Morphological Prediction of CPAP Associated Acute Respiratory Instability (Self Similarity)

This research presents a new algorithm to assess self-similarity (SS) as a
signature of increased loop gain using respiratory effort signals, and
demonstrates its utility in predicting acute failure of continuous positive
airway pressure (CPAP) therapy. By analyzing effort signals from 2,145 split-
night polysomnography studies, our model achieved strong predictive
performance with area under the curve values of 0.82 and 0.84 for ROC and
precision-recall curves, respectively. The SS metric combined with central
apnea index and hypoxic burden outperformed conventional metrics alone,
providing a more accurate, noninvasive approach to phenotyping obstructive
sleep apnea for precision treatment strategies.

This data publication includes data and code needed to reproduce the results
in this publication:

[Nassi TE, Oppersma E, Labarca G, Donker DW, Westover MB, Thomas RJ.
Morphological Prediction of CPAP Associated Acute Respiratory Instability. Ann
Am Thorac Soc. 2024 Sep 17;22(1):138-49\. doi: 10.1513/AnnalsATS.202311-979OC.
Epub ahead of print. PMID: 39288402; PMCID:
PMC11708763.](https://pubmed.ncbi.nlm.nih.gov/39288402/)