DOI: 10.4103/sjoh.sjoh_31_26 ISSN: 1319-8491

Predictability of Different Scoring Systems for Suspected Airway Foreign Body in Children: A Retrospective Study

Zahraa Jumah Almuhanna, Mohammed S. Alhaddad, Norah A. Boukhamssein, Fatimah A. Alwosaibi, Hussain A. Alsheef
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Abstract

Background:

Foreign body aspiration (FBA) is a frequent pediatric emergency; however, its diagnosis remains challenging due to nonspecific symptoms and clinical presentations, which may delay its management.

Objective:

The objective is to evaluate the diagnostic usefulness of the FOBAS (FBA score) and Janahi et al . scoring systems in children undergoing rigid bronchoscopy for suspected FBA, and to develop a locally applicable scoring model based on clinical and examination findings.

Methodology:

A retrospective cohort study was conducted of all patients <14 years who underwent rigid bronchoscopy for suspected FBA between 2019 and 2024. Each case was scored using the FOBAS and Janahi et al . Systems, and correlated with bronchoscopy outcomes. Multivariate logistic regression was used to identify independent predictors and generate a weighted scoring model.

Results:

Of the 54 patients, 42 (77.8%) had a confirmed airway foreign body. Choking ( P = 0.009) and sudden, persistent cough ( P = 0.038) were the only history elements significantly associated with positive bronchoscopy. Chest radiography lacked discriminatory value, with over half of positive bronchoscopy cases (54.8%) showing normal X-rays. The proposed scoring model demonstrated good discrimination (area under the curve [AUC] = 0.748), and 81% of confirmed cases fell into the high-risk category. The FOBAS system showed slightly better performance (AUC = 0.750) compared to the Janahi model (AUC = 0.739).

Conclusion:

Choking and sudden persistent cough were the strongest predictors of FBA, whereas chest radiography had limited diagnostic value. The proposed scoring system showed good discrimination and may serve as a practical tool. Larger prospective studies are needed to validate this model.

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