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1.
Am J Transplant ; 22(7): 1884-1892, 2022 07.
Article in English | MEDLINE | ID: covidwho-1956680

ABSTRACT

The development of donor-specific antibodies (DSA) after lung transplantation is common and results in adverse outcomes. In kidney transplantation, Belatacept has been associated with a lower incidence of DSA, but experience with Belatacept in lung transplantation is limited. We conducted a two-center pilot randomized controlled trial of de novo immunosuppression with Belatacept after lung transplantation to assess the feasibility of conducting a pivotal trial. Twenty-seven participants were randomized to Control (Tacrolimus, Mycophenolate Mofetil, and prednisone, n = 14) or Belatacept-based immunosuppression (Tacrolimus, Belatacept, and prednisone until day 89 followed by Belatacept, Mycophenolate Mofetil, and prednisone, n = 13). All participants were treated with rabbit anti-thymocyte globulin for induction immunosuppression. We permanently stopped randomization and treatment with Belatacept after three participants in the Belatacept arm died compared to none in the Control arm. Subsequently, two additional participants in the Belatacept arm died for a total of five deaths compared to none in the Control arm (log rank p = .016). We did not detect a significant difference in DSA development, acute cellular rejection, or infection between the two groups. We conclude that the investigational regimen used in this study is associated with increased mortality after lung transplantation.


Subject(s)
Lung Transplantation , Tacrolimus , Abatacept/therapeutic use , Antilymphocyte Serum/therapeutic use , Graft Rejection/drug therapy , Graft Rejection/etiology , Graft Rejection/prevention & control , Graft Survival , Humans , Immunosuppression Therapy , Immunosuppressive Agents/therapeutic use , Lung Transplantation/adverse effects , Mycophenolic Acid/therapeutic use , Pilot Projects , Prednisone
2.
Epidemiology ; 33(4): 457-464, 2022 Jul 01.
Article in English | MEDLINE | ID: covidwho-1922359

ABSTRACT

BACKGROUND: Explicit knowledge of total community-level immune seroprevalence is critical to developing policies to mitigate the social and clinical impact of SARS-CoV-2. Publicly available vaccination data are frequently cited as a proxy for population immunity, but this metric ignores the effects of naturally acquired immunity, which varies broadly throughout the country and world. Without broad or random sampling of the population, accurate measurement of persistent immunity post-natural infection is generally unavailable. METHODS: To enable tracking of both naturally acquired and vaccine-induced immunity, we set up a synthetic random proxy based on routine hospital testing for estimating total immunoglobulin G (IgG) prevalence in the sampled community. Our approach analyzed viral IgG testing data of asymptomatic patients who presented for elective procedures within a hospital system. We applied multilevel regression and poststratification to adjust for demographic and geographic discrepancies between the sample and the community population. We then applied state-based vaccination data to categorize immune status as driven by natural infection or by vaccine. RESULTS: We validated the model using verified clinical metrics of viral and symptomatic disease incidence to show the expected biologic correlation of these entities with the timing, rate, and magnitude of seroprevalence. In mid-July 2021, the estimated immunity level was 74% with the administered vaccination rate of 45% in the two counties. CONCLUSIONS: Our metric improves real-time understanding of immunity to COVID-19 as it evolves and the coordination of policy responses to the disease, toward an inexpensive and easily operational surveillance system that transcends the limits of vaccination datasets alone.


Subject(s)
COVID-19 , SARS-CoV-2 , Antibodies, Viral , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Immunoglobulin G , Seroepidemiologic Studies , Vaccination
3.
Patterns (N Y) ; 2(8): 100310, 2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1763926

ABSTRACT

We discuss several issues of statistical design, data collection, analysis, communication, and decision-making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324951

ABSTRACT

We discuss several issues of statistical design, data collection, analysis, communication, and decision making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature;rather, we use examples to illustrate statistical points that we think are important.

5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308839

ABSTRACT

Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification (MRP) to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our MRP method to track viral behavior in a mixed urban-suburban-rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of SARS-CoV-2 earlier and more accurately than currently accepted metrics.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-294843

ABSTRACT

We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coefficients. The proposed methods can be used for hierarchical mixed effects models with partial pooling over one group of predictors, as well as random effects models with partial pooling over two groups of predictors. We demonstrate the performance of the methods on two applications, one involving U.S. opinion polls and one involving the modeling of COVID-19 outbreaks in Israel using survey data. The algorithms involve analytical marginalization of regression coefficients followed by numerical integration of the remaining low-dimensional density. The dominant cost of the algorithms is an eigendecomposition computed once for each value of the outside parameter of integration. Our approach drastically reduces run times compared to state-of-the-art Markov chain Monte Carlo (MCMC) algorithms. The latter, in addition to being computationally expensive, can also be difficult to tune when applied to hierarchical models.

