Lung protective ventilation based on donor size is associated with a lower risk of severe primary graft dysfunction after lung transplantation

BACKGROUND

Mechanical ventilation immediately after lung transplantation may impact the development of primary graft dysfunction (PGD), particularly in cases of donor-recipient size mismatch as ventilation is typically based on recipient rather than donor size.

METHODS

We conducted a retrospective cohort study of adult bilateral lung transplant recipients at our center between January 2010 and January 2017. We defined donor-based lung protective ventilation (dLPV) as 6 to 8 ml/kg of donor ideal body weight and plateau pressure <30 cm H₂O. We calculated the donor-recipient predicted total lung capacity (pTLC) ratio and used logistic regression to examine relationships between pTLC ratio, dLPV and PGD grade 3 at 48 to 72 hours. We used Cox proportional hazards modelling to examine the relationship between pTLC ratio, dLPV and 1-year survival.

RESULTS

The cohort included 373 recipients; 24 (6.4%) developed PGD grade 3 at 48 to 72 hours, and 213 (57.3%) received dLPV. Mean pTLC ratio was 1.04 ± 0.18. dLPV was associated with significantly lower risks of PGD grade 3 (OR = 0.44; 95% CI: 0.29-0.68, p < 0.001) and 1-year mortality (HR = 0.49; 95% CI: 0.29-0.8, p = 0.018). There was a significant association between pTLC ratio and the risk of PGD grade 3, but this was attenuated by the use of dLPV.

CONCLUSIONS

dLPV is associated with decreased risk of PGD grade 3 at 48 to 72 hours and decreased 1-year mortality. Additionally, dLPV attenuates the association between pTLC and both PGD grade 3 and 1-year mortality. Donor-based ventilation strategies may help to mitigate the risk of PGD and other adverse outcomes associated with size mismatch after lung transplantation.

Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data

It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, Bayesian hierarchical modeling yields an adaptive procedure that uses the data to determine how much adjustment to perform. The result is a novel analysis with increased statistical efficiency compared with the default analysis based on difference estimates. We demonstrate this procedure on two real examples, as well as on a series of simulated datasets. We show that the increased efficiency can have real-world consequences in terms of the conclusions that can be drawn from the experiments. We also discuss the relevance of this work to causal inference and statistical design and analysis more generally.

Know your population and know your model: Using model-based regression and poststratification to generalize findings beyond the observed sample

Psychology research often focuses on interactions, and this has deep implications for inference from nonrepresentative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This can be seen as an extension of multilevel regression and poststratification (MRP), a method used in political science and other areas of survey research, where researchers wish to generalize from a sparse and possibly nonrepresentative sample to the general population. In this article, we discuss areas where this method can be used in the psychological sciences. We use our method to estimate the norming distribution for the Big Five Personality Scale using open source data. We argue that large open data sources like this and other collaborative data sources can potentially be combined with MRP to help resolve current challenges of generalizability and replication in psychology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

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.

Fallout of Lead Over Paris From the 2019 Notre-Dame Cathedral Fire

The roof and spire of Notre-Dame cathedral in Paris that caught fire and collapsed on 15 April 2019 were covered with 460 t of lead (Pb). Government reports documented Pb deposition immediately downwind of the cathedral and a twentyfold increase in airborne Pb concentrations at a distance of 50 km in the aftermath. For this study, we collected 100 samples of surface soil from tree pits, parks, and other sites in all directions within 1 km of the cathedral. Concentrations of Pb measured by X-ray fluorescence range from 30 to 9,000 mg/kg across the area, with a higher proportion of elevated concentrations to the northwest of the cathedral, in the direction of the wind prevailing during the fire. By integrating these observations with a Gaussian process regression model, we estimate that the average concentration of Pb in surface soil downwind of the cathedral is 430 (95% interval, 300-590) mg/kg, nearly double the average Pb concentration in the other directions of 240 (95% interval, 170-320) mg/kg. The difference corresponds to an integrated excess Pb inventory within a 1 km radius of 1.0 (95% interval, 0.5-1.5) t, about 0.2% of all the Pb covering the roof and spire. This is over 6 times the estimated amount of Pb deposited downwind 1-50 km from the cathedral. To what extent the concentrated fallout within 1 km documented here temporarily exposed the downwind population to Pb is difficult to confirm independently because too few soil, dust, and blood samples were collected immediately after the fire.

