Hennepin Healthcare, Minneapolis, MN.
University of Florida, College of Medicine, Jacksonville, FL; Northwestern University, Feinberg School of Medicine, Chicago, IL.
Ann Emerg Med. 2024 Sep;84(3):246-258. doi: 10.1016/j.annemergmed.2024.02.009. Epub 2024 Mar 25.
Compare physician gestalt to existing screening tools for identifying sepsis in the initial minutes of presentation when time-sensitive treatments must be initiated.
This prospective observational study conducted with consecutive encounter sampling took place in the emergency department (ED) of an academic, urban, safety net hospital between September 2020 and May 2022. The study population included ED patients who were critically ill, excluding traumas, transfers, and self-evident diagnoses. Emergency physician gestalt was measured using a visual analog scale (VAS) from 0 to 100 at 15 and 60 minutes after patient arrival. The primary outcome was an explicit sepsis hospital discharge diagnosis. Clinical data were recorded for up to 3 hours to compare Systemic Inflammatory Response Syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), Modified Early Warning Score (MEWS), and a logistic regression machine learning model using Least Absolute Shrinkage and Selection Operator (LASSO) for variable selection. The screening tools were compared using receiver operating characteristic analysis and area under the curve calculation (AUC).
A total of 2,484 patient-physician encounters involving 59 attending physicians were analyzed. Two hundred seventy-five patients (11%) received an explicit sepsis discharge diagnosis. When limited to available data at 15 minutes, initial VAS (AUC 0.90; 95% confidence interval [CI] 0.88, 0.92) outperformed all tools including LASSO (0.84; 95% CI 0.82 to 0.87), qSOFA (0.67; 95% CI 0.64 to 0.71), SIRS (0.67; 95% 0.64 to 0.70), SOFA (0.67; 95% CI 0.63 to 0.70), and MEWS (0.66; 95% CI 0.64 to 0.69). Expanding to data available at 60 minutes did not meaningfully change results.
Among adults presenting to an ED with an undifferentiated critical illness, physician gestalt in the first 15 minutes of the encounter outperformed other screening methods in identifying sepsis.
当需要开始治疗时，比较医生的整体判断与现有的用于识别脓毒症的筛选工具，以在患者就诊的最初几分钟内进行判断。
本项前瞻性观察性研究采用连续抽样方法，于 2020 年 9 月至 2022 年 5 月在学术性城市医疗保障医院的急诊科进行。研究人群包括除创伤、转院和显而易见的诊断之外，处于危急状态的急诊科患者。使用视觉模拟量表（VAS）在患者到达后 15 分钟和 60 分钟时对急诊医生的整体判断进行 0 到 100 的测量。主要结局是明确的脓毒症出院诊断。记录临床数据长达 3 小时，以比较全身炎症反应综合征（SIRS）、序贯器官衰竭评估（SOFA）、快速 SOFA（qSOFA）、改良早期预警评分（MEWS）和使用最小绝对值收缩和选择算子（LASSO）进行变量选择的逻辑回归机器学习模型。使用接收者操作特征分析和曲线下面积计算（AUC）比较筛选工具。
共分析了涉及 59 名主治医生的 2484 例患者-医生就诊。275 例患者（11%）获得了明确的脓毒症出院诊断。当仅考虑 15 分钟时的可用数据时，初始 VAS（AUC 0.90；95%置信区间[CI]0.88，0.92）优于所有工具，包括 LASSO（0.84；95%CI 0.82 至 0.87）、qSOFA（0.67；95%CI 0.64 至 0.71）、SIRS（0.67；95%CI 0.64 至 0.70）、SOFA（0.67；95%CI 0.63 至 0.70）和 MEWS（0.66；95%CI 0.64 至 0.69）。扩展到 60 分钟时的可用数据并没有显著改变结果。
在因未分化危急疾病就诊于急诊科的成年人中，在就诊的最初 15 分钟内，医生的整体判断在识别脓毒症方面优于其他筛选方法。
