1Infection Control Unit,Massachusetts General Hospital,Boston,Massachusetts.
3Harvard Medical School,Boston,Massachusetts.
Infect Control Hosp Epidemiol. 2018 Jul;39(7):826-833. doi: 10.1017/ice.2018.97. Epub 2018 May 17.
OBJECTIVETo validate a system to detect ventilator associated events (VAEs) autonomously and in real time.DESIGNRetrospective review of ventilated patients using a secure informatics platform to identify VAEs (ie, automated surveillance) compared to surveillance by infection control (IC) staff (ie, manual surveillance), including development and validation cohorts.SETTINGThe Massachusetts General Hospital, a tertiary-care academic health center, during January-March 2015 (development cohort) and January-March 2016 (validation cohort).PATIENTSVentilated patients in 4 intensive care units.METHODSThe automated process included (1) analysis of physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); (2) querying the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and (3) retrieval and interpretation of microbiology reports. The cohorts were evaluated as follows: (1) manual surveillance by IC staff with independent chart review; (2) automated surveillance detection of ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and possible VAP (PVAP); (3) senior IC staff adjudicated manual surveillance-automated surveillance discordance. Outcomes included sensitivity, specificity, positive predictive value (PPV), and manual surveillance detection errors. Errors detected during the development cohort resulted in algorithm updates applied to the validation cohort.RESULTSIn the development cohort, there were 1,325 admissions, 479 ventilated patients, 2,539 ventilator days, and 47 VAEs. In the validation cohort, there were 1,234 admissions, 431 ventilated patients, 2,604 ventilator days, and 56 VAEs. With manual surveillance, in the development cohort, sensitivity was 40%, specificity was 98%, and PPV was 70%. In the validation cohort, sensitivity was 71%, specificity was 98%, and PPV was 87%. With automated surveillance, in the development cohort, sensitivity was 100%, specificity was 100%, and PPV was 100%. In the validation cohort, sensitivity was 85%, specificity was 99%, and PPV was 100%. Manual surveillance detection errors included missed detections, misclassifications, and false detections.CONCLUSIONSManual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.Infect Control Hosp Epidemiol 2018;826-833.
目的 自主实时检测呼吸机相关性事件（VAEs）的系统验证。
设计 使用安全信息平台对通气患者进行回顾性分析，以识别 VAEs（即自动监测），并与感染控制（IC）人员的监测（即手动监测）进行比较，包括开发和验证队列。
地点 马萨诸塞州综合医院，一所三级保健学术中心，于 2015 年 1 月至 3 月（开发队列）和 2016 年 1 月至 3 月（验证队列）期间。
患者 4 个重症监护病房的通气患者。
方法 自动处理过程包括：（1）分析生理数据以检测呼气末正压（PEEP）和吸入氧分数（FiO2）的增加；（2）查询电子病历（EHR）以获取白细胞减少或白细胞增多和抗生素起始数据；（3）检索和解释微生物学报告。对队列进行了以下评估：（1）IC 工作人员进行手动监测，并进行独立图表审查；（2）自动监测检测呼吸机相关条件（VAC）、感染相关呼吸机相关并发症（IVAC）和可能的呼吸机相关性肺炎（PVAP）；（3）高级 IC 工作人员对手动监测-自动监测不一致进行裁决。结果包括敏感性、特异性、阳性预测值（PPV）和手动监测检测错误。在开发队列中发现的错误导致对验证队列应用算法更新。
结果 在开发队列中，有 1325 例入院，479 例通气患者，2539 天通气和 47 例 VAE。在验证队列中，有 1234 例入院，431 例通气患者，2604 天通气和 56 例 VAE。在手动监测中，在开发队列中，敏感性为 40%，特异性为 98%，PPV 为 70%。在验证队列中，敏感性为 71%，特异性为 98%，PPV 为 87%。在自动监测中，在开发队列中，敏感性为 100%，特异性为 100%，PPV 为 100%。在验证队列中，敏感性为 85%，特异性为 99%，PPV 为 100%。手动监测检测错误包括漏检、误分类和假阳性。
结论 手动监测容易出现人为错误。自动监测更准确、更高效，适用于 VAE 监测。
传染病控制与医院流行病学 2018；826-833。
