Credible Interval Research Articles

An Economic Evaluation of Direct Oral Penicillin Challenge for De-Labelling Low Risk Patients With a Penicillin Allergy Label.

Removing inaccurate penicillin allergy labels (PALs) can reduce unnecessary exposure to 'watch' and 'reserve' groups of antibiotics and thereby reduce antimicrobial resistance. The most efficient model for a non-allergy-specialist-led penicillin allergy de-labelling (PADL) service has not been established. To determine the costs to the UK National Health Service of a direct oral penicillin challenge (DPC) for low-risk patients with a PAL in three hospitals in England, each with a different non-allergy-specialist delivery model: pharmacist-led, nurse-led, and mixed multidisciplinary. Cost analysis of the DPC pathway, including resources related to staff time and antibiotics. The effect of de-labelling on healthcare utilisation over 5 years was modelled using data from the published literature. In total, 2257 patients from the Acute Medical or Infectious Disease Unit (AMU/IDU), Pre-surgical, and Haematology-Oncology departments were screened. Subsequently, 126 underwent DPC, and 122 were de-labelled. Twenty-two of these were de-labelled in time to affect their antibiotic regimen; 6 from AMU/IDU and 16 Pre-surgery. The DPC represented 22%-23% of the pathway cost in the pharmacist-led and mixed models, and 15% in the nurse-led model. Across departments and models, the cost per de-labelled patient varied between £577 (95% Credible Interval: 370, 633) for haematology-oncology patients to £2329 (947, 19,504) for AMU/IDU patients, both under the nurse-led model. After 5 years, recouping costs was unlikely for AMU/IDU patients under any model or for all patients combined under the mixed model. The penicillin allergy de-labelling pathway cost was ≥ 4-fold that of the DPC alone. Costs were up to 3 times higher in an acute compared to an elective setting. No short-term cost savings were identified from proactive or opportunistic penicillin allergy de-labelling in this study.

Development of a Microsimulation Model to Project the Future Prevalence of Childhood Cancer in Ontario, Canada.

Estimates of the future prevalence of childhood cancer are informative for health system planning but are underutilized. We describe the development of a pediatric oncology microsimulation model for prevalence (POSIM-Prev) and illustrate its application to produce projections of incidence, survival, and limited-duration prevalence of childhood cancer in Ontario, Canada, until 2040. POSIM-Prev is a population-based, open-cohort, discrete-time microsimulation model. The model population was updated annually from 1970 to 2040 to account for births, deaths, net migration, and incident cases of childhood cancer. Prevalent individuals were followed until death, emigration, or the last year of simulation. Median population-based outcomes with 95% credible intervals (CrIs) were generated using Monte Carlo simulation. The methodology to derive model inputs included generalized additive modeling of cancer incidence, parametric survival modeling, and stochastic population forecasting. Individual-level data from provincial cancer registries for years 1970 to 2019 informed cancer-related model inputs and internal validation. The number of children (aged 0-14 y) diagnosed with cancer in Ontario is projected to rise from 414 (95% CrI: 353-486) in 2020 to 561 (95% CrI: 481-653) in 2039. The 5-y overall survival rate for 2030-2034 is estimated to reach 90% (95% CrI: 88%-92%). By 2040, 24,088 (95% CrI: 22,764-25,648) individuals with a history of childhood cancer (diagnosed in Ontario or elsewhere) are projected to reside in the province. The model accurately reproduced historical trends in incidence, survival, and prevalence when validated. The rising incidence and prevalence of childhood cancer will create increased demand for both acute cancer care and long-term follow-up services in Ontario. The POSIM-Prev model can be used to support long-range health system planning and future health technology assessments in jurisdictions that have access to similar model inputs. This article describes the development of a population-based, discrete-time microsimulation model that can simulate incident and prevalent cases of childhood cancer in Ontario, Canada, until 2040.Use of an open cohort framework allowed for estimation of the potential impact of net migration on childhood cancer prevalence.In addition to supporting long-term health system planning, this model can be used in future health technology assessments, by providing a demographic profile of incident and prevalent cases for model conceptualization and budget impact purposes.This modeling framework is adaptable to other jurisdictions and disease areas where individual-level data for incidence and survival are available.

Nutritional Anemia Reductions Due to Food Fortification Among Women of Childbearing Age: A Literature Review and Bayesian Meta-Analysis.

