Suchergebnisse

Jaber S Alqahtani,1,2 Yousef S Aldabayan,3 Abdulelah M Aldhahir,4 Ahmad M Al Rajeh,3 Swapna Mandal,1,5,* John R Hurst1,5,* 1UCL Respiratory, University College London, London, UK; 2Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; 3Respiratory Care Department, College of Applied Medical Sciences, King Faisal University, Al-Hasa, Saudi Arabia; 4Respiratory Care Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia; 5Respiratory Medicine, Royal Free London NHS Foundation Trust, London, UK*These authors contributed equally to this workCorrespondence: Jaber S AlqahtaniUCL Respiratory, University College London, Rowland Hill Street, London, NW3 2PF, UKEmail Alqahtani-Jaber@hotmail.comBackground: Readmission following COPD exacerbation is a common and challenging clinical problem. New approaches to predicting readmissions are required to help mitigate risk and develop novel interventions.Methods: We conducted a prospective cohort study in 82 COPD patients admitted due to an exacerbation of COPD. Lung function measures [spirometry, forced oscillation technique (FOT) indices and peak inspiratory flow rate (PIFR)], inflammatory biomarkers and patient-reported outcomes including previous exacerbation history, breathlessness, quality of life and frailty were measured at admission and discharge. We prospectively followed patients for 30 and 90 days to identify predictors for readmission.Results: The readmission rate within 30 days was 38%, and 56% within 90 days. Previous exacerbations, higher COPD Assessment Test score at discharge, frailty, reduced PIFR and increased length of stay were significantly associated with 30-day readmission. PIFR at discharge and frailty had the highest predictive ability for 30-day readmission using area under receiver operating characteristic curves (AUC 0.86, 95% CI 0.78– 0.95, p < 0.001 and AUC 0.81, 95% CI 0.71– 0.90, p < 0.001, respectively). Ninety-day readmissions were significantly associated with previous exacerbations and hospitalisations, higher CAT score at discharge, frailty, depression, lower PIFR and greater expiratory flow limitation (EFL) in the supine position. The best predictive variable in multivariable analysis for both 30- and 90-day readmission was PIFR at discharge.Conclusion: PIFR, CAT score, frailty, and EFL were found to be associated with 30- and 90-day readmission following COPD exacerbation. These findings help identify those at highest risk and to optimise care prior to discharge.Keywords: COPD, exacerbation, admission, readmission, predictor, risk factor, PIFR, frailty, EFL

Abdulelah M Aldhahir,1 Jaber S Alqahtani,2 Malik A Althobiani,3,4 Saeed M Alghamdi,5,6 Abeer F Alanazi,7 Norah Alnaim,8 Abdullah A Alqarni,3 Hassan Alwafi9 1Respiratory Therapy Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia; 2Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; 3Department of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia; 4UCL Respiratory, University College London, London, WC1E 6BT, UK; 5Clinical Technology Department, Respiratory Care Program, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia; 6National Heart and Lung Institute, Imperial College London, London, UK; 7Department of Pharmaceutical and Biological Sciences, UCL School of Pharmacy, London, UK; 8Department of Computer Science, College of Science and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 9Faculty of Medicine, Umm Al-Qura University, Mecca, Saudi ArabiaCorrespondence: Abdulelah M Aldhahir, Respiratory Therapy Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia, Email Aldhahir.abdulelah@hotmail.comBackground: General population knowledge, satisfaction, and barriers to using Seha app have not been evaluated from a large-scale perspective. Therefore, this study aimed to explore current knowledge, satisfaction, and barriers of using Seha app and identify the most common mobile health application used among the general population in Saudi Arabia.Methods: A cross-sectional online survey, consisting of 25 questions, was distributed among the general population of Saudi Arabia. Descriptive statistics were used to describe the respondents' characteristics. Categorical variables were reported as frequencies and percentages. A chi-square (χ 2) test was conducted to assess the statistical difference between respondents' demographic characteristics and their knowledge and use of the app.Results: Overall, 5008 respondents, both Saudi (3723: 74%) and non-Saudi (1285: 26%) as well as male 2142 (43%) and female 2866 (57%), across the Kingdom of Saudi Arabia completed the online survey. A total of 2921 (58%) had heard of the Seha app, although only 1286 (25%) had used the app. Higher percentages of users were from the western region, females and those within the age group of ≥ 51 years old, 388 users (29%: P< 0.001), 804 (28%; P< 0.001) and 67 (35%; P=0.013), respectively. Consulting a doctor was the most frequently utilized service, 576 users (58%). Respondents strongly agreed 402 (41%) that Seha was easy to use, and 538 (54%) strongly agreed that they would recommend Seha to others. The most common barrier of using Seha was a lack of knowledge about the app and its benefits, at 1556 (35%). Overall, the Tawakkalna app was the most utilized mobile health application provided by MOH used 2170 (48%).Conclusion: Utilization of the Seha app is quite low due to a lack of knowledge about the app and its benefits. Thus, the MOH should promote public awareness about the app and its benefits.Keywords: E-health, telemedicine, Saudi Arabia, Saudi Ministry of Health, mobile app, user satisfaction, mHealth

