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1.
JAMA Netw Open ; 4(11): e2134241, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1508587

ABSTRACT

Importance: The influence of sleep-disordered breathing (SDB) and sleep-related hypoxemia in SARS-CoV-2 viral infection and COVID-19 outcomes remains unknown. Controversy exists regarding whether to continue treatment for SDB with positive airway pressure given concern for aerosolization with limited data to inform professional society recommendations. Objective: To investigate the association of SDB (identified via polysomnogram) and sleep-related hypoxia with (1) SARS-CoV-2 positivity and (2) World Health Organization (WHO)-designated COVID-19 clinical outcomes while accounting for confounding including obesity, underlying cardiopulmonary disease, cancer, and smoking history. Design, Setting, and Participants: This case-control study was conducted within the Cleveland Clinic Health System (Ohio and Florida) and included all patients who were tested for COVID-19 between March 8 and November 30, 2020, and who had an available sleep study record. Sleep indices and SARS-CoV-2 positivity were assessed with overlap propensity score weighting, and COVID-19 clinical outcomes were assessed using the institutional registry. Exposures: Sleep study-identified SDB (defined by frequency of apneas and hypopneas using the Apnea-Hypopnea Index [AHI]) and sleep-related hypoxemia (percentage of total sleep time at <90% oxygen saturation [TST <90]). Main Outcomes and Measures: Outcomes were SARS-CoV-2 infection and WHO-designated COVID-19 clinical outcomes (hospitalization, use of supplemental oxygen, noninvasive ventilation, mechanical ventilation or extracorporeal membrane oxygenation, and death). Results: Of 350 710 individuals tested for SARS-CoV-2, 5402 (mean [SD] age, 56.4 [14.5] years; 3005 women [55.6%]) had a prior sleep study, of whom 1935 (35.8%) tested positive for SARS-CoV-2. Of the 5402 participants, 1696 were Black (31.4%), 3259 were White (60.3%), and 822 were of other race or ethnicity (15.2%). Patients who were positive vs negative for SARS-CoV-2 had a higher AHI score (median, 16.2 events/h [IQR, 6.1-39.5 events/h] vs 13.6 events/h [IQR, 5.5-33.6 events/h]; P < .001) and increased TST <90 (median, 1.8% sleep time [IQR, 0.10%-12.8% sleep time] vs 1.4% sleep time [IQR, 0.10%-10.8% sleep time]; P = .02). After overlap propensity score-weighted logistic regression, no SDB measures were associated with SARS-CoV-2 positivity. Median TST <90 was associated with the WHO-designated COVID-19 ordinal clinical outcome scale (adjusted odds ratio, 1.39; 95% CI, 1.10-1.74; P = .005). Time-to-event analyses showed sleep-related hypoxia associated with a 31% higher rate of hospitalization and mortality (adjusted hazard ratio, 1.31; 95% CI, 1.08-1.57; P = .005). Conclusions and Relevance: In this case-control study, SDB and sleep-related hypoxia were not associated with increased SARS-CoV-2 positivity; however, once patients were infected with SARS-CoV-2, sleep-related hypoxia was an associated risk factor for detrimental COVID-19 outcomes.


Subject(s)
COVID-19 , Cause of Death , Hospitalization , Severity of Illness Index , Sleep Apnea Syndromes/complications , Aged , COVID-19/complications , COVID-19/mortality , COVID-19/therapy , Case-Control Studies , Continuous Positive Airway Pressure , Delivery of Health Care, Integrated , Extracorporeal Membrane Oxygenation , Female , Florida , Hospital Mortality , Humans , Hypoxia , Logistic Models , Male , Middle Aged , Odds Ratio , Ohio , Respiration, Artificial , Risk Factors , SARS-CoV-2 , Sleep , Sleep Apnea Syndromes/pathology , Sleep Apnea Syndromes/therapy
3.
Thorax ; 76(7): 704-713, 2021 07.
Article in English | MEDLINE | ID: covidwho-1322844