Food fortification can deliver essential micronutrients to populations at a large scale, thereby reducing nutritional anemia. This study aimed to review and meta-analyze the literature on the impact of wheat flour, maize flour, rice, and oil (singly or combined) fortification on women's (10-49 years) hemoglobin and anemia. A search of 17 databases yielded 2284 results. Longitudinal, pre-post cross-sectional, efficacy, and effectiveness studies were included. Primary outcomes were changes in hemoglobin concentration and anemia prevalence. Studies were synthesized using arm-based network meta-analysis. In women who consumed fortified rice, hemoglobin mean change was 3.24 g/L (95% credibility interval (CrI) 0.9, 5.98), higher than for women in the control, with a 99.1% probability that the true mean difference was > 0. Hemoglobin was 2.08 g/L (95% CrI -0.76, 4.35) higher in women who consumed wheat flour versus control, with a 93.5% probability that the true mean difference was > 0. After rice fortification, anemia prevalence in women was 1.38 percentage points (95% CrI -106.6, 99.2) lower than for control women, with a 51.2% probability that the true mean difference was < 0. Wheat flour fortification decreased anemia prevalence by 1.84 percentage points (95% CrI -93.4, 92.4) with a 52.72% probability that the true mean difference was < 0. The treatment effects of fortified maize flour and fortified oil could not be calculated due to the absence of control arms compared to the intervention arms. Fortified rice and wheat flour appear likely to modestly increase hemoglobin and may also reduce anemia in women of childbearing age.

Age and Sex Differences in Efficacy of Treatments for Type 2 Diabetes: A Network Meta-Analysis.

Sodium-glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and dipeptidyl peptidase 4 (DPP4) inhibitors improve hyperglycemia, and SGLT2 inhibitors and GLP-1 receptor agonists reduce the risk of major adverse cardiovascular events (MACEs) among individuals with type 2 diabetes. It is not clear whether efficacy varies by age or sex. To assess whether age or sex are associated with differences in the efficacy of SGLT2 inhibitors, GLP-1 receptor agonists, and DPP4 inhibitors. The MEDLINE and Embase databases and US and Chinese clinical trial registries were searched for articles published from inception to November 2022; in August 2024, the search was updated to capture the trial results. Two reviewers screened for randomized clinical trials of SGLT2 inhibitors, GLP-1 receptor agonists, or DPP4 inhibitors vs a placebo or active comparator in adults with type 2 diabetes. Individual participant data and aggregate data were used to estimate age × treatment interactions and sex × treatment interactions in multilevel network meta-regression models. Hemoglobin A1c (HbA1c) and MACEs. Of the 601 eligible trials identified (592 trials with 309 503 participants reported HbA1c; mean age, 58.9 [SD, 10.8] years; 42.3% were female and 23 trials with 168 489 participants reported MACEs; mean age, 64.0 [SD, 8.6] years; 35.3% were female), individual participant data were obtained for 103 trials (103 reported HbA1c and 6 reported MACEs). The use of SGLT2 inhibitors (vs placebo) was associated with less HbA1c lowering with increasing age for monotherapy (absolute reduction [AR], 0.24% [95% credible interval {CrI}, 0.10% to 0.38%] per 30-year increment in age), for dual therapy (AR, 0.17% [95% CrI, 0.10% to 0.24%]), and for triple therapy (AR, 0.25% [95% CrI, 0.20% to 0.30%]). The use of GLP-1 receptor agonists was associated with greater HbA1c lowering with increasing age for monotherapy (AR, -0.18% [95% CrI, -0.31% to -0.05%] per 30-year increment in age) and for dual therapy (AR, -0.24% [95% CrI, -0.40% to -0.07%]), but not for triple therapy (AR, 0.04% [95% CrI, -0.02% to 0.11%]). The use of DPP4 inhibitors was associated with slightly better HbA1c lowering in older people for dual therapy (AR, -0.09% [95% CrI, -0.15% to -0.03%] per 30-year increment in age), but not for monotherapy (AR, -0.08% [95% CrI, -0.18% to 0.01%]) or triple therapy (AR, -0.01% [95% CrI, -0.06% to 0.05%]). The relative reduction in MACEs with use of SGLT2 inhibitors was greater in older vs younger participants per 30-year increment in age (hazard ratio, 0.76 [95% CrI, 0.62 to 0.93]), and the relative reduction in MACEs with use of GLP-1 receptor agonists was less in older vs younger participants (hazard ratio, 1.47 [95% CrI, 1.07 to 2.02]). There was no consistent evidence for sex × treatment interactions with use of SGLT2 inhibitors and GLP-1 receptor agonists. The SGLT2 inhibitors and GLP-1 receptor agonists were associated with lower risk of MACEs. Analysis of age × treatment interactions suggested that SGLT2 inhibitors were more cardioprotective in older than in younger people despite smaller reductions in HbA1c; GLP-1 receptor agonists were more cardioprotective in younger people.

Persistent organochlorine pesticides and cardiometabolic outcomes among middle-aged Latina women in a California agricultural community: The CHAMACOS Maternal Cognition Study.

