# A Noninvasive Five-Parameter Bedside Score for Predicting a Difficult Airway in the Emergency Department: A Prospective Observational Comparative Study

## TL;DR

This study introduces a new five-parameter scoring system to help emergency doctors quickly identify patients with difficult airways, improving safety and efficiency in airway management.

## Contribution

A novel, simple bedside scoring system combining five clinical parameters for predicting difficult airways in the emergency department.

## Key findings

- The new score achieved 79.27% sensitivity and 71.2% specificity in predicting difficult airways.
- The score showed good discriminative ability with an AUC of 0.752 in ROC analysis.
- The score outperformed individual predictors and the Modified Mallampati classification when used alone.

## Abstract

Introduction: Airway management is a critical and challenging component of emergency care, requiring skilled interventions to ensure safe intubation, particularly in difficult airway scenarios. Early identification is essential to minimize intubation attempts and reduce major airway-related complications. Although several assessment tools and scoring systems have been proposed, their effectiveness as unstructured individual predictors remains uncertain. This study aimed to develop a simple scoring system for predicting difficult airways in the emergency department (ED).

Aim: To develop and preliminarily validate an innovative, easy-to-use, and uncomplicated scoring system for predicting the difficult airway in the ED.

Methodology: This prospective observational study was conducted in the ED of a tertiary medical center in India (January 2023-August 2025) and included 200 conscious adult patients. Five bedside predictors (body mass index (BMI), retrognathia, upper lip bite test (ULBT), thyromental distance (TMD), and hyomental distance) were assessed to develop a composite score for difficult airway prediction. The Modified Mallampati classification (MMC) (Class 3/4) was used as the reference standard for initial validation prior to assessment against the Cormack-Lehane classification. Diagnostic performance of individual predictors and the composite score was evaluated using sensitivity, specificity, predictive values, and receiver operating characteristic (ROC) analysis.

Results: Among 200 patients (n=200), 82 (n=82) were classified with a MMC of 3/4, resulting in a 41% incidence (n=82) of difficult airways. Among individual parameters, BMI >30 kg/m² demonstrated the highest individual sensitivity (79.3%), while the ULBT showed the highest specificity (94.9%). The novel scoring system achieved an overall sensitivity of 79.27% and a specificity of 71.2%. The short TMD was the most frequently observed positive individual predictor (n=59). The McNemar test indicated a statistically significant difference in the proportions of discordant classifications between the new score and the MMC (p=0.006), suggesting they do not classify patients identically. However, the kappa coefficient of 0.522 demonstrated a moderate level of overall agreement. ROC analysis demonstrated good discriminative ability (AUC=0.752, 95% CI: 0.683-0.822, p<0.001), validating the score’s diagnostic strength.

Conclusion: This study presents a tentative confirmation of a concise, five-parameter bedside scoring tool that displays strong discriminative ability in predicting a challenging airway in the ED environment. The score outperforms individual predictors and the MMC when used on its own by synthesizing predictable clinical and anatomical variables that can be obtained in a short period of time. Its high negative predictive value makes it easy to rule out airway difficulty early and to proactively deal with airway management. To verify the applicability and clinical relevance of the suggested tool, multicenter validation is necessary.