Key result
Eight predictive factors identified Acute Heart Failure Syndrome with an AUC of 0.86, while unstructured clinical gestalt (≥5) performed very well alone with an AUC of 0.91.
Why the study?
Early identification of acute heart failure syndrome among ED patients presenting with acute dyspnoea can facilitate the introduction of appropriate treatments.
Does clinical gestalt or specific predictive criteria accurately diagnose acute heart failure syndrome in emergency department patients with acute dyspnoea?
Observational (n=341)
Yes
Does clinical gestalt or specific predictive criteria accurately diagnose acute heart failure syndrome in emergency department patients with acute dyspnoea?
Unstructured clinical gestalt by emergency physicians is highly accurate (AUC 0.91) for diagnosing acute heart failure in patients with acute dyspnea, performing better than a derived predictive model.
OBJECTIVES: The early identification of patients with Acute Heart Failure Syndrome (AHFS) among patients admitted to the Emergency Department (ED) with dyspnoea can facilitate the introduction of appropriate treatments. The objectives are to identify the predictive factors for AHFS diagnosis in patients with acute dyspnoea (primary objective) and the clinical 'gestalt' (secondary objective) in ED. METHODS: PREDICA is an observational, prospective, multicentre study. The enrolment of patients admitted to the ED for nontraumatic acute dyspnoea and data collection on admission were recorded by the patient's emergency physician. The AHFS endpoints were assessed following a duplicate expert evaluation by pairs of cardiologists and emergency physicians. Step-by-step logistic regression was used to retain predictive criteria, and the area under the receiver operating characteristic (ROC) curve of the model was constructed to assess the ability of the selected factors to identify real cases. The probability of AHFS was estimated on a scale from 1 to 10 based on the emergency physician's perception and understanding (gestalt). RESULTS: Among 341 patients consecutively enrolled in three centres, 149 (44%) presented AHFS. Eight predictive factors of AHFS were detected with a performance test showing an area under the model ROC curve of 0.86. Gestalt greater than or equal to five showed sensitivity of 78% and specificity of 90% (AUC 0.91) and diagnosed 88% of AHF in our population. CONCLUSIONS: We identified several independant predictors of final AHFS diagnosis. They should contribute to the development of diagnostic strategies in ED. However, unstructured gestalts seem to perform very well alone.
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Roncalli et al. (2019) conducted an observational in Acute dyspnoea (n=341). Predictive factors and clinical gestalt was evaluated on Acute Heart Failure Syndrome (AHFS) diagnosis (AUC 0.86). Eight predictive factors identified Acute Heart Failure Syndrome with an AUC of 0.86, while unstructured clinical gestalt (≥5) performed very well alone with an AUC of 0.91.
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