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1.
PLoS Med ; 19(5): e1004015, 2022 May.
Article in English | MEDLINE | ID: covidwho-1865333

ABSTRACT

BACKGROUND: Healthcare workers (HCWs), particularly those from ethnic minority groups, have been shown to be at disproportionately higher risk of infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) compared to the general population. However, there is insufficient evidence on how demographic and occupational factors influence infection risk among ethnic minority HCWs. METHODS AND FINDINGS: We conducted a cross-sectional analysis using data from the baseline questionnaire of the United Kingdom Research study into Ethnicity and Coronavirus Disease 2019 (COVID-19) Outcomes in Healthcare workers (UK-REACH) cohort study, administered between December 2020 and March 2021. We used logistic regression to examine associations of demographic, household, and occupational risk factors with SARS-CoV-2 infection (defined by polymerase chain reaction (PCR), serology, or suspected COVID-19) in a diverse group of HCWs. The primary exposure of interest was self-reported ethnicity. Among 10,772 HCWs who worked during the first UK national lockdown in March 2020, the median age was 45 (interquartile range [IQR] 35 to 54), 75.1% were female and 29.6% were from ethnic minority groups. A total of 2,496 (23.2%) reported previous SARS-CoV-2 infection. The fully adjusted model contained the following dependent variables: demographic factors (age, sex, ethnicity, migration status, deprivation, religiosity), household factors (living with key workers, shared spaces in accommodation, number of people in household), health factors (presence/absence of diabetes or immunosuppression, smoking history, shielding status, SARS-CoV-2 vaccination status), the extent of social mixing outside of the household, and occupational factors (job role, the area in which a participant worked, use of public transport to work, exposure to confirmed suspected COVID-19 patients, personal protective equipment [PPE] access, aerosol generating procedure exposure, night shift pattern, and the UK region of workplace). After adjustment, demographic and household factors associated with increased odds of infection included younger age, living with other key workers, and higher religiosity. Important occupational risk factors associated with increased odds of infection included attending to a higher number of COVID-19 positive patients (aOR 2.59, 95% CI 2.11 to 3.18 for ≥21 patients per week versus none), working in a nursing or midwifery role (1.30, 1.11 to 1.53, compared to doctors), reporting a lack of access to PPE (1.29, 1.17 to 1.43), and working in an ambulance (2.00, 1.56 to 2.58) or hospital inpatient setting (1.55, 1.38 to 1.75). Those who worked in intensive care units were less likely to have been infected (0.76, 0.64 to 0.92) than those who did not. Black HCWs were more likely to have been infected than their White colleagues, an effect which attenuated after adjustment for other known risk factors. This study is limited by self-selection bias and the cross sectional nature of the study means we cannot infer the direction of causality. CONCLUSIONS: We identified key sociodemographic and occupational risk factors associated with SARS-CoV-2 infection among UK HCWs, and have determined factors that might contribute to a disproportionate odds of infection in HCWs from Black ethnic groups. These findings demonstrate the importance of social and occupational factors in driving ethnic disparities in COVID-19 outcomes, and should inform policies, including targeted vaccination strategies and risk assessments aimed at protecting HCWs in future waves of the COVID-19 pandemic. TRIAL REGISTRATION: The study was prospectively registered at ISRCTN (reference number: ISRCTN11811602).


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19 Vaccines , Cohort Studies , Communicable Disease Control , Cross-Sectional Studies , Female , Health Personnel , Humans , Male , Middle Aged , Minority Groups , Pandemics , Risk Factors , SARS-CoV-2 , United Kingdom/epidemiology
2.
Int J Ophthalmol ; 15(4): 527-532, 2022.
Article in English | MEDLINE | ID: covidwho-1798638

