'Like walking with someone as opposed to trying to catch up to them'-Dynamics at play when clinicians and young people formulate together.

OBJECTIVE: The aim of the present study was to explore the social process of formulation in talk therapy between young people and clinicians. DESIGN: Qualitative semi-structured interview study. METHOD: Ten young people (male = 6, female = 4, age range = 16-23 years) and nine clinicians from various disciplines within a youth mental health service were interviewed. Constructivist grounded theory was used for the analysis. RESULTS: Four themes were constructed from the data; a 'level playing field' between young person and clinician enables formulation, formulating is a constant process of getting it right and getting it wrong, emotional expression and attunement get us closer to each other and to understanding, and 'formulation versus diagnosis' can create tension in the therapy room. The constructivist grounded theory devised demonstrated how the dynamics of power, collaboration, openness, and the therapeutic relationship are constantly in flux during the process of formulation. CONCLUSION: The paper presents a constructivist grounded theory which incorporates dynamics relating to power, collaboration, and openness. The importance of the therapeutic relationship is also emphasised. The theory encourages continuous and recursive personal reflection by the therapist as to how they can be optimally attuned to the dynamics of power, collaboration, and openness with young people.

The Power of Preference Signaling: A Monumental Shift in the Orthopaedic Surgery Application Process.

INTRODUCTION: Orthopaedic surgery has consistently been one of the most competitive specialties in the US residency selection process. This is due in part to the steady upward trend in average applications received per program and average applications submitted per applicant, which is of growing concern. With the implementation of the Preference Signaling Program, the total number of applications has now dropped for the first time in many years, indicating signaling may improve the application process. The hypothesis is that signaling has led to a decrease in applications sent by applicants and a decrease in applications received by programs. METHODS: A 7-question survey regarding their interview and match statistics was sent to orthopaedic surgery residency programs that participated in the Electronic Residency Application Service during the 2023-2024 application cycle. A response from the program director/administrator was then recorded. RESULTS: Our program search yielded 159 programs with 106 respondents (66.7%). 82 programs (78.8%) solely interviewed applicants who signaled their program. 92.7% of current interns signaled the program where they matched, and 88 programs (84.6%) matched only applicants who signaled. 95 programs (89.6%) revealed that implementing signaling has improved the application process. CONCLUSION: Most of the programs only interviewed applicants who also signaled, and nearly all matched orthopaedic surgery applicants from the 2022-2023 cycle signaled their matching program. Orthopaedic surgery applicants should consider only applying to 30 programs and using all 30 available signals. Applicants should also be more confident knowing that beyond the 30 signals they use, there is limited support to say that they will receive an interview outside of these 30 applications. Orthopaedic surgery programs will also now have the ability to allocate more time to applicants most interested in their program, given the reduction of applications.

Robust identification of environmental exposures and community characteristics predictive of rapid lung disease progression.

Environmental exposures and community characteristics have been linked to accelerated lung function decline in people with cystic fibrosis (CF), but geomarkers, the measurements of these exposures, have not been comprehensively evaluated in a single study. To determine which geomarkers have the greatest predictive potential for lung function decline and pulmonary exacerbation (PEx), a retrospective longitudinal cohort study was performed using novel Bayesian joint covariate selection methods, which were compared with respect to PEx predictive accuracy. Non-stationary Gaussian linear mixed effects models were fitted to data from 151 CF patients aged 6-20 receiving care at a CF Center in the midwestern US (2007-2017). The outcome was forced expiratory volume in 1 s of percent predicted (FEV1pp). Target functions were used to predict PEx from established criteria. Covariates included 11 routinely collected clinical/demographic characteristics and 45 geomarkers comprising 8 categories. Unique covariate selections via four Bayesian penalized regression models (elastic-net, adaptive lasso, ridge, and lasso) were evaluated at both 95 % and 90 % credible intervals (CIs). Resultant models included one to 6 geomarkers (air temperature, percentage of tertiary roads outside urban areas, percentage of impervious nonroad outside urban areas, fine atmospheric particulate matter, fraction achieving high school graduation, and motor vehicle theft) representing weather, impervious descriptor, air pollution, socioeconomic status, and crime categories. Adaptive lasso had the lowest information criteria. For PEx predictive accuracy, covariate selection from the 95 % CI elastic-net had the highest area under the receiver-operating characteristic curve (mean ± standard deviation; 0.780 ± 0.026) along with the 95 % CI ridge and lasso methods (0.780 ± 0.027). The 95 % CI elastic-net had the highest sensitivity (0.773 ± 0.083) while the 95 % CI adaptive lasso had the highest specificity (0.691 ± 0.087), suggesting the need for different geomarker sets depending on monitoring goals. Surveillance of certain geomarkers embedded in prediction algorithms can be used in real-time warning systems for PEx onset.

Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM.

BACKGROUND: Sodium-glucose cotransporter 2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) reduce the risk of major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head clinical trials. OBJECTIVES: The aim of this study was to compare the cardiovascular effectiveness of SGLT2is, GLP-1 RAs, dipeptidyl peptidase-4 inhibitors (DPP4is), and clinical sulfonylureas (SUs) as second-line antihyperglycemic agents in T2DM. METHODS: Across the LEGEND-T2DM (Large-Scale Evidence Generation and Evaluation Across a Network of Databases for Type 2 Diabetes Mellitus) network, 10 federated international data sources were included, spanning 1992 to 2021. In total, 1,492,855 patients with T2DM and cardiovascular disease (CVD) on metformin monotherapy were identified who initiated 1 of 4 second-line agents (SGLT2is, GLP-1 RAs, DPP4is, or SUs). Large-scale propensity score models were used to conduct an active-comparator target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, on-treatment Cox proportional hazards models were fit for 3-point MACE (myocardial infarction, stroke, and death) and 4-point MACE (3-point MACE plus heart failure hospitalization) risk and HR estimates were combined using random-effects meta-analysis. RESULTS: Over 5.2 million patient-years of follow-up and 489 million patient-days of time at risk, patients experienced 25,982 3-point MACE and 41,447 4-point MACE. SGLT2is and GLP-1 RAs were associated with lower 3-point MACE risk than DPP4is (HR: 0.89 [95% CI: 0.79-1.00] and 0.83 [95% CI: 0.70-0.98]) and SUs (HR: 0.76 [95% CI: 0.65-0.89] and 0.72 [95% CI: 0.58-0.88]). DPP4is were associated with lower 3-point MACE risk than SUs (HR: 0.87; 95% CI: 0.79-0.95). The pattern for 3-point MACE was also observed for the 4-point MACE outcome. There were no significant differences between SGLT2is and GLP-1 RAs for 3-point or 4-point MACE (HR: 1.06 [95% CI: 0.96-1.17] and 1.05 [95% CI: 0.97-1.13]). CONCLUSIONS: In patients with T2DM and CVD, comparable cardiovascular risk reduction was found with SGLT2is and GLP-1 RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of SGLT2is and GLP-1 RAs should be prioritized as second-line agents in those with established CVD.

High baseline perivascular space volume in basal ganglia is associated with attention and executive function decline in Parkinson's disease.

BACKGROUND: Pathologic perivascular spaces (PVS), the fluid-filled compartments surrounding brain vasculature, may underlie cognitive decline in Parkinson's disease (PD). However, whether this impacts specific cognitive domains has not been investigated. OBJECTIVES: This study examined the relationship of PVS volume at baseline with domain-specific and global cognitive change over 2 years in PD individuals. METHODS: A total of 39 individuals with PD underwent 3T T1w magnetic resonance imaging to determine PVS volume fraction (PVS volume normalized to total regional volume) within (i) centrum semiovale, (ii) prefrontal white matter (medial orbitofrontal, rostral middle frontal, and superior frontal), and (iii) basal ganglia. A neuropsychological battery included assessment of cognitive domains and global cognitive function at baseline and after 2 years. RESULTS: Higher basal ganglia PVS at baseline was associated with greater decline in attention, executive function, and global cognition scores. CONCLUSIONS: While previous reports have associated elevated PVS volume in the basal ganglia with decline in global cognition in PD, our findings show such decline may affect the attention and executive function domains.

