Machine learning prediction of the change in sleepiness following continuous positive airway pressure in obstructive sleep apnea: a multi-cohort analysis.

IF 4.9 2区医学 Q1 Medicine

Sleep Pub Date : 2026-08-20 DOI:10.1093/sleep/zsag222

Eric Staykov, Juha Töyräs, Dwayne L Mann, Timo Leppänen, Samu Kainulainen, Ali Azarbarzin, Scott A Sands, Philip I Terrill

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引用次数: 0

Abstract

Study objectives: To predict the improvement in sleepiness following continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea (OSA) using baseline characteristics.

Methods: Data from five polysomnography cohorts (HomePAP, BestAIR, ABC, STAGES, and MESA; total n=2332) were analyzed. Associations between polysomnographic metrics and Epworth Sleepiness Scale (ESS) were assessed using multivariable linear regression. Cross-validated LASSO regression was used to select predictor variables. Prediction of ESS improvement was evaluated in CPAP-treated participants (n=213) using linear regression models trained on 139 participants and tested in a holdout group (n=74).

Results: Cross-sectional analysis revealed significant associations between baseline ESS and ventilatory burden (0.56 points/SD; 95% CI 0.34-0.78), hypoxic burden (0.51; 0.28-0.74), the apnea-hypopnea index (AHI, 0.45; 0.23-0.68), time below 90% oxygen saturation (0.36; 0.15-0.57), and flow limitation severity (0.31; 0.10-0.53). Baseline ESS and baseline ventilatory burden were the most frequently selected predictors of ESS improvement. Using these two variables in a regression model, predicted and actual ESS improvement were correlated in the holdout group (adjusted R2=0.313). The model classified CPAP responders (≥2 points ESS improvement) more accurately than current clinical guidelines (78.1% [3.5% SD] versus 68.4% [3.7%]). Substituting the AHI for ventilatory burden slightly reduced accuracy (77.3% [3.4%]). Charts were developed to assist in predicting ESS improvement from baseline metrics.

Conclusions: Models incorporating baseline ESS and either ventilatory burden or AHI predict ESS improvement and may help identify patients with OSA likely to benefit from CPAP. The ventilatory burden model demonstrated modestly better performance, while the AHI-based model offers greater clinical applicability.

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阻塞性睡眠呼吸暂停患者持续气道正压通气后困倦变化的机器学习预测：一项多队列分析

研究目的：利用基线特征预测阻塞性睡眠呼吸暂停（OSA）患者持续气道正压通气（CPAP）治疗后嗜睡的改善情况。方法：对来自5个多导睡眠图队列（HomePAP、BestAIR、ABC、STAGES和MESA，总n=2332）的数据进行分析。采用多变量线性回归评估多导睡眠图指标与Epworth嗜睡量表（ESS）之间的相关性。采用交叉验证LASSO回归选择预测变量。在接受cpap治疗的参与者（n=213）中，使用对139名参与者进行训练的线性回归模型评估ESS改善的预测，并在不接受cpap治疗的参与者（n=74）中进行测试。结果：横断面分析显示，基线ESS与呼吸负荷（0.56分/SD； 95% CI 0.34-0.78）、缺氧负荷（0.51;0.28-0.74）、呼吸暂停低通气指数（AHI, 0.45; 0.23-0.68）、低于90%氧饱和度的时间（0.36;0.15-0.57）和血流受限严重程度（0.31;0.10-0.53）之间存在显著相关性。基线ESS和基线通气负荷是ESS改善最常用的预测指标。在回归模型中使用这两个变量，预测和实际的ESS改善在抵抗组中是相关的（调整R2=0.313）。该模型对CPAP应答者（ESS改善≥2分）的分类比目前的临床指南更准确（78.1% [3.5% SD]对68.4%[3.7%]）。用AHI代替呼吸负荷的准确性略有降低（77.3%[3.4%]）。开发图表以帮助从基线指标预测ESS的改善。结论：纳入基线ESS和通气负担或AHI的模型预测ESS改善，并可能有助于识别可能从CPAP获益的OSA患者。通气负荷模型表现出稍好的性能，而基于ahi的模型具有更大的临床适用性。

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来源期刊

Sleep Medicine-Neurology (clinical)

CiteScore

8.70

自引率

10.70%

发文量

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