Polysomnographic Assessment of Sleep Disturbances in Cancer Development A Historical Multicenter Clinical Cohort Study

BackgroundMany cellular processes are controlled by sleep. Therefore, alterations in sleep might be expected to stress biological systems that could influence malignancy risk.Research questionWhat is the association between polysomnographic measures of sleep disturbances and incident cancer, and what is the validity of cluster analysis in identifying polysomnography phenotypes?Study design and methodsWe conducted a retrospective multicenter cohort study using linked clinical and provincial health administrative data on consecutive adults free of cancer at baseline with polysomnography data collected between 1994 and 2017 in four academic hospitals in Ontario, Canada. Cancer status was derived from registry records. Polysomnography phenotypes were identified by k-means cluster analysis. A combination of validation statistics and distinguishing polysomnographic features was used to select clusters. Cox cause-specific regressions were used to assess the relationship between identified clusters and incident cancer.ResultsAmong 29,907 individuals, 2,514 (8.4%) received a diagnosis of cancer over a median of 8.0 years (interquartile range, 4.2-13.5 years). Five clusters were identified: mild (mildly abnormal polysomnography findings), poor sleep, severe OSA or sleep fragmentation, severe desaturations, and periodic limb movements of sleep (PLMS). The associations between cancer and all clusters compared with the mild cluster were significant while controlling for clinic and year of polysomnography. When additionally controlling for age and sex, the effect remained significant only for PLMS (adjusted hazard ratio [aHR], 1.26; 95% CI, 1.06-1.50) and severe desaturations (aHR, 1.32; 95% CI, 1.04-1.66). Further controlling for confounders, the effect remained significant for PLMS, but was attenuated for severe desaturations.InterpretationIn a large cohort, we confirmed the importance of polysomnographic phenotypes and highlighted the role that PLMS and oxygenation desaturation may play in cancer. Using this study's findings, we also developed an Excel (Microsoft) spreadsheet (polysomnography cluster classifier) that can be used to validate the identified clusters on new data or to identify which cluster a patient belongs to.Trial registryClinicalTrials.gov; Nos.: NCT03383354 and NCT03834792; URL: www.Clinicaltrialsgov

Trends in outpatient and inpatient visits for separate ambulatory-care-sensitive conditions during the first year of the COVID-19 pandemic: a province-based study

BackgroundThe COVID-19 pandemic led to global disruptions in non-urgent health services, affecting health outcomes of individuals with ambulatory-care-sensitive conditions (ACSCs).MethodsWe conducted a province-based study using Ontario health administrative data (Canada) to determine trends in outpatient visits and hospitalization rates (per 100,000 people) in the general adult population for seven ACSCs during the first pandemic year (March 2020–March 2021) compared to previous years (2016–2019), and how disruption in outpatient visits related to acute care use. ACSCs considered were chronic obstructive pulmonary disease (COPD), asthma, angina, congestive heart failure (CHF), hypertension, diabetes, and epilepsy. We used time series auto-regressive integrated moving-average models to compare observed versus projected rates.ResultsFollowing an initial reduction (March–May 2020) in all types of visits, primary care outpatient visits (combined in-person and virtual) returned to pre-pandemic levels for asthma, angina, hypertension, and diabetes, remained below pre-pandemic levels for COPD, and rose above pre-pandemic levels for CHF (104.8 vs. 96.4, 95% CI: 89.4–104.0) and epilepsy (29.6 vs. 24.7, 95% CI: 22.1–27.5) by the end of the first pandemic year. Specialty visits returned to pre-pandemic levels for COPD, angina, CHF, hypertension, and diabetes, but remained above pre-pandemic levels for asthma (95.4 vs. 79.5, 95% CI: 70.7–89.5) and epilepsy (53.3 vs. 45.6, 95% CI: 41.2–50.5), by the end of the year. Virtual visit rates increased for all ACSCs. Among ACSCs, reductions in hospitalizations were most pronounced for COPD and asthma. CHF-related hospitalizations also decreased, albeit to a lesser extent. For angina, hypertension, diabetes, and epilepsy, hospitalization rates reduced initially, but returned to pre-pandemic levels by the end of the year.ConclusionThis study demonstrated variation in outpatient visit trends for different ACSCs in the first pandemic year. No outpatient visit trends resulted in increased hospitalizations for any ACSC; however, reductions in rates of asthma, COPD, and CHF hospitalizations persisted

