Study Objectives: Continuous positive airway pressure (CPAP) is the most effective treatment for obstructive sleep apnea (OSA). However, its effectiveness is limited by poor long-term compliance. Few recent studies have investigated the effectiveness of high-flow nasal cannula (HFNC) in treating OSA; however, its role remains uncertain. This study aimed to determine the effectiveness of HFNC, compared with CPAP, in the treatment of patients with OSA.
Methods: This prospective open-label randomized crossover trial was conducted on treatment-naïve, newly-diagnosed patients with OSA. Participants underwent a CPAP and a HFNC titration studies in one-of-two crossover sequences. The American Academy of Sleep Medicine guidelines for CPAP titration were followed for titration of both: CPAP and HFNC. The initial flow rate of HFNC was set at 10 L/min, and the flow rate was increased by 10 L/min, up to a maximum of 60 L/min, to eliminate all respiratory events.
Results: Sixty-eight participants completed the study. Compared to the diagnostic PSG, the apnea-hypopnea index (AHI) decreased by a median of 52% with HFNC therapy [18-77, p value < 0.001]. Clinically acceptable titration was observed in 48% of patients receiving HFNC therapy, whereas 53% experienced a ≥50% reduction in the AHI. The efficacy of HFNC decreased as OSA severity increased. However, CPAP therapy provided superior control of OSA, with a lower AHI (5.8 vs. 16.6, p values < 0.001). Sleep architecture significantly improved with CPAP; however, declined with HFNC.
Conclusions: HFNC serves as a viable option for patients intolerant to CPAP, although careful patient selection is essential.
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http://dx.doi.org/10.5664/jcsm.11640 | DOI Listing |
Intern Emerg Med
March 2025
Department of Chest Disease, Ankara University School of Medicine, Ankara, Turkey.
Int J Emerg Med
March 2025
Department of Emergency Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Thung Phaya Thai, Ratchathewi, Bangkok, 10400, Thailand.
Introduction: High-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) are widely utilized respiratory support modalities for patients presenting with suspected sepsis and respiratory distress.This study aims to compare the 48-hour intubation rates between HFNC and NIV therapies in patients with suspected sepsis and respiratory distress.
Methods: This retrospective cohort study collected data over a 2-year period (January 2022 to December 2023) from patients presenting to the ED of Ramathibodi Hospital with suspected sepsis who received respiratory support with either HFNC or NIV.
Health SA
February 2025
Department of Speech-Language Pathology and Audiology, Faculty of Humanities, University of Pretoria, Pretoria, South Africa.
Background: Introduction of oral feeding for young children receiving high-flow oxygen has recently gained interest. With limited literature, there are varied opinions regarding the safety of oral feeding in this population.
Aim: This study describes speech-language therapists' (SLTs) views on oral feeding for infants receiving high-flow oxygen.
World J Clin Pediatr
March 2025
Department of Pediatrics, Institute of Child Health and Hospital for Children, Madras Medical College, Chennai 600003, Tamil Nādu, India.
Background: In multisystem inflammatory syndrome in children (MIS-C) with coronavirus disease 2019, there was paucity of data from low-income and middle-income countries on cardiovascular involvement and its longitudinal outcomes. We planned to estimate the pattern of cardiovascular involvement among children with MIS-C and its mid-term outcomes.
Aim: To determine association between cardiovascular abnormalities and clinical and laboratory parameters.
Crit Care
March 2025
School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
Background: Early identification of patients with acute hypoxemic respiratory failure (AHRF) who are at risk of failing high-flow nasal cannula (HFNC) therapy could facilitate closer monitoring, and timely adjustment/escalation of treatment. We aimed to establish whether machine learning (ML) models could predict HFNC outcome, early in the course of treatment, with greater accuracy than currently used clinical indices.
Methods: We developed ML models trained using measurements made within the first 2 h of treatment from 184 AHRF patients (37% HFNC failures) treated at the respiratory ICU of the University Hospital of Modena between 2018 and 2023.
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