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2.
Clin Respir J ; 14(3): 214-221, 2020 Mar.
Article in English | MEDLINE | ID: covidwho-1455532

ABSTRACT

BACKGROUND: Patients with neuromuscular disorders (NMDs) are likely to develop respiratory failure which requires noninvasive ventilation (NIV). Ventilation via a mouthpiece (MPV) is an option to offer daytime NIV. OBJECTIVES: To determine the preferred equipment for MPV by patients with NMDs. METHODS: Two MPV equipment sets were compared in 20 patients with NMDs. Set 1, consisted of a non-dedicated ventilator for MPV (PB560, Covidien) with a plastic angled mouthpiece. Set 2, consisted of a dedicated MPV ventilator (Trilogy 100, Philips Respironics) without backup rate and kiss trigger combined with a silicone straw mouthpiece. The Borg dyspnea score, ventilator free time, transcutaneous oxygen saturation (SpO2) and carbon dioxide tension (TcCO2 ) were recorded with and without MPV. Patient perception was assessed by a 17-items list. RESULTS: Carbon dioxide tension measurements and total perception score were not different between the two MPV sets. Dyspnea score was lower with the non-dedicated versus dedicated equipment, 1 (0.5) versus 3 (1-6), P < 0.01. All patients with a ventilator free time lower than 6 hours preferred a set backup rate rather than a kiss trigger. Sixty five percent of patients preferred the commercial arm support and 55% preferred the plastic angled mouthpiece. CONCLUSIONS: Dedicated and non-dedicated MPV equipment are deemed effective and comfortable. Individualization of arm support and mouthpiece is advised to ensure success of MPV. A ventilator free time lower than 6 hours seems to be a useful indicator to a priori set a backup rate rather than a rate at zero associated to the kiss trigger.


Subject(s)
Neuromuscular Diseases/complications , Noninvasive Ventilation/instrumentation , Respiratory Insufficiency/therapy , Ventilators, Mechanical/statistics & numerical data , Adolescent , Adult , Blood Gas Monitoring, Transcutaneous/methods , Carbon Dioxide/metabolism , Case-Control Studies , Cross-Over Studies , Dyspnea/diagnosis , Equipment Design , Female , Humans , Male , Perception , Time Factors , Ventilators, Mechanical/trends , Young Adult
4.
Occup Environ Med ; 78(9): 679-690, 2021 09.
Article in English | MEDLINE | ID: covidwho-1362002

ABSTRACT

OBJECTIVES: To synthesise evidence concerning the range of filtering respirators suitable for patient care and guide the selection and use of different respirator types. DESIGN: Comparative analysis of international standards for respirators and rapid review of their performance and impact in healthcare. DATA SOURCES: Websites of international standards organisations, Medline and Embase, hand-searching of references and citations. STUDY SELECTION: Studies of healthcare workers (including students) using disposable or reusable respirators with a range of designs. We examined respirator performance, clinician adherence and performance, comfort and impact, and perceptions of use. RESULTS: We included standards from eight authorities across Europe, North and South America, Asia and Australasia and 39 research studies. There were four main findings. First, international standards for respirators apply across workplace settings and are broadly comparable across jurisdictions. Second, effective and safe respirator use depends on proper fitting and fit testing. Third, all respirator types carry a burden to the user of discomfort and interference with communication which may limit their safe use over long periods; studies suggest that they have little impact on specific clinical skills in the short term but there is limited evidence on the impact of prolonged wearing. Finally, some clinical activities, particularly chest compressions, reduce the performance of filtering facepiece respirators. CONCLUSION: A wide range of respirator types and models is available for use in patient care during respiratory pandemics. Careful consideration of performance and impact of respirators is needed to maximise protection of healthcare workers and minimise disruption to care.


Subject(s)
COVID-19/epidemiology , Disposable Equipment/statistics & numerical data , Equipment Reuse/statistics & numerical data , Ventilators, Mechanical/statistics & numerical data , Disposable Equipment/standards , Equipment Reuse/standards , Health Personnel/statistics & numerical data , Humans , Pandemics/statistics & numerical data , Ventilators, Mechanical/standards
5.
PLoS One ; 16(4): e0249285, 2021.
Article in English | MEDLINE | ID: covidwho-1167111

ABSTRACT

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. OBJECTIVES: To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. METHODS: Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients' data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. RESULTS: Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. CONCLUSION: Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.


