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1.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22269222

RESUMO

Vaccines have played a central role in mitigating severe disease and death from COVID-19 in the past 12 months. However, efficacy wanes over time and this loss of protection is being compounded by the emergence of the Omicron variant. By fitting an immunological model to population-level vaccine effectiveness data, we estimate that neutralizing antibody titres for Omicron are reduced by 3.9-fold (95% CrI 2.9-5.5) compared to the Delta variant. Under this model, we predict that 90 days after boosting with the Pfizer-BioNTech vaccine, efficacy against severe disease (admission to hospital) declines to 95.9% (95% CrI 95.4%-96.3%) against the Delta variant and 78.8% (95% CrI 75.0%-85.1%) against the Omicron variant. Integrating this immunological model within a model of SARS-CoV-2 transmission, we demonstrate that the size of the Omicron wave will depend on the degree of past exposure to infection across the population, with relatively small Omicron waves in countries that previously experienced a large Delta wave. We show that booster doses can have a major impact in mitigating the epidemic peak, although in many settings it remains possible that healthcare capacity could still be challenged. This is particularly the case in "zero-COVID" countries where there is little prior infection-induced immunity and therefore epidemic peaks will be higher. Where dose supply is limited, targeting boosters to the highest risk groups to ensure continued high protection in the face of waning immunity is of greater benefit than giving these doses as primary vaccination to younger age-groups. In many settings it is likely that health systems will be stretched, and it may therefore be necessary to maintain and/or reintroduce some level of NPIs to mitigate the worst impacts of the Omicron variant as it replaces the Delta variant.

2.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21268270

RESUMO

ImportanceUniversal paid sick-leave (PSL) policies have been implemented in jurisdictions to mitigate the spread of SARS-CoV-2. However empirical data regarding health and economic consequences of PSL policies is scarce. ObjectiveTo estimate effects of a universal PSL policy in Ontario, Canadas most populous province. DesignAn agent-based model (ABM) to simulate SARS-CoV-2 transmission informed by data from Statistics Canada, health administrative sources, and from the literature. SettingOntario from January 1st to May 1st, 2021. ParticipantsA synthetic population (1 million) with occupation and household characteristics representative of Ontario residents (14.5 million). ExposureA base case of existing employer-based PSL alone versus the addition of a 3-or 10-day universal PSL policy to facilitate testing and self-isolation among workers infected with SARS-CoV-2 themselves or because of infected household members. Main Outcome(s) and Measure(s)Number of SARS-CoV-2 infections and COVID-19 hospitalizations, worker productivity, lost wages, and presenteeism (going to a workplace while infected). ResultsIf a 3- and 10-day universal PSL were implemented over the 4-month study period, then compared with the base-case, the PSL policies were estimated to reduce cumulative SARS-CoV-2 cases by 85,531 (95% credible interval, CrI -2,484; 195,318) and 215,302 (81,500; 413,742), COVID-19 hospital admissions by 1,307 (-201; 3,205) and 3,352 (1,223; 6,528), numbers of workers forgoing wages by 558 (-327;1,608) and 7,406 (6,764; 8,072), and numbers of workers engaged in presenteeism by 24,499 (216; 54,170) and 279,863 (262,696; 295,449). Hours of productivity loss were estimated to be 10,854,379 (10,212,304; 11,465,635) in the base case, 17,446,525 (15,934,321; 18,854,683) in the 3-day scenario, and 26,127,165 (20,047,239; 29,875,161) in the 10-day scenario. Lost wages were $5,256,316 ($4,077,280; $6,804,983) and $12,610,962 ($11,463,128; $13,724,664) lower in the 3 day and 10 day scenarios respectively, relative to the base case. Conclusions and RelevanceExpanded access to PSL is estimated to reduce total numbers of COVID-19 cases, reduce presenteeism of workers with SARS-CoV-2 at workplaces, and mitigate wage loss experienced by workers. Competing interestsThe authors have no competing interests relevant to this article to disclose. FundingSupported by COVID-19 Rapid Research Funding (C-291-2431272-SANDER). This research was further supported, in part, by a Canada Research Chair in Economics of Infectious Diseases held by Beate Sander (CRC-950-232429). The study sponsor had no role in the design, collection, analysis, interpretation of the data, manuscript preparation or the decision to submit for publication. Author ContributionsConceptualization: PP, JDR, BS, DN Data Curation: PP, JDR, BS, DN Formal Analysis: PP, JDR, DN Methodology: PP, JDR, BS, DN Supervision: PP, DN, BS Validation: PP, JDR, BS, DN First Draft: PP, JDR, BS, DN Review and Edit PP, JDR, BS, DN Key pointsO_ST_ABSQuestionC_ST_ABSWhat could be the health and economic consequence of more generous paid sick leave policies in the context of the COVID-19 pandemic? FindingsMore generous policies are estimated to reduce SARS-CoV-2 infections (and thus COVID-19 hospitalizations), lost wages and presence of individuals with infection at workplaces. MeaningMore generous paid sick leave can be a valuable addition to other COVID-19 public health interventions.

