Amland Robert C, Burghart Mark, Overhage J Marc
Population Health, Cerner Corporation, Kansas City, Missouri, USA.
JAMIA Open. 2019 Jun 11;2(3):339-345. doi: 10.1093/jamiaopen/ooz014. eCollection 2019 Oct.
To examine performance of a sepsis surveillance system in a simulated environment where modifications to parameters and settings for identification of at-risk patients can be explored in-depth.
This was a multiple center observational cohort study. The study population comprised 14 917 adults hospitalized in 2016. An expert-driven rules algorithm was applied against 15.1 million data points to simulate a system with binary notification of sepsis events. Three system scenarios were examined: a scenario as derived from the second version of the Consensus Definitions for Sepsis and Septic Shock (SEP-2), the same scenario but without systolic blood pressure (SBP) decrease criteria (near SEP-2), and a conservative scenario with limited parameters. Patients identified by scenarios as being at-risk for sepsis were assessed for suspected infection. Multivariate binary logistic regression models estimated mortality risk among patients with suspected infection.
First, the SEP-2-based scenario had a hyperactive, unreliable parameter SBP decrease >40 mm Hg from baseline. Second, the near SEP-2 scenario demonstrated adequate reliability and sensitivity. Third, the conservative scenario had modestly higher reliability, but sensitivity degraded quickly. Parameters differed in predicting mortality risk and represented a substitution effect between scenarios.
Configuration of parameters and alert criteria have implications for patient identification and predicted outcomes.
Performance of scenarios was associated with scenario design. A single hyperactive, unreliable parameter may negatively influence adoption of the system. A trade-off between modest improvements in alert reliability corresponded to a steep decline in condition sensitivity in scenarios explored.
在模拟环境中检验脓毒症监测系统的性能，以便深入探究对识别高危患者的参数和设置进行修改的情况。
这是一项多中心观察性队列研究。研究人群包括2016年住院的14917名成年人。采用专家驱动的规则算法，针对1510万个数据点模拟一个脓毒症事件二元通知系统。研究了三种系统场景：源自脓毒症和脓毒性休克共识定义第二版（SEP-2）的场景、相同场景但无收缩压（SBP）下降标准（接近SEP-2）的场景以及参数有限的保守场景。对各场景识别出的脓毒症高危患者进行疑似感染评估。多变量二元逻辑回归模型估计疑似感染患者的死亡风险。
首先，基于SEP-2的场景有一个过度活跃、不可靠的参数，即SBP较基线下降>40 mmHg。其次，接近SEP-2的场景显示出足够的可靠性和敏感性。第三，保守场景的可靠性略高，但敏感性迅速下降。各参数在预测死亡风险方面存在差异，且在不同场景间呈现替代效应。
参数配置和警报标准对患者识别及预测结果有影响。
各场景的性能与场景设计相关。一个过度活跃、不可靠的单一参数可能对系统的采用产生负面影响。在所探究的场景中，警报可靠性的适度提高与病情敏感性的急剧下降之间存在权衡。