Environmental exposure to endocrine disrupting compounds is hypothesized to increase risk of cardiovascular disease through effects on obesity, hypertension, dyslipidemia, and insulin resistance. We examined the relationship between serum concentrations of persistent organochlorine pesticides and biologic markers of inflammation and cardiometabolic disease, measured over a decade later, in a cohort of middle-aged and primarily immigrant Latina women living in an underserved agricultural community in California. We used data from the Center for the Health Assessment of Mothers and Children of Salinas-Maternal Cognition Study (CHAMACOS-MCS). We included 468 women who had concentrations of organochlorine pesticides measured in serum collected in 2009-2011 and complete follow-up data in 2022-2024 (blood draw, anthropometry, personal interview). We used Bayesian hierarchical regression models (BHM) to examine the independent effects of five highly correlated pesticides with continuous and binary measures of cardiometabolic disease and inflammation. Participants averaged 49.0 (±5.5) years at follow-up. In BHM models, a 10-fold increase in p,p'-dichlorodiphenyltrichloroethane (DDT) and β-hexacyclohexane (β-HCH) was positively associated with BMI (DDT: adj-β=1.26, 95% Credible Interval (CrI): 0.33, 2.20; β-HCH: adj-β=1.56, 95%CrI: 0.45, 2.67) and waist circumference (DDT: adj-β=2.75, 95%CrI: 0.65, 4.85; β-HCH: adj-β=3.74, 95%CrI: 1.24, 6.23). Although credible intervals crossed the null, consistent positive associations were observed for DDT and β-HCH with blood pressure and for DDT with insulin resistance. Trans-nonachlor was positively associated with triglycerides (log-TRIG: adj-β=0.08, 95%CrI: 0.02, 0.13). β-HCH was positively associated with inflammatory markers (log-hsCRP: adj-β=0.11, 95%CrI: 0.03, 0.19; log-IL-6: adj-β=0.08, 95%CrI: 0.03, 0.14). With over a decade of follow-up, we extend evidence on previously reported associations of DDT and β-HCH with several measures of obesity. In addition, we provide new evidence suggesting associations with biomarkers of blood pressure, insulin resistance, dyslipidemia and inflammation, supporting the hypothesis that exposure may have long-term influences on cardiovascular disease risk.

Pamrevlumab plus nab-paclitaxel/gemcitabine (Pam + GA) as first- and second-line therapy in metastatic pancreatic cancer (mPDAC): Results from Precision Promise (PrP) Bayesian platform trial.

673 Background: Pamrevlumab (Pam) is a fully human recombinant monoclonal antibody against connective tissue growth factor. Early clinical data with Pam plus chemotherapy showed a favorable safety profile and potential efficacy in PDAC. In PrP, Pam + GA was tested as first line (Line 1) and second line (Line 2) therapy for mPDAC vs GA. PrP is a phase 2/3, innovative Bayesian adaptive platform trial sponsored by the Pancreatic Cancer Action Network testing multiple experimental arms efficiently against common controls (1). Methods: Randomization is 70% (adaptive amongst experimental arms in stage 1) and 15%:15% amongst two control arms (GA, mFOLFIRINOX). Pam + GA graduates from stage 1 to 2 if the Bayesian predictive power (PP) of eventual success is ≥ 35% for one of the arm’s signatures [Line 1, Line 2 or Line 1 &amp; 2 (All)]. All participants (pts) are followed for 12 months (mos) after last pt randomized and treated in Pam + GA. Stages 1 and 2 are combined for final analysis. Efficacy is defined by overall survival (OS) hazard ratio (HR, experimental vs control), by a Bayesian statistical model. Superiority (HR &lt; 1) is claimed at final analysis if the Bayesian probability of superiority is ≥ 98%. Results: Pam + GA entered PrP in Jun 2021. Between 6/2021 - 1/2023, at 23 US sites, 317 pts were treated and are included in the mITT analysis. 213 pts were treated in the Pam + GA arm (102 Line 1; 111 Line 2), 45 in the GA arm (23 Line 1; 22 Line 2), 31 in the mFOLFIRINOX arm and 28 in other experimental arms. All treated pts before and during Pam + GA enrollment were in the Bayesian model, with outcomes adjusted via a time machine to increase the trial’s statistical power. Baseline characteristics were balanced across the arms. Pam + GA met criteria to graduate to stage 2 in Sep 2022 in the All signature (Line 1 &amp; 2). At final analysis, Pam + GA did not meet the OS primary endpoint [model estimated HR: 1.18 (95% credible interval, 0.88, 1.56), posterior Pr(HR &lt; 1) = 0.14, below specified ≥ 0.98]. No benefit was seen in PFS nor ORR (Table). No new safety signals were seen. Conclusions: PrP, the first Bayesian platform trial in mPDAC, performed as designed; Pam + GA did not improve OS versus GA in Line 1 and Line 2 mPDAC. The novel Bayesian design warrants further study in mPDAC to enhance efficiency of drug development. Future designs should explore different strategies to allocate patients to control arm(s) vs experimental arms and accommodate novel agents intended to benefit subsets of mPDAC. 1. Picozzi, ASCO TPS 4188, 2022. Clinical trial information: NCT04229004 . Line 1 Line 2 GA Pam + GA Hazard Ratio GA Pam + GA Hazard Ratio Model Estimated mOS (mos) 11.3 9.7 1.18(95% CI* 0.88, 1.56) 7.8 6.6 1.18(95% CI* 0.88, 1.56) mPFS (mos) 5.3 5.9 0.64(95% CI^ 0.36, 1.14) 7.0 3.9 1.35(95% CI^ 0.78, 2.33) ORR (%) 26.1 35.3 4.5 9.0 *Credible Interval. ^Confidence Interval.

