Evidence map›Paper›PMID 41769567›Full record

ArticleCureus2026

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

Anagani Hrushikesh, Chinnam Vishnupriya, Chetla Rakesh, Meesaraganda Chucharitha, Vanjavakam Sahithya, Sreekrishnan Trikkur, Gireesh Kumar

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anagani HrushikeshDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Ernakulam, IND.
Chinnam VishnupriyaDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Ernakulam, IND.
Chetla RakeshDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Ernakulam, IND.
Meesaraganda ChucharithaHospital Administration, AIG Hospital, Hyderabad, IND.
Vanjavakam SahithyaHealth Economic Outcomes Research, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, IND.
Sreekrishnan TrikkurDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Ernakulam, IND.
Gireesh KumarDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Ernakulam, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAirway 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).

aimTo 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.

resultsAmong 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.

conclusionThis 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.

Indexed as

body mass index: bmidifficult airway managementemergency departmentsnovel scoring systemprediction of difficult airway

Identifiers

PMID41769567
PMCPMC12948641

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.