ABSTRACT

AIM: To share clinical pattern of presentation, the modalities of surgical intervention and the one month post-surgical outcome of rhino-orbito-mucormycosis (ROCM) cases. METHODS: All COVID associated mucormycosis (CAM) patients underwent comprehensive multidisciplinary examination by ophthalmologist, otorhinolaryngologist and physician. Patients with clinical and radiological evidence of orbital apex involvement were included in the study. Appropriate medical and surgical intervention were done to each patient. Patients were followed up one-month post intervention. RESULTS: Out of 89 CAM patients, 31 (34.8%) had orbital apex syndrome. Sixty-six (74.2%) of such patients had pre-existing diabetes mellitus, 18 (58%) patients had prior documented use of steroid use, and 55 (61.8%) had no light perception (LP) presenting vision. Blepharoptosis, proptosis, complete ophthalmoplegia were common clinical findings. Seventeen (19.1%) of such patients had variable amount of cavernous sinus involvement. Endoscopic debridement of paranasal sinuses and orbit with or without eyelid sparing limited orbital exenteration was done in most cases, 34 (38.2%) patients could retain vision in the affected eye. CONCLUSION: Orbital apex involvement in CAM patients occur very fast. It not only leads to loss of vision but also sacrifice of the eyeball, orbital contents and eyelids. Early diagnosis and prompt intervention can preserve life, vision and spare mutilating surgeries.

3.
Journal of the American College of Cardiology (JACC) ; 79(9):2395-2395, 2022.
Article in English | Academic Search Complete | ID: covidwho-1749990
4.
Clin Pract Epidemiol Ment Health ; 17(1): 280-286, 2021.
Article in English | MEDLINE | ID: covidwho-1745218

ABSTRACT

Background: The recent pandemic of COVID-19 caused havoc on the health system globally and raised a lot of questions and issues. Treatment for cancer is an emergency that cannot be taken back, particularly in an era of global pandemics. Cancer treatment mainly includes chemotherapy, surgery, radiotherapy, and palliative care, and because of the pandemic, all of these treatments are affected. The COVID-19 pandemic also had a potential effect on the quality of life and mental health of patients as well as health workers. Objective: This systematic review was intended to discuss the quality of life of people with cancer in the era of the COVID-19 pandemic in India in the light of the best available facts. Methods: An extensive literature search was done on PubMed, Medline, Embase, Clinical Key and Google Scholar databases till 3rd Feb 2021. Out of 1455 research articles, 06 research articles were included in this systematic review. Results: The results showed that cancer treatment delivery was as per standard safety protocol and the best treatment decisions were made by scheduling and setting priority. Till data, no direct research was conducted on the Indian continent to assess the quality of life of cancer patients in the COVID-19 era. The effect on the quality of life of cancer patients is very large and needs to be explored more by further research. Issues to be discussed with health care administrators and policy makers further. The tele-oncology method of cancer care delivery to patients is another rational option which is applicable as well. Conclusion: This systematic review demonstrated up-to-date evidence regarding the quality of life of cancer patients in the COVID-19 era in India. No research has been done to assess the quality of life of cancer patients. Still, the area is unrevealed, but evidence from other global studies indicates an altered quality of life for cancer patients. To maintain quality of life, cancer physicians should make evidence-based decisions and incorporate multidisciplinary management into decision making.