Effects of Exercise-Based ACL Injury Prevention Interventions on Knee Motion in Athletes: A Systematic Review and Bayesian Network Meta-Analysis.

OBJECTIVE: To compare the effectiveness of injury prevention programs (IPPs) for improving high-risk knee motion patterns in the context of reducing the risk of noncontact anterior cruciate ligament injury. DESIGN: Systematic review with Bayesian network meta-analysis. DATA SOURCES: PubMed, Embase, Web of Science, Cochrane Library, and the Cumulative Index to Nursing and Allied Health Literature were searched until September 10, 2023. ELIGIBILITY CRITERIA: We included randomized controlled trials involving athletes without prior anterior cruciate ligament injury. The trials had to provide data on peak knee flexion and valgus angles, obtained from 2-leg drop vertical jump, single-leg drop vertical jump, or side-step cutting test. RESULTS: The network meta-analysis synthesized data from 22 randomized controlled trials involving 878 participants, evaluating 12 different IPPs. Results of meta-analyses indicated that, in jumping tests, the external focus instructions (mean difference [MD] = 26; credible interval [CrI] = 7.5, 44; surface under the cumulative ranking curve [SUCRA] = 0.94) and internal focus of attention (MD = 19; CrI = 0.19, 37; SUCRA = 0.81) was effective for increasing peak knee flexion, and core stability training ranked first for reducing knee valgus (MD = -4.40; CrI = -7.7, -1.1; SUCRA = 0.96). None of the IPPs revealed statistically significant effects for the cutting test. CONCLUSION: The external focus instructions and internal focus of attention increased knee flexion, while core stability exercise reduced knee valgus in jumping tests. Intervention effects were inconsistent for cutting tests. J Orthop Sports Phys Ther 2025;55(2):1-14. Epub 20 December 2024. doi:10.2519/jospt.2024.12720.

A Bayesian Approach to Correcting the Attenuation Bias of Regression Using Polygenic Risk Score.

Polygenic risk score (PRS) has become increasingly popular for predicting the value of complex traits. In many settings, PRS is used as a covariate in regression analysis to study the association between different phenotypes. However, measurement error in PRS causes attenuation bias in the estimation of regression coefficients. In this paper, we employ a Bayesian approach to accounting for the measurement error of PRS and correcting the attenuation bias in linear and logistic regression. Through simulation, we show that our approach is able to obtain approximately unbiased estimation of coefficients and credible intervals with correct coverage probability. We also empirically compare our Bayesian measurement error model to the conventional regression model by analyzing real traits in the UK Biobank. The results demonstrate the effectiveness of our approach as it significantly reduces the error in coefficient estimates.

Antiviral efficacy of fluoxetine in early symptomatic COVID-19: an open-label, randomised, controlled, adaptive platform trial (PLATCOV).

