A new clinical algorithm scoring for management of suspected foreign body aspiration in children

Abstract

Background: Foreign Body Aspiration (FBA) is a serious problem in children and delays in diagnosis and management can be devastating. The history is often vague, with subtle physical and chest radiograph abnormalities. This study aims to determine the indications for bronchoscopy in children with suspected FBA and evaluate the key clinical and statistically significant predictors of FBA, based on the patients' historical, physical and radiological findings at presentation. Methods: This is a retrospective observational study, including patients who were admitted between January 2001 to January 2011 with suspected FBA. Their presenting history, physical exam, radiological and bronchoscopic findings were analyzed. Results: Three hundred children with a mean age of 2.1±1.7years were included. In children with both abnormal physical and radiological findings, 47.2% had proven FBA. If either was abnormal, the likelihood reduced to 32-33.3%; if both were normal, only 7.4% had a FB. Witnessed choking (adjusted OR 2.1, 95% CI 1.03-4.3; P=0.041), noisy breathing/stridor/dysphonia (adjusted OR 2.7, 95% CI 1.2-6.2; P=0.015), new onset/recurrent /persistent wheeze (adjusted OR 4.6, 95% CI 1.8-11.8; P=0.002), abnormal radiological findings (adjusted OR 4.0, 95% CI 1.9-8.5; P<0.001), and unilateral reduced air entry (adjusted OR 2.9, 95% CI 1.5-5.5; P=0.001) were significant predictors of FBA (P<0.05). When three or more risk factors were present, the cumulative proportion of children with proven FBA increased significantly. The discriminative ability of the model was found to be good; the area under the ROC curve value was 0.76 (95% CI 0.70, 0.82). The predicted cutoff score derived using ROC analysis was found to co-relate well with known clinically significant predictors of FBA. This supports our algorithm and scoring system. Conclusions: A high index of suspicion is required in diagnosing airway FB. Our proposed clinical algorithm and scoring system hopes to empower physicians to accurately predict patients with a high likelihood of FBA.