5.
EClinicalMedicine ; 46: 101346, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1739677

ABSTRACT

Background: Several countries now have mandatory SARS-CoV-2 vaccination for healthcare workers (HCWs) or the general population. HCWs' views on this are largely unknown. Using data from the nationwide UK-REACH study we aimed to understand UK HCW's views on improving SARS-CoV-2 vaccination coverage, including mandatory vaccination. Methods: Between 21st April and 26th June 2021, we administered an online questionnaire via email to 17 891 UK HCWs recruited as part of a longitudinal cohort from across the UK who had previously responded to a baseline questionnaire (primarily recruited through email) as part of the United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers (UK-REACH) nationwide prospective cohort study. We categorised responses to a free-text question "What should society do if people do not get vaccinated against COVID-19?" using qualitative content analysis. We collapsed categories into a binary variable: favours mandatory vaccination or not, using logistic regression to calculate its demographic predictors, and its occupational, health, and attitudinal predictors adjusted for demographics. Findings: Of 5633 questionnaire respondents, 3235 answered the free text question. Median age of free text responders was 47 years (IQR 36-56) and 2705 (74.3%) were female. 18% (n = 578) favoured mandatory vaccination (201 [6%] participants for HCWs and others working with vulnerable populations; 377 [12%] for the general population), but the most frequent suggestion was education (32%, n = 1047). Older HCWs (OR 1.84; 95% CI 1.44-2.34 [≥55 years vs 16 years to <40 years]), HCWs vaccinated against influenza (OR 1.49; 95% CI 1.11-2.01 [2 vaccines vs none]), and with more positive vaccination attitudes generally (OR 1.10; 95% CI 1.06-1.15) were more likely to favour mandatory vaccination, whereas female HCWs (OR= 0.79, 95% CI 0.63-0.96, vs male HCWs) and Black HCWs (OR=0.46, 95% CI 0.25-0.85, vs white HCWs) were less likely to. Interpretation: Only one in six of the HCWs in this large, diverse, UK-wide sample favoured mandatory vaccination. Building trust, educating, and supporting HCWs who are hesitant about vaccination may be more acceptable, effective, and equitable. Funding: MRC-UK Research and Innovation grant (MR/V027549/1) and the Department of Health and Social Care (DHSC) via the National Institute for Health Research (NIHR). Core funding was also provided by NIHR Biomedical Research Centres.

6.
Indian J Surg ; : 1-9, 2022 Mar 11.
Article in English | MEDLINE | ID: covidwho-1739432

ABSTRACT

The new coronavirus (COVID-19) infection, first detected in Wuhan, China in 2019 has become a pandemic that has spread to nearly every country in the world. Through October 11, 2021, more than 23 billion confirmed cases and 4.8 million fatalities were reported globally. The bulk of individuals afflicted in India during the first wave were elderly persons. The second wave, however, resulted in more severe diseases and mortality in even younger age groups due to mutations in the wild virus. Symptoms may range from being asymptomatic to fatal acute respiratory distress syndrome (ARDS). In addition to respiratory symptoms, patients may present with gastrointestinal symptoms such as stomach pain, vomiting, loose stools, or mesenteric vein thrombosis. The frequency of patients presenting with thromboembolic symptoms has recently increased. According to certain studies, the prevalence of venous thromboembolism among hospitalized patients ranges from 9 to 25%. It was also shown that the incidence is significantly greater among critically sick patients, with a prevalence of 21-31%. Although the exact origin of thromboembolism is unknown, it is considered to be produced by several altered pathways that manifest as pulmonary embolism, myocardial infarction, stroke, limb gangrene, and acute mesenteric ischemia. Acute mesenteric ischemia (AMI) is becoming an increasingly prevalent cause of acute surgical abdomen in both intensive care unit (ICU) and emergency room (ER) patients. Mesenteric ischemia should be evaluated in situations with unexplained stomach discomfort. In suspected situations, appropriate imaging techniques and early intervention, either non-surgical or surgical, are necessary to avert mortality. The purpose of this article is to look at the data on acute mesenteric ischemia in people infected with COVID-19.

