Churpek Matthew M, Adhikari Richa, Edelson Dana P
Department of Medicine, University of Chicago, Chicago, IL, United States.
Department of Medicine, University of Chicago, Chicago, IL, United States.
Resuscitation. 2016 May;102:1-5. doi: 10.1016/j.resuscitation.2016.02.005. Epub 2016 Feb 16.
Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient's current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends.
Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC).
A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC -0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05).
Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores.
早期发现病房内患者的病情恶化情况可能会改善治疗结果，而大多数早期预警评分仅利用患者当前的生命体征。生命体征随时间变化的趋势所具有的附加价值尚未得到充分描述。我们研究了加入趋势信息是否能提高准确性，以及哪种方法最适合对趋势进行建模。
本观察性队列研究纳入了五年期间在五家医院住院的患者，60%的数据用于模型推导，40%用于验证。利用生命体征来预测心脏骤停、转入重症监护病房和死亡的综合结局。使用受试者操作特征曲线下面积（AUC）比较了利用当前值和不同趋势方法的模型的准确性。
共纳入269,999例患者入院病例，产生了16,452个结局。总体而言，与仅包含当前生命体征的模型相比，趋势信息提高了准确性（AUC分别为0.78和0.74；p<0.001）。导致准确性平均提高最大的方法是生命体征斜率（AUC提高0.013）和最小值（AUC提高0.012），而与前一个值的变化导致AUC平均变差（AUC变化-0.002）。添加趋势信息时，收缩压的AUC增加最多（AUC提高0.05）。
生命体征趋势提高了旨在检测病房内危重病的模型的准确性。我们的研究结果对床边临床医生和早期预警评分的开发具有重要意义。