Prevalence of Sleep Disordered Breathing in Obese Hypoxemic Individuals, and Association of Adherence to Positive Airway Pressure Treatment with Long Term Oxygen Use and Rates of Health Care Utilization in a Single Centre in Alberta Canada

Obesity affects 25-30% of Canadians. Obese individuals are at increased risk of obstructive sleep apnea (OSA) and the obesity hypoventilation syndrome (OHS), which may cause or complicate chronic hypoxemia. This thesis reports the prevalence of OSA and OHS in a cohort with obesity and chronic hypoxemia referred for sleep testing. Obstructive sleep apnea and OHS were highly prevalent, affecting 80% and 51% respectively. The obesity hypoventilation syndrome was more common than chronic obstructive pulmonary disease in this cohort. Adherence to treatment of OSA and OHS with positive airway pressure therapy was associated with an increase in mean arterial oxygen levels as well as a reduction in the number of individuals requiring oxygen. Adherence with PAP therapy was also associated with a reduction in the rate of hospitalizations though the frequency of outpatient visits rose. These findings support current provincial policies for testing for OSA and OHS in obese hypoxemic individuals

Use of a Level 3 Portable Monitor for The Diagnosis and Management of Sleep-Disordered Breathing in an Inpatient Tertiary Care Setting

BACKGROUND: Sleep-disordered breathing (SDB) may impact the course of medical illness among hospitalized patients. Access to testing during hospitalization to assess this may be limited by wait times for laboratory polysomnography. Level 3 portable monitoring (PM) may provide an alternative

Use of a Level 3 Portable Monitor for The Diagnosis and Management of Sleep-Disordered Breathing in an Inpatient Tertiary Care Setting

BACKGROUND: Sleep-disordered breathing (SDB) may impact the course of medical illness among hospitalized patients. Access to testing during hospitalization to assess this may be limited by wait times for laboratory polysomnography. Level 3 portable monitoring (PM) may provide an alternative

Treatment of Sleep Disordered Breathing Liberates Obese Hypoxemic Patients from Oxygen.

Obese hypoxemic patients have a high prevalence of sleep disordered breathing (SDB). It is unclear to what extent treatment of SDB can improve daytime hypoxemia.We performed a retrospective cohort study of obese hypoxemic individuals, all of whom underwent polysomnography, arterial blood gas analysis, and subsequent initiation of positive airway pressure (PAP) therapy for SDB. Patients were followed for one year for change in partial pressure of arterial oxygen and the need for supplemental oxygen.One hundred and seventeen patients were treated with nocturnal PAP and had follow-up available. Adherence to PAP was satisfactory in 60%, and was associated with a significant improvement in daytime hypoxemia and hypercapnea; 56% of these patients were able to discontinue supplemental oxygen. Adherence to PAP therapy and the baseline severity of OSA predicted improvement in hypoxemia, but only adherence to PAP therapy predicted liberation from supplemental oxygen.The identification and treatment of SDB in obese hypoxemic patients improves daytime hypoxemia. It is important to identify SDB in these patients, since supplemental oxygen can frequently be discontinued following treatment with PAP therapy

Predictors of improvement in PaO₂.

<p>*Statistically significant</p><p>Abbreviations: A-a gradient alveolar arterial gradient, AHI apnea hypopnea index, BMI body mass index, FEV1 forced expiratory volume in 1 second, FVC forced vital capacity, 95%CI 95% confidence interval.</p><p>Predictors of improvement in PaO₂.</p

Improvement in need for supplemental oxygen therapy in patients treated with PAP therapy.

<p>CPAP continuous positive airway pressure, NIV non-invasive ventilation.</p

Baseline characteristics of all patients with follow-up (N = 138).

<p>*Mean (standard deviation)</p><p>^$ Proportion (95% confidence interval)</p><p>Abbreviations: A-a gradient alveolar to arterial gradient, AHI apnea hypopnea index, BMI body mass index, COPD chronic obstructive pulmonary disease, FEV1 forced expiratory volume in 1 second, FVC forced vital capacity, OSA obstructive sleep apnea, OHS the obesity hypoventilation syndrome, PaO₂ arterial partial pressure of oxygen, PaCO₂ arterial partial pressure of carbon dioxide, mmHg millimeter of mercury, 95%CI 95% confidence interval</p><p>Baseline characteristics of all patients with follow-up (N = 138).</p