7.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-318441

ABSTRACT

Background: The primary focus of the study was to evaluate the impact of COVID-19 pandemic on the management of common gastrointestinal cancer patients and develop baseline data to form cancer care policy. Methods: The patient data was collected for two study periods: between 1 Jan 2020 to 22 March 2020 (T1) and 23 March 2020 and 30 May 2020 (T2). Objectives of the study were to evaluate the differences in patient presentation, treatment patterns/modifications and 100 - day case fatality rates across the study periods T1 and T2. Findings: There were 20,112 footfalls between 1 Jan 2020 and 22 March 2020 and 10,804 footfalls between 23 March 2020 and 30 May 2020. 3177 patients in T1 and 1069 patients in T2 with gastrointestinal cancers were evaluated. A greater proportion of patients were planned for systemic therapy (70.5% vs. 74.8%, OR-0.85;95% CI:0.75- 0.96;p= 0.007), monotherapy (18% vs 23%, OR-0.81;95% CI:0.72-0.91;p<0.001) and doublet therapy (34% vs 41%, OR-0.8;95% CI:0.72-0.88;p<0.001), chemotherapy at reduced doses during T2 (10% vs 16, OR 0.86;95% CI:0.79 - 0.93;p= <0.001). Overall 470 and 158 patients were operated during T1 and T2 respectively, with fewer patients with ASA III status operated during T2 (3% vs 0, OR-1.35;95%CI:1.29-1.41, p=0.028). In patients receiving systemic therapy, there were 263 deaths (8.3%) during T1, while 95 (8.9%) deaths were seen in T2 with no significant differences in 100-day case fatality rates (OR 0.93;95% CI: 0.72-1.18;p=0.54). There were no differences in post-operative mortality rates between T1 and T2 (3% vs. 2%, OR 1.09;95% CI: 0.86-1.38, p=0.55). Interpretation: Maintaining standard of care treatment with limited modifications during COVID-19 pandemic for patients receiving chemotherapy or undergoing surgery results in short term outcomes comparable to the pre-COVID-19 scenario.Trial Registration: Clinical Trials Registry of India (CTRI) registration;REF/2020/07/035004.Funding Statement: None.Declaration of Interests: Dr Vikas Ostwal and Dr Anant Ramaswamy declare the receipt of the grants to Tata Memorial Centre, Mumbai for other biomedical (investigator initiated) studies from Reddy’s lab pvt ltd and Cadila Pharmaceutical pvt ltd. Dr Vikas Ostwal received a travel grant from Novartis pvt ltd for an advisory meeting. No conflict of interest / financial interest related to this work. Ethics Approval Statement: IEC/0620/3480/001

8.
J Family Med Prim Care ; 10(12): 4611-4612, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1689977
9.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-306115

ABSTRACT

We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05 for mechanical ventilation requirement and mortality prediction, respectively. Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances where inclusion of radiomic features in DL improves model predictions, something that might be explored in other pathologies. The models proposed in this study and the prognostic information they provide might aid physician decision making and resource allocation during the COVID-19 pandemic.

10.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327401

ABSTRACT

Objectives To investigate how ethnicity and other sociodemographic, work, and physical health factors are related to mental health in UK healthcare and ancillary workers (HCWs), and how structural inequities in these factors may contribute to differences in mental health by ethnicity. Design Cross-sectional analysis of baseline data from the UK-REACH national cohort study Setting HCWs across UK healthcare settings. Participants 11,695 HCWs working between December 2020-March 2021. Main outcome measures Anxiety or depression symptoms (4-item Patient Health Questionnaire, cut-off >3), and Post-Traumatic Stress Disorder (PTSD) symptoms (3-item civilian PTSD Checklist, cut-off >5). Results Asian, Black, Mixed/multiple and Other ethnic groups had greater odds of PTSD than the White ethnic group. Differences in anxiety/depression were less pronounced. Younger, female HCWs, and those who were not doctors had increased odds of symptoms of both PTSD and anxiety/depression. Ethnic minority HCWs were more likely to experience the following work factors that were also associated with mental ill-health: workplace discrimination, feeling insecure in raising workplace concerns, seeing more patients with COVID-19, reporting lack of access to personal protective equipment (PPE), and working longer hours and night shifts. Ethnic minority HCWs were also more likely to live in a deprived area and have experienced bereavement due to COVID-19. After adjusting for sociodemographic and work factors, ethnic differences in PTSD were less pronounced and ethnic minority HCWs had lower odds of anxiety/depression compared to White HCWs. Conclusions Ethnic minority HCWs were more likely to experience PTSD and disproportionately experienced work and sociodemographic factors associated with PTSD, anxiety and depression. These findings could help inform future work to develop workplace strategies to safeguard HCWs’ mental health. This will only be possible with adequate investment in staff recruitment and retention, alongside concerted efforts to address inequities due to structural discrimination. Summary box What is already known on this topic The pandemic is placing healthcare workers under immense pressure, and there is currently a mental health crisis amongst NHS staff Ethnic inequities in health outcomes are driven by structural discrimination, which occurs inside and outside the workplace Investigating ethnic inequities in the mental health of healthcare workers requires large diverse studies, of which few exist What this study adds In UK-REACH (N=11,695), ethnic minority staff had higher odds of Post-Traumatic Stress Disorder symptoms;we report many other factors associated with mental-ill health, including those experienced disproportionately by ethnic minority staff, such as workplace discrimination, contact with more patients with COVID-19, and bereavement due to COVID-19 These findings underline the moral and practical need to care for staff mental health and wellbeing, which includes tackling structural inequities in the workplace;improving staff mental health may also reduce workforce understaffing due to absence and attrition

