Predicting the impact of CPAP on brain health: A study using the sleep EEG‐derived brain age index

Author:

Yook Soonhyun1ORCID,Park Hea Ree2,Joo Eun Yeon3,Kim Hosung1

Affiliation:

1. USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC University of Southern California Los Angeles California 90033 USA

2. Department of Neurology Inje University College of Medicine, Ilsan Paik Hospital Goyang 10380 Korea

3. Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine Sungkyunkwan University Seoul 06351 Korea

Abstract

AbstractObjectiveThis 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.MethodsWe 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.ResultsCPAP‐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.Interpretation.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.

Funder

National Institutes of Health

National Heart, Lung, and Blood Institute

Publisher

Wiley

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