Rossetti Sarah C, Dykes Patricia C, Knaplund Chris, Cho Sandy, Withall Jennifer, Lowenthal Graham, Albers David, Lee Rachel Y, Jia Haomiao, Bakken Suzanne, Kang Min-Jeoung, Chang Frank Y, Zhou Li, Bates David W, Daramola Temiloluwa, Liu Fang, Schwartz-Dillard Jessica, Tran Mai, Bokhari Syed Mohtashim Abbas, Thate Jennifer, Cato Kenrick D
Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY, USA.
Columbia University Irving Medical Center, School of Nursing, New York, NY, USA.
Nat Med. 2025 Apr 2. doi: 10.1038/s41591-025-03609-7.
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pragmatic trial with cluster-randomization of 74 clinical units (37 intervention; 37 usual care) across 2 health systems. Eligible adult hospital encounters were included. We tested if outcomes differed between patients whose care teams were and patients whose care teams were not informed by the CONCERN EWS. Coprimary outcomes were in-hospital mortality (examined as instantaneous risk) and length of stay. Secondary outcomes were cardiopulmonary arrest, sepsis, unanticipated intensive care unit transfers and 30-day hospital readmission. Among 60,893 hospital encounters (33,024 intervention; 27,869 usual care), intervention group encounters had 35.6% decreased instantaneous risk of death (adjusted hazard ratio (HR), 0.64; 95% confidence interval (CI), 0.53-0.78; P < 0.0001), 11.2% decreased length of stay (adjusted incidence rate ratio, 0.91; 95% CI, 0.90-0.93; P < 0.0001), 7.5% decreased instantaneous risk of sepsis (adjusted HR, 0.93; 95% CI, 0.86-0.99; P = 0.0317) and 24.9% increased instantaneous risk of unanticipated intensive care unit transfer (adjusted HR, 1.25; 95% CI, 1.09-1.43; P = 0.0011) compared with usual-care group encounters. No adverse events were reported. A machine learning-based EWS, modeled on nursing surveillance patterns, decreased inpatient deterioration risk with statistical significance. ClinicalTrials.gov registration: NCT03911687 .
注册护士录入的沟通性叙述性关切（CONCERN）早期预警系统（EWS）在其机器学习算法中使用实时护理监测文档模式来识别病情恶化风险。我们进行了一项为期1年的多中心实用性试验，对2个医疗系统中的74个临床单元进行整群随机分组（37个干预组；37个常规护理组）。纳入符合条件的成年住院病例。我们测试了护理团队收到和未收到CONCERN EWS通知的患者之间的结局是否存在差异。共同主要结局是住院死亡率（作为即时风险进行检查）和住院时间。次要结局是心肺骤停、脓毒症、意外转入重症监护病房和30天内再次入院。在60893例住院病例中（33024例干预组；27869例常规护理组），与常规护理组病例相比，干预组病例的即时死亡风险降低了35.6%（调整后风险比（HR）为0.64；95%置信区间（CI）为0.53 - 0.78；P < 0.0001），住院时间缩短了11.2%（调整后发病率比为0.91；95% CI为0.90 - 0.93；P < 0.0001），脓毒症的即时风险降低了7.5%（调整后HR为0.93；95% CI为0.86 - 0.99；P = 0.0317），意外转入重症监护病房的即时风险增加了24.9%（调整后HR为1.25；95% CI为1.09 - 1.43；P = 0.0011）。未报告不良事件。一种基于机器学习的EWS，以护理监测模式为模型，显著降低了住院患者的病情恶化风险。ClinicalTrials.gov注册号：NCT03911687 。