11.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327069

ABSTRACT

Key Features of the UK-REACH Cohort (Profile in a nutshell) The UK-REACH Cohort was established to understand why ethnic minority healthcare workers (HCWs) are at risk of poorer outcomes from COVID-19 when compared to their white ethnic counterparts in the United Kingdom (UK). Through study design, it contains a uniquely high percentage of participants from ethnic minority backgrounds about whom a wide range of qualitative and quantitative data has been collected. A total of 17891 HCWs aged 16-89 years (mean age: 44) have been recruited from across the UK via all major healthcare regulators, individual National Health Service (NHS) hospital trusts and UK HCW membership bodies who advertised the study to their registrants/staff to encourage participation in the study. Data available include linked healthcare records for 25 years from the date of consent and consent to obtain genomic sequencing data collected via saliva. Online questionnaires include information on demographics, COVID-19 exposures at work and home, redeployment in the workforce due to COVID-19, mental health measures, workforce attrition, and opinions on COVID-19 vaccines, with baseline (n=15 119), 6 (n=5632) and 12-month follow-up data captured. Request data access and collaborations by following documentation found at https://www.uk-reach.org/main/data_sharing .

12.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326551

ABSTRACT

Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire due to the high cost of annotation. Thus, it is desirable to learn a robust and transferable representation in an unsupervised manner to benefit tasks that lack labeled data. Unlike natural images, medical images have their own domain prior;e.g., we observe that many pulmonary diseases, such as the COVID-19, manifest as changes in the lung tissue texture rather than the anatomical structure. Therefore, we hypothesize that studying only the texture without the influence of structure variations would be advantageous for downstream prognostic and predictive modeling tasks. In this paper, we propose a generative framework, the Lung Swapping Autoencoder (LSAE), that learns factorized representations of a CXR to disentangle the texture factor from the structure factor. Specifically, by adversarial training, the LSAE is optimized to generate a hybrid image that preserves the lung shape in one image but inherits the lung texture of another. To demonstrate the effectiveness of the disentangled texture representation, we evaluate the texture encoder $Enc

14.
Surg J (N Y) ; 7(4): e366-e373, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1607952

ABSTRACT

Introduction In response to the national coronavirus disease 2019 (COVID-19) pandemic, all hospitals and medical institutes gave priority to COVID-19 screening and to the management of patients who required hospitalization for COVID-19 infection. Surgical departments postponed all elective operative procedures and provided only essential surgical care to patients who presented with acute surgical conditions or suspected malignancy. Ample literature has emerged during this pandemic regarding the guidelines for safe surgical care. We report our experience during the lockdown period including the surgical procedures performed, the perioperative care provided, and the specific precautions implemented in response to the COVID-19 crisis. Materials and Methods We extracted patient clinical data from the medical records of all surgical patients admitted to our tertiary care hospital between the March 24th, 2020 and May 31st, 2020. Data collected included: patient demographics, surgical diagnoses, surgical procedures, nonoperative management, and patient outcomes. Results Seventy-seven patients were included in this report: 23 patients were managed medically, 28 patients underwent a radiologic intervention, and 23 patients required an operative procedure. In total eight of the 77 patients died due to ongoing sepsis, multiorgan failure, or advanced malignancy. Conclusion During the COVID-19 lockdown period, our surgical team performed many lifesaving surgical procedures and appropriately selected cancer operations. We implemented and standardized essential perioperative measures to reduce the spread of COVID-19 infection. When the lockdown measures were phased out a large number of patients remained in need of delayed elective and semi-elective operative treatment. Hospitals, medical institutes, and surgical leadership must adjust their priorities, foster stewardship of limited surgical care resources, and rapidly implement effective strategies to assure perioperative safety for both patients and operating room staff during periods of crisis.

