Bayesian Hierarchical Regression Model Research Articles

Trends and projections of age-appropriate vaccination coverage in 41 low- and middle- income countries in Asia and Sub-Saharan Africa, 2000–2030

IntroductionRoutine immunization programs have focused on increasing vaccination coverage, which is equally important for decreasing vaccine-preventable diseases (VPDs), particularly in low- and lower-middle-income countries (LMICs). We estimated the trends and projections of age-appropriate vaccination coverage at the regional and national levels, as well as place of residence and wealth index in LMICs.MethodsIn total, 174 nationally representative household surveys from 2000 to 2020 from 41 LMICs were included in this study. Bayesian hierarchical regression models were used to estimate trends and projections of age-appropriate vaccination.ResultsThe trend in coverage of age-appropriate Bacillus Calmette-Guérin (BCG), third dose of diphtheria, tetanus, and pertussis (DTP3), third dose of polio (polio3), and measles-containing vaccine (MCV) increased rapidly from 2000 to 2020 in LMICs. Findings indicate substantial increases at the regional and national levels, and by area of residence and socioeconomic status between 2000 and 2030. The largest rise was observed in East Africa, followed by South and Southeast Asia. However, out of the 41 countries, only 10 countries are estimated to achieve 90% coverage of the BCG vaccine by 2030, five of DTP3, three of polio3, and none of MCV. Additionally, by 2030, wider pro-urban and -rich inequalities are expected in several African countries.ConclusionSignificant progress in age-appropriate vaccination coverage has been made in LMICs from 2000 to 2020. Despite this, projections show many countries will not meet the 2030 coverage goals, with persistent urban–rural and socioeconomic disparities. Therefore, LMICs must prioritize underperforming areas and reduce inequalities through stronger health systems and increased community engagement to ensure high coverage and equitable vaccine access.

Mesoscale activity drives the habitat suitability of yellowfin tuna in the Gulf of Mexico

Yellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries’ catch data spanning 2012–2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.

Effects of IV fluid restriction according to site-specific intensity of standard fluid treatment-protocol.

Variation in usual practice in fluid trials assessing lower versus higher volumes may affect overall comparisons. To address this, we will evaluate the effects of heterogeneity in treatment intensity in the Conservative versus Liberal Approach to Fluid Therapy of Septic Shock in Intensive Care trial. This will reflect the effects of differences in site-specific intensities of standard fluid treatment due to local practice preferences while considering participant characteristics. We will assess the effects of heterogeneity in treatment intensity across one primary (all-cause mortality) and three secondary outcomes (serious adverse events or reactions, days alive without life support and days alive out of hospital) after 90 days. We will classify sites based on the site-specific intensity of standard fluid treatment, defined as the mean differences in observed versus predicted intravenous fluid volumes in the first 24 h in the standard-fluid group while accounting for differences in participant characteristics. Predictions will be made using a machine learning model including 22 baseline predictors using the extreme gradient boosting algorithm. Subsequently, sites will be grouped into fluid treatment intensity subgroups containing at least 100 participants each. Subgroups differences will be assessed using hierarchical Bayesian regression models with weakly informative priors. We will present the full posterior distributions of relative (risk ratios and ratios of means) and absolute differences (risk differences and mean differences) in each subgroup. This study will provide data on the effects of heterogeneity in treatment intensity while accounting for patient characteristics in critically ill adult patients with septic shock. The European Clinical Trials Database (EudraCT): 2018-000404-42, ClinicalTrials. gov: NCT03668236.

Pre-pregnancy ozone and ultrafine particulate matter exposure during second year of life associated with decreased cognitive and adaptive functioning at aged 2–5 years