Subject(s)
COVID-19/mortality , Machine Learning , Pandemics/statistics & numerical data , Respiration, Artificial/statistics & numerical data , Ventilators, Mechanical/statistics & numerical data , Aged , Emergency Service, Hospital/trends , Female , Hospital Mortality/trends , Humans , Male , Middle Aged , Retrospective Studies
6.
PLoS One ; 16(2): e0246720, 2021.
Article in English | MEDLINE | ID: covidwho-1088757

ABSTRACT

Filtering facepiece respirators (FFRs) and medical masks are widely used to reduce the inhalation exposure of airborne particulates and biohazardous aerosols. Their protective capacity largely depends on the fraction of these that are filtered from the incoming air volume. While the performance and physics of different filter materials have been the topic of intensive study, less well understood are the effects of mask sealing. To address this, we introduce an approach to calculate the influence of face-seal leakage on filtration ratio and fit factor based on an analytical model and a finite element method (FEM) model, both of which take into account time-dependent human respiration velocities. Using these, we calculate the filtration ratio and fit factor for a range of ventilation resistance values relevant to filter materials, 500-2500 Pa∙s∙m-1, where the filtration ratio and fit factor are calculated as a function of the mask gap dimensions, with good agreement between analytical and numerical models. The results show that the filtration ratio and fit factor are decrease markedly with even small increases in gap area. We also calculate particle filtration rates for N95 FFRs with various ventilation resistances and two commercial FFRs exemplars. Taken together, this work underscores the critical importance of forming a tight seal around the face as a factor in mask performance, where our straightforward analytical model can be readily applied to obtain estimates of mask performance.


Subject(s)
Filtration/methods , Respiratory Protective Devices/statistics & numerical data , Aerosols/analysis , Air Filters , Equipment Design , Finite Element Analysis , Humans , Inhalation Exposure/analysis , Masks/statistics & numerical data , Masks/trends , Materials Testing/methods , Models, Theoretical , N95 Respirators/statistics & numerical data , Particle Size , Respiration , Respiratory Protective Devices/standards , Ventilators, Mechanical/statistics & numerical data , Ventilators, Mechanical/trends
7.
J Diabetes Sci Technol ; 15(5): 1005-1009, 2021 09.
Article in English | MEDLINE | ID: covidwho-1085175

ABSTRACT

The COVID-19 pandemic raised distinct challenges in the field of scarce resource allocation, a long-standing area of inquiry in the field of bioethics. Policymakers and states developed crisis guidelines for ventilator triage that incorporated such factors as immediate prognosis, long-term life expectancy, and current stage of life. Often these depend upon existing risk factors for severe illness, including diabetes. However, these algorithms generally failed to account for the underlying structural biases, including systematic racism and economic disparity, that rendered some patients more vulnerable to these conditions. This paper discusses this unique ethical challenge in resource allocation through the lens of care for patients with severe COVID-19 and diabetes.


Subject(s)
COVID-19/therapy , Diabetes Complications/therapy , Diabetes Mellitus/therapy , Resource Allocation , COVID-19/complications , COVID-19/epidemiology , Diabetes Complications/economics , Diabetes Complications/epidemiology , Diabetes Mellitus/economics , Diabetes Mellitus/epidemiology , Health Services Accessibility/economics , Health Services Accessibility/ethics , Health Services Accessibility/standards , Health Services Accessibility/statistics & numerical data , Health Status Disparities , Healthcare Disparities/economics , Healthcare Disparities/ethics , Healthcare Disparities/organization & administration , Healthcare Disparities/statistics & numerical data , Humans , Pandemics , Racism/ethics , Racism/statistics & numerical data , Resource Allocation/economics , Resource Allocation/ethics , Resource Allocation/organization & administration , Resource Allocation/statistics & numerical data , Triage/economics , Triage/ethics , United States/epidemiology , Ventilators, Mechanical/economics , Ventilators, Mechanical/statistics & numerical data , Ventilators, Mechanical/supply & distribution
8.
AANA J ; 89(1): 62-69, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1049415