Development and validation of a patient-level model to predict dementia across a network of observational databases.

BACKGROUND: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. METHODS: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55-84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. RESULTS: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. CONCLUSIONS: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

Epigenome-wide association study of fluoride exposure during early adolescence and DNA methylation among U.S. children.

Exposure to fluoride in early childhood has been associated with altered cognition, intelligence, attention, and neurobehavior. Fluoride-related neurodevelopment effects have been shown to vary by sex and very little is known about the mechanistic processes involved. There is limited research on how fluoride exposure impacts the epigenome, potentially leading to changes in DNA methylation of specific genes regulating key developmental processes. In the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS), urine samples were analyzed using a microdiffusion method to determine childhood urinary fluoride adjusted for specific gravity (CUFsg) concentrations. Whole blood DNA methylation was assessed using the Infinium MethylationEPIC BeadChip 850 k Array. In a cross-sectional analysis, we interrogated epigenome-wide DNA methylation at 775,141 CpG loci across the methylome in relation to CUFsg concentrations in 272 early adolescents at age 12 years. Among all participants, higher concentrations of CUF were associated with differential methylation of one CpG (p < 6 × 10-8) located in the gene body of GBF1 (cg25435255). Among females, higher concentrations of CUFsg were associated with differential methylation of 7 CpGs; only three CpGs were differentially methylated among males with no overlap of significant CpGs observed among females. Secondary analyses revealed several differentially methylated regions (DMRs) and CpG loci mapping to genes with key roles in psychiatric outcomes, social interaction, and cognition, as well as immunologic and metabolic phenotypes. While fluoride exposure may impact the epigenome during early adolescence, the functional consequences of these changes are unclear warranting further investigation.

Variability in personal exposure to ultrafine and fine particles by microenvironment among adolescents in Cincinnati.

Personal exposure to air pollution is influenced by an individual's time-activity patterns, but data regarding personal exposure to air pollution among children populations is lacking. The objective of this study was to characterize personal exposure to both PM2.5 and ultrafine particles (UFPs) using two portable real-time monitors, combined with GPS logging, and describe the relationship between these exposures across time and microenvironments among adolescents with asthma. Participants completed personal exposure monitoring for seven consecutive days and PM2.5 and UFP concentrations experienced in five microenvironments were determined using GPS location and mobility data. Average UFP and PM2.5 exposure varied across microenvironments with the highest average UFP exposure concentrations observed in transit (10,910 ± 27,297 p/cc), though correlations between UFP and PM2.5 concentrations in transit were low (0.24) and did not reach statistical significance (p > 0.05). We calculated exposure time ratios for each participant. Across participants, UFP exposures within the transit environment demonstrated the highest ratio (average exposure-time ratio = 1.91) though only 3 % of overall sampling time among all participants was monitored in transit (74/2840 h). We did not observe similar trends among PM2.5 exposures. The correlations between UFP and PM2.5 exposures varied throughout the day, with an overall correlation ranging from moderate to high among participants. Identifying microenvironments and activities where high exposure to PM occurs may offer potential targets for interventions to reduce overall exposures among sensitive groups.

Fear of Reinjury, Psychological Factors, and Sport Played Have Negative Impact on Return to Sport Following Medial Patellofemoral Ligament Reconstruction for Patellar Instability.

