Effect of CPAP Therapy on Kidney Function in Patients With Chronic Kidney Disease: A Pilot Randomized Controlled Trial.

BACKGROUND: OSA is common in chronic kidney disease (CKD) and may accelerate a decline in kidney function. It is not clear whether treatment of OSA with CPAP improves kidney function. RESEARCH QUESTION: Does treatment with CPAP improve kidney function in patients with CKD and coexisting OSA? STUDY DESIGN AND METHODS: A randomized, controlled, nonblinded, parallel clinical trial was performed of patients with stages 3 and 4 CKD and coexisting OSA comparing the effect of CPAP vs usual care on the estimated glomerular filtration rate (eGFR) and the urine albumin to creatinine ratio (ACR) over 12 months. RESULTS: Fifty-seven patients were enrolled and 30 were randomized to CPAP. They had moderately severe CKD (eGFR, 38.4 ± 1.5 mL/min/1.73 m2) and significant OSA and nocturnal hypoxemia (oxygen desaturation index: 23.9 events/h; interquartile range [IQR], 20.3 events/h; mean peripheral capillary oxygen saturation: 89.5%; IQR, 1.7%); 60% had baseline albuminuria (ACR, > 3 mg/mmol). No significant difference was found between CPAP and usual care in the change in eGFR and ACR over 12 months. Although some improvement in eGFR occurred with CPAP therapy in patients with a lower risk of CKD progression, this did not reach statistical significance. INTERPRETATION: Although CPAP did not provide additional renal benefits over usual care in all CKD patients, some evidence suggested that CPAP slowed the decline in eGFR in CKD patients with a lower risk of CKD progression. These preliminary data support the need for larger clinical trials exploring the effects of CPAP on kidney function. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT02420184; URL: www.clinicaltrials.gov.

Effects of Wait Times on Treatment Adherence and Clinical Outcomes in Patients With Severe Sleep-Disordered Breathing: A Secondary Analysis of a Noninferiority Randomized Clinical Trial.

Importance: Sleep-disordered breathing (SDB) is common and associated with substantial adverse health consequences. Long wait times for SDB care are commonly reported; however, it is unclear whether wait times for care are associated with clinical outcomes. Objective: To evaluate the association of wait times for care with clinical outcomes for patients with severe SDB. Design, Setting, and Participants: This study is a secondary analysis of a randomized clinical noninferiority trial comparing management by alternative care practitioners (ACPs) with traditional sleep physician-led care between October 2014 and May 2017. The study took place at Foothills Medical Centre Sleep Centre, a tertiary care multidisciplinary sleep clinic at the University of Calgary. Patients with severe SDB (defined as a respiratory event index ≥30 events per hour during home sleep apnea testing, mean nocturnal oxygen saturation ≤85%, or suspected sleep hypoventilation syndrome) were recruited for the study. Patients were excluded if they were suspected of having a concomitant sleep disorder other than SDB or had previously been treated with positive airway pressure (PAP) therapy for SDB. Data were analyzed from October 2017 to January 2020. Main Outcomes and Measures: Outcomes were assessed 3 months after treatment initiation with adherence to PAP therapy as the primary outcome. Secondary outcomes included Epworth Sleepiness Scale score, health-related quality of life, and patient satisfaction measured using the Visit-Specific Satisfaction Instrument-9. Multiple regression models were used to assess the associations between wait times and each of the outcomes. t tests were used to compare wait times for patients who were adherent to PAP therapy (≥4 hours per night for 70% of nights) with those for nonadherent patients. Results: One hundred fifty-six patients (112 [71.8%] men; mean [SD] age, 56 [12] years) were included in the analysis. The mean time from referral to initial visit was 88 days (95% CI, 79 to 96 days), and the mean time to treatment was 123 days (95% CI, 112 to 133 days). Shorter wait time to treatment initiation was associated with adherence to PAP therapy (odds ratio, 0.99; 95% CI, 0.98 to 0.99; P = .04), greater improvement in Epworth Sleepiness Scale score (mean coefficient, -9.37; 95% CI, -18.51 to -0.24; P = .04), and higher Visit-Specific Satisfaction Instrument-9 score (mean coefficient, -0.024; 95% CI, -0.047 to -0.0015; P = .04) at 3 months. Compared with nonadherent patients, those who were adherent to treatment waited a mean of 15 fewer days (95% CI, 12 to 19 days) for initial assessment (P = .07) and 30 fewer days (95% CI, 23 to 35 days) for treatment initiation (P = .008). Conclusions and Relevance: Earlier initiation of treatment for severe SDB was associated with better PAP adherence and greater improvements in daytime sleepiness and patient satisfaction. These findings suggest that system interventions to improve timely access may modify patient behavior and improve clinical outcomes. Trial Registration: ClinicalTrials.gov Identifier: NCT02191085.