ABSTRACT

The coronavirus disease 2019 (COVID-19) respiratory illness has increased the amount of people needing airway rescue and the support of mechanical ventilators. In doing so, the pandemic has increased the demand of healthcare professionals to manage these critically ill individuals. Certified Registered Nurse Anesthetists (CRNAs), who are trained experts in airway management and mechanical ventilation with experience in intensive care units (ICUs), rise to this challenge. However, many CRNAs may be unfamiliar with advancements in critical care ventilators. The purpose of this review is to provide a resource for CRNAs returning to the ICU to manage patients requiring invasive mechanical ventilation. The most common ventilator modes found in anesthesia machine ventilators and ICU ventilators are reviewed, as are the lung-protective ventilation strategies, including positive end-expiratory pressure, used to manage patients with COVID-19-induced acute respiratory distress syndrome. Adjuncts to mechanical ventilation, recruitment maneuvers, prone positioning, and extracorporeal membrane oxygenation are also reviewed. More research is needed concerning the management of COVID-19-infected patients, and CRNAs must become familiar with their ICU units' individual ventilator machine, but this brief review provides a good place to start for those returning to the ICU.


Subject(s)
Anesthesia/statistics & numerical data , Anesthesia/standards , COVID-19/therapy , Critical Care/standards , Respiration, Artificial/standards , Respiratory Distress Syndrome/therapy , Ventilators, Mechanical/standards , Critical Care/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Pandemics , Practice Guidelines as Topic , Respiration, Artificial/statistics & numerical data , SARS-CoV-2 , Ventilators, Mechanical/statistics & numerical data
9.
Chest ; 159(2): 634-652, 2021 02.
Article in English | MEDLINE | ID: covidwho-973941

ABSTRACT

BACKGROUND: Early in the coronavirus disease 2019 (COVID-19) pandemic, there was serious concern that the United States would encounter a shortfall of mechanical ventilators. In response, the US government, using the Defense Production Act, ordered the development of 200,000 ventilators from 11 different manufacturers. These ventilators have different capabilities, and whether all are able to support COVID-19 patients is not evident. RESEARCH QUESTION: Evaluate ventilator requirements for affected COVID-19 patients, assess the clinical performance of current US Strategic National Stockpile (SNS) ventilators employed during the pandemic, and finally, compare ordered ventilators' functionality based on COVID-19 patient needs. STUDY DESIGN AND METHODS: Current published literature, publicly available documents, and lay press articles were reviewed by a diverse team of disaster experts. Data were assembled into tabular format, which formed the basis for analysis and future recommendations. RESULTS: COVID-19 patients often develop severe hypoxemic acute respiratory failure and adult respiratory defense syndrome (ARDS), requiring high levels of ventilator support. Current SNS ventilators were unable to fully support all COVID-19 patients, and only approximately half of newly ordered ventilators have the capacity to support the most severely affected patients; ventilators with less capacity for providing high-level support are still of significant value in caring for many patients. INTERPRETATION: Current SNS ventilators and those on order are capable of supporting most but not all COVID-19 patients. Technologic, logistic, and educational challenges encountered from current SNS ventilators are summarized, with potential next-generation SNS ventilator updates offered.


Subject(s)
COVID-19/therapy , Respiration, Artificial/methods , Respiratory Distress Syndrome/therapy , Respiratory Insufficiency/therapy , Strategic Stockpile , Ventilators, Mechanical/statistics & numerical data , Humans , Intensive Care Units , Respiration, Artificial/instrumentation , SARS-CoV-2 , United States , Ventilators, Mechanical/standards , Ventilators, Mechanical/supply & distribution
10.
Trials ; 21(1): 883, 2020 Oct 26.
Article in English | MEDLINE | ID: covidwho-892368