15.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296840

ABSTRACT

Background Vaccination is key to successful prevention of COVID-19 particularly nosocomial acquired infection in health care workers (HCWs). ‘Vaccine hesitancy’ is common in the population and in HCWs, and like COVID-19 itself, hesitancy is more frequent in ethnic minority groups. UK-REACH (United Kingdom Research study into Ethnicity and COVID-19 outcomes) is a large-scale study of COVID-19 in UK HCWs from diverse ethnic backgrounds, which includes measures of vaccine hesitancy. The present study explores predictors of vaccine hesitancy using a ‘phenomic approach’, considering several hundred questionnaire-based measures. Methods UK-REACH includes a questionnaire study encompassing 12,431 HCWs who were recruited from December 2020 to March 2021 and completed a lengthy online questionnaire (785 raw items;392 derived measures;260 final measures). Ethnicity was classified using the Office for National Statistics’ five (ONS5) and eighteen (ONS18) categories. Missing data were handled by multiple imputation. Variable selection used the islasso package in R , which provides standard errors so that results from imputations could be combined using Rubin’s rules. The data were modelled using path analysis, so that predictors, and predictors of predictors could be assessed. Significance testing used the Bayesian approach of Kass and Raftery, a ‘very strong’ Bayes Factor of 150, N=12,431, and a Bonferroni correction giving a criterion of p<4.02 × 10 −8 for the main regression, and p<3.11 × 10 −10 for variables in the path analysis. Results At the first step of the phenomic analysis, six variables were direct predictors of greater vaccine hesitancy: Lower pro-vaccination attitudes;no flu vaccination in 2019-20;pregnancy;higher COVID-19 conspiracy beliefs;younger age;and lower optimism the roll-out of population vaccination. Overall 44 lower variables in total were direct or indirect predictors of hesitancy, with the remaining 215 variables in the phenomic analysis not independently predicting vaccine hesitancy. Key variables for predicting hesitancy were belief in conspiracy theories of COVID-19 infection, and a low belief in vaccines in general. Conspiracy beliefs had two main sets of influences: Higher Fatalism, which was influenced a) by high external and chance locus of control and higher need for closure, which in turn were associated with neuroticism, conscientiousness, extraversion and agreeableness;and b) by religion being important in everyday life, and being Muslim. receiving information via social media, not having higher education, and perceiving greater risks to self, the latter being influenced by higher concerns about spreading COVID, greater exposure to COVID-19, and financial concerns. There were indirect effects of ethnicity, mediated by religion. Religion was more important for Pakistani and African HCWs, and less important for White and Chinese groups. Lower age had a direct effect on hesitancy, and age and female sex also had several indirect effects on hesitancy. Conclusions The phenomic approach, coupled with a path analysis revealed a complex network of social, cognitive, and behavioural influences on SARS-Cov-2 vaccine hesitancy from 44 measures, 6 direct and 38 indirect, with the remaining 215 measures not having direct or indirect effects on hesitancy. It is likely that issues of trust underpin many associations with hesitancy. Understanding such a network of influences may help in tailoring interventions to address vaccine concerns and facilitate uptake in more hesistant groups. Funding UKMRI-MRC and NIHR