The selective serotonin reuptake inhibitors (SSRIs) fluoxetine and fluvoxamine were repurposed for the treatment of early COVID-19 based on their antiviral activity invitro, and observational and clinical trial evidence suggesting they prevented progression to severe disease. However, these SSRIs have not been recommended in therapeutic guidelines and their antiviral activity invivo has not been characterised. PLATCOV is an open-label, multicentre, phase 2, randomised, controlled, adaptive pharmacometric platform trial running in Thailand, Brazil, Pakistan, and Laos. We recruited low-risk adult outpatients aged 18-50 with early symptomatic COVID-19 (symptoms <4 days) between 5 April 2022 and 8 May 2023. Patients were assigned using block randomisation to one of eleven treatment arms including oral fluoxetine (40mg/day for 7 days), or no study drug. Uniform randomisation ratios were applied across the active treatment groups while the no study drug group comprised ≥20% of patients at all times. The primary endpoint was the rate of oropharyngeal viral clearance assessed until day 7. Measurements were taken daily between days 0 and 7 and analysed in a modified intention-to-treat population (>2 days follow-up).The viral clearance rate was estimated under a Bayesian hierarchical linear model fitted to the log10 viral densities measured in standardised duplicate oropharyngeal swab eluates taken daily over one week (18 measurements per patient). Secondary endpoints were all-cause hospital admission at 28 days, and time to resolution of fever and symptoms. This ongoing trial is registered at ClinicalTrials.gov (NCT05041907). 271 patients were concurrently randomised to either fluoxetine (n=120) or no study drug (n=151). All patients had received at least one COVID-19 vaccine dose and 67% were female (182/271). In the primary analysis, viral clearance rates following fluoxetine were compatible with a small or no increase relative to the no study drug arm (15% increase; 95% credible interval (CrI):-2 to 34%). There were no deaths or hospitalisations in either arm. There were no significant differences in times to symptom resolution or fever clearance between the fluoxetine and the no study drug arms (although only a quarter of patients were febrile at baseline). Fluoxetine was well tolerated, there were no serious adverse events and only one grade 3 adverse event in the intervention arm. Overall, the evidence from this study is compatible with fluoxetine having a weak invivo antiviral activity against SARS-CoV-2, although the primary endpoint is also compatible with no effect. This level of antiviral efficacy is substantially less than with other currently available antiviral drugs. Wellcome Trust Grant ref: 223195/Z/21/Z through the COVID-19 Therapeutics Accelerator.

Accuracy of ICD Influenza Discharge Diagnosis Codes in Hospitalized Adults From the Valencia Region, Spain, in the Pre-COVID-19 Period 2012/2013 to 2017/2018.

International Classification of Diseases (ICD) codes obtained from real-world data can be used to identify influenza cases for epidemiological research but, without validation, may introduce biases. The objective of this study was to validate ICD influenza discharge diagnoses using real-time reverse transcription-polymerase chain reaction (RT-PCR) laboratory-confirmed influenza (LCI) results. The study was conducted during six influenza seasons (2012/2013-2017/2018) in the Valencia Hospital Surveillance Network for the Study of Influenza (VAHNSI). Patients aged 18+ years were identified via active-surveillance and had to meet an influenza-like illness (ILI) case definition to be included. All patients were tested for influenza by real-time RT-PCR. Main and secondary influenza discharge diagnosis codes were extracted from hospital discharge letters. Positive predictive values (PPVs) and the complementary of the sensitivities (1-Sensitivity) of ICD codes with corresponding 95% credible intervals (CrIs) were estimated via binomial Bayesian regression models. A total of 13,545 patients were included, with 2257 (17%) positive for influenza. Of 2257 LCI cases, 1385 (61%) were not ICD-coded as influenza. Overall, 74.73% (95% CrI: 63.24-84.44) of LCI were not-ICD coded as influenza (1-Sensitivity) after adjustment. Sensitivity improved across seasons and with increasing age. Average PPV was 74.02% (95% CrI: 68.58-79.17), ranging from 43.71% to 81.57% between seasons. Using only main and secondary discharge diagnosis codes for influenza detection markedly underestimates the full burden of influenza in hospitalized patients. Future studies, including post-COVID context, using prospective surveillance for ILI are required to assess the validity of hospital discharge data as a tool for determining influenza-related burden of disease.

Navigating the Bayes maze: The psychologist's guide to Bayesian statistics, a hands-on tutorial with R code.

Bayesian statistics has gained substantial popularity in the social sciences, particularly in psychology. Despite its growing prominence in the psychological literature, many researchers remain unacquainted with Bayesian methods and their advantages. This tutorial addresses the needs of curious applied psychology researchers and introduces Bayesian analysis as an accessible and powerful tool. We begin by comparing Bayesian and frequentist approaches, redefining fundamental terms from both perspectives with practical illustrations. Our exploration of Bayesian statistics includes Bayes's Theorem, likelihood, prior and posterior distributions, various prior types, and Markov-Chain Monte Carlo (MCMC) methods, supplemented by graphical aids for clarity. To bridge theory and practice, we employ a psychological research example with real, open data. We analyse the data using both frequentist and Bayesian approaches, providing R code and comprehensive supporting information, and emphasising best practices for interpretation and reporting. We discuss and demonstrate how to interpret parameter estimates and credible intervals, among other essential topics. Throughout, we maintain an accessible and user-friendly language, focusing on practical implications, intuitive examples, and actionable recommendations.