BackgroundThis study sought to investigate the association of prenatal and early life exposure to a mixture of air pollutants on cognitive and adaptive outcomes separately in children with or without autism spectrum disorder (ASD). MethodsUtilizing data from the CHARGE case-control study (birth years: 2000–2016), we predicted daily air concentrations of NO2, O3, and particulate matter <0.1 μm (PM0.1), between 0.1 and 2.5 μm (PM0.1-2.5), and between 2.5 and 10 μm (PM2.5-10) using chemical transport models with ground-based monitor adjustments. Exposures were evaluated for pre-pregnancy, each trimester, and the first two years of life. Individual and combined effects of pollutants were assessed with Vineland Adaptive Behavior Scales (VABS) and Mullen Scales of Early Learning (MSEL), separately for children with ASD (n = 660) and children without ASD (typically developing (TD) and developmentally delayed (DD) combined; n = 753) using hierarchical Bayesian Kernel Machine Regression (BKMR) models with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. ResultsPre-pregnancy Ozone was strongly negatively associated with all scores in the non-ASD group (group posterior inclusion probability (gPIP) = 0.83–1.00). The PM group during year 2 was also strongly negatively associated with all scores in the non-ASD group (gPIP = 0.59–0.93), with PM0.1 driving the group association (conditional PIP (cPIP) = 0.73–0.96). Weaker and less consistent associations were observed between PM0.1-2.5 during pre-pregnancy and ozone during year 1 and VABS scores in the ASD group. ConclusionsThese findings prompt further investigation into ozone and ultrafine PM as potential environmental risk factors for neurodevelopment.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model

BackgroundMultidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). MethodsWe developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FindingsWe found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. InterpretationOur results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option.

National, regional, and global estimates of low birthweight in 2020, with trends from 2000: a systematic analysis

Low birthweight (LBW; <2500 g) is an important predictor of health outcomes throughout the life course. We aimed to update country, regional, and global estimates of LBW prevalence for 2020, with trends from 2000, to assess progress towards global targets to reduce LBW by 30% by 2030. For this systematic analysis, we searched population-based, nationally representative data on LBW from Jan 1, 2000, to Dec 31, 2020. Using 2042 administrative and survey datapoints from 158 countries and areas, we developed a Bayesian hierarchical regression model incorporating country-specific intercepts, time-varying covariates, non-linear time trends, and bias adjustments based on data quality. We also provided novel estimates by birthweight subgroups. An estimated 19·8 million (95% credible interval 18·4-21·7 million) or 14·7% (13·7-16·1) of liveborn newborns were LBW worldwide in 2020, compared with 22·1 million (20·7-23·9 million) and 16·6% (15·5-17·9) in 2000-an absolute reduction of 1·9 percentage points between 2000 and 2020. Using 2012 as the baseline, as this is when the Global Nutrition Target began, the estimated average annual rate of reduction from 2012 to 2020 was 0·3% worldwide, 0·85% in southern Asia, and 0·59% in sub-Saharan Africa. Nearly three-quarters of LBW births in 2020 occurred in these two regions: of 19 833 900 estimated LBW births worldwide, 8 817 000 (44·5%) were in southern Asia and 5 381 300 (27·1%) were in sub-Saharan Africa. Of 945 300 estimated LBW births in northern America, Australia and New Zealand, central Asia, and Europe, approximately 35·0% (323 700) weighed less than 2000 g: 5·8% (95% CI 5·2-6·4; 54 800 [95% CI 49 400-60 800]) weighed less than 1000 g, 9·0% (8·7-9·4; 85 400 [82 000-88 900]) weighed between 1000 g and 1499 g, and 19·4% (19·0-19·8; 183 500 [180 000-187 000]) weighed between 1500 g and 1999 g. Insufficient progress has occurred over the past two decades to meet the Global Nutrition Target of a 30% reduction in LBW between 2012 and 2030. Accelerating progress requires investments throughout the lifecycle focused on primary prevention, especially for adolescent girls and women living in the most affected countries. With increasing numbers of births in facilities and advancing electronic information systems, improvements in the quality and availability of administrative LBW data are also achievable. The Children's Investment Fund Foundation; the UNDP-UNFPA-UNICEF-WHO World Bank Special Programme of Research, Development and Research Training in Human Reproduction; and the Bill & Melinda Gates Foundation.

