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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 &amp;lt; 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.

A scalable method of determining physiological endotypes of sleep apnea from a polysomnographic sleep study