Incident cardiovascular disease risk prediction using extensive oximetry patterns

[摘要] Obstructive sleep apnea (OSA) has been associated with increasedrisk of developing cardiovascular disease (CVD) in multiple studies[1–3]. However, the nature of its association requires further investigation, including how to best predict which patients ultimatelydevelop CVD. This is a challenging task, given the heterogeneousinformation provided by the apnea–hypopnea index (AHI; whichmay obscure OSA endotypes) [4, 5] on one hand, and the complexnature of polysomnographic signals (with minimal guidance ondefining optimal signals) on the other. Identifying an optimal signal or combination of signals that improves the accuracy of CVDrisk prediction would improve risk stratification and personalizedmedicine. Ideally a search across a broad range of candidate signals would also provide insights on potential biological mechanisms and contribute to the design of improved OSA clinical trialmeasures when appropriately considering the larger biologicalcontext that contributes to these signals [6]. Testing dozens of candidate signals among thousands of participants remains technically challenging, even when considering cleaned and harmonizedPSG datasets provided by resources such as the National SleepResearch Resource (NSRR).

[学科分类] 生理学