Sampling design methods for making improved lake management decisions

AbstractThe ecological status of lakes is important for understanding an ecosystem's biodiversity as well as for service water quality and policies related to land use and agricultural run‐off. If the status is weak, then decisions about management alternatives need to be made. We assess the value of information of lake monitoring in Finland, where lakes are abundant. With reasonable ecological values and restoration costs, the value of information analysis can be compared with the survey's costs. Data are worth gathering if the expected value from the data exceeds the costs. From existing data, we specify a hierarchical Bayesian spatial logistic regression model for the ecological status of lakes. We then rely on functional approximations and Laplace approximations to get closed‐form expressions for the value of information of a sampling design. The case study contains thousands of lakes. The combinatorially difficult design problem is to wisely pick the right subset of lakes for data gathering. To solve this optimization problem, we study the performance of various heuristics: greedy forward algorithms, exchange algorithms and Bayesian optimization approaches. The value of information increases quickly when adding lakes to a small design but then flattens out. Good designs are usually composed of lakes that are difficult to manage, while also balancing a variety of covariates and geographic coverage. The designs achieved by forward selection are reasonably good, but we can outperform them with the more nuanced search algorithms. Statistical designs clearly outperform other designs selected according to simpler criteria.

Joint Learning of Full-Structure Noise in Hierarchical Bayesian Regression Models.

We consider the reconstruction of brain activity from electroencephalography (EEG). This inverse problem can be formulated as a linear regression with independent Gaussian scale mixture priors for both the source and noise components. Crucial factors influencing the accuracy of the source estimation are not only the noise level but also its correlation structure, but existing approaches have not addressed the estimation of noise covariance matrices with full structure. To address this shortcoming, we develop hierarchical Bayesian (type-II maximum likelihood) models for observations with latent variables for source and noise, which are estimated jointly from data. As an extension to classical sparse Bayesian learning (SBL), where across-sensor observations are assumed to be independent and identically distributed, we consider Gaussian noise with full covariance structure. Using the majorization-maximization framework and Riemannian geometry, we derive an efficient algorithm for updating the noise covariance along the manifold of positive definite matrices. We demonstrate that our algorithm has guaranteed and fast convergence and validate it in simulations and with real MEG data. Our results demonstrate that the novel framework significantly improves upon state-of-the-art techniques in the real-world scenario where the noise is indeed non-diagonal and full-structured. Our method has applications in many domains beyond biomagnetic inverse problems.

Relationship between sex, body size, and cardiac resynchronization therapy benefit: A patient-level meta-analysis of randomized controlled trials

BackgroundWomen might benefit more than men from cardiac resynchronization therapy (CRT) and do so at shorter QRS durations. ObjectiveThis meta-analysis was performed to determine whether sex-based differences in CRT effects are better accounted for by height, body surface area (BSA), or left ventricular end-diastolic dimension (LVEDD). MethodsWe analyzed patient-level data from CRT trials (MIRACLE, MIRACLE ICD, MIRACLE ICD II, REVERSE, RAFT, COMPANION, and MADIT-CRT) using bayesian hierarchical Weibull regression models. Relationships between QRS duration and CRT effects were examined overall and in sex-stratified cohorts; additional analyses indexed QRS duration by height, BSA, or LVEDD. End points were heart failure hospitalization (HFH) or death and all-cause mortality. ResultsCompared with men (n = 5628), women (n = 1439) were shorter (1.62 [interquartile range, 1.57–1.65] m vs 1.75 [1.70–1.80] m; P < .001), with smaller BSAs (1.76 [1.62–1.90] m2 vs 2.02 [1.89–2.16] m2; P < .001). In adjusted sex-stratified analyses, the reduction in HFH or death was greater for women (hazard ratio, 0.54; credible interval, 0.42–0.70) than for men (hazard ratio, 0.77; credible interval, 0.66–0.89; Pinteraction = .009); results were similar for all-cause mortality even after adjustment for height, BSA, and LVEDD. Sex-specific differences were observed only in nonischemic cardiomyopathy. The effect of CRT on HFH or death was observed at a shorter QRS duration for women (126 ms) than for men (145 ms). Indexing QRS duration by height, BSA, or LVEDD attenuated sex-specific QRS duration thresholds for the effects of CRT on HFH or death but not on mortality. ConclusionAlthough body size partially explains sex-specific QRS duration thresholds for CRT benefit, it is not associated with the magnitude of CRT benefit. Indexing QRS duration for body size might improve selection of patients for CRT, particularly with a “borderline” QRS duration. ClinicalTrials.gov registrationNCT00271154, NCT00251251, NCT00267098, NCT00180271

Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables-Bayesian Models vs. Machine Learning Models.