3.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268948

RESUMO

Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how machine learning may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection or magnified during model development. For example, if one class is over-presented or errors/inconsistencies in practice are reflected in the training data, then a model can be biased by these. To evaluate our adversarial training framework, we used the statistical definition of equalized odds. We evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments, and aimed to mitigate regional (hospital) and ethnic biases present. We trained our framework on a large, real-world COVID-19 dataset and demonstrated that adversarial training demonstrably improves outcome fairness (with respect to equalized odds), while still achieving clinically-effective screening performances (NPV>0.98). We compared our method to the benchmark set by related previous work, and performed prospective and external validation on four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.

4.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22269211

RESUMO

BackgroundEmerging data suggest that SARS-CoV-2 Omicron variant of concern (VOC)is associated with reduced risk of severe disease. The extent to which this reflects a difference in the inherent virulence of Omicron, or just higher levels of population immunity, is currently not clear. MethodsRdRp target delay (RTD: a difference in cycle threshold value of RdRp - E > 3.5) in the Seegene Allplex 2019-nCoV PCR assay is a proxy marker for the Delta VOC. The absence of this proxy marker in the period of transition to Omicron was used to identify suspected Omicron VOC infections. Cox regression was performed for the outcome of hospital admission in those who tested positive for SARS-CoV-2 on the Seegene Allplex assay from 1 November to 14 December 2021 in the Western Cape Province, South Africa, public sector. Vaccination status at time of diagnosis, as well as prior diagnosed infection and comorbidities, were adjusted for. Results150 cases with RTD (proxy for Delta) and 1486 cases without RTD (proxy for Omicron) were included. Cases without RTD had a lower hazard of admission (adjusted Hazard Ratio [aHR] of 0.56, 95% confidence interval [CI] 0.34-0.91). Complete vaccination was protective of admission with an aHR of 0.45 (95%CI 0.26-0.77). ConclusionOmicron has resulted in a lower risk of hospital admission, compared to contemporaneous Delta infection in the Western Cape Province, when using the proxy marker of RTD. Under-ascertainment of reinfections with an immune escape variant like Omicron remains a challenge to accurately assessing variant virulence.

5.
Flavio Azevedo Figueiredo; Lucas Emanuel Ferreira Ramos; Rafael Tavares Silva; Magda Carvalho Pires; Daniela Ponce; Rafael Lima Rodrigues de Carvalho; Alexandre Vargas Schwarzbold; Amanda de Oliveira Maurilio; Ana Luiza Bahia Alves Scotton; Andresa Fontoura Garbini; Barbara Lopes Farace; Barbara Machado Garcia; Carla Thais Candida Alves Silva; Christiane Correa Rodrigues Cimini Cimini; Cintia Alcantara de Carvalho; Cristiane dos Santos Dias; Daniel Vitorio Silveira; Euler Roberto Fernandes Manenti; Evelin Paola de Almeida Cenci; Fernando Anschau; Fernando Graca Aranha; Filipe Carrilho de Aguiar; Frederico Bartolazzi; Giovanna Grunewald Vietta; Guilherme Fagundes Nascimento; Helena Carolina Noal; Helena Duani; Heloisa Reniers Vianna; Henrique Cerqueira Guimaraes; Joice Coutinho de Alvarenga; Jose Miguel Chatkin; Julia Parreiras Drumond de Moraes; Juliana Machado Rugolo; Karen Brasil Ruschel; Karina Paula Medeiros Prado Martins; Luanna Silva Monteiro Menezes; Luciana Siuves Ferreira Couto; Luis Cesar de Castro; Luiz Antonio Nasi; Maderson Alvares de Souza Cabral; Maiara Anschau Floriani; Maira Dias Souza; Maira Viana Rego Souza e Silva; Marcelo Carneiro; Mariana Frizzo de Godoy; Maria Aparecida Camargos Bicalho; Maria Clara Pontello Barbosa Lima; Matheus Carvalho Alves Nogueira; Matheus Fernandes Lopes Martins; Milton Henriques Guimaraes-Junior; Natalia da Cunha Severino Sampaio; Neimy Ramos de Oliveira; Patricia Klarmann Ziegelmann; Pedro Guido Soares Andrade; Pedro Ledic Assaf; Petronio Jose de Lima Martelli; POLIANNA DELFINO PEREIRA; Raphael Castro Martins; Rochele Mosmann Menezes; Saionara Cristina Francisco; Silvia Ferreira Araujo; Talita Fischer Oliveira; Thainara Conceicao de Oliveira; Thais Lorenna Souza Sales; Yuri Carlotto Ramires; Milena Soriano Marcolino.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268631