PURPOSE: To analyze factors that affect return to sport after medial patellofemoral ligament reconstruction (MPFLR), such as psychological factors, sport played, and a positive apprehension test following surgery, and to determine the average return to sport rates and time to return to sport. METHODS: A literature search was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Included studies met the following criteria: patients underwent MPFLR for patellar instability, return to sport was recorded, and a factor that affected return to sport was mentioned. Search terms included medial patellofemoral ligament, tibial tubercle osteotomy, tibial tubercle transfer, return to play, and return to sport. RESULTS: Eighteen of 632 identified studies met inclusion criteria, and 1,072 patients who underwent MFPLR were recorded. Return-to-sport rates and mean/median time ranged from 60.0% to 100% and 3 to 10.4 months, respectively. Of the patients, 55.6% to 84.0% returned to sport without decreasing the level of competition. Six of 12 studies (50.0%) reported fear of reinjury as the top reason for patients not returning or returning at a lower level of sport. Volleyball/handball had the lowest return to the same level following surgery (18.2%-50.0%). CONCLUSIONS: Athletes who underwent MPFLR following recurrent patellar instability returned to sport at a range of 60.0% to 100%. Return to sport at the same level or higher was found to have a lower maximum rate at 55.6% to 84.0%. Fear of reinjury and sport played were found to have a substantial impact on ability to return to sport. Surgeons can use this information to advise patients on expectations following surgery. LEVEL OF EVIDENCE: Level IV, systematic review of Level III and IV studies.

The Legacy of Redlining: Increasing Childhood Asthma Disparities Through Neighborhood Poverty.

RATIONALE: Identifying the root causes of racial disparities in childhood asthma is critical for health equity. OBJECTIVES: To determine if the 1930's racist policy of redlining led to present-day disparities in childhood asthma by increasing community-level poverty and decreasing neighborhood socioeconomic position (SEP). METHODS: We categorized census tracts at birth of participants from the Children's Respiratory and Environmental Workgroup birth cohort consortium into A, B, C, or D categories as defined by the Home Owners Loan Corporation (HOLC), with D being the highest perceived risk. Surrogates of present-day neighborhood-level SEP were determined for each tract including the percentage of low-income households, the CDC's social vulnerability index (SVI), and other tract-level variables. We performed causal mediation analysis, which, under the assumption of no unmeasured confounding, estimates the direct and mediated pathways by which redlining may cause asthma disparities through census tract-level mediators adjusting for individual-level covariates. MEASUREMENTS AND MAIN RESULTS: Of 4,849 children, the cumulative incidence of asthma through age 11 was 26.6% and 13.2% resided in census tracts with a HOLC grade of D. In mediation analyses, residing in grade D tracts (aOR = 1.03 [95%CI 1.01,1.05]) was significantly associated with childhood asthma, with 79% of this increased risk mediated by percentage of low-income households; results were similar for SVI and other tract-level variables. CONCLUSIONS: The historical structural racist policy of redlining led to present-day asthma disparities in part through decreased neighborhood SEP. Policies aimed at reversing the effects of structural racism should be considered to create more just, equitable, and healthy communities.

Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels.

OBJECTIVES: To automatically construct a drug indication taxonomy from drug labels using generative Artificial Intelligence (AI) represented by the Large Language Model (LLM) GPT-4 and real-world evidence (RWE). MATERIALS AND METHODS: We extracted indication terms from 46 421 free-text drug labels using GPT-4, iteratively and recursively generated indication concepts and inferred indication concept-to-concept and concept-to-term subsumption relations by integrating GPT-4 with RWE, and created a drug indication taxonomy. Quantitative and qualitative evaluations involving domain experts were performed for cardiovascular (CVD), Endocrine, and Genitourinary system diseases. RESULTS: 2909 drug indication terms were extracted and assigned into 24 high-level indication categories (ie, initially generated concepts), each of which was expanded into a sub-taxonomy. For example, the CVD sub-taxonomy contains 242 concepts, spanning a depth of 11, with 170 being leaf nodes. It collectively covers a total of 234 indication terms associated with 189 distinct drugs. The accuracies of GPT-4 on determining the drug indication hierarchy exceeded 0.7 with "good to very good" inter-rater reliability. However, the accuracies of the concept-to-term subsumption relation checking varied greatly, with "fair to moderate" reliability. DISCUSSION AND CONCLUSION: We successfully used generative AI and RWE to create a taxonomy, with drug indications adequately consistent with domain expert expectations. We show that LLMs are good at deriving their own concept hierarchies but still fall short in determining the subsumption relations between concepts and terms in unregulated language from free-text drug labels, which is the same hard task for human experts.

