Multimodal Remote Monitoring of High Cardiovascular Risk Patients With OSA Initiating CPAP

Pépin, Jean‐Louis; Jullian‐Desayes, Ingrid; Sapène, Marc; Treptow, Erika; Joyeux‐Faure, Marie; Benmerad, Mériem; Bailly, Sébastien; Grillet, Y.; Stach, B.; Richard, Patrick; Lévy, Patrick; Muir, Jean-François; Tamisier, Renaud

doi:10.1016/j.chest.2018.11.007

Cited by 60 publications

(63 citation statements)

References 37 publications

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“…The long-term rate of CPAP discontinuation is consistent across studies at between 20% and 40% (13). No clear improvement in mean CPAP adherence has been seen over the last 20 years, despite considerable technical advances, behavioral interventions, and multimodal telemonitoring systems (29,30). This leaves thousands of patients with symptomatic OSA with untreated daytime sleepiness.…”

Section: Discussionmentioning

confidence: 92%

Pitolisant for Daytime Sleepiness in Patients with Obstructive Sleep Apnea Who Refuse Continuous Positive Airway Pressure Treatment. A Randomized Trial

Dauvilliers

Verbraecken

Partinen

et al. 2020

Am J Respir Crit Care Med

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248

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Rationale: Excessive daytime sleepiness is a common disabling symptom in obstructive sleep apnea syndrome. Objectives: To evaluate the efficacy and safety of pitolisant, a selective histamine H3 receptor antagonist with wake-promoting effects, for the treatment of daytime sleepiness in patients with moderate to severe obstructive sleep apnea refusing continuous positive airway pressure treatment. Methods: In an international, multicenter, double-blind, randomized (3:1), placebo-controlled, parallel-design trial, pitolisant was individually titrated at up to 20 mg/d over 12 weeks. The primary endpoint was the change in the Epworth Sleepiness Scale score. Key secondary endpoints were maintenance of wakefulness assessed on the basis of the Oxford Sleep Resistance test, safety, Clinical Global Impression of severity, patient's global opinion, EuroQol quality-oflife questionnaire, and Pichot fatigue questionnaire. Measurements and Main Results: A total of 268 patients with obstructive sleep apnea (75% male; mean age, 52 yr; apnea-hypopnea index, 49/h; baseline sleepiness score, 15.7) were randomized (200 to pitolisant and 68 to placebo) and analyzed on an intention-totreat basis. The Epworth Sleepiness Scale score was reduced more with pitolisant than with placebo (22.8; 95% confidence interval, 24.0 to 21.5; P , 0.001). Wake maintenance tests were not improved. The Pichot fatigue score was reduced with pitolisant. The overall impact of pitolisant was confirmed by both physicians' and patients' questionnaires. Adverse event incidence, mainly headache, insomnia, nausea, and vertigo, was similar in the pitolisant and placebo groups (29.5% and 25.4%, respectively), with no cardiovascular or other significant safety concerns. Conclusions: Pitolisant significantly reduced self-reported daytime sleepiness and fatigue and improved patient-reported outcomes and physician disease severity assessment in sleepy patients with obstructive sleep apnea refusing or nonadherent to continuous positive airway pressure. Clinical trial registered with www.clinicaltrials.gov (NCT01072968) and EU Clinical Trials Register (EudraCT 2009-017251-94).

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Section: Discussionmentioning

confidence: 92%

Pitolisant for Daytime Sleepiness in Patients with Obstructive Sleep Apnea Who Refuse Continuous Positive Airway Pressure Treatment. A Randomized Trial

Dauvilliers

Verbraecken

Partinen

et al. 2020

Am J Respir Crit Care Med

Self Cite

248

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“…Device-based telemonitoring is rapidly emerging as an interesting tool for remote follow-up in heart failure patients with a pacemaker, ICD, or CRT device [ 3 ]. Also, in other branches of medicine (wireless follow-up of glycaemia in diabetic patients, follow-up of therapeutic adherence to continuous positive airway pressure therapy in obstructive sleep apnea [ 12 , 13 ]), remote patient follow-up allows for faster processing of specific patient data to control larger patient cohorts. These features rapidly evolved from “gadgets” to useful tools, especially when overall health care system capacity is stretched to its maximum, as is the case with this COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