RESUMO

BackgroundAcute kidney injury (AKI) is frequently associated with COVID-19 and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalized COVID-19 patients. MethodsThis study is part of the multicentre cohort, the Brazilian COVID-19 Registry. A total of 5,212 adult COVID-19 patients were included between March/2020 and September/2020. We evaluated four categories of predictor variables: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) the need for mechanical ventilation at any time during hospitalization. Variable selection was performed using generalized additive models (GAM) and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. The accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Risk groups were proposed based on predicted probabilities: non-high (up to 14.9%), high (15.0 - 49.9%), and very high risk ([≥] 50.0%). ResultsThe median age of the model-derivation cohort was 59 (IQR 47-70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalization. The validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. Thirty-two variables were tested and four important predictors of the need for KRT during hospitalization were identified using GAM: need for mechanical ventilation, male gender, higher creatinine at admission, and diabetes. The MMCD score had excellent discrimination in derivation (AUROC = 0.929; 95% CI 0.918-0.939) and validation (AUROC = 0.927; 95% CI 0.911-0.941) cohorts an good overall performance in both cohorts (Brier score: 0.057 and 0.056, respectively). The score is implemented in a freely available online risk calculator (https://www.mmcdscore.com/). ConclusionThe use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalized COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.

6.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22269157

RESUMO

Well parameterised epidemiological models including accurate representation of contacts, are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here we fit age-stratified models, including re-estimation of relative contact rates between age-classes, to public data describing the 2020-21 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions, and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing Approximate Bayesian Computation (ABC) methodology with model-based proposals (MBP) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalisation rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrisation of dynamic transmission models that can inform data-driven public health policy and interventions.

7.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22269178

RESUMO

BackgroundWe investigated the effect of HIV on COVID-19 outcomes with attention to selection bias due to differential testing and to comorbidity burden. MethodsRetrospective cohort analysis using four hierarchical outcomes: positive SARS-CoV-2 test, COVID-19 hospitalization, intensive care unit (ICU) admission, and hospital mortality. The effect of HIV status was assessed using traditional covariate-adjusted, inverse probability weighted (IPW) analysis based on covariate distributions for testing bias (testing IPWs), HIV infection status (HIV IPWs), and combined models. Among PWH, we evaluated whether CD4 count and HIV plasma viral load (pVL) discriminated between those who did or did not develop study outcomes using receiver operating characteristic analysis. ResultsBetween March and November 2020, 63,319 people were receiving primary care services at UCSD, of whom 4,017 were people living with HIV (PWH). PWH had 2.1 times the odds of a positive SARS-CoV-2 test compared to those without HIV after weighting for potential testing bias, comorbidity burden, and HIV-IPW (95% CI 1.6-2.8). Relative to persons without HIV, PWH did not have an increased rate of COVID-19 hospitalization after controlling for comorbidities and testing bias [adjusted incidence rate ratio (aIRR): 0.5, 95% CI: 0.1 - 1.4]. PWH had neither a different rate of ICU admission (aIRR:1.08, 95% CI; 0.31 - 3.80) nor in-hospital death (aIRR:0.92, 95% CI; 0.08 - 10.94) in any examined model. Neither CD4 count nor pVL predicted any of the hierarchical outcomes among PWH. ConclusionsPWH have a higher risk of COVID-19 diagnosis but similar outcomes compared to those without HIV. Summary pointAfter considering the effects of potential bias due to differential testing, comorbidities, and other patient characteristics, people with HIV had an increased rate of SARS-CoV-2 positivity and similar rates of hospitalization, ICU admission, and death.