Assessing Covariate Balance with Small Sample Sizes.

Propensity score adjustment addresses confounding by balancing covariates in subject treatment groups through matching, stratification, inverse probability weighting, etc. Diagnostics ensure that the adjustment has been effective. A common technique is to check whether the standardized mean difference for each relevant covariate is less than a threshold like 0.1. For small sample sizes, the probability of falsely rejecting the validity of a study because of chance imbalance when no underlying balance exists approaches 1. We propose an alternative diagnostic that checks whether the standardized mean difference statistically significantly exceeds the threshold. Through simulation and real-world data, we find that this diagnostic achieves a better trade-off of type 1 error rate and power than standard nominal threshold tests and not testing for sample sizes from 250 to 4000 and for 20 to 100,000 covariates. In network studies, meta-analysis of effect estimates must be accompanied by meta-analysis of the diagnostics or else systematic confounding may overwhelm the estimated effect. Our procedure for statistically testing balance at both the database level and the meta-analysis level achieves the best balance of type-1 error rate and power. Our procedure supports the review of large numbers of covariates, enabling more rigorous diagnostics.

Particulate Matter Exposure and Default Mode Network Equilibrium During Early Adolescence.

Background: Air pollution exposure has been associated with adverse cognitive and mental health outcomes in children, adolescents, and adults, although youth may be particularly susceptible given ongoing brain development. However, the neurodevelopmental mechanisms underlying the associations among air pollution, cognition, and mental health remain unclear. We examined the impact of particulate matter (PM2.5) on resting-state functional connectivity (rsFC) of the default mode network (DMN) and three key attention networks: dorsal attention, ventral attention, and cingulo-opercular. Methods: Longitudinal changes in rsFC within/between networks were assessed from baseline (9-10 years) to the 2-year follow-up (11-12 years) in 10,072 youth (M ± SD = 9.93 + 0.63 years; 49% female) from the Adolescent Brain Cognitive Development (ABCD®) study. Annual ambient PM2.5 concentrations from the 2016 calendar year were estimated using hybrid ensemble spatiotemporal models. RsFC was estimated using functional neuroimaging. Linear mixed models were used to test associations between PM2.5 and change in rsFC over time while adjusting for relevant covariates (e.g., age, sex, race/ethnicity, parental education, and family income) and other air pollutants (O3, NO2). Results: A PM2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM2.5 exposure was associated with alterations in the development and equilibrium of the DMN-a network implicated in self-referential processing-and anticorrelated attention networks, which may impact trajectories of cognitive and mental health symptoms across adolescence.

An open source knowledge graph ecosystem for the life sciences.

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

The autophagy inhibitor NSC185058 suppresses mTORC1-mediated protein anabolism in cultured skeletal muscle.

The mammalian target of rapamycin (mTOR), and specifically the mTOR complex 1 (mTORC1) is the central regulator of anabolism in skeletal muscle. Among the many functions of this kinase complex is the inhibition of the catabolic process of autophagy; however, less work has been done in investigating the role of autophagy in regulating mTORC1 signaling. Using an in vitro model to better understand the pathways involved, we activated mTORC1 by several different means (growth factors, leucine supplementation, or muscle contraction), alone or with the autophagy inhibitor NSC185058. We found that inhibiting autophagy with NSC185058 suppresses mTORC1 activity, preventing any increase in cellular protein anabolism. These decrements were the direct result of action on the mTORC1 kinase, which we demonstrate, for the first time, cannot function when autophagy is inhibited by NSC185058. Our results indicate that, far from being a matter of unidirectional action, the relationship between mTORC1 and the autophagic cascade is more nuanced, with autophagy serving as an mTORC1 input, and mTORC1 inhibition of autophagy as a form of homeostatic feedback to regulate anabolic signaling. Future studies of cellular metabolism will have to consider this fundamental intertwining of protein anabolism and catabolism, and how it ultimately serves to regulate muscle proteostasis.

Effectiveness and implementation of lower-intensity weight management interventions delivered by the non-specialist workforce in postnatal women: a mixed-methods systematic review.

