Point-of-care prediction model of loop gain in patients with obstructive sleep apnea: development and validation

High loop gain (unstable ventilatory control) is an important-but difficult to measure-contributor to obstructive sleep apnea (OSA) pathogenesis, predicting OSA sequelae and/or treatment response. Our objective was to develop and validate a clinical prediction tool of loop gain. A retrospective coho...

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Bibliographic Details
Published in:BMC pulmonary medicine 2022-04, Vol.22 (1), p.158-158, Article 158
Main Authors: Schmickl, Christopher N, Orr, Jeremy E, Kim, Paul, Nokes, Brandon, Sands, Scott, Manoharan, Sreeganesh, McGinnis, Lana, Parra, Gabriela, DeYoung, Pamela, Owens, Robert L, Malhotra, Atul
Format: Article
Language:English
Subjects:
Adult
Algorithms
Apnea
Care and treatment
Clinical decision rules
Clinical trials
Cohort Studies
Complications
Feature selection
Humans
Mean square errors
Patients
Point-of-Care Systems
Polysomnography
Precision medicine
Prediction models
Prognosis
Pulmonology
Regression analysis
Respiration
Retrospective Studies
Sleep
Sleep apnea
Sleep apnea syndromes
Sleep apnea, obstructive
Sleep Apnea, Obstructive - therapy
Sleep disorders
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Summary:High loop gain (unstable ventilatory control) is an important-but difficult to measure-contributor to obstructive sleep apnea (OSA) pathogenesis, predicting OSA sequelae and/or treatment response. Our objective was to develop and validate a clinical prediction tool of loop gain. A retrospective cohort of consecutive adults with OSA (apnea-hypopnea index, AHI > 5/hour) based on in-laboratory polysomnography 01/2017-12/2018 was randomly split into a training and test-set (3:1-ratio). Using a customized algorithm ("reference standard") loop gain was quantified from raw polysomnography signals on a continuous scale and additionally dichotomized (high > 0.7). Candidate predictors included general patient characteristics and routine polysomnography data. The model was developed (training-set) using linear regression with backward selection (tenfold cross-validated mean square errors); the predicted loop gain of the final linear regression model was used to predict loop gain class. More complex, alternative models including lasso regression or random forests were considered but did not meet pre-specified superiority-criteria. Final model performance was validated on the test-set. The total cohort included 1055 patients (33% high loop gain). Based on the final model, higher AHI (beta = 0.0016; P 
ISSN:1471-2466
1471-2466
DOI:10.1186/s12890-022-01950-y