Carl J. Stepnowsky

S and full research articles published in SLEEP, the official journal of the APSS. Sleep and sleep disorder scientific findings.

Improved data transmission technologies have facilitated data collected from positive airway pressure (PAP) devices in the home environment. Although clinicians’ treatment decisions increasingly rely on autoscoring of respiratory events by the PAP device, few studies have specifically examined the accuracy of autoscored respiratory events in the home environment in ongoing PAP use. “PAP efficacy” studies were conducted in which participants wore PAP simultaneously with an Embletta sleep system (Embla, Inc., Broomfield, CO), which was directly connected to the ResMed AutoSet S8 (ResMed, Inc., San Diego, CA) via a specialized cable. Mean PAP-scored Apnea-Hypopnea Index (AHI) was 14.2 ± 11.8 (median: 11.7; range: 3.9–46.3) and mean manual-scored AHI was 9.4 ± 10.2 (median: 7.7; range: 1.2–39.3). Ratios between the mean indices were calculated. PAP-scored HI was 2.0 times higher than the manual-scored HI. PAP-scored AHI was 1.5 times higher than the manual-scored AHI, and PAP-scored AI was 1.04 of manual-scored AI. In this sample, PAP-scored HI was on average double the manual-scored HI. Given the importance of PAP efficacy data in tracking treatment progress, it is important to recognize the possible bias of PAP algorithms in overreporting hypopneas. The most likely cause of this discrepancy is the use of desaturations in manual hypopnea scoring.

Obstructive sleep apnea (OSA) is a prevalent and serious medical condition characterized by repeated complete or partial obstructions of the upper airway during sleep and is prevalent in 2% to 4% of working middle-aged adults. Nasal continuous positive airway pressure (CPAP) is the gold-standard treatment for OSA. Because compliance rates with CPAP therapy are disappointingly low, effective interventions are needed to improve CPAP compliance among patients diagnosed with OSA. The aim was to determine whether wireless telemonitoring of CPAP compliance and efficacy data, compared to usual clinical care, results in higher CPAP compliance and improved OSA outcomes. 45 patients newly diagnosed with OSA were randomized to either telemonitored clinical care or usual clinical care and were followed for their first 2 months of treatment with CPAP therapy. CPAP therapists were not blinded to the participants' treatment group. 20 participants in each group received the designated intervention. Patients randomized to telemonitored clinical care used CPAP an average of 4.1 +/- 1.8 hours per night, while the usual clinical care patients averaged 2.8 +/- 2.2 hours per night (P = .07). Telemonitored patients used CPAP on 78% +/- 22% of the possible nights, while usual care patients used CPAP on 60% +/- 32% of the nights (P = .07). No statistically significant differences between the groups were found on measures of CPAP efficacy, including measures of mask leak and the Apnea-Hypopnea Index. Patients in the telemonitored group rated their likelihood to continue using CPAP significantly higher than the patients in the usual care group. Patients in both groups were highly satisfied with the care they received and rated themselves as "not concerned" that their CPAP data were being wirelessly monitored. Telemonitoring of CPAP compliance and efficacy data and rapid use of those data by the clinical sleep team to guide the collaborative (ie, patient and provider) management of CPAP treatment is as effective as usual care in improving compliance rates and outcomes in new CPAP users. This study was designed as a pilot-larger, well-powered studies are necessary to fully evaluate the clinical and economic efficacy of telemonitoring for this population.

OBJECTIVE: To examine the nightly variability of sleep‐disordered breathing (SDB) as measured by the apnea‐hypopnea index (AHI).STUDY DESIGN AND SETTING: Retrospective comparison of 3 sequential nights of testing performed in the home in 1091 patients who were referred for diagnostic testing of SDB.RESULTS: The Pearson and Intraclass correlation coefficients ranged between 0.88 and 0.90 for each pair of nights. Based on night 1, approximately 90% of patients were classified consistently with “AHI‐high” (the highest AHI measured across the 3 nights) using an AHI threshold of 5. However, 10% were misclassified on night 1 relative to the highest AHI level.CONCLUSION AND SIGNIFICANCE: These findings suggest that (1) 1 night of diagnostic testing for SDB is not sufficient to diagnosis SDB in approximately 1 of every 10 cases, and (2) there is little, if any, significant nightly change in SDB in the home environment. EBM rating: D. (Otolaryngol Head Neck Surg 2004;131:837–43.)

With the advent of ambulatory blood pressure monitoring has come the awareness that blood pressure (BP) normally drops, or “dips,” at night by roughly 10%. A number of pathological conditions have been associated with the nondipping of nocturnal BP. In general, researchers have looked at dipping in neurological and cardiovascular disorders. We examined the extent to which nocturnal nondipping might be influenced by relatively gross measures of social environment. This study examined 78 healthy adults and adults with mild hypertension who were not currently receiving medication, aged 25 to 52 years (mean age = 38.2). Forty-two participants self-identified as black and 36 identified as white. Age, body mass index, apnea–hypopnea index, screening BP, ethnicity, and socioeconomic status (SES) were significantly associated with nocturnal BP dipping, accounting for 41% of the variance in dipping (F[6,51] = 5.473, p < .001). When SES was entered on the last step of a hierarchical regression analysis, it independently accounted for 8% of the variance in dipping, even after accounting for ethnicity, such that the lower the SES, the more the nondipping. It remains to be seen what aspect of the social environment may be driving this association between nondipping and lower social class. However, investigators might consider including social class in their models in future studies.

Unreliability associated with scoring sleep variables is a potentially problematic issue in clinical and research studies. When scoring unreliability is unrecognized, it can contribute to the following: increase variability in the measures of interest, decrease a study's ability to detect important relationships, attenuate correlation coefficients, and increase clinical trial costs.