Lower-intensity interventions delivered in primary and community care contacts could provide more equitable and scalable weight management support for postnatal women. This mixed-methods systematic review aimed to explore the effectiveness, implementation, and experiences of lower-intensity weight management support delivered by the non-specialist workforce. We included quantitative and qualitative studies of any design that evaluated a lower-intensity weight management intervention delivered by non-specialist workforce in women up to 5 years post-natal, and where intervention effectiveness (weight-related and/or behavioural outcomes), implementation and/or acceptability were reported. PRISMA guidelines were followed, and the review was prospectively registered on PROSPERO (CRD42022371828). Nine electronic databases were searched to identify literature published between database inception to January 2023. This was supplemented with grey literature searches and citation chaining for all included studies and related reviews (completed June 2023). Screening, data extraction and risk of bias assessments were performed in duplicate. Risk of bias was assessed using the Joanna Briggs Institute appraisal tools. Narrative methods were used to synthesise outcomes. Seven unique studies described in 11 reports were included from the Netherlands (n = 2), and the United Kingdom, Germany, Taiwan, Finland, and the United States (n = 1 each). All studies reported weight-related outcomes; four reported diet; four reported physical activity; four reported intervention implementation and process outcomes; and two reported intervention acceptability and experiences. The longest follow-up was 13-months postnatal. Interventions had mixed effects on weight-related outcomes: three studies reported greater weight reduction and/or lower postnatal weight retention in the intervention group, whereas four found no difference or mixed effects. Most studies reporting physical activity or diet outcomes showed no intervention effect, or mixed effects. Interventions were generally perceived as acceptable by women and care providers, although providers had concerns about translation into routine practice. The main limitations of the review were the limited volume of evidence available, and significant heterogeneity in interventions and outcome reporting which limited meaningful comparisons across studies. There is a need for more intervention studies, including process evaluations, with longer follow-up in the postnatal period to understand the role of primary and community care in supporting women's weight management. Public Health Wales was the primary funder of this review.

A randomized double-blinded trial to assess recurrence of systemic allergic reactions following COVID-19 mRNA vaccination.

BACKGROUND: Systemic allergic reactions (sARs) following coronavirus disease 2019 (COVID-19) mRNA vaccines were initially reported at a higher rate than after traditional vaccines. OBJECTIVE: We aimed to evaluate the safety of revaccination in these individuals and to interrogate mechanisms underlying these reactions. METHODS: In this randomized, double-blinded, phase 2 trial, participants aged 16 to 69 years who previously reported a convincing sAR to their first dose of COVID-19 mRNA vaccine were randomly assigned to receive a second dose of BNT162b2 (Comirnaty) vaccine and placebo on consecutive days in a blinded, 1:1 crossover fashion at the National Institutes of Health. An open-label BNT162b2 booster was offered 5 months later if the second dose did not result in severe sAR. None of the participants received the mRNA-1273 (Spikevax) vaccine during the study. The primary end point was recurrence of sAR following second dose and booster vaccination; exploratory end points included biomarker measurements. RESULTS: Of 111 screened participants, 18 were randomly assigned to receive study interventions. Eight received BNT162b2 second dose followed by placebo; 8 received placebo followed by BNT162b2 second dose; 2 withdrew before receiving any study intervention. All 16 participants received the booster dose. Following second dose and booster vaccination, sARs recurred in 2 participants (12.5%; 95% CI, 1.6 to 38.3). No sAR occurred after placebo. An anaphylaxis mimic, immunization stress-related response (ISRR), occurred more commonly than sARs following both vaccine and placebo and was associated with higher predose anxiety scores, paresthesias, and distinct vital sign and biomarker changes. CONCLUSIONS: Our findings support revaccination of individuals who report sARs to COVID-19 mRNA vaccines. Distinct clinical and laboratory features may distinguish sARs from ISRRs.

Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study.

PURPOSE: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DESIGN: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. SUBJECTS: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion). METHODS: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate. MAIN OUTCOME MEASURES: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. RESULTS: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60). CONCLUSIONS: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.