ABSTRACT

OBJECTIVES: General: To assess the safety, efficacy and dose response of convalescent plasma (CP) transfusion in severe COVID-19 patients Specific: a. To identify the appropriate effective dose of CP therapy in severe patients b. To identify the efficacy of the therapy with their end point based on clinical improvement within seven days of treatment or until discharge whichever is later and in-hospital mortality c. To assess the clinical improvement after CP transfusion in severe COVID-19 patients d. To assess the laboratory improvement after CP transfusion in severe COVID-19 patients TRIAL DESIGN: This is a multicentre, multi-arm phase II Randomised Controlled Trial. PARTICIPANTS: Age and sex matched COVID-19 positive (by RT-PCR) severe cases will be enrolled in this trial. Severe case is defined by the World Health Organization (W.H.O) clinical case definition. The inclusion criteria are 1. Respiratory rate > 30 breaths/min; PLUS 2. Severe respiratory distress; or SpO2 ≤ 88% on room air or PaO2/FiO2≤ 300 mm of Hg, PLUS 3. Radiological (X-ray or CT scan) evidence of bilateral lung infiltrate, AND OR 4. Systolic BP < 90 mm of Hg or diastolic BP <60 mm of Hg. AND/OR 5. Criteria 1 to 4 AND or patient in ventilator support Patients' below18 years, pregnant and lactating women, previous history of allergic reaction to plasma, patients who have already received plasma from a different source will be excluded. Patients will be enrolled at Bangabandhu Sheikh Mujib Medical University (BSMMU) hospital, Dhaka medical college hospital (DMCH) and Mugda medical college hospital (MuMCH). Apheretic plasma will be collected at the transfusion medicine department of SHNIBPS hospital, ELISA antibody titre will be done at BSMMU and CMBT and neutralizing antibody titre will be checked in collaboration with the University of Oxford. Patients who have recovered from COVID-19 will be recruited as donors of CP. The recovery criteria are normality of body temperature for more than 3 days, resolution of respiratory symptoms, two consecutively negative results of sputum SARS-CoV-2 by RT-PCR assay (at least 24 hours apart) 22 to 35 days of post onset period, and neutralizing antibody titre ≥ 1:160. INTERVENTION AND COMPARATOR: This RCT consists of three arms, a. standard care, b. standard care and 200 ml CP and c. standard care and 400 ml CP. Patients will receive plasma as a single transfusion. Intervention arms will be compared to the standard care arm. MAIN OUTCOMES: The primary outcome will be time to clinical improvement within seven days of treatment or until discharge whichever is later and in-hospital mortality. The secondary outcome would be improvement of laboratory parameters after therapy (neutrophil, lymphocyte ratio, CRP, serum ferritin, SGPT, SGOT, serum creatinine and radiology), length of hospital stay, length of ICU stay, reduction in proportion of deaths, requirement of ventilator and duration of oxygen and ventilator support. RANDOMISATION: Randomization will be done by someone not associated with the care or assessment of the patients by means of a computer generated random number table using an allocation ratio of 1:1:1. BLINDING (MASKING): This is an open level study; neither the physician nor the patients will be blinded. However, the primary and secondary outcome (oxygen saturations, PaO2/FiO2, BP, day specific laboratory tests) will be recorded using an objective automated method; the study staff will not be able to influence the recording of these data. NUMBER TO BE RANDOMISED (SAMPLE SIZE): No similar study has been performed previously. Therefore no data are available that could be used to generate a sample size calculation. This phase II study is required to provide some initial data on efficacy and safety that will allow design of a larger study. The trial will recruit 60 participants (20 in each arm). TRIAL STATUS: Protocol version 1.4 dated May 5, 2020 and amended version 1.5, dated June 16, 2020. First case was recruited on May 27, 2020. By August 10, 2020, the trial had recruited one-third (21 out of 60) of the participants. The recruitment is expected to finish by October 31, 2020. TRIAL REGISTRATION: Clinicaltrials.gov ID: NCT04403477 . Registered 26 May, 2020 FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trial's website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this letter serves as a summary of the key elements of the full protocol.


Subject(s)
Betacoronavirus/genetics , Blood Transfusion/methods , Coronavirus Infections/therapy , Pneumonia, Viral/therapy , Bangladesh/epidemiology , COVID-19 , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Dose-Response Relationship, Immunologic , Female , Hospital Mortality/trends , Humans , Immunization, Passive/adverse effects , Immunization, Passive/methods , Male , Pandemics , Patient Discharge/statistics & numerical data , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , SARS-CoV-2 , Safety , Severity of Illness Index , Time Factors , Treatment Outcome , Ventilators, Mechanical/statistics & numerical data
11.
Pharmacol Res Perspect ; 8(6): e00666, 2020 12.
Article in English | MEDLINE | ID: covidwho-882366

