RESUMO
STUDY OBJECTIVE: Compare physician gestalt to existing screening tools for identifying sepsis in the initial minutes of presentation when time-sensitive treatments must be initiated. METHODS: 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). RESULTS: 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. CONCLUSION: 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.