Related Experiment Video
Updated: Jun 2, 2025
Image Acquisition using Portable Sonography for Emergency Airway Management
Published on: September 28, 2022
"The M-APNE score: an objective screening tool for OSA highlighting the area under the inspiratory flow-volume curve"
Celal Satici1, Damla Azakli2, Sinem Nedime Sokucu1
1Yedikule Chest Disease and Thoracic Surgery Training and Research Hospital, Pulmonology, Istanbul, Turkey.
Background:
Polysomnography (PSG) is resource-intensive but remains the gold standard for diagnosing Obstructive Sleep Apnea (OSA). We aimed to develop a screening tool to better allocate resources by identifying individuals at higher risk for OSA, overcoming limitations of current tools that may under-diagnose based on self-reported symptoms.
Methods:
A total of 884 patients (490 diagnosed with OSA) were included, which was divided into the training, validation, and test sets. Using multivariate logistic regression analyses, we developed a scoring system incorporating male sex, age, sawtooth pattern, area under the inspiratory flow-volume curve (AreaFI), and neck circumference to objectively identify patients at higher risk of OSA. Sensitivity and specificity were evaluated using area under the curve (AUC) metrics. The M-APNE Score was compared to other non-symptom-based tools, the No-Apnea Score and the Symptomless Multivariable Apnea Prediction (sMVAP) model, using the Delong test.
Results:
The M-APNE Score showed sensitivity rates of 79.3% in the training set, 70.8% in the test, and 80% in the validation set. ROC analysis for M-APNE score yielded AUCs of 0.82 in the training, 0.76 in the test, 0.82 in the validation set. The discriminative accuracy of M-APNE Score were found to be better than the No-Apnea Score (AUC = 0.82 vs. 0.76, p < 0.001) and the sMVAP (AUC = 0.82 vs. 0.75, p = 0.001) in the training set. Hosmer Lemeshow test indicated good calibration for M-Apne Score (p = 0.46).
Conclusions:
The M-APNE Score is a robust and objective tool for OSA screening, potentially reducing classification errors and improving accuracy.
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