7.
Epidemiology ; 32(6): 792-799, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1443121

ABSTRACT

Throughout the coronavirus disease 2019 (COVID-19) pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our approach to tracking viral behavior in a mixed urban-suburban-rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) earlier and more accurately than currently accepted metrics. See video abstract at, http://links.lww.com/EDE/B859.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19 Testing , Hospitals , Humans , Pandemics
8.
JCI Insight ; 6(4)2021 02 22.
Article in English | MEDLINE | ID: covidwho-1150281

ABSTRACT

BackgroundMitochondrial DNA (MT-DNA) are intrinsically inflammatory nucleic acids released by damaged solid organs. Whether circulating cell-free MT-DNA quantitation could be used to predict the risk of poor COVID-19 outcomes remains undetermined.MethodsWe measured circulating MT-DNA levels in prospectively collected, cell-free plasma samples from 97 subjects with COVID-19 at hospital presentation. Our primary outcome was mortality. Intensive care unit (ICU) admission, intubation, vasopressor, and renal replacement therapy requirements were secondary outcomes. Multivariate regression analysis determined whether MT-DNA levels were independent of other reported COVID-19 risk factors. Receiver operating characteristic and area under the curve assessments were used to compare MT-DNA levels with established and emerging inflammatory markers of COVID-19.ResultsCirculating MT-DNA levels were highly elevated in patients who eventually died or required ICU admission, intubation, vasopressor use, or renal replacement therapy. Multivariate regression revealed that high circulating MT-DNA was an independent risk factor for these outcomes after adjusting for age, sex, and comorbidities. We also found that circulating MT-DNA levels had a similar or superior area under the curve when compared against clinically established measures of inflammation and emerging markers currently of interest as investigational targets for COVID-19 therapy.ConclusionThese results show that high circulating MT-DNA levels are a potential early indicator for poor COVID-19 outcomes.FundingWashington University Institute of Clinical Translational Sciences COVID-19 Research Program and Washington University Institute of Clinical Translational Sciences (ICTS) NIH grant UL1TR002345.


Subject(s)
COVID-19/diagnosis , Cell-Free Nucleic Acids/blood , DNA, Mitochondrial/blood , Severity of Illness Index , Aged , Aged, 80 and over , Biomarkers/blood , COVID-19/mortality , COVID-19/therapy , COVID-19/virology , Female , Follow-Up Studies , Hospital Mortality , Humans , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Prospective Studies , ROC Curve , Renal Replacement Therapy/statistics & numerical data , Respiration, Artificial/statistics & numerical data , Risk Factors , SARS-CoV-2/isolation & purification , Vasoconstrictor Agents/therapeutic use
9.
Chance ; 33(3):58, 2020.
Article in English | ProQuest Central | ID: covidwho-1089526

ABSTRACT

Gelman offers insights about evidence versus truth in the coronavirus pandemic. Coronavirus tests were hard to come by at that time, and everyone knew that the number of confirmed cases was much lower than the total number of people exposed, but it was not clear how much lower. The Stanford study was posted on the preprint server medRxiv on April 11, and its authors were soon writing op-eds and explaining the implications of their findings on national television. The key result from their preprint was a range of estimates of 2.5% to 4.2% for the prevalence rate, implying between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases.

10.
Lancet Public Health ; 6(1): e30-e38, 2021 01.
Article in English | MEDLINE | ID: covidwho-1072037

ABSTRACT

BACKGROUND: Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. METHODS: Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated cross-sectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. FINDINGS: Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280 327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29-0·54) to 0·06% (0·04-0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient-facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17-24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17-24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45-68%, dependent on calendar time. INTERPRETATION: Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards. FUNDING: Department of Health and Social Care.


Subject(s)
COVID-19/epidemiology , Public Health Surveillance/methods , Residence Characteristics , Adolescent , Adult , Aged , COVID-19/diagnosis , COVID-19 Testing , Child , Child, Preschool , England/epidemiology , Female , Health Surveys , Humans , Male , Middle Aged , Prevalence , Young Adult
11.
J Heart Lung Transplant ; 40(3): 169-171, 2021 03.
Article in English | MEDLINE | ID: covidwho-1002543

ABSTRACT

We are entering 2021 with an expanding and effective COVID-19 vaccine armamentarium. Recent interim results from COVID-19 vaccine trials, including more than 80,000 participants worldwide, demonstrate remarkable efficacy and low rate of serious adverse events. Based on experience with other vaccines in transplant recipients and knowing the risk of severe COVID-19 in this population, we believe that COVID-19 vaccines provide potential benefit with minimal risk. We strongly support and encourage COVID-19 vaccination of our transplant recipients.


Subject(s)
COVID-19 Vaccines/pharmacology , COVID-19/prevention & control , Organ Transplantation , Pandemics , SARS-CoV-2/immunology , Transplant Recipients , Vaccination/methods , COVID-19/epidemiology , Humans
12.
Journal of the Royal Statistical Society Series C-Applied Statistics ; 2020.
Article | WHO COVID | ID: covidwho-733195

ABSTRACT

When testing for a rare disease, prevalence estimates can be highly sensitive to uncertainty in the specificity and sensitivity of the test. Bayesian inference is a natural way to propagate these uncertainties, with hierarchical modelling capturing variation in these parameters across experiments. Another concern is the people in the sample not being representative of the general population. Statistical adjustment cannot without strong assumptions correct for selection bias in an opt-in sample, but multilevel regression and post-stratification can at least adjust for known differences between the sample and the population. We demonstrate hierarchical regression and post-stratification models with code in Stan and discuss their application to a controversial recent study of SARS-CoV-2 antibodies in a sample of people from the Stanford University area. Wide posterior intervals make it impossible to evaluate the quantitative claims of that study regarding the number of unreported infections. For future studies, the methods described here should facilitate more accurate estimates of disease prevalence from imperfect tests performed on non-representative samples.

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