ABSTRACT

Conflicting evidence exists about the effect of angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) on COVID-19 clinical outcomes. We aimed to provide a comprehensive/updated evaluation of the effect of ACEIs/ARBs on COVID-19-related clinical outcomes, including exploration of interclass differences between ACEIs and ARBs, using a systematic review/meta-analysis approach conducted in Medline (OVID), Embase, Scopus, Cochrane library, and medRxiv from inception to 22 May 2020. English studies that evaluated the effect of ACEIs/ARBs among patients with COVID-19 were included. Studies' quality was appraised using the Newcastle-Ottawa Scale. Data were analyzed using the random-effects modeling stratified by exposure (ACEIs/ARBs, ACEIs, and ARBs). Heterogeneiity was assessed using I2 statistic. Several subgroup analyses were conducted to explore the impact of potential confounders. Overall, 27 studies were eligible. The pooled analyses showed nonsignificant associations between ACEIs/ARBs and death (OR:0.97, 95%CI:0.75,1.27), ICU admission (OR:1.09;95%CI:0.65,1.81), death/ICU admission (OR:0.67; 95%CI:0.52,0.86), risk of COVID-19 infection (OR:1.01; 95%CI:0.93,1.10), severe infection (OR:0.78; 95%CI:0.53,1.15), and hospitalization (OR:1.15; 95%CI:0.81,1.65). However, the subgroup analyses indicated significant association between ACEIs/ARBs and hospitalization among USA studies (OR:1.59; 95%CI:1.03,2.44), peer-reviewed (OR:1.93, 95%CI:1.38,2.71), good quality and studies which reported adjusted measure of effect (OR:1.30, 95%CI:1.10,1.50). Significant differences were found between ACEIs and ARBs with the latter being significantly associated with lower risk of acquiring COVID-19 infection (OR:0.24; 95%CI: 0.17,0.34). In conclusion, high-quality evidence exists for the effect of ACEIs/ARBs on some COVID-19 clinical outcomes. For the first time, we provided evidence, albeit of low quality, on interclass differences between ACEIs and ARBs for some of the reported clinical outcomes.


Subject(s)
Angiotensin Receptor Antagonists/pharmacology , Angiotensin-Converting Enzyme Inhibitors/pharmacology , Betacoronavirus/drug effects , Cardiovascular Diseases/drug therapy , Coronavirus Infections/mortality , Pneumonia, Viral/mortality , Adult , Aged , Angiotensin Receptor Antagonists/adverse effects , Angiotensin Receptor Antagonists/therapeutic use , Angiotensin-Converting Enzyme Inhibitors/adverse effects , Angiotensin-Converting Enzyme Inhibitors/therapeutic use , COVID-19 , Cardiovascular Diseases/complications , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Female , Hospital Mortality/trends , Hospitalization/statistics & numerical data , Humans , Hypertension/complications , Hypertension/drug therapy , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Observational Studies as Topic , Outcome Assessment, Health Care , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Risk Assessment , SARS-CoV-2 , Ventilators, Mechanical/adverse effects , Ventilators, Mechanical/statistics & numerical data
12.
Respir Care ; 65(9): 1378-1381, 2020 09.
Article in English | MEDLINE | ID: covidwho-745222

ABSTRACT

COVID-19 is devastating health systems globally and causing severe ventilator shortages. Since the beginning of the outbreak, the provision and use of ventilators has been a key focus of public discourse. Scientists and engineers from leading universities and companies have rushed to develop low-cost ventilators in hopes of supporting critically ill patients in developing countries. Philanthropists have invested millions in shipping ventilators to low-resource settings, and agencies such as the World Health Organization and the World Bank are prioritizing the purchase of ventilators. While we recognize the humanitarian nature of these efforts, merely shipping ventilators to low-resource environments may not improve outcomes of patients and could potentially cause harm. An ecosystem of considerable technological and human resources is required to support the usage of ventilators within intensive care settings. Medical-grade oxygen supplies, reliable electricity, bioengineering support, and consumables are all needed for ventilators to save lives. However, most ICUs in resource-poor settings do not have access to these resources. Patients on ventilators require continuous monitoring from physicians, nurses, and respiratory therapists skilled in critical care. Health care workers in many low-resource settings are already exceedingly overburdened, and pulling these essential human resources away from other critical patient needs could reduce the overall quality of patient care. When deploying medical devices, it is vital to align the technological intervention with the clinical reality. Low-income settings often will not benefit from resource-intensive equipment, but rather from contextually appropriate devices that meet the unique needs of their health systems.


