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    Long-term mortality risk in obstructive sleep apnea: the critical role of oxygen desaturation index
    (Springer Science and Business Media Deutschland GmbH, 2024) Azaklı, Damla; Satıcı, Celal; Sokucu, Sinem Nedime; Aydın, Şenay; Atasever, Furkan; Özdemir, Cengiz
    Background: Mortality predictors in obstructive sleep apnea (OSA) patients yet to be comprehensively understood, especially within large cohorts undergoing long-term follow-up. We aimed to determine the independent predictors of mortality in OSA patients. Methods: In our retrospective cohort study, 3,541 patients were included and survival data was obtained from electronic medical records. Demographic characteristics, anthropometric measurements, comorbidities, laboratory tests, and polysomnography parameters were analyzed for the survived and deceased patient groups. Univariate and multivariate Cox regression analyses were performed to determine independent predictors of all-cause mortality in patients followed for at least 5 years. Results: Among all patients, 2,551 (72%) patients were male, with a mean age of 49.7 years. 231 (6.5%) patients had died. Deceased patients were significantly older and had higher waist-to-hip ratio and Epworth Sleepiness Scale (p < 0.001, p < 0.001, p = 0.003). OSA (nonpositional and not-rapid eye movement-related), periodic limb movements in sleep and Comorbidities of Sleep Apnea Score ≥ 1 were found to be associated with increased mortality (p < 0.001). Systemic immune-inflammation index was also significantly higher in the deceased group (p < 0.001). Higher oxygen desaturation index (ODI) and apnea-hypopnea index (AHI) were associated with increased mortality (p < 0.001). Due to the high correlation between ODI and AHI, two separate multivariate Cox regression models were created. While AHI lost its significance in the multivariate analysis, ODI remained significantly higher in the deceased patient group (HR = 1.007, 1.001–1.013, p = 0.01). Conclusion: ODI, as the only polysomnography parameter, emerged as an independent predictor of mortality in OSA patients. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
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    “The M-APNE score: an objective screening tool for OSA highlighting the area under the inspiratory flow-volume curve”
    (Springer science and business media deutschland GmbH, 2025) Satıcı, Celal; Azaklı, Damla; Sokucu, Sinem Nedime; Aydın, Senay; Atasever, Furkan; Özdemir, Cengiz
    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.