A comparison of Bayesian and frequentist confidence intervals in the presence of a late Universe degeneracy

Hubble tension is a problem in one-dimensional (1D) posteriors, since local H0 determinations are only sensitive to a single parameter. Projected 1D posteriors for ΛCDM cosmological parameters become more non-Gaussian with increasing effective redshift when the model is fitted to redshift-binned data in the late Universe. We explain mathematically why this non-Gaussianity arises and show, using observational Hubble data (OHD), that Markov chain Monte Carlo (MCMC) marginalisation leads to 1D posteriors that fail to track the χ2 minimum at 68% confidence level in high redshift bins. To gain a second perspective, we resort to profile likelihoods as a complementary technique. Doing so, we observe that z≳1 cosmic chronometer (CC) data currently prefer a non-evolving (constant) Hubble parameter over a Planck-ΛCDM cosmology at ∼2σ. Within the Hubble tension debate, it is imperative that subsamples of data sets with differing redshifts yield similar H0 values. In addition, we confirm that MCMC degeneracies observed in 2D posteriors are not due to curves of constant χ2. Finally, on the assumption that the Planck-ΛCDM cosmological model is correct, using profile likelihoods we confirm a >2σ2 \\sigma $$\\end{document}]]> discrepancy with Planck-ΛCDM in a combination of CC and baryon acoustic oscillations (BAO) data beyond z∼1.5. This confirms a discrepancy reported earlier with fresh methodology.

Bovine besnoitiosis: Assessment of the diagnostic accuracy of three different tests using a Bayesian latent class model approach and clinical characterization of the disease.

Bovine besnoitiosis, a disease caused by the tissue cyst-forming apicomplexan Besnoitia besnoiti, is re-emerging in Europe, leading to significant impairment of health and production, as well as economic losses. The early detection of the disease is of the utmost importance for the implementation of effective control measures, yet this is a challenge due to the lack of specific early clinical signs. The objectives of our study were 1) to estimate the diagnostic accuracy of three tests to detect B. besnoiti in naturally exposed cattle (histopathology-skin (HIS-SK); PCR-skin (PCR-SK); and parallel PCR of nasal and scleroconjunctival swabs (PCR-NS-SC)) using a Bayesian latent class model (BLCM) and 2) to describe the clinical presentation of besnoitiosis in the studied animals. The study involved 54 adult Limousin cattle. Biosecurity measures were assessed and scored as medium. At clinical examination, a sire was diagnosed with a form of besnoitiosis between the end of the acute phase and the beginning of the chronic phase. Furthermore, 29 animals displaying a subclinical infection, characterized by the presence of scleroconjunctival cysts, were identified. The PCR-SK and PCR-NS-SC were able to detect B. besnoitia. The diagnostic performance of PCR-SK, PCR-NS-SC and HIS-SK was evaluated. The BLCM indicated that HIS-SK had the highest specificity (99.1 %, 95 % posterior probability interval PI: 96-100 %), while PCR-SK and PCR-NS-SC demonstrated higher sensitivities (91.0 %, 95 % PI: 68-100 %, and 85.0 %, 95 % PI: 67-100 %, respectively). The study concludes that the use of a parallel PCR-NS-SC could represent a viable alternative for the early detection of B. besnoiti, providing a less invasive method to monitor and control bovine besnoitiosis at the herd level.

Niraparib in patients with BRCA -mutated unresectable or recurrent biliary tract, pancreatic and other gastrointestinal cancers: An investigator-initiated phase 2 trial (NIR-B trial).

589 Background: It has been reported that there are BRCA1/2 -mutated patients in biliary tract, pancreatic, and other gastrointestinal cancers. Niraparib is a poly(ADP-ribose) polymerase (PARP) inhibitor, and PARP inhibitors exert their cytotoxicity against cancer cells in the context of homologous recombination deficiency, such as BRCA mutations. Methods: Main eligibility criteria are unresectable, advanced or recurrent biliary tract cancers (BTC; cohort A), pancreatic cancers (PC; cohort B), and other gastrointestinal cancers (cohort C) with BRCA1/2 gene mutations identified by germline test or genomic profiling test with either circulating tumor DNA (ctDNA) or tumor tissue, refractory or intolerant to previous treatments, and adequate organ function. Patients with body weight of 77 kg or more and a platelet count of 150,000 /µL or more receive 300 mg of niraparib, and less than 77 kg or having a platelet count less than 150,000 /µL received 200 mg of niraparib, orally once daily, until disease progression or intolerable adverse events occurred. Primary endpoint was the investigator-assessed objective response rate (ORR) in each cohort with a threshold response rate of 10% and an expected response rate of 35%. Key secondary endpoints were progression-free survival (PFS), overall survival (OS), disease control rate (DCR), and safety. Furthermore, pre-treatment ctDNA was collected and analyzed by Guardant360. Results: A total of 62 pts, 26 in cohort A, 26 in cohort B, and 10 in cohort C were enrolled between March 2021 and April 2023. Because one patient in cohort C did not receive protocol treatment, 61 pts were identified for primary efficacy and safety analysis. Median number of prior regimens was 1/2/2 (range, 1–2/1–2/1-6). In cohort A/B/C, the confirmed ORR was 15.4/15.4/0% [95% highest posterior density credible interval, 8.4–27.9/8.4–27.9/0.0–25.9]. The DCR, median (m) PFS, mOS were 57.7/53.8/22.2% [95% confidence interval (CI), 36.9–76.6/33.4–73.4/2.8–60.0], 2.7/1.8/1.4 months (95% CI, 1.5–4.1/1.4–9.3/1.0–2.8), 7.8/9.5/7.4 months (95% CI, 5.9–9.8/3.5–NE/2.9–8.6), respectively. In the pts with BRCA mutations by Guardant360 (cohort A/B/C were 18/21/8), ORR was 11.1/19.0/0% (95% CI, 2.0-30.2/6.5–38.3/0.0–28.3). The most common treatment-emergent adverse events were thrombocytopenia (31.1%), anemia (27.9%), neutropenia (14.8%), nausea (36.1%), fatigue (29.5%), anorexia (24.6%). Conclusions: Although niraparib had signs of clinical activity in pts with BRCA -mutated BTC and PC, the primary endpoint was not achieved statistically. No new safety signal was observed. Alternative approaches, such as evaluation in biomarker-selected patients or in combination with other agents, may demonstrate greater clinical activity of niraparib in this setting. Clinical trial information: jRCT2011200023.