Length of stay (LoS) prediction is deemed important for a medical institution's operational and logistical efficiency. Sound estimates of a patient's stay increase clinical preparedness and reduce aberrations. Various statistical methods and techniques are used to quantify and predict the LoS of a patient based on pre-operative clinical features. This study evaluates and compares the results of Bayesian (simple Bayesian regression and hierarchical Bayesian regression) models and machine learning (ML) regression models against multiple evaluation metrics for the problem of LoS prediction of cardiac patients admitted to Tabba Heart Institute, Karachi, Pakistan (THI) between 2015 and 2020. In addition, the study also presents the use of hierarchical Bayesian regression to account for data variability and skewness without homogenizing the data (by removing outliers). LoS estimates from the hierarchical Bayesian regression model resulted in a root mean squared error (RMSE) and mean absolute error (MAE) of 1.49 and 1.16, respectively. Simple Bayesian regression (without hierarchy) achieved an RMSE and MAE of 3.36 and 2.05, respectively. The average RMSE and MAE of ML models remained at 3.36 and 1.98, respectively.

Using viral sequence diversity to estimate time of HIV infection in infants.

Age at HIV acquisition may influence viral pathogenesis in infants, and yet infection timing (i.e. date of infection) is not always known. Adult studies have estimated infection timing using rates of HIV RNA diversification, however, it is unknown whether adult-trained models can provide accurate predictions when used for infants due to possible differences in viral dynamics. While rates of viral diversification have been well defined for adults, there are limited data characterizing these dynamics for infants. Here, we performed Illumina sequencing of gag and pol using longitudinal plasma samples from 22 Kenyan infants with well-characterized infection timing. We used these data to characterize viral diversity changes over time by designing an infant-trained Bayesian hierarchical regression model that predicts time since infection using viral diversity. We show that diversity accumulates with time for most infants (median rate within pol = 0.00079 diversity/month), and diversity accumulates much faster than in adults (compare previously-reported adult rate within pol = 0.00024 diversity/month [1]). We find that the infant rate of viral diversification varies by individual, gene region, and relative timing of infection, but not by set-point viral load or rate of CD4+ T cell decline. We compare the predictive performance of this infant-trained Bayesian hierarchical regression model with simple linear regression models trained using the same infant data, as well as existing adult-trained models [1]. Using an independent dataset from an additional 15 infants with frequent HIV testing to define infection timing, we demonstrate that infant-trained models more accurately estimate time since infection than existing adult-trained models. This work will be useful for timing HIV acquisition for infants with unknown infection timing and for refining our understanding of how viral diversity accumulates in infants, both of which may have broad implications for the future development of infant-specific therapeutic and preventive interventions.

Frailty’s influence on 30-day mortality in old critically ill ICU patients: a bayesian analysis evaluating the clinical frailty scale

IntroductionFrailty is widely acknowledged as influencing health outcomes among critically ill old patients. Yet, the traditional understanding of its impact has predominantly been through frequentist statistics. We endeavored to explore this association using Bayesian statistics aiming to provide a more nuanced understanding of this multifaceted relationship.MethodsOur analysis incorporated a cohort of 10,363 older (median age 82 years) patients from three international prospective studies, with 30-day all-cause mortality as the primary outcome. We defined frailty as Clinical Frailty Scale ≥ 5. A hierarchical Bayesian logistic regression model was employed, adjusting for covariables, using a range of priors. An international steering committee of registry members reached a consensus on a minimal clinically important difference (MCID).ResultsIn our study, the 30-day mortality was 43%, with rates of 38% in non-frail and 51% in frail groups. Post-adjustment, the median odds ratio (OR) for frailty was 1.60 (95% CI 1.45–1.76). Frailty was invariably linked to adverse outcomes (OR > 1) with 100% probability and had a 90% chance of exceeding the minimal clinically important difference (MCID) (OR > 1.5). For the Clinical Frailty Scale (CFS) as a continuous variable, the median OR was 1.19 (1.16–1.22), with over 99% probability of the effect being more significant than 1.5 times the MCID. Frailty remained outside the region of practical equivalence (ROPE) in all analyses, underscoring its clinical importance regardless of how it is measured.ConclusionsThis research demonstrates the significant impact of frailty on short-term mortality in critically ill elderly patients, particularly when the Clinical Frailty Scale (CFS) is used as a continuous measure. This approach, which views frailty as a spectrum, enables more effective, personalized care for this vulnerable group. Significantly, frailty was consistently outside the region of practical equivalence (ROPE) in our analysis, highlighting its clinical importance.