8.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21268036

RESUMO

ObjectivesDogs can be trained to identify several substances not detected by humans, corresponding to specific volatile organic compounds (VOCs). The presence of VOCs, triggered by SARS-CoV-2 infection, was tested in sweat from Long COVID patients. Patients and methodsAn axillary sweat sample of Long COVID patients and of COVID-19 negative, asymptomatic individuals was taken at home to avoid any hospital contact. Swabs were randomly placed in olfaction detection cones, and the material sniffed by at least 2 trained dogs. ResultsForty-five Long COVID patients, mean age 45 (6-71), 73.3% female, with prolonged symptoms evolving for a mean of 15.2 months (5-22) were tested. Dogs discriminated in a positive way 23/45 (51.1%). Long COVID patients versus 0/188 (0%) control individuals (p<.0001). ConclusionThis study suggests the persistence of a viral infection in some Long COVID patients and the possibility of providing a simple, highly sensitive, non-invasive test to detect viral presence, during acute and extended phases of COVID-19.

9.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22269045

RESUMO

BackgroundThe Omicron (B.1.1.529) variant of SARS-CoV-2 has rapidly achieved global dissemination, accounting for most infections in the United States by December 2021. Risk of severe outcomes associated with Omicron infections, as compared to earlier SARS-CoV-2 variants, remains unclear. MethodsWe analyzed clinical and epidemiologic data from cases testing positive for SARS-CoV-2 infection within the Kaiser Permanente Southern California healthcare system from November 30, 2021 to January 1, 2022, using S gene target failure (SGTF) as assessed by the ThermoFisher TaqPath ComboKit assay as a proxy for Omicron infection. We fit Cox proportional hazards models to compare time to any hospital admission and hospital admissions associated with new-onset respiratory symptoms, intensive care unit (ICU) admission, mechanical ventilation, and mortality among cases with Omicron and Delta (non-SGTF) variant infections. We fit parametric competing risk models to compare lengths of hospital stay among admitted cases with Omicron and Delta variant infections. ResultsOur analyses included 52,297 cases with SGTF (Omicron) and 16,982 cases with non-SGTF (Delta [B.1.617.2]) infections, respectively. Hospital admissions occurred among 235 (0.5%) and 222 (1.3%) of cases with Omicron and Delta variant infections, respectively. Among cases first tested in outpatient settings, the adjusted hazard ratios for any subsequent hospital admission and symptomatic hospital admission associated with Omicron variant infection were 0.48 (0.36-0.64) and 0.47 (0.35-0.62), respectively. Rates of ICU admission and mortality after an outpatient positive test were 0.26 (0.10-0.73) and 0.09 (0.01-0.75) fold as high among cases with Omicron variant infection as compared to cases with Delta variant infection. Zero cases with Omicron variant infection received mechanical ventilation, as compared to 11 cases with Delta variant infections throughout the period of follow-up (two-sided p<0.001). Median duration of hospital stay was 3.4 (2.8-4.1) days shorter for hospitalized cases with Omicron variant infections as compared to hospitalized patients with Delta variant infections, reflecting a 69.6% (64.0-74.5%) reduction in hospital length of stay. ConclusionsDuring a period with mixed Delta and Omicron variant circulation, SARS-CoV-2 infections with presumed Omicron variant infection were associated with substantially reduced risk of severe clinical endpoints and shorter durations of hospital stay. Trial registrationNot applicable

10.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22269002

RESUMO

ObjectivesTo describe the use and findings of cardiopulmonary imaging - chest X-ray (cX-ray), echocardiography (cEcho), chest CT (cCT), lung ultrasound (LUS)) and/or cardiac magnetic resonance imaging (cMRI) - in COVID-19-associated hospitalizations in Latin America (LATAM) BackgroundThe SARS-Cov-2 is one of the largest and most active threats to healthcare in living memory. There is an information gap on imaging services resources (ISR) used and their findings during the pandemic in LATAM. MethodsThis was a multicenter, prospective, observational study of COVID-19 inpatients conducted from March to December 2020 from 12 high-complexity centers in nine LATAM countries. Adults (> 18 yrs) with at least one imaging modality performed, followed from admission until discharge and/or in-hospital death, were included. ResultsWe studied 1435 hospitalized patients (64% males) with a median age of 58 years classified into three regions: 262 from Mexico (Mx), 428 from Central America and Caribbean (CAC), and 745 from South America (SAm). More frequent comorbidities were overweight/obesity (61%), hypertension (45%), and diabetes (27%). During hospitalization, 58% were admitted to ICU. The in-hospital mortality was 28% (95%CI 25-30) highest in Mx (37%). The most frequent cardiopulmonary imaging performed were cCT (61%)-more frequent in Mx and SAm-, and cX-ray (46%) -significantly used in CAC-. The cEcho was carried out in 18%, similarly among regions, and LUS in 7%, more frequently in Mx. The cMRI was performed in only one patient in the cohort. Abnormal findings on the cX-ray were related to peripheral (63%) or basal infiltrates (52%), and in cCT with ground glass infiltrates (89%). Both were more commonly in Mx. In LUS, interstitial syndrome (56%) was the most related abnormal finding, predominantly in Mx and CAC. ConclusionsThe use and findings of cardiopulmonary imaging in LATAM varied between regions and may have been influenced by clinical needs, the personnel protection measures and/or hospitalization location. Condensed AbstractThe SARS-Cov-2 is one of the largest and most active threats to healthcare in living memory. There is limited information on imaging services resources (ISR) used and their findings during the pandemic in LATAM. To our knowledge, RIMAC aimed the first international, multicenter study at registering the use and findings of cardiopulmonary imaging modalities performed for the diagnosis, prognosis, and treatment of patients hospitalized for infection with SARS-CoV-2 in Latin America. We studied their demographic parameters, comorbidities, in-hospital events, laboratory results, and treatments focusing on their impact in clinical complications.

