Introduction: Cystic fibrosis (CF) is an autosomal recessive genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, primarily affecting the respiratory and digestive systems. Respiratory rehabilitation techniques play a crucial role in managing pulmonary symptoms and maintaining lung function in CF patients. Although various techniques have been developed and applied, there is currently no globally recognised optimal respiratory rehabilitation regimen. This study intends to conduct a network meta-analysis to comprehensively evaluate and compare the effectiveness of different respiratory rehabilitation techniques in CF patients.
Methods And Analysis: The following key electronic bibliographic databases will be searched from inception to September 2024: Medline, Embase, Cochrane Library, Web of Science, CINAHL and Physiotherapy Evidence Database. We will include randomised controlled trials (RCTs) and quasi-RCTs that compare the efficacy of various respiratory rehabilitation techniques in CF patients, such as airway clearance techniques, exercise training and inspiratory muscle training. The primary outcomes will be lung function (forced expiratory volume in 1 s and forced vital capacity) and exercise capacity (VO2 max and 6 min walk test). Secondary outcomes will include quality of life, frequency of pulmonary exacerbations, hospitalisation rates and adverse events. If permitted, data will be synthesised using traditional pairwise meta-analysis and network meta-analysis, with the quality of evidence assessed using the Grading of Recommendations Assessment, Development and Evaluation approach.
Ethics And Dissemination: Ethical approval will not be required for this protocol. The results of the final review will be disseminated via peer-reviewed journals and conference presentations.
Prospero Registration Number: CRD42024574551.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667402 | PMC |
http://dx.doi.org/10.1136/bmjopen-2024-092747 | DOI Listing |
Respirology
January 2025
Department of Respiratory Care, College of Medical Applied Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Special Series: Leading Women in Respiratory Clinical Sciences.
View Article and Find Full Text PDFSci Rep
January 2025
Clinical Infection, Microbiology & Immunology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
It is established that patients hospitalised with COVID-19 often have ongoing morbidity affecting activity of daily living (ADL), employment, and mental health. However, little is known about the relative outcomes in patients with COVID-19 neurological or psychiatric complications. We conducted a UK multicentre case-control study of patients hospitalised with COVID-19 (controls) and those who developed COVID-19 associated acute neurological or psychiatric complications (cases).
View Article and Find Full Text PDFBMJ
January 2025
Division of Pulmonary and Critical Care Medicine, Department of Medicine; and Department of Physical Medicine and Rehabilitation. Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Approximately half of critically ill adults experience intensive care unit acquired weakness (ICUAW). Patients who develop ICUAW may have negative outcomes, including longer duration of mechanical ventilation, greater length of stay, and worse mobility, physical functioning, quality of life, and mortality. Early physical rehabilitation interventions have potential for improving ICUAW; however, randomized trials show inconsistent findings on the efficacy of these interventions.
View Article and Find Full Text PDFInt J Surg
January 2025
Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.
Background: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
January 2025
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.
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