缺氧负荷算法：一种评估儿童阻塞性睡眠呼吸暂停间歇性缺氧的综合指标——一项回顾性队列研究

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中文摘要

背景： 儿童阻塞性睡眠呼吸暂停（OSA）涉及睡眠期间反复发生的上气道阻塞，导致间歇性缺氧（IH），并促成神经认知、心血管和代谢并发症。多导睡眠监测（PSG）和阻塞性呼吸暂停低通气指数（OAHI）常用于诊断；然而，传统夜间缺氧指标如最低血氧饱和度（LSpO2）、平均血氧饱和度（MSpO2）、血氧饱和度低于90%的总时间（T90）以及氧减饱和度指数≥3%（ODI3）未能捕捉累积负荷。因此，缺氧负荷（HB）作为一种反映去饱和事件频率、深度和持续时间的综合指标而受到关注；然而，其在儿童OSA中的应用和临床价值仍不确定。本研究旨在评估HB在诊断儿童OSA中的临床价值，并将其诊断性能与传统夜间缺氧指标进行比较。
方法： 我们对2020年12月至2025年11月期间在天津市儿童医院因疑似OSA接受PSG检查的115名儿童的PSG数据进行了回顾性分析。数据使用内部开发的Matlab软件进行分析。比较了五种夜间IH参数（LSpO2、MSpO2、T90、ODI3和HB）在四组（正常、轻度、中度和重度OSA）中的差异表现。所有数据均根据AASM评分手册（2.6版）进行人工评分。
结果： 该队列（72名男性，43名女性；平均年龄6.44±2.86岁）根据OAHI分为正常组（n=15）、轻度组（n=63）、中度组（n=23）和重度OSA组（n=14）。在严重程度组间，ODI3、T90和HB存在显著差异。相关性分析显示，LSpO2和MSpO2与OAHI呈弱至中度负相关（分别为r=-0.490和r=-0.543）。ODI3（r=0.788）和T90（r=0.700）呈中度正相关。重要的是，HB与OAHI呈强相关（r=0.801）。ROC曲线分析表明，LSpO2（AUC=0.689）和MSpO2（AUC=0.614）的诊断能力较弱。ODI3（AUC=0.857）和T90（AUC=0.701）显示出中等价值，而HB（AUC=0.888）表现出最佳的诊断性能。
结论： 我们成功开发了HB算法，证明其在儿童OSA诊断中具有显著优势。与仅反映氧去饱和单一维度的传统指标相比，HB整合了缺氧的频率、深度和持续时间。这为儿童OSA的早期筛查和临床评估提供了一种稳健、客观的替代方法。

英文摘要

BACKGROUND: Pediatric obstructive sleep apnea (OSA) involves recurrent upper-airway obstruction during sleep, which leads to intermittent hypoxia (IH) and contributes to neurocognitive, cardiovascular, and metabolic complications. Polysomnography (PSG) and the Obstructive Apnea-Hypopnea Index (OAHI) are commonly used for diagnosis; however, conventional nocturnal hypoxic metrics such as lowest oxygen saturation (LSpO2), mean oxygen saturation (MSpO2), total time with oxygen saturation below 90% (T90), and oxygen desaturation index ≥3% (ODI3) fail to capture the cumulative burden. Hypoxic burden (HB) has therefore gained attention as an integrated measure that reflects the frequency, depth, and duration of desaturation events; however, its use and clinical value in pediatric OSA remain uncertain. This study aims to evaluate the clinical value of HB in diagnosing pediatric OSA and compare its diagnostic performance with conventional nocturnal hypoxic metrics.
METHODS: We conducted a retrospective analysis of PSG data from 115 children with suspected OSA at Tianjin Children's Hospital between December 2020 and November 2025. Data were analyzed using internally developed Matlab software. The differential performance of five nocturnal IH parameters (LSpO2, MSpO2, T90, ODI3, and HB) was compared across four groups: normal, mild, moderate, and severe OSA. All data were manually scored according to the AASM Scoring Manual (version 2.6).
RESULTS: The cohort (72 males, 43 females; mean age 6.44±2.86 years) was distributed into normal (n=15), mild (n=63), moderate (n=23), and severe OSA (n=14) groups based on OAHI. Significant differences were observed among severity groups for ODI3, T90, and HB. Correlation analysis showed LSpO2 and MSpO2 had weak-to-moderate negative correlations with OAHI (r=-0.490 and r=-0.543, respectively). ODI3 (r=0.788) and T90 (r=0.700) showed moderate positive correlations. Importantly, HB demonstrated a strong correlation with OAHI (r=0.801). ROC curve analysis indicated weak diagnostic capability for LSpO2 (AUC =0.689) and MSpO2 (AUC =0.614). ODI3 (AUC =0.857) and T90 (AUC =0.701) showed moderate value, while HB (AUC =0.888) demonstrated the best diagnostic performance.
CONCLUSIONS: We successfully developed an HB algorithm, demonstrating its significant advantage in pediatric OSA diagnosis. Compared to traditional indices that reflect only a single dimension of oxygen desaturation, HB integrates the frequency, depth, and duration of hypoxia. This provides a robust, objective alternative for the early screening and clinical assessment of children with OSA.