11.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22269068

RESUMO

BackgroundThere was a national roll out of COVID Virtual Wards (CVW) during Englands second COVID-19 wave (Autumn 2020 - Spring 2021). These services used remote pulse oximetry monitoring for COVID-19 patients following discharge from hospital. A key aim was to enable rapid detection of patient deterioration. It was anticipated that the services would support early discharge and avoid readmissions, reducing pressure on beds. This study is an evaluation of the impact of the CVW services on hospital activity. MethodsUsing retrospective patient-level hospital admissions data, we built multivariate models to analyse the relationship between the implementation of CVW services and hospital activity outcomes: length of COVID-19 related stays and subsequent COVID-19 readmissions within 28 days. We used data from more than 98% of recorded COVID-19 hospital stays in England, where the patient was discharged alive between mid-August 2020 and late February 2021. FindingsWe found a longer length of stay for COVID-19 patients discharged from hospitals where a CVW was available, when compared to patients discharged from hospitals where there was no CVW (adjusted IRR 1{middle dot}05, 95% CI 1{middle dot}01 to 1{middle dot}09). We found no evidence of a relationship between the availability of CVW and subsequent rates of readmission for COVID-19 (adjusted OR 0{middle dot}95, 95% CI 0{middle dot}89 to 1{middle dot}02). InterpretationWe found no evidence of early discharges or reduced readmissions associated with the roll out of COVID Virtual Wards across England. Our analysis made pragmatic use of national-scale hospital data, but it is possible that a lack of specific data (for example, on which patients were enrolled) may have meant that true impacts, especially at a local level, were not ultimately discernible. FundingThis is independent research funded by the National Institute for Health Research, Health Services & Delivery Research programme and NHSEI. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPost-hospital virtual wards have been found to have a positive impact on patient outcomes when focussed on patients with specific diseases, for example those with heart disease. There has been less evidence of impact for more heterogenous groups of patients. While these services have been rolled out at scale in England, there has been little evidence thus far that post-hospital virtual wards (using pulse oximetry monitoring) have helped to reduce the length of stay of hospitalised COVID-19 patients, or rates of subsequent readmissions for COVID-19. Added value of this studyThis national-scale study provides evidence that the rollout of post-hospital discharge virtual ward services for COVID-19 patients in England did not reduce lengths of stay in hospital, or rates of readmission. Implications of all the available evidenceWhile there is currently an absence of evidence of positive impacts for COVID-19 patients discharged to a virtual ward, our study emphasises the need for quality data to be collected as part of future service implementation.

12.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268977

RESUMO

BackgroundAn accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions. Research objectiveTo develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to APACHE IVa and SOFA. MethodsA retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. AUROC was calculated for C-TIME, APACHE IVa and SOFA. ResultsThe median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO2/FiO2 ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72-0.79), vs 0.67 (0.64-0.71) and 0.59 (0.55-0.62) for APACHE and SOFA, respectively (Chi2 P<0.0001). ConclusionsC-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona.edu/ctime.

