睡眠结构、夜间氧合与呼吸紊乱与复合PSG衍生节律事件指数的序贯关联

文献信息

中文摘要

背景： 睡眠呼吸障碍（SDB），尤其是阻塞性睡眠呼吸暂停（OSA），与包括心律失常在内的心血管并发症相关。然而，呼吸、氧合和睡眠结构指标对复合PSG衍生节律事件指数的相对贡献仍不完全清楚。
目的： 探讨在接受多导睡眠图（PSG）检查的患者中，睡眠结构、夜间氧合和呼吸紊乱指标与复合PSG衍生节律事件指数的序贯关联。
方法： 从PSG记录中提取人口学、呼吸、氧合和睡眠结构变量。主要结局为复合PSG衍生节律事件计数，根据PSG导出的心律失常指数估算为round(心律失常指数 × 总睡眠时间小时数)，该指数仅在总睡眠时间内计算。负二项回归使用log(总睡眠时间小时数)作为偏移量。由于结局显示过度离散，采用以log(总睡眠时间小时数)为偏移量的负二项回归。由于原始14个预测变量模型对样本量而言过度参数化，事后选择了一个六预测变量模型（年龄、性别、BMI、AHI、T90和睡眠效率）。该简化模型未在原始分析前预先设定。保留睡眠效率是因为其在探索性14预测变量模型中显示出强信号。因此，所得p值应解释为乐观且探索性的。采用序贯模型构建方法（模型1：年龄、性别、BMI、AHI；模型2：加T90；模型3：加睡眠效率）以记录各领域的增量贡献。完全调整的14预测变量模型仅作为补充分析拟合。报告Wald 95% CI和bootstrap百分位95% CI（2000次重抽样）。
结果： 共纳入41名参与者（平均年龄47.8 ± 13.5岁；BMI中位数29.4 kg/m2）。重度OSA（AHI ≥ 30次/小时）占56.1%。心律失常指数中位数为3.2次/小时，具有明显过度离散。在六预测变量模型中，仅较高的睡眠效率与较低的心律失常率相关（每增加1个百分点，IRR = 0.946，95% CI 0.921-0.972，p < 0.001；bootstrap 95% CI 0.888-0.984）。T90在Wald推断中相关（IRR = 1.034，95% CI 1.006-1.063，p = 0.016），但该关联在bootstrap重抽样下不稳健（95% CI 0.979-1.067）。AHI与心律失常率无关（IRR = 1.000，95% CI 0.987-1.014，p = 0.954）。年龄、性别和BMI也不显著。14预测变量探索性模型的AIC略低（411.4），但BIC明显更高（438.8），与过拟合一致。
结论： 在这个探索性选择的睡眠中心队列中，AHI和其他呼吸事件指标与复合PSG衍生节律事件指数无独立关联。仅较高的睡眠效率显示出与较低心律失常率的稳健关联，但该发现是假设生成性的，可能反映未测量的混杂。这些结果需要在更大规模的前瞻性研究中确认，不应指导临床风险分层。

英文摘要

BACKGROUND: Sleep-disordered breathing (SDB), particularly obstructive sleep apnea (OSA), has been associated with cardiovascular complications including cardiac arrhythmias. However, the relative contributions of respiratory, oxygenation, and sleep architecture indices to composite PSG-derived rhythm-event index remain incompletely understood.
OBJECTIVE: To investigate the sequential associations of sleep architecture, nocturnal oxygenation, and respiratory disturbance indices with the composite PSG-derived rhythm-event index in patients undergoing polysomnography (PSG).
METHODS: Demographic, respiratory, oxygenation and sleep-architecture variables were extracted from PSG records. The primary outcome was a composite PSG-derived rhythm-event count, estimated as round(arrhythmia index × TST_hours) from the PSG-exported arrhythmia index, which was calculated during total sleep time only. Negative binomial regression used an offset of log(TST_hours). Because the outcome demonstrated overdispersion, negative binomial regression with an offset of log(TST hours) was used. Because the original 14-predictor model was over-parameterized for the sample size, a six-predictor model (age, sex, BMI, AHI, T90, and sleep efficiency) was chosen post hoc. This reduced model was not prespecified before the original analysis. Sleep efficiency was retained because it showed a strong signal in the exploratory 14-predictor model. The resulting p-values should therefore be interpreted as optimistic and exploratory. A sequential model-building approach (Model 1: age, sex, BMI, AHI; Model 2: plus T90; Model 3: plus sleep efficiency) was used to document the incremental contribution of each domain. A fully adjusted 14-predictor model was fitted only as a supplementary analysis. Wald 95% CIs and bootstrap percentile 95% CIs (2000 resamples) are reported.
RESULTS: Forty-one participants were included (mean age 47.8 ± 13.5 years; median BMI 29.4 kg/m2). Severe OSA (AHI ≥ 30 events/hour) was present in 56.1%. Median arrhythmia index was 3.2 events/hour, with marked overdispersion. In the six-predictor model, only higher sleep efficiency was associated with lower arrhythmia rate (IRR = 0.946 per 1 percentage-point increase, 95% CI 0.921-0.972, p < 0.001; bootstrap 95% CI 0.888-0.984). T90 was associated in the Wald inference (IRR = 1.034, 95% CI 1.006-1.063, p = 0.016) but this association was not robust under bootstrap resampling (95% CI 0.979-1.067). AHI was not associated with arrhythmia rate (IRR = 1.000, 95% CI 0.987-1.014, p = 0.954). Age, sex and BMI were also not significant. A 14-predictor exploratory model had a marginally lower AIC (411.4) but a substantially higher BIC (438.8), consistent with overfitting.
CONCLUSION: In this exploratory selected sleep-center cohort, AHI and other respiratory-event indices were not independently associated with the composite PSG-derived rhythm-event index. Only higher sleep efficiency showed a robust association with lower arrhythmia rate, but this finding is hypothesis-generating and may reflect unmeasured confounding. These results require confirmation in larger prospective studies and should not guide clinical risk stratification.