Residual Events during Use of CPAP: Prevalence, Predictors, and Detection Accuracy

Reiter, Joel; Zleik, Bashar; Bazalakova, Mihaela H.; Mehta, Pankaj; Thomas, Robert J.

doi:10.5664/jcsm.6056

Cited by 29 publications

(14 citation statements)

References 12 publications

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“…Multi-modal therapy, such as low dose acetazolamide plus CPAP [ 168 , 169 ], may reasonably be considered in future clinical trials. The quality of data, specifically direct visualization of respiratory waveforms from current generation CPAP devices, can also detect residual disease burden and help identify those who could benefit from multi-modal therapy [ 170 ]. The question should not be whether CPAP is beneficial, but whether effective sleep therapy is beneficial.…”

Section: Obstructive Sleep Apneamentioning

confidence: 99%

“…The overall findings suggest what really matters is the therapeutic effectiveness, which is determined by CPAP adherence, CPAP efficacy, apnea burden [ 148 - 150 ], and possibly disease phenotype [ 165 - 167 ]. Apnea burden is driven by residual or emergent sleep apnea during CPAP-on and-off periods along with the duration of CPAP-off periods [ 150 - 152 , 170 ]. In future trials for primary and secondary prevention in OSA, we need to adopt methods to determine the therapeutic effectiveness by measuring apnea burden, and to test whether effective therapy (defined by minimal or sufficiently low apnea burden, e.g., measured average AHI <5 over the entire treatment period including both CPAP-on and -off state in sleep) reduces the risk of stroke and other cardiovascular events.…”

Section: Obstructive Sleep Apneamentioning

confidence: 99%

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Sleep Disturbances as a Risk Factor for Stroke

et al. 2018

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Sleep, a vital process of human being, is carefully orchestrated by the brain and consists of cyclic transitions between rapid eye movement (REM) and non-REM (NREM) sleep. Autonomic tranquility during NREM sleep is characterized by vagal dominance and stable breathing, providing an opportunity for the cardiovascular-neural axis to restore homeostasis, in response to use, distress or fatigue inflicted during wakefulness. Abrupt irregular swings in sympathovagal balance during REM sleep act as phasic loads on the resting cardiovascular system. Any causes of sleep curtailment or fragmentation such as sleep restriction, sleep apnea, insomnia, periodic limb movements during sleep, and shift work, not only impair cardiovascular restoration but also impose a stress on the cardiovascular system. Sleep disturbances have been reported to play a role in the development of stroke and other cardiovascular disorders. This review aims to provide updated information on the role of abnormal sleep in the development of stroke, to discuss the implications of recent research findings, and to help both stroke clinicians and researchers understand the importance of identification and management of sleep pathology for stroke prevention and care.

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Section: Obstructive Sleep Apneamentioning

confidence: 99%

Section: Obstructive Sleep Apneamentioning

confidence: 99%

Sleep Disturbances as a Risk Factor for Stroke

et al. 2018

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“…As underlined by the 2013 ATS statement, AHI flow is not a true surrogate of AHI measured by polysomnography (AHI PSG ). Indeed, previous studies have reported that AHI flow was not always correlated or concordant with AHI PSG and major differences exist between manufacturer definitions of the residual events [4, 5]. In this regard, different ROC-determined AHI flow thresholds were found for different machines (as can be expected, considering that device manufacturers all use different event definitions [3]).…”

Section: Discussionmentioning

confidence: 88%

Is the 2013 American Thoracic Society CPAP-tracking system algorithm useful for managing non-adherence in long-term CPAP-treated patients?

et al. 2019

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Background Whereas telemedicine usage is growing, the only clinical algorithm for Continuous Positive Airway Pressure (CPAP) adherence management is that stipulated by the 2013 American Thoracic Society (ATS). The capacity of the latter to predict non-adherence in long-term CPAP-treated patients has not been validated. Methods Patients from the prospective real-life InterfaceVent study (NCT03013283, study conducted in an adult cohort undergoing at least 3 months of CPAP) and eligible for ATS algorithm usage were analysed. The residual device Apnea–Hypopnea-Index (AHIflow) and High Large Leak (HLL) thresholds proposed in the ATS algorithm were evaluated for predicting adherence (i.e. AHIflow > 10/h, HLLs 95th > 24 L/min for ResMed® devices and ResMed® nasal mask, HLLs 95th > 36 l/min for ResMed® devices and ResMed® oronasal masks, HLLs > 1 h for Philips® devices and HHLs > 60 l/min for Fisher & Paykel® devices). Adherence was defined according to the 2013 ATS algorithm (i.e. CPAP use > 4 h/j for at least 70% of days). Results 650/1484 patients eligible for ATS algorithm usage were analysed (15.38% non-adherent, 74% male with a median (IQ25–75) age of 68 (61–77) years, a body mass index of 30.8 (27.7–34.5) kg/m2, an initial AHI of 39 (31–55) events/h, and CPAP-treatment-duration of 5.1 (2.2–7.8) years). Logistic regression analysis demonstrated no significant relationship between the ATS proposed AHIflow or HLL thresholds and non-adherence. Complementary ROC curve analysis failed to determine satisfactory AHIflow and HLL thresholds. Conclusion When managing non-adherence in long-term CPAP-treated patients, our data do not validate absolute AHIflow or HLL thresholds in general. Trial registration The INTERFACE-VENT study is registered on ClinicalTrials.gov (Identifier: study (NCT03013283).