13.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268908

RESUMO

COVID-19 pandemics increased patient hospitalization impacting the hospital operations and patient care beyond COVID-19 patients. Although longitudinal symptom analysis may provide prognostic utility about clinical outcomes and critical hospitalization events of COVID-19 patients, such analysis is still missing. Here, we have analyzed over 10,000 hospitalized COVID-19 patients in the Houston Methodist Hospital at the Texas Medical Center from the beginning of pandemics till April of 2020. Our study used statistical and regression analysis over symptoms grouped into symptom groups based on their anatomical locations. Symptom intensity analysis indicated that symptoms peaked at the time of admission and subsided within the first week of hospitalization for most of the patients. Patients surviving the infection (n=9,263), had faster remission rates, usually within the first days of hospitalization compared to sustained symptom for the deceased patient group (n=1,042). The latter had also a longer hospitalization stay and more comorbidities including diabetes, cardiovascular, and kidney disease. Inflammation-associated systemic symptoms (Systemic) such as fever and chills, and lower respiratory system specific symptoms (Lower Respiratory System) such as shortness of breath and pneumonia, were the most informative for the analysis of longitudinal symptom dynamics. Our results suggest that the symptom remission rate could possess prognostic utility in evaluating patient hospitalization stay and clinical outcomes early in hospitalization. We believe knowledge and information about symptom remission rates can be used to improve hospital operations and patient care by using common and relatively easy to process source of information.

14.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268963

RESUMO

BackgroundNeutralizing monoclonal antibodies (mAbs) are authorized for early symptomatic COVID-19 patients. Whether mAbs are effective against the SARS-CoV-2 Delta variant, among vaccinated patients, or for prevention of mortality remains unknown. ObjectiveTo evaluate the effectiveness of mAb treatment in preventing progression to severe disease during the Delta phase of the pandemic and based on key baseline risk factors. Design, Setting, and PatientsObservational cohort study of non-hospitalized adult patients with SARS-CoV-2 infection from November 2020-October 2021, using electronic health records from a statewide health system plus state-level vaccine and mortality data. Using propensity matching, we selected approximately 2.5 patients not receiving mAbs for each patient who received mAbs. ExposureNeutralizing mAb treatment under emergency use authorization Main OutcomesThe primary outcome was 28-day hospitalization; secondary outcomes included mortality and severity of hospitalization. ResultsOf 36,077 patients with SARS-CoV-2 infection, 2,675 receiving mAbs were matched to 6,677 not receiving mAbs. Compared to mAb-untreated patients, mAb-treated patients had lower all-cause hospitalization (4.0% vs 7.7%; adjusted OR 0.48, 95%CI 0.38-0.60) and all-cause mortality (0.1% vs. 0.9%; adjusted OR 0.11, 95%CI 0.03-0.29) to day 28; differences persisted to day 90. Among hospitalized patients, mAb-treated patients had shorter hospital length of stay (5.8 vs. 8.5 days) and lower risk of mechanical ventilation (4.6% vs. 16.6%). Relative effectiveness was similar in preventing hospitalizations during the Delta variant phase (adjusted OR 0.35, 95%CI 0.25-0.50) and across subgroups. Lower number-needed-to-treat (NNT) to prevent hospitalization were observed for subgroups with higher baseline risk of hospitalization (e.g., multiple comorbidities (NNT=17) and not fully vaccinated (NNT=24) vs. no comorbidities (NNT=88) and fully vaccinated (NNT=81). ConclusionReal-world evidence demonstrated mAb effectiveness in reducing hospitalization among COVID-19 outpatients, including during the Delta variant phase, and conferred an overall 89% reduction in 28-day mortality. Early outpatient treatment with mAbs should be prioritized, especially for individuals with highest risk for hospitalization.

15.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268883

RESUMO

IntroductionCOVID-19 pandemic affects all populations worldwide, including adolescents. Adolescents can develop a severe form of COVID-19, especially with comorbidity underlying. The prior studies of the mRNA COVID-19 vaccine showed excellent effectiveness in adolescents. Therefore, this study aimed to evaluate the safety and effectiveness of the BBIBP-CorV vaccine with the immunobridging approach in Thai adolescents. MethodsThis single-center, prospective cohort study compared the immunogenicity after 2 doses of the BBIBO-CorV vaccine with 21 days interval of participants aged 12-17 years with 18-30 years at Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand. The key eligible criteria were healthy or had stable pre-existing comorbidity participants, aged 12-17 years. The primary endpoint was the anti-receptor binding domain antibody concentration at 4 weeks after dose 2 of the vaccine. In addition, safety profiles were solicited adverse events within 7 days after each dose of vaccine and any adverse events through 1 month after dose 2 of the vaccine. ResultsFour weeks after the second vaccination, the GMC of anti-RBD antibody in the adolescent cohort was 102.9 BAU/mL (95%CI; 91.0-116.4) and 36.9 BAU/mL (95%CI; 30.9-44.0) in the adult cohort. The GMR of the adolescent cohort was 2.79 (95%CI; 2.25-3.46, p-value; <0.0001) compared with the adult cohort which met non-inferiority criteria. The reactogenicity was slightly less reported in the adolescent cohort compared with the adult cohort. No serious adverse events were reported in both cohorts. ConclusionVaccination with the BBIBP-CorV vaccine in the adolescent participants was safe and effective.

