The prediction of obstructive sleep apnea severity based on anthropometric and Mallampati indices

Abstract

Obstructive sleep apnea (OSA) is a common health issue with serious complications. Regarding the high cost of the polysomnography (PSG), sensitive and inexpensive screening tools are necessary. The objective of this study was to evaluate the predictive value of anthropometric and Mallampati indices for OSA severity in both genders. Materials and Methods: In a cross-sectional study, we evaluated anthropometric data and the Mallampati classification for the patients (n = 205) with age >18 and confirmed OSA in PSG (Apnea-Hypopnea Index [AHI] >5). For predicting the severity of OSA, we applied a decision tree (C5.0) algorithm, with input and target variables considering two models (Model 1: AHI ≥15 with Mallampati >2, age >51 years, and neck circumference [NC] >36 cm and Model 2: AHI ≥30 with condition: gender = female, body mass index (BMI) >35.8, and age >44 years or gender = male, Mallampati ≥2, and abdominal circumference (AC) >112 then AHI ≥30). Results: About 54.1% of the patients were male. Mallampati, age, and NCs are important factors in predicting moderate OSA. The likelihood of moderate OSA severity based on Model 1 was 94.16%. In severe OSA, Mallampati, BMI, age, AC, and gender are more predictive. In Model 2, gender had a significant role. The likelihood of severe OSA based on Model 2 in female patients was 89.98% and in male patients was 90.32%. Comparison of the sensitivity and specificity of both models showed a higher sensitivity of Model 1 (93.5%) and a higher specificity of Model 2 (89.66%). Conclusion: For the prediction of moderate and severe OSA, anthropometric and Mallampati indices are important factors.