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INTRODUCTION: In addition to the accumulation of amyloid plaques and neurofibrillary tangles, the presence of excess neural activity is a pathological hallmark of Alzheimer's disease (AD) and a prognostic indicator for progression of AD pathology and clinical/cognitive worsening in mild cognitive impairment due to Alzheimer's disease (MCI due to AD). The HOPE4MCI clinical study tested the efficacy of a therapeutic with demonstrated ability to normalize heightened neural activity in the hippocampus in a randomized controlled trial of 78 weeks duration in patients with MCI due to AD. METHODS: One hundred and sixty‐four participants were randomized to placebo (n = 83) or AGB101 (n = 81), an extended‐release formulation of low dose (220 mg) levetiracetam. The primary endpoint was the change in Clinical Dementia Rating Scale Sum of Boxes score (CDR‐SB) comparing follow up at 18 months to baseline. The goal of the primary efficacy analysis was to estimate the difference between the AGB101 and placebo arms in the mean change of the primary endpoint. RESULTS: The mean change in CDR‐SB was estimated to be 1.12 (95% confidence interval [CI]: 0.66, 1.69) for the AGB101 arm and 1.22 (95% CI: 0.75, 1.78) for the placebo arm. The estimated difference between arms is ‐0.10 (95% CI: ‐0.85, 0.58), which was not statistically significant. In a prespecified analysis, the difference was ‐0.45 (95% CI: ‐1.43, 0.53) for ApoE‐4 noncarriers and ‐0.10 (95% CI: ‐0.92, 0.72) for apolipoprotein E (ApoE)‐4 carriers. DISCUSSION: The possibility that ApoE‐4 carriers and noncarriers will respond differently to therapeutic intervention is consistent with recently reported findings from biologics and the present results show further testing of AGB101 in patients with MCI due to AD who are noncarriers of the ApoeE‐4 allele is warranted. Conclusions from the HOPE4MCI study are limited primarily due to the small sample size and results can only be regarded as a guide to future research. [ABSTRACT FROM AUTHOR]

Objectives: We examined whether an array of scleroderma autoantibodies associates with risk of cancer and could be useful tools for risk stratification. Methods: Scleroderma cancer cases and scleroderma controls without cancer from the Johns Hopkins Scleroderma Center and the University of Pittsburgh Scleroderma Center were studied. Sera were assayed by Lineblot and enzyme‐linked immunosorbent assay (ELISA) for autoantibodies against centromere, topoisomerase 1, RNA polymerase (POLR) 3, PM/Scl, Th/To, NOR90, U3 RNP, Ku, Ro52, U1RNP, and RNPC3. Logistic regression models were constructed to examine whether distinct autoantibodies associated with overall cancer at any time and cancer‐associated scleroderma (cancer occurring three years before and after scleroderma onset). The effects of having more than one autoantibody on cancer were further examined using random forest analysis. Results: A total of 676 cases and 687 controls were studied. After adjusting for relevant covariates, anti‐POLR3 (odds ratio [OR] 1.47, 95% confidence interval [CI] 1.03–2.11) and monospecific anti‐Ro52 (OR 2.19, 95% CI 1.29–3.74) were associated with an increased overall cancer risk, whereas anticentromere (OR 0.69, 95% CI 0.51–0.93) and anti‐U1RNP (OR 0.63, 95% CI 0.43–0.93) were associated with lower risk. When examining risk of cancer‐associated scleroderma, these immune responses remained associated with increased or decreased risk: anti‐POLR3 (OR 2.28, 95% CI 1.33–3.91), monospecific anti‐Ro52 (OR 2.58, 95% CI 1.05–6.30), anticentromere (OR 0.39, 95% CI 0.20–0.74), and anti‐U1RNP (OR 0.32, 95% CI 0.11–0.93). Anti‐Ro52 plus anti‐U1RNP or anti‐Th/To was associated with decreased cancer risk compared with anti‐Ro52 alone. Conclusions: These data suggest that five distinct scleroderma immune responses, alone or in combination, may be useful tools to stratify the risk of cancer for scleroderma patients. Further study examining cancer risk in autoantibody subgroups relative to the general population is warranted. [ABSTRACT FROM AUTHOR]

Objectives Ectopic calcification (calcinosis) is a common complication of SSc, but a subset of SSc patients has a heavy burden of calcinosis. We examined whether there are unique risk factors for a heavy burden of calcinosis, as compared with a light burden or no calcinosis. Methods We reviewed the medical records of all patients in the Johns Hopkins Scleroderma Center Research Registry with calcinosis to quantify calcinosis burden using pre-specified definitions. We performed latent class analysis to identify SSc phenotypic classes. We used multinomial logistic regression to determine whether latent phenotypic classes and autoantibodies were independent risk factors for calcinosis burden. Results Of all patients, 29.4% (997/3388) had calcinosis, and 13.5% (130/963) of those with calcinosis had a heavy burden. The latent phenotypic class with predominantly diffuse skin disease and higher disease severity (characterized by pulmonary hypertension, interstitial lung disease, cardiomyopathy, severe RP, gastrointestinal involvement, renal crisis, myopathy and/or tendon friction rubs) was associated with an increased risk of both a heavy burden [odds ratio (OR) 6.92, 95% CI 3.66, 13.08; P  < 0.001] and a light burden (OR 2.88, 95% CI 2.11, 3.95; P  < 0.001) of calcinosis compared with the phenotypic class with predominantly limited skin disease. Autoantibodies to PM/Scl were strongly associated with a heavy burden of calcinosis (OR 17.31, 95% CI 7.72, 38.81; P  < 0.001) and to a lesser degree a light burden of calcinosis (OR 3.59, 95% CI 1.84, 7.00; P  < 0.001). Conclusions Calcinosis burden is associated with cumulative SSc-related tissue damage. Independent of disease severity, autoantibodies to PM/Scl are also associated with a heavy burden of calcinosis. [ABSTRACT FROM AUTHOR]