16.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268729

RESUMO

BackgroundThere is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in geriatric settings. MethodsWe performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID-19) in four outbreak-affected wards, and all SARS-CoV-2 RT-PCR positive HCWs from a Swiss university-affiliated geriatric acute-care hospital that admitted both Covid-19 and non-Covid-19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS-CoV-2 involving patients and HCWs, in order to determine who infected whom. We evaluated general transmission patterns according to type of case (HCWs working in dedicated Covid-19 cohorting wards: HCWcovid; HCWs working in non-Covid-19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid-19: patientnoso) by deriving the proportion of infections attributed to each type of case across all posterior trees and comparing them to random expectations. ResultsDuring the study period (March 1 to May 7, 2020) we included 180 SARS-CoV-2 positive cases: 127 HCWs (91 HCWcovid, 36 HCWoutbreak) and 53 patients. The attack rates ranged from 10-19% for patients, and 21% for HCWs. We estimated that there were 16 importation events (3 patients, 13 HCWs) that jointly led to 16 secondary cases. Most patient-to-patient transmission events involved patients having shared a ward (97.6%, 95% credible interval [CrI] 90.4-100%), in contrast to those having shared a room (44.4%, 95%CrI 27.8-62.5%). Transmission events tended to cluster by type of case: patientnoso were almost twice as likely to be infected by other patientnoso than expected (observed:expected ratio 1.91, 95%CrI 1.08 - 4.00, p = 0.02); similarly, HCWoutbreak were more than twice as likely to be infected by other HCWoutbreak than expected (2.25, 95%CrI 1.00-8.00, p = 0.04). The proportion of infectors of HCWcovid were as expected as random. The proportions of high transmitters ([≥]2 secondary cases) were significantly higher among HCWoutbreak than patientnoso in the late phases (26.2% vs. 13.4%, p<2.2e-16) of the outbreak. ConclusionsMost importation events were linked to HCW. Unexpectedly, transmission between HCWcovid was more limited than transmission between patients and HCWoutbreak. This highlights gaps in infection control and suggests possible areas of improvements to limit the extent of nosocomial transmission.

17.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268975

RESUMO

BackgroundCorona Virus Disease 2019 (COVID-19) presentation resembles common flu or can be more severe; it can result in hospitalization with significant morbidity and/or mortality. We made an attempt to develop a predictive model and a scoring system to improve the diagnostic efficiency for COVID-19 mortality via analysis of clinical features and laboratory data on admission. MethodsWe retrospectively enrolled 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were extracted from the medical records and analyzed using multiple logistic regression analysis. ResultsA novel mortality risk score (COVID-19 BURDEN) was calculated, incorporating risk factors from this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (>16.2s), diastolic blood pressure ([≤]75 mmHg), BUN (>23 mg/dL), and raised LDH (>731 U/L) are the features comprising the scoring system. The patients are triaged to the groups of low- (score <4) and high-risk (score [≥] 4) groups. The area under the curve, sensitivity, and specificity for predicting non-response to medical therapy with scores of [≥] 4 were 0.831, 78.12%, and 70.95%, respectively. ConclusionUsing this scoring system in COVID-19 patients, the severity of the disease will be determined in the early stages of the disease, which will help to reduce hospital care costs and improve its quality and outcome.

18.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268792

RESUMO

Vaccines represent the best tool to prevent the severity course and fatal consequences of pandemic by new Coronavirus 2019 infection (SARS-CoV-2). Considering the limited data on vaccination of pediatric oncohematological patients, we develop a Consensus document to support the Italian pediatric hematological oncological (AIEOP) centers in a scientifically correct communication with families and patients and to promote vaccination. The topics of the Consensus were: SARS-CoV-2 infection and disease (COVID-19) in the pediatric subjects; COVID-19 vaccines (type, schedule); which and when to vaccinate; contraindications and risk of serious adverse events; rare adverse events; third dose and vaccination after COVID-19; and other general prevention measures. Using the Delphi methodology for Consensus, 21 statements and their corresponding rationale were elaborated and discussed with the representatives of 31 centers, followed by voting. AIEOP Centers showed an overall agreement on all the statements that were therefore approved. This consensus document highlights that children and adolescents affected by hematological and oncological diseases are a fragile category. Vaccination plays an important role to prevent COVID-19, to permit the regular administration of chemotherapy or other treatments, to perform control visits and hospital admissions, and to prevent treatment delays.