The effect of particulate matter 2.5 on seasonal influenza transmission in 1,330 counties, China: A Bayesian spatial analysis based on Köppen Geiger climate zones classifications.

Previous research has linked seasonal influenza transmission with particulate matters (PM2.5). However, the effect of PM2.5 on seasonal influenza transmission varied by region. This study aims explore how PM2.5 influenced seasonal influenza transmission in the elderly across 1330 counties in two Köppen Geiger climate zones in China, incorporating the socio-economic factors to enhance climate-driven early warning systems (EWS) for influenza. Data included weekly 2015-2019 influenza cases in those aged >65 from China's national influenza surveillance system for 1330 counties in two Köppen Geiger climate zones: Temperate, Hot Summer with Dry Winter (Cwa) and No Dry Season (Cfa). PM2.5 data from 2015 to 2019 were sourced from Copernicus Atmosphere Monitoring Services. Additional data on floating population, population density and Gross Domestic Product (GDP) per capita were collected from pertinent departments. A Bayesian spatial autoregressive model assessed the association of PM2.5 and influenza transmission after adjustment of socio-economic factors. Our research results showed PM2.5 (per 1μg/m³ increase) was linked to increased influenza transmission in the Cwa zone during winter season (Relative Risk (RR)=1.023, 95% Credible Interval (CI):1.008-1.040) but not in the Cfa winter (RR=1.003, 95% CI: 0.992-1.015). Floating population significantly enhanced transmission in both zones (highest RR=1.362, 95% CI:1.181-1.583), while GDP per capita growth was associated with reduced transmission risk (highest RR=0.619, 95% CI: 0.445-0.861). The study identifies PM2.5 as a significant factor influencing influenza transmission in the elderly, with effects varying by climate zone, suggesting the need to incorporate PM2.5 and socio-economic factors into seasonal influenza EWS.

Lower vs. higher blood pressure targets during intensive care of comatose patients resuscitated from out-of-hospital cardiac arrest-a Bayesian analysis of the BOX trial.

The Blood Pressure and Oxygenation (BOX) targets after out-of-hospital cardiac arrest trial found no statistically significant differences in mortality or neurological outcomes with mean arterial blood pressure targets of 63 vs. 77 mmHg in patients receiving intensive care post-cardiac arrest. In this study, we aimed to evaluate the effect on 1-year mortality and assess heterogeneity in treatment effects (HTEs) using Bayesian statistics. We analyzed 1-year all-cause mortality, 1-year neurological outcomes, and plasma neuron-specific enolase (NSE) at 48 h using Bayesian logistic and linear regressions primarily with weakly informative priors. HTE was assessed according to age, plasma lactate, time to return of spontaneous circulation, primary shockable rhythm, history of hypertension, and ST-segment elevation myocardial infarction. Absolute and relative differences are presented with probabilities of any clinical benefit and harm. All 789 patients in the intention-to-treat cohort were included. The risk difference (RD) for 1-year mortality was 1.5%-points [95% credible interval (CrI): -5.1 to 8.1], with <33% probability of benefit with the higher target. There was 33% probability for a better neurological outcome (RD: 1.5%-points; 95% CrI: -5.3 to 8.3) and 35.1% for lower NSE levels (mean difference: 1.5 µg/L, 95% CrI: -6.0 to 9.1). HTE analyses suggested potential harms of the higher blood pressure target in younger patients. The effects of a higher blood pressure target on overall mortality among comatose patients resuscitated from out-of-hospital cardiac arrest were uncertain. A potential effect modification according to age warrants additional investigation. ClinicalTrials.gov ID NCT03141099.

