# Derivation of an updated brief multivariable prediction model to detect panic-related anxiety in emergency department patients with cardiopulmonary complaints

## TL;DR

This study created a new model to detect panic-related anxiety in emergency patients with heart or lung symptoms, improving diagnosis and reducing repeat visits.

## Contribution

A novel symptom-based prediction model for identifying panic-related anxiety in ED patients with cardiopulmonary complaints.

## Key findings

- The model achieved 88% AUC with a cutoff of ≥3 symptoms.
- It correctly classified 82.9% of patients with 78.4% sensitivity and 85.7% specificity.
- Implementation could reduce ED revisits and improve patient outcomes.

## Abstract

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.

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.

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.

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.