A consensus-based transparency checklist

We present a consensus-based checklist to improve and document the transparency of research reports in social and behavioural research. An accompanying online application allows users to complete the form and generate a report that they can submit with their manuscript or post to a public repository.

Childhood obesity intervention studies: A narrative review and guide for investigators, authors, editors, reviewers, journalists, and readers to guard against exaggerated effectiveness claims

Being able to draw accurate conclusions from childhood obesity trials is important to make advances in reversing the obesity epidemic. However, obesity research sometimes is not conducted or reported to appropriate scientific standards. To constructively draw attention to this issue, we present 10 errors that are commonly committed, illustrate each error with examples from the childhood obesity literature, and follow with suggestions on how to avoid these errors. These errors are as follows: using self-reported outcomes and teaching to the test; foregoing control groups and risking regression to the mean creating differences over time; changing the goal posts; ignoring clustering in studies that randomize groups of children; following the forking paths, subsetting, p-hacking, and data dredging; basing conclusions on tests for significant differences from baseline; equating "no statistically significant difference" with "equally effective"; ignoring intervention study results in favor of observational analyses; using one-sided testing for statistical significance; and stating that effects are clinically significant even though they are not statistically significant. We hope that compiling these errors in one article will serve as the beginning of a checklist to support fidelity in conducting, analyzing, and reporting childhood obesity research.

Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan

This report presents a new implementation of the Besag-York-Mollié (BYM) model in Stan, a probabilistic programming platform which does full Bayesian inference using Hamiltonian Monte Carlo (HMC). We review the spatial auto-correlation models used for areal data and disease risk mapping, and describe the corresponding Stan implementations. We also present a case study using Stan to fit a BYM model for motor vehicle crashes injuring school-age pedestrians in New York City from 2005 to 2014 localized to census tracts. Stan efficiently fit our multivariable BYM model having a large number of observations (n=2095 census tracts) with small outcome counts < 10 in the majority of tracts. Our findings reinforced that neighborhood income and social fragmentation are significant correlates of school-age pedestrian injuries. We also observed that nationally-available census tract estimates of commuting methods may serve as a useful indicator of underlying pedestrian densities.

The Implementation of Randomization Requires Corrected Analyses. Comment on “Comprehensive Nutritional and Dietary Intervention for Autism Spectrum Disorder—A Randomized, Controlled 12-Month Trial, Nutrients Nutrients 2018, 10, 369”

We commend Adams et al [...].

Impact of SLCO1B3 polymorphisms on clinical outcomes in lung allograft recipients receiving mycophenolic acid

Single-nucleotide polymorphisms (SNPs) in genes involved in mycophenolic acid (MPA) metabolism have been shown to contribute to variable MPA exposure, but their clinical effects are unclear. We aimed to determine if SNPs in key genes in MPA metabolism affect outcomes after lung transplantation. We performed a retrospective cohort study of 275 lung transplant recipients, 228 receiving mycophenolic acid and a control group of 47 receiving azathioprine. Six SNPs known to regulate MPA exposure from the SLCO, UGT and MRP2 families were genotyped. Primary outcome was 1-year survival. Secondary outcomes were 3-year survival, nonminimal (≥A2 or B2) acute rejection, and chronic lung allograft dysfunction (CLAD). Statistical analyses included time-to-event Kaplan-Meier with log-rank test and Cox regression modeling. We found that SLCO1B3 SNPs rs4149117 and rs7311358 were associated with decreased 1-year survival [rs7311358 HR 7.76 (1.37-44.04), p = 0.021; rs4149117 HR 7.28 (1.27-41.78), p = 0.026], increased risk for nonminimal acute rejection [rs4149117 TT334/T334G: OR 2.01 (1.06-3.81), p = 0.031; rs7311358 GG699/G699A: OR 2.18 (1.13-4.21) p = 0.019] and lower survival through 3 years for MPA patients but not for azathioprine patients. MPA carriers of either SLCO1B3 SNP had shorter survival after CLAD diagnosis (rs4149117 p = 0.048, rs7311358 p = 0.023). For the MPA patients, Cox regression modeling demonstrated that both SNPs remained independent risk factors for death. We conclude that hypofunctional SNPs in the SLCO1B3 gene are associated with an increased risk for acute rejection and allograft failure in lung transplant recipients treated with MPA.