Screening for obstructive sleep apnea in patients with cancer - a machine learning approach.

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.

Cancer-related Fatigue in Lung Cancer: A Research Agenda: An Official American Thoracic Society Research Statement.

Background: Fatigue is the most common symptom among cancer survivors. Cancer-related fatigue (CRF) may occur at any point in the cancer care continuum. Multiple factors contribute to CRF development and severity, including cancer type, treatments, presence of other symptoms, comorbidities, and medication side effects. Clinically, increasing physical activity, enhancing sleep quality, and recognizing sleep disorders are integral to managing CRF. Unfortunately, CRF is infrequently recognized, evaluated, or treated in lung cancer survivors despite more frequent and severe symptoms than in other cancers. Therefore, increased awareness and understanding of CRF are needed to improve health-related quality of life in lung cancer survivors. Objectives: 1) To identify and prioritize knowledge and research gaps and 2) to develop and prioritize research questions to evaluate mechanistic, diagnostic, and therapeutic approaches to CRF among lung cancer survivors. Methods: We convened a multidisciplinary panel to review the available literature on CRF, focusing on the impacts of physical activity, rehabilitation, and sleep disturbances in lung cancer. We used a three-round modified Delphi process to prioritize research questions. Results: This statement identifies knowledge gaps in the 1) detection and diagnostic evaluation of CRF in lung cancer survivors; 2) timing, goals, and implementation of physical activity and rehabilitation; and 3) evaluation and treatment of sleep disturbances and disorders to reduce CRF. Finally, we present the panel's initial 32 research questions and seven final prioritized questions. Conclusions: This statement offers a prioritized research agenda to 1) advance clinical and research efforts and 2) increase awareness of CRF in lung cancer survivors.

OSA in Professional Transport Operations: Safety, Regulatory, and Economic Impact.

OSA is common among commercial vehicle operators (CVOs) in all modes of transportation, including truck, bus, air, rail, and maritime operations. OSA is highly prevalent and increases the risk of drowsiness-related crashes in CVOs. Internationally, specific regulations regarding its identification and management vary widely or do not exist; medical examiners and sleep medicine specialists are urged to use available guidance documents in their absence. Education, screening, prompt identification and treatment, and ongoing surveillance to ensure effective therapy can lower the risk of fatigue-related crashes.