Tobacco use and motivation to stop smoking among long-term smokers who are ineligible for lung cancer screening.

BACKGROUND: The importance of smoking cessation interventions in lung cancer screening participants has been highlighted. This study aimed to describe the smoking habits of individuals who were ineligible for lung cancer screening and to investigate whether this encounter may represent an opportunity to reduce tobacco use. METHODS: Ever smokers between the ages of 55 and 80 and ≥1.5% lung cancer risk over 6 years or having smoked ≥30 pack-years and with no more than 15 years of smoking abstinence were eligible to participate in the Alberta Lung Cancer Screening Program (ALCSP). A baseline questionnaire exploring tobacco use was administered to all interested individuals as part of the eligibility determination for the program. RESULTS: Among 504 individuals, 254 (50.4%) met the criteria for the ALCSP and 250 (49.6%) were non-eligible for screening. Non-eligible individuals were slightly younger (mean=60.2 vs. 63.1 years, p-value <0.001), and less likely to be current smokers (26.0% vs. 48.8%, p-value <0.001). Non-eligible smokers had a lower degree of addiction compared to eligible group, as measured by the Fagerström Test of Nicotine Dependence (Median=4.0 vs 6.0, p-value=0.001), but still in the "moderately dependent" range for this test. There were no significant differences in motivation to quit (98.5% vs. 97.6%, p-value=0.689), or motivation to receive help with their quit attempt (89.2% vs. 90.3%, p-value=0.813) between these two groups. Only 7.7% of non-eligible and 2.4% of eligible current smokers were currently in a smoking cessation program. CONCLUSION: A significant proportion of individuals applying to, but not qualifying for a lung cancer screening program are active smokers with significant nicotine dependence. Very few are currently participating in active smoking cessation programs but almost all are interested in quitting and in receiving help with quit attempts. Future studies need to investigate the most effective approaches for smoking cessation in this substantial group of older, long-term smokers, capitalizing on their motivation to receive cessation assistance.

Use of electronic data and existing screening tools to identify clinically significant obstructive sleep apnea.

OBJECTIVES: To assess the ability of electronic health data and existing screening tools to identify clinically significant obstructive sleep apnea (OSA), as defined by symptomatic or severe OSA. METHODS: The present retrospective cohort study of 1041 patients referred for sleep diagnostic testing was undertaken at a tertiary sleep centre in Calgary, Alberta. A diagnosis of clinically significant OSA or an alternative sleep diagnosis was assigned to each patient through blinded independent chart review by two sleep physicians. Predictive variables were identified from online questionnaire data, and diagnostic algorithms were developed. The performance of electronically derived algorithms for identifying patients with clinically significant OSA was determined. Diagnostic performance of these algorithms was compared with versions of the STOP-Bang questionnaire and adjusted neck circumference score (ANC) derived from electronic data. RESULTS: Electronic questionnaire data were highly sensitive (>95%) at identifying clinically significant OSA, but not specific. Sleep diagnostic testing-determined respiratory disturbance index was very specific (specificity ≥95%) for clinically relevant disease, but not sensitive (<35%). Derived algorithms had similar accuracy to the STOP-Bang or ANC, but required fewer questions and calculations. CONCLUSIONS: These data suggest that a two-step process using a small number of clinical variables (maximizing sensitivity) and objective diagnostic testing (maximizing specificity) is required to identify clinically significant OSA. When used in an online setting, simple algorithms can identify clinically relevant OSA with similar performance to existing decision rules such as the STOP-Bang or ANC.

Granulomatous Pneumocystis jiroveci pneumonia associated with immune reconstituted HIV.

Pneumocystis jiroveci pneumonia uncommonly presents with pulmonary nodules and granulomatous inflammation. An unusual case of granulomatous P jiroveci pneumonia in an HIV patient with a CD4(+) lymphocyte count of greater than 200 cells/mm(3), occurring in the context of immune reconstitution with highly active antiretroviral therapy, is described. The case highlights the importance of establishing this diagnosis to institute appropriate therapy.