Respiratory medicine · 2025
Morpho-clinical insights into moderate-to-severe obstructive sleep apnea.
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Riachy Moussa, Riachy Sophie, Riachy Albert, Mallat Farid, Medawar Nicolas, Hanna Thalia, Braidy Kristy, Al Halabi Dalia, El Chaer Joe Elio, Chammas Karl, El Kik Antoine, Eid Hind
Аннотация
INTRODUCTION: Obstructive sleep apnea syndrome results from recurrent upper airway obstruction during sleep. Moderate-to-severe is common in referred sleep clinics and poses significant health risks. We assessed whether a morphotype model, a clinical-complaints model, and a combined morpho-clinical model can predict MS-OSA.
METHOD: In a prospective cohort of referred adults undergoing type I polysomnography (n = 925, 2018-2024), we recorded demographics/anthropometry and two novel dynamic metrics, the cricomental angle (CMA) and inspiratory Mallampati score (I-MS). Logistic regression models were developed and internally validated with 80-20 split and 10-fold cross validation. Pre-specified operating points were chosen for interpretability.
RESULTS: Among 925 patients (72.8 % male, average body mass index (BMI) of 30.3 ± 5.6 kg/m2, mean AHI 32.7 ± 24.4), daytime drowsiness (75.5 %) and snoring (74.6 %) were prevalent. Significant predictors of MS-OSA included age, male sex, BMI, neck circumference. Higher I-MS classifications and convex CMA were associated with greater odds of MS-OSA. In internal validation the comprehensive morphotype model showed high specificity (0.96) and PPV (0.82) (rule-in). The clinical-complaints model showed higher sensitivity (0.46) and NPV (0.80) (rule-out but does not exclude MS-OSA). Adding clinical features (Berlin category 1 and arterial hypertension) to the morphotype model improved overall fit (lower Akaike information criterion 737) while maintaining high specificity.
CONCLUSION: We propose a simple three step workflow or an alternative on step option to support triage in specialist clinics. Future work should focus on refining and validating these tools across diverse populations. The proposed models are designed for triage and prioritization rather than to replace or restrict diagnostic testing.
METHOD: In a prospective cohort of referred adults undergoing type I polysomnography (n = 925, 2018-2024), we recorded demographics/anthropometry and two novel dynamic metrics, the cricomental angle (CMA) and inspiratory Mallampati score (I-MS). Logistic regression models were developed and internally validated with 80-20 split and 10-fold cross validation. Pre-specified operating points were chosen for interpretability.
RESULTS: Among 925 patients (72.8 % male, average body mass index (BMI) of 30.3 ± 5.6 kg/m2, mean AHI 32.7 ± 24.4), daytime drowsiness (75.5 %) and snoring (74.6 %) were prevalent. Significant predictors of MS-OSA included age, male sex, BMI, neck circumference. Higher I-MS classifications and convex CMA were associated with greater odds of MS-OSA. In internal validation the comprehensive morphotype model showed high specificity (0.96) and PPV (0.82) (rule-in). The clinical-complaints model showed higher sensitivity (0.46) and NPV (0.80) (rule-out but does not exclude MS-OSA). Adding clinical features (Berlin category 1 and arterial hypertension) to the morphotype model improved overall fit (lower Akaike information criterion 737) while maintaining high specificity.
CONCLUSION: We propose a simple three step workflow or an alternative on step option to support triage in specialist clinics. Future work should focus on refining and validating these tools across diverse populations. The proposed models are designed for triage and prioritization rather than to replace or restrict diagnostic testing.