ABSTRACT

BACKGROUND: Poor sleep may contribute to chronic kidney disease (CKD) through several pathways, including hypoxia-induced systemic and intraglomerular pressure, inflammation, oxidative stress and endothelial dysfunction. However, few studies have investigated the association between multiple objectively measured sleep dimensions and CKD. METHODS: We investigated the cross-sectional association between sleep dimensions and CKD among 1895 Multi-Ethnic Study of Atherosclerosis Sleep Ancillary Study participants who completed in-home polysomnography, wrist actigraphy and a sleep questionnaire. Using Poisson regression models with robust variance, we estimated separate prevalence ratios (PR) and 95% CIs for moderate-to-severe CKD (glomerular filtration rate <60 mL/min/1.73 m2 or albuminuria >30 mg/g) among participants according to multiple sleep dimensions, including very short (≤5 hours) sleep, Apnoea-Hypopnoea Index and sleep apnoea-specific hypoxic burden (SASHB) (total area under the respiratory event-related desaturation curve divided by total sleep duration, %min/hour)). Regression models were adjusted for sociodemographic characteristics, health behaviours and clinical characteristics. RESULTS: Of the 1895 participants, mean age was 68.2±9.1 years, 54% were women, 37% were white, 28% black, 24% Hispanic/Latino and 11% Asian. Several sleep metrics were associated with higher adjusted PR of moderate-to-severe CKD: very short versus recommended sleep duration (PR=1.40, 95% CI 1.06 to 1.83); SASHB (Box-Cox transformed SASHB: PR=1.06, 95% CI 1.02 to 1.12); and for participants in the highest quintile of SASHB plus sleep apnoea: PR=1.28, 95% CI 1.01 to 1.63. CONCLUSIONS: Sleep apnoea associated hypoxia and very short sleep, likely representing independent biological mechanisms, were associated with a higher moderate-to-severe CKD prevalence, which highlights the potential role for novel interventions.


Subject(s)
Atherosclerosis/complications , Hypoxia/etiology , Renal Insufficiency, Chronic/complications , Sleep Apnea Syndromes/complications , Sleep/physiology , Actigraphy , Aged , Aged, 80 and over , Atherosclerosis/ethnology , Cross-Sectional Studies , Female , Humans , Hypoxia/physiopathology , Male , Middle Aged , Polysomnography , Prevalence , Renal Insufficiency, Chronic/ethnology , Risk Factors , Self Report , Sleep Apnea Syndromes/ethnology , Sleep Apnea Syndromes/physiopathology , United States/epidemiology
7.
Chron Respir Dis ; 18: 1479973120986806, 2021.
Article in English | MEDLINE | ID: covidwho-1069523

ABSTRACT

We examined the relative contribution of pulmonary diseases (chronic obstructive pulmonary disease, asthma and sleep apnea) to mortality risks associated with Coronavirus Disease (COVID-19) independent of other medical conditions, health risks, and sociodemographic factors. Data were derived from a large US-based case series of patients with COVID-19, captured from a quaternary academic health network covering New York City and Long Island. From March 2 to May 24, 2020, 11,512 patients who were hospitalized were tested for COVID-19, with 4,446 (38.62%) receiving a positive diagnosis for COVID-19. Among those who tested positive, 959 (21.57%) died of COVID-19-related complications at the hospital. Multivariate-adjusted Cox proportional hazards modeling showed mortality risks were strongly associated with greater age (HR = 1.05; 95% CI: 1.04-1.05), ethnic minority (Asians, Non-Hispanic blacks, and Hispanics) (HR = 1.26; 95% CI, 1.10-1.44), low household income (HR = 1.29; 95% CI: 1.11, 1.49), and male sex (HR = 0.85; 95% CI: 0.74, 0.97). Higher mortality risks were also associated with a history of COPD (HR = 1.27; 95% CI: 1.02-1.58), obesity (HR = 1.19; 95% CI: 1.04-1.37), and peripheral artery disease (HR = 1.33; 95% CI: 1.05-1.69). Findings indicate patients with COPD had the highest odds of COVID-19 mortality compared with patients with pre-existing metabolic conditions, such as obesity, diabetes and hypertension. Sociodemographic factors including increased age, male sex, low household income, ethnic minority status were also independently associated with greater mortality risks.


Subject(s)
Asthma/complications , COVID-19/mortality , Hospital Mortality , Pulmonary Disease, Chronic Obstructive/complications , Sleep Apnea Syndromes/complications , Urban Health/statistics & numerical data , Adult , Aged , Aged, 80 and over , COVID-19/complications , COVID-19/diagnosis , Female , Humans , Male , Middle Aged , New York City/epidemiology , Proportional Hazards Models , Risk Factors , Socioeconomic Factors
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