16.
IEEE J Biomed Health Inform ; 25(11): 4110-4118, 2021 11.
Article in English | MEDLINE | ID: covidwho-1570200

ABSTRACT

Almost 25% of COVID-19 patients end up in ICU needing critical mechanical ventilation support. There is currently no validated objective way to predict which patients will end up needing ventilator support, when the disease is mild and not progressed. N = 869 patients from two sites (D1: N = 822, D2: N = 47) with baseline clinical characteristics and chest CT scans were considered for this study. The entire dataset was randomly divided into 70% training, D1train (N = 606) and 30% test-set (Dtest: D1test (N = 216) + D2 (N = 47)). An expert radiologist delineated ground-glass-opacities (GGOs) and consolidation regions on a subset of D1train, (D1train_sub, N = 88). These regions were automatically segmented and used along with their corresponding CT volumes to train an imaging AI predictor (AIP) on D1train to predict the need of mechanical ventilators for COVID-19 patients. Finally, top five prognostic clinical factors selected using univariate analysis were integrated with AIP to construct an integrated clinical and AI imaging nomogram (ClAIN). Univariate analysis identified lactate dehydrogenase, prothrombin time, aspartate aminotransferase, %lymphocytes, albumin as top five prognostic clinical features. AIP yielded an AUC of 0.81 on Dtest and was independently prognostic irrespective of other clinical parameters on multivariable analysis (p<0.001). ClAIN improved the performance over AIP yielding an AUC of 0.84 (p = 0.04) on Dtest. ClAIN outperformed AIP in predicting which COVID-19 patients ended up needing a ventilator. Our results across multiple sites suggest that ClAIN could help identify COVID-19 with severe disease more precisely and likely to end up on a life-saving mechanical ventilation.


Subject(s)
COVID-19 , Artificial Intelligence , Humans , Lung , Nomograms , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed , Ventilators, Mechanical
17.
Expert Rev Respir Med ; 15(12): 1525-1537, 2021 12.
Article in English | MEDLINE | ID: covidwho-1500937

ABSTRACT

INTRODUCTION: Limited data exist regarding the long-term pulmonary sequelae of COVID-19. Identifying features utilizing multiple imaging modalities engenders a clearer picture of the illness's long-term consequences. AREAS COVERED: This review encompasses the common pulmonary findings associated with different imaging modalities during acute and late remission stages of COVID-19 pneumonia. EXPERT OPINION: Chest x-ray, a common preliminary diagnostic imaging technique, is not optimal for extended care due to limited tissue contrast resolution providing suboptimal assessment of pulmonary pathology and subtle interval changes. Ultrasound may be utilized on a case-by-case basis in certain patient populations, or in countries with limited resources. Chest CT's accessibility, high tissue contrast and spatial resolution make it the foremost modality for long-term COVID-19 follow-up. While MRI can viably monitor extrapulmonary disease due to its lack of radiation and high inherent soft-tissue contrast, it has limited pulmonary utility due to motion artifact and alveolar gas decreasing lung signal. Although 18F-FDG-PET/CT is costly and has limited specificity, it can provide molecular level data and inflammation quantification. Lung perfusion scintigraphy may also explain COVID-19 induced thromboembolic events and persistent dyspnea despite normal structural imaging and testing results. Correlating the long-term pulmonary findings of COVID-19 with each imaging modality is essential in elucidating the post-recovery course.


Subject(s)
COVID-19 , Humans , Lung/diagnostic imaging , Positron Emission Tomography Computed Tomography , SARS-CoV-2 , Tomography, X-Ray Computed
18.
Lancet Reg Health Eur ; 9: 100180, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1461657