Jaber S Alqahtani,1,2 Renata G Mendes,3 Abdulelah Aldhahir,1,4 Daniel Rowley,5 Mohammed D AlAhmari,2,6 George Ntoumenopoulos,7 Saeed M Alghamdi,8,9 Jithin K Sreedharan,2 Yousef S Aldabayan,10 Tope Oyelade,11 Ahmed Alrajeh,10 Carlo Olivieri,12 Maher AlQuaimi,13 Jerome Sullivan,14 Mohammed A Almeshari,15 Antonio Esquinas16 1UCL Respiratory, University College London, London, UK; 2Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; 3Department of Physical Therapy, Cardiopulmonary Physiotherapy Laboratory, Federal University of São Carlos, São Paulo, Brazil; 4Respiratory Care Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia; 5Pulmonary Diagnostics & Respiratory Therapy Services, University of Virginia Medical Center, Charlottesville, VA, USA; 6Dammam Health Network, Dammam, Saudi Arabia; 7Consultant Physiotherapist, Physiotherapy Department St Vincent's Hospital Sydney, Sydney, NSW, Australia; 8National Heart and Lung Institute, Imperial College London, London, UK; 9Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia; 10Respiratory Care, King Faisal University, Al-Ahsa, Saudi Arabia; 11UCL Institute for Liver and Digestive Health, London, UK; 12Emergency Department, Ospedale Sant'Andrea, Vercelli 13100, Italy; 13Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 14President, International Council for Respiratory Care, Professor Emeritus & Respiratory Care Program Director, The University of Toledo, Toledo, OH, USA; 15Rehabilitation Health Sciences Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia; 16Director International NIV School, Intensive Care Unit, Hospital Morales Meseguer, Murcia, SpainCorrespondence: Jaber S AlqahtaniUCL Respiratory, University College London, Rowland Hill Street, London NW3 2PF, UKEmail Alqahtani-Jaber@hotmail.comBackground: As the global outbreak of COVID-19 continues to ravage the world, it is important to understand how frontline clinicians manage ventilatory support and the various limiting factors.Methods: An online survey composed of 32 questions was developed and validated by an international expert panel.Results: Overall, 502 respondents from 40 countries across six continents completed the survey. The mean number (±SD) of ICU beds was 64 ± 84. The most popular initial diagnostic tools used for treatment initiation were arterial blood gas (48%) and clinical presentation (37.5%), while the national COVID-19 guidelines were the most used (61.2%). High flow nasal cannula (HFNC) (53.8%), non-invasive ventilation (NIV) (47%), and invasive mechanical ventilation (IMV) (92%) were mostly used for mild, moderate, and severe COVID-19 cases, respectively. However, only 38.8%, 56.6% and 82.9% of the respondents had standard protocols for HFNC, NIV, and IMV, respectively. The most frequently used modes of IMV and NIV were volume control (VC) (36.1%) and continuous positive airway pressure/pressure support (CPAP/PS) (40.6%). About 54% of the respondents did not adhere to the recommended, regular ventilator check interval. The majority of the respondents (85.7%) used proning with IMV, with 48.4% using it for 12– 16 hours, and 46.2% had tried awake proning in combination with HFNC or NIV. Increased staff workload (45.02%), lack of trained staff (44.22%) and shortage of personal protective equipment (PPE) (42.63%) were the main barriers to COVID-19 management.Conclusion: Our results show that general clinical practices involving ventilatory support were highly heterogeneous, with limited use of standard protocols and most frontline clinicians depending on isolated and varied management guidelines. We found increased staff workload, lack of trained staff and shortage of PPE to be the main limiting factors affecting global COVID-19 ventilatory support management.Keywords: COVID-19, ventilation, respiratory, clinical management, proning, mechanical ventilation, NIV, HFNC