19.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21268536

RESUMO

BackgroundLong COVID or long-term complication after COVID-19 has the ability to affect health and quality of life. Knowledge about the burden and predictors could aid in their prevention and management. Most of the studies are from high-income countries and focus on severe cases. We did this study to estimate the prevalence and identify the characteristics and predictors of Long COVID among our patients. MethodologyWe recruited adult ([≥]18 years) patients who were diagnosed as Reverse Transcription Polymerase Chain Reaction (RTPCR) confirmed SARS-COV-2 infection and were either hospitalized or tested on outpatient basis. Eligible participants were followed up telephonically after four weeks of diagnosis of SARS-COV-2 infection to collect data on sociodemographic, clinical history, vaccination history, Cycle threshold (Ct) values during diagnosis and other variables. Characteristics of Long COVID were elicited, and multivariable logistic regression was done to find the predictors of Long COVID. ResultsWe have analyzed 487 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47). Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) participants. Prevalence of Long COVID among patients with mild/moderate disease (n = 415) was 23.4% (95% CI: 19.5%,27.7%) as compared to 62.5% (95% CI: 50.7%,73%) in severe/critical cases(n=72). The most common Long COVID symptom was fatigue (64.8%) followed by cough (32.4%). Statistically significant predictors of Long COVID were - Pre-existing medical conditions (Adjusted Odds ratio (aOR)=2.00, 95% CI: 1.16,3.44), having a more significant number of symptoms during acute phase of COVID-19 disease (aOR=11.24, 95% CI: 4.00,31.51), two doses of COVID-19 vaccination (aOR=2.32, 95% CI: 1.17,4.58), the severity of illness (aOR=5.71, 95% CI: 3.00,10.89) and being admitted to hospital (Odds ratio (OR)=3.89, 95% CI: 2.49,6.08). ConclusionA considerable proportion of COVID-19 cases reported Long COVID symptoms. More research is needed in Long COVID to objectively assess the symptoms and find the biological and radiological markers.

20.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22268674

RESUMO

IntroductionThere is a lack of studies in adolescents with COVID-19 from developing countries. We aimed to describe the clinical profile and risk factors for severe disease in adolescents hospitalized with COVID-19. MethodsA retrospective analysis of a prospectively admitted cohort of COVID-19 patients was performed at a tertiary hospital in north India. Adolescents aged 12 to 18 years who were hospitalized during the first wave (March 2020 to December 2020) and the second wave (March 2021 to June 2021) of the pandemic were included. Data on the demographic details, clinical presentation, laboratory parameters, disease severity at admission, treatments received, and in-hospital outcomes were retrieved and logistic regression was used to identify the risk factors for occurrence of moderate or severe disease. ResultsThe study included 197 adolescents with median (IQR) age 15 (13-17) years, of whom 117 (59.4%) were male. Among these, 170 (86.3%) were admitted during the 1st wave. Underlying comorbidities were present in 9 (4.6%) patients. At the time of hospital admission, 60 (30.9%) patients were asymptomatic. In the severity grading, 148 (84.6%) had mild, 16 (9.1%) had moderate, and 11 (6.3%) had severe disease. Fever (14.9%) and cough (14.9%) were the most commonly encountered symptoms. The median (IQR) duration of hospital stay was 10 (8-13) days and 6 (3.1%) patients died in hospital. The odds of moderate to severe disease were 3.8 for second wave, 1.9 for fever and 1.1 for raised C reactive protein (CRP). ConclusionIn our single-center study from northern India, adolescents admitted with COVID-19 had predominantly asymptomatic or mild disease. Admission during the second wave of COVID-19 pandemic, presence of fever and raised CRP were risk factors for moderate or severe disease. Lay SummaryFrom 3rd January 2022 onwards, adolescents between 15 to 18 years of age in India will be given Covaxin vaccine, as per the latest Indian government guidelines. In our study, we aimed to describe the clinical profile and risk factors for severe disease in adolescents hospitalized with COVID-19. Our study included 197 adolescents. 170 (86.3%) of them were admitted during the 1st wave and the rest 27 (13.7%) during the 2nd wave. At the time of hospital admission, 60 (30.9%) patients were asymptomatic. In the severity grading, 148 (84.6%) had mild, 16 (9.1%) had moderate, and 11 (6.3%) had severe disease. Fever (14.9%) and cough (14.9%) were the most commonly encountered symptoms. The median (IQR) duration of hospital stay was 10 (8-13) days and 6 (3.1%) patients died in hospital. 2nd wave, fever and high C reactive protein increased the odds of moderate to severe disease.

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