Background Prior observation has shown differences in COVID-19 hospitalization risk between SARS-CoV-2 variants, but limited information describes hospitalization outcomes. Methods Inpatients with COVID-19 at 5 hospitals in the eastern United States were included if they had hypoxia, tachypnea, tachycardia, or fever, and SARS-CoV-2 variant data, determined from whole-genome sequencing or local surveillance inference. Analyses were stratified by history of SARS-CoV-2 vaccination or infection. The average effect of SARS-CoV-2 variant on 28-day risk of severe disease, defined by advanced respiratory support needs, or death was evaluated using models weighted on propensity scores derived from baseline clinical features. Results Severe disease or death within 28 days occurred for 977 (29%) of 3369 unvaccinated patients and 269 (22%) of 1230 patients with history of vaccination or prior SARS-CoV-2 infection. Among unvaccinated patients, the relative risk of severe disease or death for Delta variant compared with ancestral lineages was 1.30 (95% confidence interval [CI]: 1.11–1.49). Compared with Delta, the risk for Omicron patients was.72 (95% CI:.59–.88) and compared with ancestral lineages was.94 (.78–1.1). Among Omicron and Delta infections, patients with history of vaccination or prior SARS-CoV-2 infection had half the risk of severe disease or death (adjusted hazard ratio:.40; 95% CI:.30–.54), but no significant outcome difference by variant. Conclusions Although risk of severe disease or death for unvaccinated inpatients with Omicron was lower than with Delta, it was similar to ancestral lineages. Severe outcomes were less common in vaccinated inpatients, with no difference between Delta and Omicron infections. [ABSTRACT FROM AUTHOR]

Key Points: Question: Can DNA sequencing of blood from adults with acute myeloid leukemia (AML) in first remission prior to allogeneic hematopoietic cell transplant identify patients at increased risk of subsequent relapse and death? Findings: In patients with AML in first complete remission who received a transplant from March 1, 2013, through December 31, 2017 (discovery, n = 371) or from January 1, 2018, through February 14, 2019 (validation, n = 451), the presence of residual FLT3 internal tandem duplication (FLT3-ITD) and/or NPM1 DNA variants before transplant were associated with significantly increased rates of relapse (validation cohort difference, 47%; 95% CI, 26% to 69%) and significantly worse survival (validation cohort difference, −24%; 95% CI, −39 to −9%) at 3 years, compared with those without these markers. Meaning: Among patients with AML in first remission prior to allogeneic hematopoietic cell transplant, the persistence of FLT3-ITD or NPM1 variants in the blood at an allele fraction of 0.01% or higher was associated with increased relapse and worse survival compared with those without variants detected. Importance: Preventing relapse for adults with acute myeloid leukemia (AML) in first remission is the most common indication for allogeneic hematopoietic cell transplant. The presence of AML measurable residual disease (MRD) has been associated with higher relapse rates, but testing is not standardized. Objective: To determine whether DNA sequencing to identify residual variants in the blood of adults with AML in first remission before allogeneic hematopoietic cell transplant identifies patients at increased risk of relapse and poorer overall survival compared with those without these DNA variants. Design, Setting, and Participants: In this retrospective observational study, DNA sequencing was performed on pretransplant blood from patients aged 18 years or older who had undergone their first allogeneic hematopoietic cell transplant during first remission for AML associated with variants in FLT3, NPM1, IDH1, IDH2, or KIT at 1 of 111 treatment sites from 2013 through 2019. Clinical data were collected, through May 2022, by the Center for International Blood and Marrow Transplant Research. Exposure: Centralized DNA sequencing of banked pretransplant remission blood samples. Main Outcomes and Measures: The primary outcomes were overall survival and relapse. Day of transplant was considered day 0. Hazard ratios were reported using Cox proportional hazards regression models. Results: Of 1075 patients tested, 822 had FLT3 internal tandem duplication (FLT3-ITD) and/or NPM1 mutated AML (median age, 57.1 years, 54% female). Among 371 patients in the discovery cohort, the persistence of NPM1 and/or FLT3-ITD variants in the blood of 64 patients (17.3%) in remission before undergoing transplant was associated with worse outcomes after transplant (2013-2017). Similarly, of the 451 patients in the validation cohort who had undergone transplant in 2018-2019, 78 patients (17.3%) with residual NPM1 and/or FLT3-ITD variants had higher rates of relapse at 3 years (68% vs 21%; difference, 47% [95% CI, 26% to 69%]; HR, 4.32 [95% CI, 2.98 to 6.26]; P <.001) and decreased survival at 3 years (39% vs 63%; difference, −24% [2-sided 95% CI, −39% to −9%]; HR, 2.43 [95% CI, 1.71 to 3.45]; P <.001). Conclusions and Relevance: Among patients with acute myeloid leukemia in first remission prior to allogeneic hematopoietic cell transplant, the persistence of FLT3 internal tandem duplication or NPM1 variants in the blood at an allele fraction of 0.01% or higher was associated with increased relapse and worse survival compared with those without these variants. Further study is needed to determine whether routine DNA-sequencing testing for residual variants can improve outcomes for patients with acute myeloid leukemia. In this observational study, targeted deep DNA sequencing was performed to test the hypothesis that detection of specific residual AML-associated variants in the blood of patients in first remission prior to allogeneic hematopoietic cell transplant would be associated with higher rates of relapse and mortality after transplant [ABSTRACT FROM AUTHOR]

Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test population. Quantification under dataset shift has been addressed only for single-class (categorical) predictions and assuming perfect knowledge of the true labels on a small subset of the test population. We propose generalized Bayes quantification learning (GBQL) that uses the entire compositional predictions from probabilistic classifiers and allows for uncertainty in true class labels for the limited labeled test data. Instead of positing a full model, we use a model-free Bayesian estimating equation approach to compositional data using Kullback–Leibler loss-functions based only on a first-moment assumption. The idea will be useful in Bayesian compositional data analysis in general as it is robust to different generating mechanisms for compositional data and allows 0's and 1's in the compositional outputs thereby including categorical outputs as a special case. We show how our method yields existing quantification approaches as special cases. Extension to an ensemble GBQL that uses predictions from multiple classifiers yielding inference robust to inclusion of a poor classifier is discussed. We outline a fast and efficient Gibbs sampler using a rounding and coarsening approximation to the loss functions. We establish posterior consistency, asymptotic normality and valid coverage of interval estimates from GBQL, which to our knowledge are the first theoretical results for a quantification approach in the presence of local labeled data. We also establish finite sample posterior concentration rate. Empirical performance of GBQL is demonstrated through simulations and analysis of real data with evident dataset shift. for this article are available online. [ABSTRACT FROM AUTHOR]

Severe COVID-19 is characterized by a prothrombotic state associated with thrombocytopenia, with microvascular thrombosis being almost invariably present in the lung and other organs at postmortem examination. We evaluated the presence of antibodies to platelet factor 4 (PF4)–polyanion complexes using a clinically validated immunoassay in 100 hospitalized patients with COVID-19 with moderate or severe disease (World Health Organization score, 4 to 10), 25 patients with acute COVID-19 visiting the emergency department, and 65 convalescent individuals. Anti-PF4 antibodies were detected in 95 of 100 hospitalized patients with COVID-19 (95.0%) irrespective of prior heparin treatment, with a mean optical density value of 0.871 ± 0.405 SD (range, 0.177 to 2.706). In contrast, patients hospitalized for severe acute respiratory disease unrelated to COVID-19 had markedly lower levels of the antibodies. In a high proportion of patients with COVID-19, levels of all three immunoglobulin (Ig) isotypes tested (IgG, IgM, and IgA) were simultaneously elevated. Antibody levels were higher in male than in female patients and higher in African Americans and Hispanics than in White patients. Anti-PF4 antibody levels were correlated with the maximum disease severity score and with significant reductions in circulating platelet counts during hospitalization. In individuals convalescent from COVID-19, the antibody levels returned to near-normal values. Sera from patients with COVID-19 induced higher levels of platelet activation than did sera from healthy blood donors, but the results were not correlated with the levels of anti-PF4 antibodies. These results demonstrate that the vast majority of patients with severe COVID-19 develop anti-PF4 antibodies, which may play a role in the clinical complications of COVID-19. [ABSTRACT FROM AUTHOR]

PURPOSE: The Bone Metastases Ensemble Trees for Survival Decision Support Platform (BMETS-DSP) provides patient-specific survival predictions and evidence-based recommendations to guide multidisciplinary management for symptomatic bone metastases. We assessed the clinical utility of the BMETS-DSP through a pilot prepost design in a simulated clinical environment. METHODS: Ten Radiation Oncology physicians reviewed 55 patient cases at two time points: without and then with the use of BMETS-DSP. Assessment included 12-month survival estimate, confidence in and likelihood of sharing estimates with patients, and recommendations for open surgery, systemic therapy, hospice referral, and radiotherapy (RT) regimen. Paired statistics compared pre- versus post-DSP outcomes. Reported statistical significance is P <.05. RESULTS: Pre- versus post-DSP, overestimation of true minus estimated survival time was significantly reduced (mean difference –2.1 [standard deviation 4.1] v –1 month [standard deviation 3.5]). Prediction accuracy was significantly improved at cut points of < 3 (72 v 79%), ≤ 6 (64 v 71%), and ≥ 12 months (70 v 81%). Median ratings of confidence in and likelihood of sharing prognosis significantly increased. Significantly greater concordance was seen in matching use of 1-fraction RT with the true survival < 3 months (70 v 76%) and < 10-fraction RT with the true survival < 12 months (55 v 62%) and appropriate use of open surgery (47% v 53%), without significant changes in selection of hospice referral or systemic therapy. CONCLUSION: This pilot study demonstrates that BMETS-DSP significantly improved physician survival estimation accuracy, prognostic confidence, likelihood of sharing prognosis, and use of prognosis-appropriate RT regimens in the care of symptomatic bone metastases, supporting future multi-institutional validation of the platform. [ABSTRACT FROM AUTHOR]