Subject(s)
Coronavirus Infections/epidemiology , Healthcare Disparities/economics , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Poverty/statistics & numerical data , Ventilators, Mechanical/statistics & numerical data , COVID-19 , Coronavirus Infections/therapy , Critical Care/organization & administration , Developing Countries , Female , Health Resources/economics , Humans , Intensive Care Units/organization & administration , Male , Nigeria , Pneumonia, Viral/therapy , United Nations , Ventilators, Mechanical/economics , World Health Organization
14.
J Hosp Infect ; 106(2): 277-282, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-704916

ABSTRACT

BACKGROUND: The shortage of single-use N95 respirator masks (NRMs) during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has prompted consideration of NRM recycling to extend limited stocks by healthcare providers and facilities. AIM: To assess potential reuse via autoclaving of NRMs worn daily in a major urban Canadian hospital. METHODS: NRM reusability was assessed following collection from volunteer staff after 2-8 h use, sterilization by autoclaving and PortaCount fit testing. A workflow was developed for reprocessing hundreds of NRMs daily. FINDINGS: Used NRMs passed fit testing after autoclaving once, with 86% passing a second reuse/autoclave cycle. A separate cohort of used masks pre-warmed before autoclaving passed fit testing. To recycle 200-1000 NRMs daily, procedures for collection, sterilization and re-distribution were developed to minimize particle aerosolization risk during NRM handling, to reject NRM showing obvious wear, and to promote adoption by staff. NRM recovery ranged from 49% to 80% across 12 collection cycles. CONCLUSION: Reuse of NRMs is feasible in major hospitals and other healthcare facilities. In sharp contrast to studies of unused NRMs passing fit testing after 10 autoclave cycles, we show that daily wear substantially reduces NRM fit, limiting reuse to a single cycle, but still increasing NRM stocks by ∼66%. Such reuse requires development of a comprehensive plan that includes communication across staffing levels, from front-line workers to hospital administration, to increase the collection, acceptance of and adherence to sterilization processes for NRM recovery.


Subject(s)
Coronavirus Infections/prevention & control , Equipment Design/standards , Equipment Reuse/standards , Hospitals, Urban/standards , Infection Control/standards , Masks/standards , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Respiratory Protective Devices/standards , Ventilators, Mechanical/standards , Betacoronavirus , COVID-19 , Canada/epidemiology , Coronavirus Infections/epidemiology , Equipment Design/statistics & numerical data , Equipment Reuse/statistics & numerical data , Hospitals, Urban/statistics & numerical data , Humans , Infection Control/methods , Masks/statistics & numerical data , Occupational Exposure/standards , Occupational Exposure/statistics & numerical data , Pneumonia, Viral/epidemiology , Respiratory Protective Devices/statistics & numerical data , SARS-CoV-2 , Ventilators, Mechanical/statistics & numerical data
15.
Emerg Infect Dis ; 26(10): 2361-2369, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-661057

ABSTRACT

Social distancing orders have been enacted worldwide to slow the coronavirus disease (COVID-19) pandemic, reduce strain on healthcare systems, and prevent deaths. To estimate the impact of the timing and intensity of such measures, we built a mathematical model of COVID-19 transmission that incorporates age-stratified risks and contact patterns and projects numbers of hospitalizations, patients in intensive care units, ventilator needs, and deaths within US cities. Focusing on the Austin metropolitan area of Texas, we found that immediate and extensive social distancing measures were required to ensure that COVID-19 cases did not exceed local hospital capacity by early May 2020. School closures alone hardly changed the epidemic curve. A 2-week delay in implementation was projected to accelerate the timing of peak healthcare needs by 4 weeks and cause a bed shortage in intensive care units. This analysis informed the Stay Home-Work Safe order enacted by Austin on March 24, 2020.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Health Policy , Health Services/supply & distribution , Health Services/statistics & numerical data , Hospital Bed Capacity , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Adolescent , Adult , Aged , COVID-19 , Child , Child, Preschool , Cities/epidemiology , Computer Simulation , Coronavirus Infections/mortality , Forecasting , Hospitalization/statistics & numerical data , Humans , Infant , Intensive Care Units/statistics & numerical data , Middle Aged , Models, Statistical , Pneumonia, Viral/mortality , Schools , Texas/epidemiology , Ventilators, Mechanical/statistics & numerical data , Young Adult
17.
J Korean Med Sci ; 35(23): e223, 2020 Jun 15.
Article in English | MEDLINE | ID: covidwho-598889

