]）或干预组（2581 [49.2%]）。其中，分别有 336 例（12.6%）和 318 例（12.3%）有确认的 OHCA。这 654 名患者的平均（SD）年龄为 70（16.1）岁，已知性别为男性的 627 名患者中，有 419 名（67.8%）。与没有机器学习辅助的标准协议相比，干预组的调度员在机器学习的帮助下识别出 296 例确认的 OHCA 病例（93.1%），而没有机器学习辅助的调度员识别出 304 例确认的 OHCA 病例（90.5%）（P=.15）。单独使用机器学习警报对确认的 OHCA 的敏感性明显高于没有警报的调度员（85.0%比 77.5%；P<0.001），但特异性（97.4%比 99.6%；P<0.001）和阳性预测值（17.8%比 55.8%；P<0.001）较低。