Background Estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in young children and risk factors for seropositivity are scarce. Using data from a prospective cohort study of households during the pre-coronavirus disease 2019 (COVID-19) vaccine period, we estimated SARS-CoV-2 seroprevalence by age and evaluated risk factors for SARS-CoV-2 seropositivity. Methods The SARS-CoV-2 Epidemiology and Response in Children (SEARCh) study enrolled 175 Maryland households (690 participants) with ≥1 child aged 0–4 years during November 2020–March 2021; individuals vaccinated against COVID-19 were ineligible. At enrollment, participants completed questionnaires about sociodemographic and health status and work, school, and daycare attendance. Participants were tested for SARS-CoV-2 antibodies in sera. Logistic regression models with generalized estimating equations (GEE) to account for correlation within households assessed predictors of individual- and household-level SARS-CoV-2 seropositivity. Results Of 681 (98.7%) participants with enrollment serology results, 55 (8.1%; 95% confidence interval [CI], 6.3%–10.4%) participants from 21 (12.0%) households were seropositive for SARS-CoV-2. Among seropositive participants, fewer children than adults reported being tested for SARS-CoV-2 infection before enrollment (odds ratio [OR] = 0.23; 95% CI,.06–.73). Seropositivity was similar by age (GEE OR vs 0–4 years: 1.19 for 5–17 years, 1.36 for adults; P =.16) and was significantly higher among adults working outside the home (GEE adjusted OR = 2.2; 95% CI, 1.1–4.4) but not among children attending daycare or school. Conclusions Before study enrollment, children and adults in this cohort had similar rates of SARS-CoV-2 infection as measured by serology. An adult household member working outside the home increased a household's odds of SARS-CoV-2 infection, whereas a child attending daycare or school in person did not. [ABSTRACT FROM AUTHOR]

Compositional data are common in many fields, both as outcomes and predictor variables. The inventory of models for the case when both the outcome and predictor variables are compositional is limited, and the existing models are often difficult to interpret in the compositional space, due to their use of complex log‐ratio transformations. We develop a transformation‐free linear regression model where the expected value of the compositional outcome is expressed as a single Markov transition from the compositional predictor. Our approach is based on estimating equations thereby not requiring complete specification of data likelihood and is robust to different data‐generating mechanisms. Our model is simple to interpret, allows for 0s and 1s in both the compositional outcome and covariates, and subsumes several interesting subcases of interest. We also develop permutation tests for linear independence and equality of effect sizes of two components of the predictor. Finally, we show that despite its simplicity, our model accurately captures the relationship between compositional data using two datasets from education and medical research. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: In the management of symptomatic bone metastases, current practice guidelines do not provide clear methodology for selecting palliative radiotherapy (RT) regimens based on specific patient and disease features. Decision support aids may offer an effective means for translating the complex data needed to render individualised treatment decisions, yet no such tools are available for use in this setting. Thus, we describe the development of the Bone Metastases Ensemble Trees for Survival‐Decision Support Platform (BMETS‐DSP), which aims to optimise selection of evidence‐based, individualised palliative RT regimens. Method: The Ottawa Decision Support Framework was used as the theoretical basis for development of BMETS‐DSP. First, we utilised stakeholder input and review of the literature to assess determinants underlying the provider decision. Based on this assessment and iterative stakeholder feedback, we developed the web‐based, provider‐facing BMETS‐DSP. Consistent with the underlying theoretical framework, our design also included assessment of decision quality using the International Patient Decision Aids Standards (IPDAS) certification checklist. Results: Stakeholder input and review of 54 evidence‐based publications identified the following determinants of the provider decision: estimated prognosis, characteristics of the target symptomatic lesion and the primary cancer type, consideration of alternative interventions, access to patient‐specific recommendations, and patient preferences. Based on these determinants, we developed the BMETS‐DSP that (1) collects patient‐specific data, (2) displays an individualised predicted survival curve, and (3) provides case‐specific, evidence‐based recommendations regarding RT, open surgery, systemic therapy, and hospice referral to aid in the decision‐making process. The finalised tool met IPDAS quality requirements. Preliminary results of a pilot assessment suggest impact of clinical outcomes. Conclusions: We describe the successful development of a provider‐facing decision support platform to aid in the provision of palliative RT in better alignment with patient and disease features. Impact of the BMETS‐DSP on decision outcomes will be further assessed in a randomised, controlled study. [ABSTRACT FROM AUTHOR]

This paper presents a model‐based method for clustering multivariate binary observations that incorporates constraints consistent with the scientific context. The approach is motivated by the precision medicine problem of identifying autoimmune disease patient subsets or classes who may require different treatments. We start with a family of restricted latent class models or RLCMs. However, in the motivating example and many others like it, the unknown number of classes and the definition of classes using binary states are among the targets of inference. We use a Bayesian approach to RLCMs in order to use informative prior assumptions on the number and definitions of latent classes to be consistent with scientific knowledge so that the posterior distribution tends to concentrate on smaller numbers of clusters and sparser binary patterns. The paper derives a posterior sampling algorithm based on Markov chain Monte Carlo with split‐merge updates to efficiently explore the space of clustering allocations. Through simulations under the assumed model and realistic deviations from it, we demonstrate greater interpretability of results and superior finite‐sample clustering performance for our method compared to common alternatives. The methods are illustrated with an analysis of protein data to detect clusters representing autoantibody classes among scleroderma patients. [ABSTRACT FROM AUTHOR]

