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The Buddhasothorn Asthma Severity Score (BASS): A practical screening tool for predicting severe asthma exacerbations
Suttipong Ittiporn1, Kanlaya Prajongdee2
1Division of Allergy and Immunology, Department of Pediatrics, Buddhasothorn Hospital, Chachoengsao, Thailand; suttipong.itt@gmail.com.
Background And Aim:
A precise scaling system of acute asthma leads to an accurate assessment of disease severity. This study aimed to compare the accuracy of the Buddhasothorn Asthma Severity Score (BASS) with the Wood-Downes-Ferrés Scale (WDFS) to recognize the severity level of acute asthma.
Materials And Methods:
A cross-sectional study was conducted comprising Thai children aged 2-15 years with acute asthma. The BASS and WFDS were rated once in the emergency department. The degree of severity was determined by frequency and type of nebulized bronchodilator administrations at the time of initial treatment. The optimum cutoff points for the area under the curve (AUC) were established to predict severe asthma exacerbations.
Results:
All 73 episodes of asthma exacerbations (EAEs) in 35 participants were analyzed. Fifty-nine (80.8%) EAEs were classified as severe. Both scales had good significance to recognize the selection of nebulized bronchodilator treatments by AUC of 0.815 (95% Confidence Interval [CI]: 0.680-0.950) in case of BASS, and AUC of 0.822 (95% CI: 0.70-0.944) in case of WDFS. Cutoff points of BASS ≥ 8 had sensitivity 72.9%, specificity 64.3%, positive predictive value (PPV) 89.6%, negative predictive value (NPV) 36.0% at an AUC of 0.718 (95% CI: 0.563-0.873) for severe exacerbations. These results were consistent for cutoff points of WDFS ≥ 5 with sensitivity 78.0%, specificity 50.0%, PPV 86.8%, NPV 35.0% at an AUC of 0.768 (95% CI: 0.650-0.886) for predicting severe exacerbations. There was no significant difference between the AUCs of both scales.
Conclusions:
Both the BASS and WDFS were good and accurate scales and effective screening tools for predicting severe asthma exacerbations in pediatric patients by optimal cutoff points.
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