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.

中文翻译：

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早期医生格式塔与常规筛查工具预测危重症急诊患者脓毒症的比较


将医生格式塔与现有的筛查工具进行比较，以便在必须开始时间敏感治疗的最初几分钟内识别脓毒症。这项前瞻性观察性研究于 2020 年 9 月至 2022 年 5 月期间在一家学术城市安全网医院的急诊科 （ED） 进行。研究人群包括重症 ED 患者，不包括创伤、转移和不证自明的诊断。在患者到达后 15 分钟和 60 分钟使用视觉模拟量表 （VAS） 从 0 到 100 测量急诊医师格式塔。主要结局是明确的脓毒症出院诊断。记录长达 3 小时的临床数据，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） 和使用最小绝对收缩和选择运算符 （LASSO） 的逻辑回归机器学习模型进行变量选择。使用受试者工作特征分析和曲线下面积计算 （AUC） 对筛选工具进行比较。共分析了 2,484 例医患，涉及 59 名主治医师。275 例患者 （11%） 接受了明确的脓毒症出院诊断。当仅限于 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 分钟时可用的数据并没有有意义地改变结果。 在因不明原因的危重疾病到急诊科就诊的成年人中，医生格式塔在相遇的前 15 分钟内在识别脓毒症方面优于其他筛查方法。