Longitudinal trajectories of vital signs and biomarkers during hospital admission of patients with COVID-19 remain poorly characterized despite their potential to provide critical insights about disease progression. We studied 1884 patients with severe acute respiratory syndrome coronavirus 2 infection from April 3, 2020, to June 25, 2020, within 1 Maryland hospital system and used a retrospective longitudinal framework with linear mixed-effects models to investigate relevant biomarker trajectories leading up to 3 critical outcomes: mechanical ventilation, discharge, and death. Trajectories of 4 vital signs (respiratory rate, ratio of oxygen saturation (Sp o 2) to fraction of inspired oxygen (Fi o 2), pulse, and temperature) and 4 laboratory values (C-reactive protein (CRP), absolute lymphocyte count (ALC), estimated glomerular filtration rate, and D-dimer) clearly distinguished the trajectories of patients with COVID-19. Before any ventilation, log(CRP), log(ALC), respiratory rate, and Sp o 2-to-Fi o 2 ratio trajectories diverge approximately 8–10 days before discharge or death. After ventilation, log(CRP), log(ALC), respiratory rate, Sp o 2-to-Fi o 2 ratio, and estimated glomerular filtration rate trajectories again diverge 10–20 days before death or discharge. Trajectories improved until discharge and remained unchanged or worsened until death. Our approach characterizes the distribution of biomarker trajectories leading up to competing outcomes of discharge versus death. Moving forward, this model can contribute to quantifying the joint probability of biomarkers and outcomes when provided clinical data up to a given moment. [ABSTRACT FROM AUTHOR]

Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific mortality fractions. These estimates may be inaccurate if CCVA is trained on non-local training data different from the local population of interest. This problem is a special case of transfer learning, i.e., improving classification within a target domain (e.g., a particular population) with the classifier trained in a source-domain. Most transfer learning approaches concern individual-level (e.g., a person's) classification. Social and health scientists such as epidemiologists are often more interested with understanding etiological distributions at the population-level. The sample sizes of their data sets are typically orders of magnitude smaller than those used for common transfer learning applications like image classification, document identification, etc. We present a parsimonious hierarchical Bayesian transfer learning framework to directly estimate population-level class probabilities in a target domain, using any baseline classifier trained on source-domain, and a small labeled target-domain dataset. To address small sample sizes, we introduce a novel shrinkage prior for the transfer error rates guaranteeing that, in absence of any labeled target-domain data or when the baseline classifier is perfectly accurate, our transfer learning agrees with direct aggregation of predictions from the baseline classifier, thereby subsuming the default practice as a special case. We then extend our approach to use an ensemble of baseline classifiers producing an unified estimate. Theoretical and empirical results demonstrate how the ensemble model favors the most accurate baseline classifier. We present data analyses demonstrating the utility of our approach. [ABSTRACT FROM AUTHOR]

Background Males experience increased severity of illness and mortality from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) compared with females, but the mechanisms of male susceptibility are unclear. Methods We performed a retrospective cohort analysis of SARS-CoV-2 testing and admission data at 5 hospitals in the Maryland/Washington DC area. Using age-stratified logistic regression models, we quantified the impact of male sex on the risk of the composite outcome of severe disease or death (World Health Organization score 5–8) and tested the impact of demographics, comorbidities, health behaviors, and laboratory inflammatory markers on the sex effect. Results Among 213 175 SARS-CoV-2 tests, despite similar positivity rates, males in age strata between 18 and 74 years were more frequently hospitalized. For the 2626 hospitalized individuals, clinical inflammatory markers (interleukin-6, C-reactive protein, ferritin, absolute lymphocyte count, and neutrophil:lymphocyte ratio) were more favorable for females than males (P <.001). Among 18–49-year-olds, male sex carried a higher risk of severe outcomes, both early (odds ratio [OR], 3.01; 95% CI, 1.75 to 5.18) and at peak illness during hospitalization (OR, 2.58; 95% CI, 1.78 to 3.74). Despite multiple differences in demographics, presentation features, comorbidities, and health behaviors, these variables did not change the association of male sex with severe disease. Only clinical inflammatory marker values modified the sex effect, reducing the OR for severe outcomes in males aged 18–49 years to 1.81 (95% CI, 1.00 to 3.26) early and 1.39 (95% CI, 0.93 to 2.08) at peak illness. Conclusions Higher inflammatory laboratory test values were associated with increased risk of severe coronavirus disease 2019 for males. A sex-specific inflammatory response to SARS-CoV-2 infection may underlie the sex differences in outcomes. [ABSTRACT FROM AUTHOR]

Age-related deficits in pattern separation have been postulated to bias the output of hippocampal memory processing toward pattern completion, which can cause deficits in accurate memory retrieval. Although the CA3 region of the hippocampus is often conceptualized as a homogeneous network involved in pattern completion, growing evidence demonstrates a functional gradient in CA3 along the transverse axis, as pattern-separated outputs (dominant in the more proximal CA3) transition to pattern-completed outputs (dominant in the more distal CA3). We examined the neural representations along the CA3 transverse axis in young (Y), aged memory-unimpaired (AU), and aged memory-impaired (AI) rats when different changes were made to the environment. Functional heterogeneity in CA3 was observed in Y and AU rats when the environmental similarity was high (altered cues or altered environment shapes in the same room), with more orthogonalized representations in proximal CA3 than in distal CA3. In contrast, AI rats showed reduced orthogonalization in proximal CA3 but showed normal (i.e., generalized) representations in distal CA3, with little evidence of a functional gradient. Under experimental conditions when the environmental similarity was low (different rooms), representations in proximal and distal CA3 remapped in all rats, showing that CA3 of AI rats is able to encode distinctive representations for inputs with greater dissimilarity. These experiments support the hypotheses that the age-related bias toward hippocampal pattern completion is due to the loss in AI rats of the normal transition from pattern separation to pattern completion along the CA3 transverse axis. [Display omitted] • Young (Y) rats show transition from pattern separation to pattern completion in CA3 • Aged memory-impaired (AI) rats show pattern completion in proximal and distal CA3 • AI rats can orthogonalize representations in two spatially, distinct environments • Aged memory-unimpaired rats show trends that are intermediate between Y and AI rats Lee et al. report that aged memory-impaired rats do not show functional heterogeneity in CA3. When the environment similarity was high, aged rats showed reduced orthogonalization in proximal CA3, resulting in the loss of the normal transition from pattern separation to pattern completion along the CA3 transverse axis. [ABSTRACT FROM AUTHOR]