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“…real versus device-provided measures). For CPAP, it was underlined that AHI flow was not always correlated or concordant with PG/PSG measures, especially when a 3% versus a 4% threshold of oxygen desaturation is used (results were worse when a PSG was used because of the additive impact of arousals (which cannot be diagnosed by the device) on the scoring) [18, 25–27]. Equivalent, exhaustive data are lacking for ASV therapy, whereas preliminary [28] or final data [8, 29, 30] are in favour of a similar discrepancy between AHI flow and AHI PSG .…”

Section: Discussionmentioning

confidence: 99%

What is the remaining status of adaptive servo-ventilation? The results of a real-life multicenter study (OTRLASV-study)

et al. 2019

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BackgroundsAs a consequence of the increased mortality observed in the SERVE-HF study, many questions concerning the safety and rational use of ASV in other indications emerged. The aim of this study was to describe the clinical characteristics of ASV-treated patients in real-life conditions.MethodsThe OTRLASV-study is a prospective, 5-centre study including patients who underwent ASV-treatment for at least 1 year. Patients were consecutively included in the study during the annual visit imposed for ASV-reimbursement renewal.Results177/214 patients were analysed (87.57% male) with a median (IQ25–75) age of 71 (65–77) years, an ASV-treatment duration of 2.88 (1.76–4.96) years, an ASV-usage of 6.52 (5.13–7.65) hours/day, and 54.8% were previously treated via continuous positive airway pressure (CPAP). The median Epworth Scale Score decreased from 10 (6–13.5) to 6 (3–9) (p < 0.001) with ASV-therapy, the apnea-hypopnea-index decreased from 50 (38–62)/h to a residual device index of 1.9 (0.7–3.8)/h (p < 0.001). The majority of patients were classified in a Central-Sleep-Apnea group (CSA; 59.3%), whereas the remaining are divided into an Obstructive-Sleep-Apnea group (OSA; 20.3%) and a Treatment-Emergent-Central-Sleep-Apnea group (TECSA; 20.3%). The Left Ventricular Ejection Fraction (LVEF) was > 45% in 92.7% of patients. Associated comorbidities/etiologies were cardiac in nature for 75.7% of patients (neurological for 12.4%, renal for 4.5%, opioid-treatment for 3.4%). 9.6% had idiopathic central-sleep-apnea. 6.2% of the patients were hospitalized the year preceding the study for cardiological reasons. In the 6 months preceding inclusion, night monitoring (i.e. polygraphy or oximetry during ASV usage) was performed in 34.4% of patients, 25.9% of whom required a subsequent setting change. According to multivariable, logistic regression, the variables that were independently associated with poor adherence (ASV-usage ≤4 h in duration) were TECSA group versus CSA group (p = 0.010), a higher Epworth score (p = 0.019) and lack of a night monitoring in the last 6 months (p < 0.05).ConclusionsIn real-life conditions, ASV-treatment is often associated with high cardiac comorbidities and high compliance. Future research should assess how regular night monitoring may optimize devices settings and patient management.Trial registrationThe OTRLASV study is registered on ClinicalTrials.gov (Identifier: NCT02429986) on 1 April 2015.

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Residual Events during Use of CPAP: Prevalence, Predictors, and Detection Accuracy

Cited by 29 publications

References 12 publications

Sleep Disturbances as a Risk Factor for Stroke

Sleep Disturbances as a Risk Factor for Stroke

Is the 2013 American Thoracic Society CPAP-tracking system algorithm useful for managing non-adherence in long-term CPAP-treated patients?

What is the remaining status of adaptive servo-ventilation? The results of a real-life multicenter study (OTRLASV-study)

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