Exploiting the Features of Clinical Judgment to Improve Assessment of Disease Severity in the Emergency Department: An Acutelines Study

Abstract

Background: Clinical judgment, also known as gestalt or gut feeling, can predict deterioration and can be easily and rapidly obtained. To date, it is unknown what clinical judgement precisely entails. The aim of this study was to elucidate which features define the clinical impression of health care professionals in the ED. Method: A nominal group technique (NGT) was used to develop a consensus-based instrument to measure the clinical impression score (CIS, scale 1–10) and to identify features associated with either a more severe or less severe estimated disease severity. This single-center observational cohort study included 517 medical patients visiting the ED. The instrument was prospectively validated.. The predictive value of each feature for the clinical impression was assessed using multivariate linear regression analyses to adjust for potential confounders and validated in the infection group. Results: The CIS at the ED was associated with ICU admission (OR 1.67 [1.37–2.03], p < 0.001), in-hospital mortality (OR 2.25 [1.33–3.81], p < 0.001), and 28-day mortality (OR 1.33 [1.07–1.65], <0.001). Dry mucous membranes, eye glance, red flags during physical examination, results of arterial blood gas analysis, heart and respiratory rate, oxygen modality, triage urgency, and increased age were associated with a higher estimated disease severity (CIS). On the other hand, behavior of family, self-estimation of the patient, systolic blood pressure, and Glascow Coma Scale were associated with a lower estimated disease severity (CIS). Conclusion: We identified several features that were associated with the clinical impression of health care professionals in the ED. Translating the subjective features and objective measurements into quantifiable parameters may aid the development of a novel triage tool to identify patients at risk of deterioration in the ED.

Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites

“…Odds ratios (ORs) with 95% confidence intervals (CIs) were computed to quantify the strength and direction of associations. Variables demonstrating an association with the outcome in the univariate logistic regression analysis ( p value < 0.20) 32 were subsequently incorporated into the multivariate logistic regression model using the backward stepwise elimination method to adjust for potential confounders and to identify independent predictors of E. coli infection subtype. Variables with a P -value higher than 0.05 were omitted from the multivariate logistic regression model.…”