Objective: To understand if autoantibodies account for racial variation in disease severity, we compared autoantibody distribution and associated phenotype between self-identified black and white systemic sclerosis (SSc) patients., Methods: 803 black and 2178 white SSc patients had systematic testing for autoantibodies using Euroimmun (centromere (ACA), RNA-polymerase III (POLR3), Scl70, PM/Scl, NOR90, Th/To, Ku, U3RNP and Ro52) and commercial ELISA (U1RNP). In this observational study, logistic regression was performed to assess the association between self-identified race and outcomes, adjusting for autoantibodies. To estimate whether the effect of race was mediated by autoantibody status, race coefficients from multivariate models including and excluding autoantibodies were compared., Results: Anti-Scl70, anti-U1RNP, anti-U3RNP, anti-Th/To, anti-Ku and anti-NOR90 were more common in the black cohort than in the white cohort, which was enriched for ACA, anti-POLR3 and anti-PM/Scl. Black individuals had a higher prevalence of severe Raynaud's, skin, lung, gastrointestinal and renal disease whereas white individuals had a higher prevalence of severe heart and muscle disease. Adjusting for autoantibodies decreased the effect of race on outcome for telangiectasias, forced vital capacity <70%, pulmonary hypertension and severe lung, heart, muscle and gastrointestinal disease by 11%-44% and increased the association between race and renal crisis and severe kidney disease by 37%-52%., Conclusions: This study is the largest systematic analysis of autoantibody responses in a geographically diverse population of black SSc patients. Black and white individuals with SSc have distinct autoantibody profiles. Autoantibodies explain only a fraction of the effect of race on clinical outcomes, suggesting other factors contribute to disparate outcomes between these groups., Competing Interests: Competing interests: RD received consulting fees from CSL Behring and Astra-Zeneca. LAS received grant funding from Horizon Pharmaceuticals, aTyr Pharmaceuticals and Kinevant Pharmaceuticals and honoraria from Janssen Pharmaceuticals. She served on the data safety monitoring board for Argenx Pharmaceuticals. LC received grant funding from Boehringer Ingelheim and consulting fees from Kyverna, Eicos Sciences, Genentech, IgM Biosciences, Lilly. She participated in an advisory capacity for Mitsubishi Tanabe and Janssen. FB received honoraria from Janssen. RMS received grant funding from Boehringer Ingelheim, Merck and Amgen; he is the CEO of FibroBiologics. JV received consulting fees from TeneoBio, and Conquest; he serves on the data safety monitoring board for Conquest. MDM received grant funding from Prometheus Biosciences, Mitsubishi Tanabe, Boehringer Ingelheiem, Eicos, Corbus and Horizon Pharmaceuticals. She received consulting fees from Cabaletta Pharmaceuticals; she served on the advisory board for Mitsubishi Tanabe and Eicos Sciences. AS has grant support from Kadmon, Arena Pharmaceuticals, Medpace and Eicos Sciences., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ on behalf of EULAR.)

Background: A significant number of women die from pregnancy and childbirth complications globally, particularly in low- and middle-income countries (LMICs). Receiving at least four antenatal care (ANC) visits may be important in reducing maternal and perinatal deaths. This study investigates factors associated with attending ≥ 4 ANC visits in Sarlahi district of southern Nepal., Methods: A secondary analysis was conducted on data from the Nepal Oil Massage Study (NOMS), a cluster-randomized, community-based longitudinal pregnancy cohort study encompassing 34 Village Development Committees. We quantified the association between receipt/attendance of ≥ 4 ANC visits and socioeconomic, demographic, morbidity, and pregnancy history factors using logistic regression; Generalized Estimating Equations were used to account for multiple pregnancies per woman., Results: All pregnancies resulting in a live birth (n=31,867) were included in the model and 31.4% of those pregnancies received 4+ ANC visits. Significant positive associations include socioeconomic factors such as participation in non-farming occupations for women (OR=1.52, 95% CI: 1.19, 1.93), higher education (OR=1.79, 95% CI: 1.66, 1.93) and wealth quintile OR=1.44, 95% CI: 1.31, 1.59), nutritional status such as non-short stature (OR=1.17, 95% CI: 1.07, 1.27), obstetric history such as adequate interpregnancy interval (OR=1.31, 95% CI: 1.19, 1.45) and prior pregnancy but no live birth (OR=2.14, 95% CI: 1.57, 2.92), symptoms such as vaginal bleeding (OR=1.35, 95% CI:1.11, 1.65) and awareness of the government's conditional cash transfer ANC program (OR=2.26, 95% CI: 2.01, 2.54). Conversely, belonging to the lower Shudra caste (OR=0.56, 95% CI: 0.47, 0.67), maternal age below 18 or above 35 (OR=0.81, 95% CI:0.74, 0.88; OR=0.77, 95% CI: 0.62, 0.96)), preterm birth (OR=0.41, 95% CI: 0.35, 0.49), parity ≥ 1 (OR=0.66, 95% CI: 0.61, 0.72), and the presence of hypertension during pregnancy (OR=0.79, 95% CI: 0.69, 0.90) were associated with decreased likelihood of attending ≥ 4 ANC visits., Conclusions: These findings underscore the importance of continuing and promoting the government's program and increasing awareness among women. Moreover, understanding these factors can guide interventions aimed at encouraging ANC uptake in the most vulnerable groups, subsequently reducing maternal-related adverse outcomes in LMICs., Trial Registration: The clinicaltrial.gov trial registration number for NOMS was #NCT01177111. Registration date was August 6 th , 2010., Competing Interests: Competing interests The authors declare that they have no competing interests.