ABSTRACT

BACKGROUND: The mortality risk of coronavirus disease 2019 (COVID-19) is higher in patients with older age, and many elderly patients are reported to require advanced respiratory support. METHODS: We reviewed medical records of 98 patients aged ≥ 65 years who were hospitalized with COVID-19 during a regional outbreak in Daegu/Gyeongsangbuk-do province of Korea. The outcome measures were in-hospital mortality and the treatment with mechanical ventilation (MV) or high-flow nasal cannula (HFNC). RESULTS: The median age of the patients was 72 years; 55.1% were female. Most (74.5%) had at least one underlying condition. Overall case fatality rate (CFR) was 20.4%, and median time to death after admission was 8 days. The CFR was 6.1% among patients aged 65-69 years, 22.7% among those aged 70-79 years, and 38.1% among those aged ≥ 80 years. The CFR among patients who required MV was 43.8%, and the proportion of patients received MV/HFNC was 28.6%. Nosocomial acquisition, diabetes, chronic lung diseases, and chronic neurologic diseases were significant risk factors for both death and MV/HFNC. Hypotension, hypoxia, and altered mental status on admission were also associated with poor outcome. CRP > 8.0 mg/dL was strongly associated with MV/HFNC (odds ratio, 26.31; 95% confidence interval, 7.78-88.92; P < 0.001), and showed better diagnostic characteristics compared to commonly used clinical scores. CONCLUSION: Patients aged ≥ 80 years had a high risk of requiring MV/HFNC, and mortality among those severe patients was very high. Severe initial presentation and laboratory abnormalities, especially high CRP, were identified as risk factors for mortality and severe hospital course.


Subject(s)
Coronavirus Infections/mortality , Coronavirus Infections/pathology , Hypoxia/pathology , Pneumonia, Viral/mortality , Pneumonia, Viral/pathology , Respiration, Artificial/statistics & numerical data , Respiratory Insufficiency/mortality , Age Factors , Aged , Aged, 80 and over , Betacoronavirus , C-Reactive Protein/analysis , COVID-19 , Female , Hospitalization , Humans , Intensive Care Units , Male , Pandemics , Republic of Korea , Retrospective Studies , Risk Factors , SARS-CoV-2 , Treatment Outcome , Ventilators, Mechanical/statistics & numerical data
18.
Swiss Med Wkly ; 150: w20277, 2020 05 04.
Article in English | MEDLINE | ID: covidwho-198135

ABSTRACT

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.


Subject(s)
Coronavirus Infections , Forecasting/methods , Health Planning/methods , Hospital Bed Capacity , Intensive Care Units/supply & distribution , Pandemics , Pneumonia, Viral , Software , Ventilators, Mechanical/supply & distribution , COVID-19 , Coronavirus Infections/epidemiology , Decision Making, Computer-Assisted , Hospital Bed Capacity/statistics & numerical data , Hospitalization/statistics & numerical data , Hospitalization/trends , Humans , Intensive Care Units/statistics & numerical data , Models, Theoretical , Pandemics/statistics & numerical data , Patient Admission/statistics & numerical data , Pneumonia, Viral/epidemiology , Software/standards , Switzerland/epidemiology , Ventilators, Mechanical/statistics & numerical data
19.
Emerg Med Australas ; 32(3): 520-524, 2020 06.
Article in English | MEDLINE | ID: covidwho-46601

ABSTRACT

EDs fulfil a frontline function during public health emergencies (PHEs) and will play a pivotal role during the COVID-19 pandemic. This perspective article draws on qualitative data from a longitudinal, ethnographic study of an Australian tertiary ED to illustrate the clinical and ethical challenges faced by EDs during PHEs. Interview data collected during the 2014 Ebola Virus Disease PHE of International Concern suggest that ED clinicians have a strong sense of professional responsibility, but this can be compromised by increased visibility of risk and sub-optimal engagement from hospital managers and public health authorities. The study exposes the tension between a healthcare worker's right to protection and a duty to provide treatment. Given the narrow window of opportunity to prepare for a surge of COVID-19 presentations, there is an immediate need to reflect and learn from previous experiences. To maintain the confidence of ED clinicians, and minimise the risk of moral injury, hospital and public health authorities must urgently develop processes to support ethical healthcare delivery and ensure adequate resourcing of EDs.


Subject(s)
Coronavirus Infections/diagnosis , Coronavirus , Disease Outbreaks/ethics , Emergency Medicine/ethics , Emergency Service, Hospital/ethics , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Ventilators, Mechanical/ethics , Betacoronavirus , COVID-19 , Coronavirus/isolation & purification , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Decision Making , Disease Outbreaks/prevention & control , Emergency Medical Services , Hemorrhagic Fever, Ebola/epidemiology , Humans , Pneumonia, Viral/diagnosis , Pneumonia, Viral/therapy , Public Health , SARS-CoV-2 , Ventilators, Mechanical/statistics & numerical data
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