Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection
Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection
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DOI：
10.1093/cid/ciab946
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发表时间：
2021-11-12
影响因子：
11.8
通讯作者：
Harrison, Lee H.
中科院分区：
文献类型：
--
作者：
Sundermann, Alexander J.;Chen, Jieshi;Harrison, Lee H.
背景大多数医院采用传统的感染预防（IP）方法进行疫情检测。我们开发了医疗保健相关传播增强检测系统（EDS-HAT），该系统结合了全基因组测序（WGS）监测和电子健康记录（EHR）的机器学习（ML），分别识别未被发现的疫情和负责的传播途径。方法2016年11月至2018年11月对卫生保健相关病原菌进行WGS监测。电子病历ML用于确定wgs检测到的疫情的传播途径，并由知识产权专家进行了调查。估计了同一时期预防的潜在感染，并与传统知识产权做法进行了比较。结果3165株分离株中，99个聚类中有2752株独特的患者分离株，其中WGS鉴定的患者分离株297株（10.8%）；集群范围为2-14例患者。65.7%的集群至少检测到1条传播途径。与此同时，传统的知识产权调查促使WGS发现了15起疑似疫情，涉及133名患者，其中确定了5起传播事件（3.8%）。如果EDS-HAT能够实时运行，就可以避免25-63次传输。研究发现，EDS-HAT比传统的IP方法更节省成本，更有效，总体节省了192408美元至692532美元。结论EDS-HAT检测到传统IP方法未发现的多起疫情，正确识别了大多数疫情的传播途径，为医院节省了大量成本。传统的知识产权做法错误地识别了没有发生传播的疫情。WGS监测与电子病历ML相结合具有节省成本和提高患者安全的潜力。对细菌病原体的全基因组测序监测和电子健康记录的机器学习发现了以前未被发现的疫情及其传播途径，这可以提高患者安全性并节省成本。
Background Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole-genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively. Methods We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared with traditional IP practice during the same period. Results Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2-14 patients. At least 1 transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192 408-$692 532. Conclusions EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.Whole-genome sequencing surveillance of bacterial pathogens and machine learning of the electronic health record finds previously undetected outbreaks and their transmission routes, which can increase patient safety and save costs.