Abstract: Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity., (Copyright © 2024 International Association for the Study of Pain.)

Importance: Persistence of FLT3 internal tandem duplication (ITD) in adults with acute myeloid leukemia (AML) in first complete remission (CR) prior to allogeneic hematopoietic cell transplant (HCT) is associated with increased relapse and death after transplant, but the association between the level of measurable residual disease (MRD) detected and clinical outcome is unknown., Objective: To examine the association between pre-allogeneic HCT MRD level with relapse and death posttransplant in adults with AML in first CR., Design, Setting, and Participants: In this cohort study, DNA sequencing was performed on first CR blood from patients with FLT3-ITD AML transplanted from March 2013 to February 2019. Clinical follow-up was through May 2022. Data were analyzed from October 2022 to December 2023., Exposure: Centralized DNA sequencing for FLT3-ITD in pre-allogeneic HCT first CR blood using a commercially available kit., Main Outcomes and Measures: The primary outcomes were overall survival and cumulative incidence of relapse, with non-relapse-associated mortality as a competing risk post-allogeneic HCT. Kaplan-Meier estimations (log-rank tests), Cox proportional hazards models, and Fine-Gray models were used to estimate the end points., Results: Of 537 included patients with FLT3-ITD AML from the Pre-MEASURE study, 296 (55.1%) were female, and the median (IQR) age was 55.6 (42.9-64.1) years. Using the variant allele fraction (VAF) threshold of 0.01% or greater for MRD positivity, the results closely aligned with those previously reported. With no VAF threshold applied (VAF greater than 0%), 263 FLT3-ITD variants (median [range] VAF, 0.005% [0.0002%-44%]), and 177 patients (33.0%) with positive findings were identified. Multivariable analyses showed that residual FLT3-ITD was the variable most associated with relapse and overall survival, with a dose-dependent correlation. Patients receiving reduced-intensity conditioning without melphalan or nonmyeloablative conditioning had increased risk of relapse and death at any given level of MRD compared with those receiving reduced-intensity conditioning with melphalan or myeloablative conditioning., Conclusions and Relevance: This study provides generalizable and clinically applicable evidence that the detection of residual FLT3-ITD in the blood of adults in first CR from AML prior to allogeneic HCT is associated with an increased risk of relapse and death, particularly for those with a VAF of 0.01% or greater. While transplant conditioning intensification, an intervention not available to all, may help mitigate some of this risk, alternative approaches will be necessary for this high-risk population of patients who are underserved by the current standard of care.

Background: Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management., Methods: We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study., Results: The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated ( p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders., Conclusion: We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: To evaluate whether latent class analysis on social determinants of health (SDoH) data can identify social risk groups that differ by adverse SDoH and vision outcomes in patients with diabetes., Methods: This was a prospective cohort study of adults ≥18 years with diabetes who completed a SDoH survey. Latent class analysis was used to cluster patients into social risk groups. The association of social risk group and severity of diabetic retinopathy, history of lapses in diabetic retinopathy care, and visual acuity was evaluated., Results: A total of 1006 participants were included. The three social risk groups differed by sociodemographic characteristics. The average age was 65, 60, and 54 in Groups 1, 2, and 3 respectively. Most (51%) patients in group 1 were non-Hispanic White, 66% in group 2 were non-Hispanic Black, and 80% in group 3 were Hispanic. Group 1 had the lowest burden of adverse SDoH per person (average 3.6), group 2 had 8.2, and group 3 had 10.5. In general, group 1 lacked diabetic retinopathy knowledge, group 2 had financial insecurity and difficulties with transportation, and group 3 had financial insecurity and did not have health insurance. Social risk group was associated with a history of lapses in diabetic retinopathy care, and presenting with worse vision., Conclusions and Translational Relevance: We identified distinct social risk groups among patients seeking care for diabetic retinopathy that differed by social needs, eye care utilization, and vision. Identifying these groups and their specific needs can help guide interventions to effectively address adverse SDoH and improve eye care utilization and vision outcomes among patients with diabetes.

