Tanii Rimi, Hayashi Kuniyoshi, Naito Takaki, Shui-Yee Wong Zoie, Yoshida Toru, Hayashi Koichi, Fujitani Shigeki
Department of Emergency and Critical Care Medicine, St Marianna University Yokohama Seibu Hospital, 1197-1 Yasushi-cho, Asahi-ku, Yokohama, Kanagawa, Japan.
Department of Emergency and Critical Care Medicine, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan.
Resusc Plus. 2024 Apr 9;18:100628. doi: 10.1016/j.resplu.2024.100628. eCollection 2024 Jun.
Although early detection of patients' deterioration may improve outcomes, most of the detection criteria use on-the-spot values of vital signs. We investigated whether adding trend values over time enhanced the ability to predict adverse events among hospitalized patients.
Patients who experienced adverse events, such as unexpected cardiac arrest or unplanned ICU admission were enrolled in this retrospective study. The association between the events and the combination of vital signs was evaluated at the time of the worst vital signs 0-8 hours before events (near the event) and at 24-48 hours before events (baseline). Multivariable logistic analysis was performed, and the area under the receiver operating characteristic curve (AUC) was used to assess the prediction power for adverse events among various combinations of vital sign parameters.
Among 24,509 in-patients, 54 patients experienced adverse events(cases) and 3,116 control patients eligible for data analysis were included. At the timepoint near the event, systolic blood pressure (SBP) was lower, heart rate (HR) and respiratory rate (RR) were higher in the case group, and this tendency was also observed at baseline. The AUC for event occurrence with reference to SBP, HR, and RR was lower when evaluated at baseline than at the timepoint near the event (0.85 [95%CI: 0.79-0.92] vs. 0.93 [0.88-0.97]). When the trend in RR was added to the formula constructed of baseline values of SBP, HR, and RR, the AUC increased to 0.92 [0.87-0.97].
Trends in RR may enhance the accuracy of predicting adverse events in hospitalized patients.
尽管早期发现患者病情恶化可能改善预后，但大多数检测标准采用生命体征的即时值。我们研究了添加随时间变化的趋势值是否能增强预测住院患者不良事件的能力。
经历不良事件（如意外心脏骤停或计划外入住重症监护病房）的患者纳入本回顾性研究。在事件发生前0至8小时（接近事件时）和事件发生前24至48小时（基线）的最差生命体征时，评估事件与生命体征组合之间的关联。进行多变量逻辑分析，并使用受试者操作特征曲线下面积（AUC）评估各种生命体征参数组合对不良事件的预测能力。
在24509名住院患者中，54名患者发生不良事件（病例组），纳入3116名符合数据分析条件的对照患者。在接近事件的时间点，病例组的收缩压（SBP）较低，心率（HR）和呼吸频率（RR）较高，在基线时也观察到这种趋势。以SBP、HR和RR为参考评估事件发生的AUC，在基线时低于接近事件的时间点（0.85[95%CI：0.79-0.92]对0.93[0.88-0.97]）。当将RR趋势添加到由SBP、HR和RR的基线值构建的公式中时，AUC增加到0.92[0.87-0.97]。
RR趋势可能提高预测住院患者不良事件的准确性。
