University of Chicago, Chicago, IL, USA.
University of Wisconsin-Madison, Madison, WI, USA.
BMC Pregnancy Childbirth. 2022 Apr 6;22(1):295. doi: 10.1186/s12884-022-04631-0.
Early warning scores are designed to identify hospitalized patients who are at high risk of clinical deterioration. Although many general scores have been developed for the medical-surgical wards, specific scores have also been developed for obstetric patients due to differences in normal vital sign ranges and potential complications in this unique population. The comparative performance of general and obstetric early warning scores for predicting deterioration and infection on the maternal wards is not known.
This was an observational cohort study at the University of Chicago that included patients hospitalized on obstetric wards from November 2008 to December 2018. Obstetric scores (modified early obstetric warning system (MEOWS), maternal early warning criteria (MEWC), and maternal early warning trigger (MEWT)), paper-based general scores (Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), and a general score developed using machine learning (electronic Cardiac Arrest Risk Triage (eCART) score) were compared using the area under the receiver operating characteristic score (AUC) for predicting ward to intensive care unit (ICU) transfer and/or death and new infection.
A total of 19,611 patients were included, with 43 (0.2%) experiencing deterioration (ICU transfer and/or death) and 88 (0.4%) experiencing an infection. eCART had the highest discrimination for deterioration (p < 0.05 for all comparisons), with an AUC of 0.86, followed by MEOWS (0.74), NEWS (0.72), MEWC (0.71), MEWS (0.70), and MEWT (0.65). MEWC, MEWT, and MEOWS had higher accuracy than MEWS and NEWS but lower accuracy than eCART at specific cut-off thresholds. For predicting infection, eCART (AUC 0.77) had the highest discrimination.
Within the limitations of our retrospective study, eCART had the highest accuracy for predicting deterioration and infection in our ante- and postpartum patient population. Maternal early warning scores were more accurate than MEWS and NEWS. While institutional choice of an early warning system is complex, our results have important implications for the risk stratification of maternal ward patients, especially since the low prevalence of events means that small improvements in accuracy can lead to large decreases in false alarms.
预警评分旨在识别有临床恶化风险的住院患者。尽管已经开发出许多用于内科-外科病房的一般评分，但由于正常生命体征范围和该特定人群中潜在并发症的差异，也为产科患者开发了特定的评分。用于预测产科病房患者恶化和感染的一般和产科预警评分的比较性能尚不清楚。
这是芝加哥大学的一项观察性队列研究，纳入了 2008 年 11 月至 2018 年 12 月期间在产科病房住院的患者。产科评分（改良产科预警系统（MEOWS）、产妇早期预警标准（MEWC）和产妇早期预警触发（MEWT））、纸质通用评分（改良早期预警评分（MEWS）和国家早期预警评分（NEWS））和使用机器学习开发的通用评分（电子心搏骤停风险分类（eCART）评分）通过接受者操作特征曲线下面积（AUC）进行比较，用于预测病房到重症监护病房（ICU）的转移和/或死亡和新感染。
共纳入 19611 例患者，其中 43 例（0.2%）发生恶化（ICU 转移和/或死亡），88 例（0.4%）发生感染。eCART 对恶化的鉴别能力最高（所有比较的 p<0.05），AUC 为 0.86，其次是 MEOWS（0.74）、NEWS（0.72）、MEWC（0.71）、MEWS（0.70）和 MEWT（0.65）。MEWC、MEWT 和 MEOWS 的准确度均高于 MEWS 和 NEWS，但在特定截断阈值下的准确度低于 eCART。预测感染时，eCART（AUC 0.77）的鉴别能力最高。
在我们的回顾性研究的限制范围内，eCART 在我们的产前和产后患者人群中对预测恶化和感染的准确性最高。产科预警评分比 MEWS 和 NEWS 更准确。虽然机构选择预警系统很复杂，但我们的结果对产妇病房患者的风险分层具有重要意义，尤其是因为事件的低患病率意味着准确性的微小提高可以导致假警报的大幅减少。
