Journal article
Screening for obstructive sleep apnea in patients with cancer - a machine learning approach
Sleep advances, v 4(1), zpad042
01 Jan 2023
PMID: 38131038
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder associated with daytime sleepiness, fatigue, and increased all-cause mortality risk in patients with cancer. Existing screening tools for OSA do not account for the interaction of cancer-related features that may increase OSA risk.Study Design and Methods This is a retrospective study of patients with cancer at a single tertiary cancer institution who underwent a home sleep apnea test (HSAT) to evaluate for OSA. Unsupervised machine learning (ML) was used to reduce the dimensions and extract significant features associated with OSA. ML classifiers were applied to principal components and model hyperparameters were optimized using k-fold cross-validation. Training models for OSA were subsequently tested and compared with the STOP-Bang questionnaire on a prospective unseen test set of patients who underwent an HSAT.Results From a training dataset of 249 patients, kernel principal component analysis (PCA) extracted eight components through dimension reduction to explain the maximum variance with OSA at 98%. Predictors of OSA were smoking, asthma, chronic kidney disease, STOP-Bang score, race, diabetes, radiation to head/neck/thorax (RT-HNT), type of cancer, and cancer metastases. Of the ML models, PCA + RF had the highest sensitivity (96.8%), specificity (92.3%), negative predictive value (92%), F1 score (0.93), and ROC-AUC score (0.88). The PCA + RF screening algorithm also performed better than the STOP-Bang questionnaire alone when tested on a prospective unseen test set.Conclusions The PCA + RF ML model had the highest accuracy in screening for OSA in patients with cancer. History of RT-HNT, cancer metastases, and type of cancer were identified as cancer-related risk factors for OSA.
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Details
- Title
- Screening for obstructive sleep apnea in patients with cancer - a machine learning approach
- Creators
- Karen A. Wong - Mécanique et Engrenage Moderne (France)Ankita Paul - Drexel UniversityPaige Fuentes - Mécanique et Engrenage Moderne (France)Diane C. Lim - Miami Vet Affairs Healthcare Syst, Dept Med, Miami, FL USAAnup Das - Drexel University, Electrical and Computer EngineeringMiranda Tan - Mécanique et Engrenage Moderne (France)
- Publication Details
- Sleep advances, v 4(1), zpad042
- Publisher
- Oxford University Press
- Number of pages
- 10
- Grant note
- P30 CA008748 / National Cancer Institute, National Institutes of Health Cancer Center Support Grant
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001585303500001
- Scopus ID
- 2-s2.0-85183176501
- Other Identifier
- 991022207152904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Clinical Neurology
- Neurosciences