ABSTRACT

BACKGROUND: In most countries, healthcare workers (HCWs) represent a priority group for vaccination against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) due to their elevated risk of COVID-19 and potential contribution to nosocomial SARS-CoV-2 transmission. Concerns have been raised that HCWs from ethnic minority groups are more likely to be vaccine hesitant (defined by the World Health Organisation as refusing or delaying a vaccination) than those of White ethnicity, but there are limited data on SARS-CoV-2 vaccine hesitancy and its predictors in UK HCWs. METHODS: Nationwide prospective cohort study and qualitative study in a multi-ethnic cohort of clinical and non-clinical UK HCWs. We analysed ethnic differences in SARS-CoV-2 vaccine hesitancy adjusting for demographics, vaccine trust, and perceived risk of COVID-19. We explored reasons for hesitancy in qualitative data using a framework analysis. FINDINGS: 11,584 HCWs were included in the cohort analysis. 23% (2704) reported vaccine hesitancy. Compared to White British HCWs (21.3% hesitant), HCWs from Black Caribbean (54.2%), Mixed White and Black Caribbean (38.1%), Black African (34.4%), Chinese (33.1%), Pakistani (30.4%), and White Other (28.7%) ethnic groups were significantly more likely to be hesitant. In adjusted analysis, Black Caribbean (aOR 3.37, 95% CI 2.11 - 5.37), Black African (aOR 2.05, 95% CI 1.49 - 2.82), White Other ethnic groups (aOR 1.48, 95% CI 1.19 - 1.84) were significantly more likely to be hesitant. Other independent predictors of hesitancy were younger age, female sex, higher score on a COVID-19 conspiracy beliefs scale, lower trust in employer, lack of influenza vaccine uptake in the previous season, previous COVID-19, and pregnancy. Qualitative data from 99 participants identified the following contributors to hesitancy: lack of trust in government and employers, safety concerns due to the speed of vaccine development, lack of ethnic diversity in vaccine studies, and confusing and conflicting information. Participants felt uptake in ethnic minority communities might be improved through inclusive communication, involving HCWs in the vaccine rollout, and promoting vaccination through trusted networks. INTERPRETATION: Despite increased risk of COVID-19, HCWs from some ethnic minority groups are more likely to be vaccine hesitant than their White British colleagues. Strategies to build trust and dispel myths surrounding the COVID-19 vaccine in these communities are urgently required. Emphasis should be placed on the safety and benefit of SARS-CoV-2 vaccination in pregnancy and in those with previous COVID-19. Public health communications should be inclusive, non-stigmatising and utilise trusted networks. FUNDING: UKRI-MRC and NIHR.

19.
Diagnostics (Basel) ; 11(10)2021 Sep 30.
Article in English | MEDLINE | ID: covidwho-1444130

ABSTRACT

In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and random forest (RF) machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using radiomic features extracted from patients' CXRs. Deep learning (DL) approaches were also explored for the clinical outcome prediction task and a novel radiomic embedding framework was introduced. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic classification models had mean area under the receiver operating characteristic curve (mAUCs) of 0.78 ± 0.05 (sensitivity = 0.72 ± 0.07, specificity = 0.72 ± 0.06) and 0.78 ± 0.06 (sensitivity = 0.70 ± 0.09, specificity = 0.73 ± 0.09), compared with expert scores mAUCs of 0.75 ± 0.02 (sensitivity = 0.67 ± 0.08, specificity = 0.69 ± 0.07) and 0.79 ± 0.05 (sensitivity = 0.69 ± 0.08, specificity = 0.76 ± 0.08) for mechanical ventilation requirement and mortality prediction, respectively. Classifiers using both expert severity scores and radiomic features for mechanical ventilation (mAUC = 0.79 ± 0.04, sensitivity = 0.71 ± 0.06, specificity = 0.71 ± 0.08) and mortality (mAUC = 0.83 ± 0.04, sensitivity = 0.79 ± 0.07, specificity = 0.74 ± 0.09) demonstrated improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances in which the inclusion of radiomic features in DL improves model predictions over DL alone. The models proposed in this study and the prognostic information they provide might aid physician decision making and efficient resource allocation during the COVID-19 pandemic.

20.
Emerg Radiol ; 28(6): 1083-1086, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1439725

ABSTRACT

For more than 1 year, COVID-19 pandemic has impacted every aspect of our lives. This paper reviews the major challenges that the radiology community faced over the past year and the impact the pandemic had on the radiology practice, radiologist-in-training education, and radiology research. The lessons learned from COVID-19 pandemic can help the radiology community to be prepared for future outbreaks and new pandemics, preserve good habits, enhance cancer screening programs, and adapt to the changes in radiology education and scientific meetings.


Subject(s)
COVID-19 , Internship and Residency , Radiology , Humans , Pandemics , Radiology/education , SARS-CoV-2
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