Objectives: To assess the association between maternal characteristics, adverse birth outcomes (small-for-gestational-age (SGA) and/or preterm) and neonatal mortality in rural Nepal., Design: This is a secondary observational analysis to identify risk factors for neonatal mortality, using data from a randomised trial to assess the impact of newborn massage with different oils on neonatal mortality in Sarlahi district, Nepal., Setting: Rural Sarlahi district, Nepal., Participants: 40 119 pregnant women enrolled from 9 September 2010 to 16 January 2017., Main Outcome: The outcome variable is neonatal death. Cox regression was used to estimate adjusted Hazard Ratios (aHRs) to assess the association between adverse birth outcomes and neonatal mortality., Results: There were 32 004 live births and 998 neonatal deaths. SGA and/or preterm birth was strongly associated with increased neonatal mortality: SGA and preterm (aHR: 7.09, 95% CI: (4.44 to 11.31)), SGA and term/post-term (aHR: 2.12, 95% CI: (1.58 to 2.86)), appropriate-for-gestational-age/large-for-gestational-age and preterm (aHR: 3.23, 95% CI: (2.30 to 4.54)). Neonatal mortality was increased with a history of prior child deaths (aHR: 1.53, 95% CI: (1.24 to 1.87)), being a twin or triplet (aHR: 5.64, 95% CI: (4.25 to 7.48)), births at health posts/clinics or in hospital (aHR: 1.34, 95% CI: (1.13 to 1.58)) and on the way to facilities or outdoors (aHR: 2.26, 95% CI: (1.57 to 3.26)). Risk was lower with increasing maternal height from <145 cm to 145-150 cm (aHR: 0.78, 95% CI: (0.65 to 0.94)) to ≥150 cm (aHR: 0.57, 95% CI: (0.47 to 0.68)), four or more antenatal care (ANC) visits (aHR: 0.67, 95% CI: (0.53 to 0.86)) and education >5 years (aHR: 0.75, 95% CI: (0.62 to 0.92))., Conclusion: SGA and/or preterm birth are strongly associated with increased neonatal mortality. To reduce neonatal mortality, interventions that prevent SGA and preterm births by promoting ANC and facility delivery, and care of high-risk infants after birth should be tested., Trial Registration Number: NCT01177111., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Objective: Preterm birth can have short-term and long-term complications for a child. Socioeconomic factors and pregnancy-related morbidities may be important to predict and prevent preterm births in low-resource settings. The objective of our study was to find prevalence and predictors of spontaneous preterm birth in rural Nepal., Design: This is a secondary observational analysis of trial data (registration number NCT01177111)., Setting: Rural Sarlahi district, Nepal., Participants: 40 119 pregnant women enrolled from 9 September 2010 to 16 January 2017., Outcome Measures: The outcome variable is spontaneous preterm birth. Generalized Estimating Equations Poisson regression with robust variance was fitted to present effect estimates as risk ratios., Result: The prevalence of spontaneous preterm birth was 14.5% (0.5% non-spontaneous). Characteristics not varying in pregnancy associated with increased risk of preterm birth were maternal age less than 18 years (adjusted risk ratio=1.13, 95% CI: 1.02 to 1.26); being Muslim (1.53, 1.16 to 2.01); first pregnancy (1.15, 1.04 to 1.28); multiple births (4.91, 4.20 to 5.75) and male child (1.10, 1.02 to 1.17). Those associated with decreased risk were maternal education >5 years (0.81, 0.73 to 0.90); maternal height ≥150 cm (0.89, 0.81 to 0.98) and being from wealthier families (0.83, 0.74 to 0.93). Pregnancy-related morbidities associated with increased risk of preterm birth were vaginal bleeding (1.53, 1.08 to 2.18); swelling (1.37, 1.17 to 1.60); high systolic blood pressure (BP) (1.47, 1.08 to 2.01) and high diastolic BP (1.41, 1.17 to 1.70) in the third trimester. Those associated with decreased risk were respiratory problem in the third trimester (0.86, 0.79 to 0.94); having poor appetite, nausea and vomiting in the second trimester (0.86, 0.80 to 0.92) and third trimester (0.86, 0.79 to 0.94); and higher weight gain from second to third trimester (0.89, 0.87 to 0.90)., Conclusion: The prevalence of preterm birth is high in rural Nepal. Interventions that increase maternal education may play a role. Monitoring morbidities during antenatal care to intervene to reduce them through an effective health system may help reduce preterm birth., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Severe COVID-19 is characterized by a prothrombotic state associated with thrombocytopenia, with microvascular thrombosis being almost invariably present in the lung and other organs at postmortem examination. We evaluated the presence of antibodies to platelet factor 4 (PF4)-polyanion complexes using a clinically validated immunoassay in 100 hospitalized patients with COVID-19 with moderate or severe disease (World Health Organization score, 4 to 10), 25 patients with acute COVID-19 visiting the emergency department, and 65 convalescent individuals. Anti-PF4 antibodies were detected in 95 of 100 hospitalized patients with COVID-19 (95.0%) irrespective of prior heparin treatment, with a mean optical density value of 0.871 ± 0.405 SD (range, 0.177 to 2.706). In contrast, patients hospitalized for severe acute respiratory disease unrelated to COVID-19 had markedly lower levels of the antibodies. In a high proportion of patients with COVID-19, levels of all three immunoglobulin (Ig) isotypes tested (IgG, IgM, and IgA) were simultaneously elevated. Antibody levels were higher in male than in female patients and higher in African Americans and Hispanics than in White patients. Anti-PF4 antibody levels were correlated with the maximum disease severity score and with significant reductions in circulating platelet counts during hospitalization. In individuals convalescent from COVID-19, the antibody levels returned to near-normal values. Sera from patients with COVID-19 induced higher levels of platelet activation than did sera from healthy blood donors, but the results were not correlated with the levels of anti-PF4 antibodies. These results demonstrate that the vast majority of patients with severe COVID-19 develop anti-PF4 antibodies, which may play a role in the clinical complications of COVID-19.