Abstract Sleep apnea is caused by several endophenotypic traits, namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Measures of these traits have shown promise for predicting outcomes of therapies (e.g. oral appliances, surgery, hypoglossal nerve stimulation, CPAP, and pharmaceuticals), which may become an integral part of precision sleep medicine. Currently, the methods Sands et al. developed for endotyping sleep apnea from polysomnography (PSG) are embedded in the original authors’ code, which is computationally expensive and requires technological expertise to run. We present a reimplementation and validation of the integrity of the original authors’ code by reproducing the endo-Phenotyping Using Polysomnography (PUP) method of Sands et al. The original MATLAB methods were reprogrammed in Python; efficient algorithms were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (PUPpy) was validated by comparing the endotypes from PUPpy with the original PUP results. Both endotyping methods were applied to 38 manually scored polysomnographic studies. Results of the new implementation were strongly correlated with the original (p < 10–6 for all): ventilation at eupnea V̇ passive (ICC = 0.97), ventilation at arousal onset V̇ active (ICC = 0.97), loop gain (ICC = 0.96), and arousal threshold (ICC = 0.90). We successfully implemented the original PUP method by Sands et al. providing further evidence of its integrity. Additionally, we created a cloud-based version for scaling up sleep apnea endotyping that can be used more easily by a wider audience of researchers and clinicians.

中文翻译：

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从多导睡眠图睡眠研究中确定睡眠呼吸暂停生理内型的可扩展方法。

摘要 睡眠呼吸暂停是由多种内表型特征引起的，即咽塌陷、肌肉代偿差、通气不稳定（高环路增益）和睡眠唤醒（低唤醒阈值）。这些特征的测量已显示出预测治疗结果（例如口腔矫治器、手术、舌下神经刺激、CPAP 和药物）的希望，这可能成为精准睡眠医学的一个组成部分。目前，Sands 等人的方法。为从多导睡眠图 (PSG) 进行睡眠呼吸暂停内型分型而开发的，嵌入在原始作者的代码中，该代码的计算成本很高，并且需要技术专业知识才能运行。我们通过重现 Sands 等人的使用多导睡眠图 (PUP) 方法的内表型分析来重新实现和验证原始作者代码的完整性。最初的 MATLAB 方法在 Python 中重新编程；开发了有效的算法来检测呼吸、计算标准化通气（移动时间平均）和模拟通气驱动（预期通气）。通过将 PUPpy 的内型与原始 PUP 结果进行比较来验证新的实现 (PUPpy)。两种内分型方法均应用于 38 项手动评分的多导睡眠图研究。新实施的结果与原来的结果密切相关（所有 p < 10-6）：eupnea V̇ 被动通气（ICC = 0.97），觉醒开始 V̇ 主动通气（ICC = 0.97），环路增益（ICC = 0.96 ) 和唤醒阈值 (ICC = 0.90)。我们成功实施了 Sands 等人的原始 PUP 方法。提供进一步证明其完整性的证据。此外，