Impact of neuromuscular block monitoring and reversal on postoperative pulmonary complications in thoracic surgery: a Bayesian analysis of the iPROVE-OLV trial.

Postoperative pulmonary complications (PPCs) are a significant concern in thoracic surgery. A modifiable factor influencing PPCs is postoperative residual neuromuscular block (NMB), which impairs respiratory muscle function. We performed a post hoc Bayesian analysis of data from the iPROVE-OLV study, a multicentre randomised controlled trial involving patients undergoing thoracic surgery with one-lung ventilation. We compared participants managed with any neuromuscular monitoring and any reversal vs those managed without either. The primary outcome was the occurrence of a composite of PPCs within the first 7 postoperative days. Of the 698 patients included, 657 received any neuromuscular monitoring and any reversal, while 41 did not. Patients managed with any neuromuscular monitoring and any reversal had a lower incidence of PPCs (20%) compared with those without either (34%). Bayesian random effect logistic regression indicated that the use of any neuromuscular monitoring and any reversal reduced PPCs with an odds ratio (OR) ranging from 0.67 (95% credibility interval, CrI, 0.39-1.11) to 0.84 (95% CrI 0.48-1.37), depending on the prior model used. The probability of benefit (OR <1) was between 77% and 94%. Subgroup analysis indicated that sugammadex was more effective than neostigmine in reducing PPCs, with a high probability of benefit (97%), and both neuromuscular monitoring and reversal reduced PCCs when evaluated separately with a high probability of benefit. Utilising neuromuscular monitoring and reversal agents significantly reduced the risk of PPCs in thoracic surgery. Sugammadex was more efficacious in reducing PPCs compared with neostigmine. These findings support the combined use of neuromuscular monitoring and reversal drugs. NCT03182062.

Obtaining a Bayesian Estimate of Coefficient Alpha Using a Posterior Normal Distribution.

A new alternative to obtain a Bayesian estimate of coefficient alpha through a posterior normal distribution is proposed and assessed through percentile, normal-theory-based, and highest probability density credible intervals in a simulation study. The results indicate that the proposed Bayesian method to estimate coefficient alpha has acceptable coverage probability performance across the majority of investigated simulation conditions.

State-Level Influenza Hospitalization Burden in the United States, 2022-2023.

Objectives. To develop a method leveraging hospital-based surveillance to estimate influenza-related hospitalizations by state, age, and month as a means of enhancing current US influenza burden estimation efforts. Methods. Using data from the Influenza Hospitalization Surveillance Network (FluSurv-NET), we extrapolated monthly FluSurv-NET hospitalization rates after adjusting for testing practices and diagnostic test sensitivities to non-FluSurv-NET states. We used a Poisson zero-inflated model with an overdispersion parameter within the Bayesian hierarchical framework and accounted for uncertainty and variability between states and across time. Model validation included checking the sensitivity of results to input data, as well as model convergence diagnostics and comparing the results with independent data sources. Results. We estimated 379 300 (90% credible interval [CrI] = 305 400, 479 300) influenza-related hospitalizations in the United States for the 2022-2023 season. Median cumulative state rates ranged widely from 23.2 to 249.0 per 100 000 people. Conclusions. Our estimates were comparable to national burden estimates incorporating other approaches while accounting for variations in the timing and geography of disease activity and changes in detection and reporting. Our results provide a complementary framework to calculate estimates at finer geographic scales. (Am J Public Health. Published online ahead of print January 30, 2025:e1-e9. https://doi.org/10.2105/AJPH.2024.307928).

Uncertainty Quantification in Shear Wave Velocity Predictions: Integrating Explainable Machine Learning and Bayesian Inference

The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized linear model (GLM) to enhance both predictive accuracy and uncertainty quantification in Vs prediction. The study utilizes an Extreme Gradient Boosting (XGBoost) algorithm coupled with Shapley Additive Explanations (SHAPs) and partial dependency analysis to identify key geotechnical parameters influencing Vs predictions. Additionally, a Bayesian GLM is developed to explicitly account for uncertainties arising from geotechnical variability. The effectiveness and predictive performance of the proposed models were validated through comparison with real case scenarios. The results highlight the unique advantages of each model. The XGBoost model demonstrates good predictive performance, achieving high coefficient of determination (R2), index of agreement (IA), Kling–Gupta efficiency (KGE) values, and low error values while effectively explaining the impact of input parameters on Vs. In contrast, the Bayesian GLM provides probabilistic predictions with 95% credible intervals, capturing the uncertainty associated with the predictions. The integration of these two approaches creates a comprehensive framework that combines the strengths of high-accuracy ML predictions with the uncertainty quantification of Bayesian inference. This hybrid methodology offers a powerful and interpretable tool for Vs prediction, providing engineers with the confidence to make informed decisions.