Updated symptom-based model with 3 symptoms achieved 78.4% sensitivity, 85.7% specificity, and 82.9% accuracy (AUC 0.88) in detecting panic-related anxiety among ED patients with cardiopulmonary complaints, outperforming original model.
Does a symptom-based multivariable prediction model accurately detect panic-related anxiety in emergency department patients with cardiopulmonary complaints?
A brief 7-symptom prediction model demonstrated strong diagnostic accuracy for identifying panic-related anxiety in emergency department patients presenting with cardiopulmonary complaints, potentially facilitating timely diagnosis and reducing repeat visits.
Effect estimate: AUC 0.88
Absolute Event Rate: 82.9% vs 77.9%
Background Patients with panic related-anxiety (i.e., panic attacks or panic disorder) frequently present to emergency departments (EDs) with cardiopulmonary complaints but are often undiagnosed, which can lead to recurrent visits and prolonged distress. This study aimed to derive a new symptom-based multivariable diagnostic prediction model to detect panic-related anxiety in ED patients with cardiopulmonary symptoms. Methods We conducted a single-blind prospective derivation study over 15 months in the ED of a major tertiary hospital in Singapore. Patients presenting with symptoms of palpitations, chest pain, dizziness, or difficulty breathing were assessed using the Structured Clinical Interview for DSM Disorders (SCID) to diagnose panic-related anxiety. A stepwise multivariable prediction model was constructed using 13 SCID-defined panic symptoms as predictors, with the diagnosis of panic-related anxiety as the outcome. Diagnostic accuracy was evaluated through sensitivity, specificity, receiver operating characteristics (ROC), and the Youden index. Results 321 eligible patients were included, with 39% meeting criteria for panic-related anxiety. The optimal cutoff (≥3 symptoms) in the derived model achieved an area under the curve (AUC) of 0.88, sensitivity of 78.4%, specificity of 85.7%, a Youden index of 64.1%, classified 82.9% correctly, positive likelihood ratio=5.4880, and negative likelihood ratio=0.2520. Conclusions This newly derived model demonstrated strong diagnostic accuracy in identifying panic-related anxiety among ED patients with cardiopulmonary complaints, suggesting its potential utility in clinical screening. Implementation of this model may facilitate timely diagnosis, reduce repeated ED visits, and improve patient outcomes.
Sung et al. (Wed,) conducted a other in Emergency department patients aged 6 years with cardiopulmonary complaints (primarily chest pain) presenting with mild to moderate symptoms, assessed for panic-related anxiety (n=321). Updated brief multivariable symptom-based prediction model assessing 7 panic symptoms (sweating, choking, trembling or shaking, fear of dying, shortness of breath, dizziness, palpitations) vs. Original 7-symptom prediction model was evaluated on Diagnosis accuracy of panic-related anxiety by SCID compared to prediction model thresholds (AUC 0.88). Updated symptom-based model with 3 symptoms achieved 78.4% sensitivity, 85.7% specificity, and 82.9% accuracy (AUC 0.88) in detecting panic-related anxiety among ED patients with cardiopulmonary complaints, outperforming original model.