A Steep Increase in the HeartLogic Index Predicts COVID-19 Disease in an Advanced Heart Failure Patient

Heggermont

Nguyen

Lau

et al. 2020

Case Reports in Cardiology

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We present a patient with severe nonischemic cardiomyopathy in whom the HeartLogic algorithm was activated on her Boston Scientific cardioverter defibrillator. She had an out-of-alert state for several months and had clinically “stable” heart failure with no hospitalizations in the last 6 months. A sudden and fast increase of the HeartLogic index preceded her presentation in the emergency ward by several days. The detailed readout of HeartLogic however had some atypical features for heart failure decompensation. The patient presented at the emergency department with an increased dyspnea and a dry cough. Clinical exam showed desaturation and was suggestive for an acute respiratory infection. Subsequent imaging with CT thorax and nasopharyngeal real-time polymerase chain reaction (RT-PCR) confirmed SARS-CoV-2 viral pneumonia (COVID-19). This case illustrates that a timely and detailed analysis of HeartLogic alerts could help in the early differentiation of disease in patients with severe heart failure.

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“…) . Information can also be obtained from the social media and the non‐intrusive collection of real‐life data concerning daily activities via smartphones and connected wearable devices, the so‐called ‘internet of things’.…”

Section: Availability Of Big Datamentioning

confidence: 99%

“…From the hospital's perspective, an important characteristic of sleep apnoea patient management is that it is predominantly ambulatory . Approaches including multimodal remote telemonitoring and telemedicine, smartphone engagement tools and internet‐based management platforms need to be supported by Big Data analysis and AI . Innovative Big Data analytics will allow us to better identify high‐risk populations and clinical scenarios that require immediate intervention .…”

Section: Opportunities Of Big Data In Understanding Sleep Breathing Dmentioning

confidence: 99%

Big Data in sleep apnoea: Opportunities and challenges

2019

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Sleep apnoea is now regarded as a highly prevalent systemic, multimorbid, chronic disease requiring a combination of long-term home-based treatments. Optimization of personalized treatment strategies requires accurate patient phenotyping. Data to describe the broad variety of phenotypes can come from electronic health records, health insurance claims, socio-economic administrative databases, environmental monitoring, social media, etc. Connected devices in and outside homes collect vast amount of data amassed in databases. All this contributes to 'Big Data' that, if used appropriately, has great potential for the benefit of health, well-being and therapeutics. Sleep apnoea is particularly well placed with regards to Big Data because the primary treatment is positive airway pressure (PAP). PAP devices, used every night over long periods by millions of patients across the world, generate an enormous amount of data. In this review, we discuss how different types of Big Data have, and could be, used to improve our understanding of sleep-disordered breathing, to identify undiagnosed sleep apnoea, to personalize treatment and to adapt health policies and better allocate resources. We discuss some of the challenges of Big Data including the need for appropriate data management, compilation and analysis techniques employing innovative statistical approaches alongside machine learning/artificial intelligence; closer collaboration between data scientists and physicians; and respect of the ethical and regulatory constraints of collecting and using Big Data. Lastly, we consider how Big Data can be used to overcome the limitations of randomized clinical trials and advance real-life evidence-based medicine for sleep apnoea.Big Data and sleep apnoea tory, Grenoble Alpes University) for the literature search and contribution to the pharmacovigilance and environmentalRespirology (2020) 25, 486-494

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Multimodal Remote Monitoring of High Cardiovascular Risk Patients With OSA Initiating CPAP

Cited by 60 publications

References 37 publications

Pitolisant for Daytime Sleepiness in Patients with Obstructive Sleep Apnea Who Refuse Continuous Positive Airway Pressure Treatment. A Randomized Trial

Pitolisant for Daytime Sleepiness in Patients with Obstructive Sleep Apnea Who Refuse Continuous Positive Airway Pressure Treatment. A Randomized Trial

A Steep Increase in the HeartLogic Index Predicts COVID-19 Disease in an Advanced Heart Failure Patient

Big Data in sleep apnoea: Opportunities and challenges

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