USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, 90033, USA.
Department of Neurology, Inje University College of Medicine, Ilsan Paik Hospital, Goyang, 10380, Korea.
Ann Clin Transl Neurol. 2024 May;11(5):1172-1183. doi: 10.1002/acn3.52032. Epub 2024 Feb 23.
This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index (BAI) to quantify CPAP's impact on brain health and identify individually varying CPAP effects on brain aging using machine learning approaches.
We retrospectively analyzed CPAP-treated (n = 98) and untreated OSA patients (n = 88) with a minimum 12-month follow-up of polysomnography. BAI was calculated by subtracting chronological age from the predicted brain age. To investigate BAI changes before and after CPAP treatment, we compared annual ΔBAI between CPAP-treated and untreated OSA patients. To identify individually varying CPAP effectiveness and factors influencing CPAP effectiveness, machine learning approaches were employed to predict which patient displayed positive outcomes (negative annual ΔBAI) based on their baseline clinical features.
CPAP-treated group showed lower annual ΔBAI than untreated (-0.6 ± 2.7 vs. 0.3 ± 2.6 years, p < 0.05). This BAI reduction with CPAP was reproduced independently in the Apnea, Bariatric surgery, and CPAP study cohort. Patients with more severe OSA at baseline displayed more positive annual ΔBAI (=accelerated brain aging) when untreated and displayed more negative annual ΔBAI (=decelerated brain aging) when CPAP-treated. Machine learning models achieved high accuracy (up to 86%) in predicting CPAP outcomes.
CPAP treatment can alleviate brain aging in OSA, especially in severe cases. Sleep EEG-derived BAI has potential to assess CPAP's impact on brain health. The study provides insights into CPAP's effects and underscores BAI-based predictive modeling's utility in OSA management.
本纵向研究旨在探讨 CPAP 治疗对阻塞性睡眠呼吸暂停（OSA）患者大脑健康的潜在积极影响。为此，我们旨在采用睡眠脑电图（EEG）衍生的大脑年龄指数（BAI）来量化 CPAP 对大脑健康的影响，并使用机器学习方法来识别个体之间 CPAP 对大脑老化的影响。
我们回顾性分析了 98 例 CPAP 治疗（CPAP 组）和 88 例未治疗 OSA 患者（未 CPAP 组）的多导睡眠图，随访时间至少 12 个月。BAI 通过从预测的大脑年龄中减去实际年龄来计算。为了研究 CPAP 治疗前后 BAI 的变化，我们比较了 CPAP 组和未 CPAP 组患者的年度 ΔBAI。为了识别个体间 CPAP 效果的差异和影响 CPAP 效果的因素，我们采用机器学习方法来预测哪些患者根据其基线临床特征显示出积极的结果（负的年度 ΔBAI）。
CPAP 组的年度 ΔBAI 低于未 CPAP 组（-0.6±2.7 年比 0.3±2.6 年，p<0.05）。在 Apnea、Bariatric surgery 和 CPAP 研究队列中，CPAP 治疗后 BAI 的降低是独立再现的。基线时 OSA 更严重的患者，未 CPAP 治疗时显示出更积极的年度 ΔBAI（=加速大脑老化），CPAP 治疗时显示出更负的年度 ΔBAI（=减缓大脑老化）。机器学习模型在预测 CPAP 结果方面具有很高的准确性（高达 86%）。
CPAP 治疗可以缓解 OSA 患者的大脑老化，尤其是在严重的情况下。睡眠 EEG 衍生的 BAI 具有评估 CPAP 对大脑健康影响的潜力。该研究提供了对 CPAP 影响的深入了解，并强调了基于 BAI 的预测模型在 OSA 管理中的应用。
