Purpose of Review <p>Central sleep apnea (CSA) defined by episodic reductions or absence of respiratory effort during sleep, is often underrecognized due to limitations in hypopnea classification. Historically, hypopneas were scored as obstructive by default, obscuring the true prevalence of CSA. This review explores the evolution of hypopnea scoring, including updates from the American Academy of Sleep Medicine, and expert-derived visual scoring strategies. We explore hypopnea classification and its impact on patient selection for transvenous phrenic nerve stimulation (TPNS), neuromodulation therapy for CSA.</p> Recent Findings <p>TPNS reduces central events and improves sleep quality, with favorable safety and adherence profiles. Improved hypopnea classification helps identify CSA phenotypes and better predicts treatment response. Future directions include integration of artificial intelligence in sleep scoring, optimizing therapy delivery, and conducting outcome studies.</p> Summary <p>Accurate hypopnea classification is essential for personalized CSA management and may expand access to effective therapies like TPNS while informing future studies.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Revisiting Hypopnea Classification and Emerging Neuromodulation Therapies in Central Sleep Apnea

  • Kara L. Dupuy-McCauley,
  • Timothy I. Morgenthaler

摘要

Purpose of Review

Central sleep apnea (CSA) defined by episodic reductions or absence of respiratory effort during sleep, is often underrecognized due to limitations in hypopnea classification. Historically, hypopneas were scored as obstructive by default, obscuring the true prevalence of CSA. This review explores the evolution of hypopnea scoring, including updates from the American Academy of Sleep Medicine, and expert-derived visual scoring strategies. We explore hypopnea classification and its impact on patient selection for transvenous phrenic nerve stimulation (TPNS), neuromodulation therapy for CSA.

Recent Findings

TPNS reduces central events and improves sleep quality, with favorable safety and adherence profiles. Improved hypopnea classification helps identify CSA phenotypes and better predicts treatment response. Future directions include integration of artificial intelligence in sleep scoring, optimizing therapy delivery, and conducting outcome studies.

Summary

Accurate hypopnea classification is essential for personalized CSA management and may expand access to effective therapies like TPNS while informing future studies.