Ultrafine particulate matter exposure during second year of life, but not before, associated with increased risk of autism spectrum disorder in BKMR mixtures model of multiple air pollutants

Prenatal and early postnatal air pollution exposures have been shown to be associated with autism spectrum disorder (ASD) risk but results regarding specific air pollutants and exposure timing are mixed and no study has investigated the effects of combined exposure to multiple air pollutants using a mixtures approach. We aimed to evaluate prenatal and early life multipollutant mixtures for the drivers of associations of air pollution with ASD. This study examined 484 typically developing (TD) and 660 ASD children from the CHARGE case-control study. Daily air concentrations for NO2, O3, ultrafine (PM0.1), fine (PM0.1-2.5), and coarse (PM2.5-10) particles were predicted from chemical transport models with statistical bias adjustment based on ground-based monitors. Daily averages were calculated for each exposure period (pre-pregnancy, each trimester of pregnancy, first and second year of life) between 2000 and 2016. Air pollution variables were natural log-transformed and then standardized. Individual and joint effects of pollutant exposure with ASD, and potential interactions, were evaluated for each period using hierarchical Bayesian Kernel Machine Regression (BKMR) models, with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. In BKMR models, the PM group was associated with ASD in year 2 (group posterior inclusion probability (gPIP) = 0.75), and marginally associated in year 1 (gPIP = 0.497). PM2.5-10 appeared to drive the association (conditional PIP (cPIP) = 0.64) in year 1, while PM0.1 appeared to drive the association in year 2 (cPIP = 0.76), with both showing a moderately strong increased risk. Pre-pregnancy O3 showed a slight J-shaped risk of ASD (gPIP = 0.55). No associations were observed for exposures during pregnancy. Pre-pregnancy O3 and year 2 p.m.0.1 exposures appear to be associated with an increased risk of ASD. Future research should examine ultrafine particulate matter in relation to ASD.

Abstract 15107: Dramatic Impact of a Liaison-Mediated Referral Strategy on Cardiac Rehabilitation Participation After Percutaneous Coronary Intervention: Insights From the Blue Cross Blue Shield of Michigan Cardiovascular Consortium

Background: While cardiac rehabilitation (CR) improves outcomes after percutaneous coronary intervention (PCI) it remains underutilized. A liaison-mediated referral, where a healthcare professional explains CR’s benefits and places a referral before discharge, may promote CR use. Hypothesis: We hypothesize a liaison-mediated CR referral is associated with greater CR participation after PCI. Methods: This was a retrospective cohort study of patients who underwent PCI across 48 hospitals in Michigan between January 2021 and April 2022 and were referred to CR before discharge. Clinical registry data was linked to Medicare and commercial claims to identify the primary outcome of CR participation, defined as attending ≥1 CR session within 90 days of discharge from the index PCI admission. Bayesian hierarchical logistic regression modeling was used to compare CR participation between patients with and without a liaison referral, adjusting for patient characteristics. Cox-proportional hazard modeling was used to compare days elapsed between discharge and first CR session between liaison cohorts. Results: Among 9,023 patients referred to CR after PCI, 4,323 (47.9%) underwent a liaison referral and 3,390 (37.6%) attended ≥1 CR session within 90 days of discharge. The liaison and non-liaison cohorts had a mean age of 69.3 vs 69.4 years, and 68.3% vs 68.4% were male, respectively. The liaison cohort had a higher unadjusted 90-day CR participation rate compared with the non-liaison cohort (Figure). After adjustment, the liaison cohort had a significantly higher odds of attending ≥1 CR session and a shorter delay in attending the first session compared with the non-liaison cohort (28 vs 33 days, p&lt;0.001) (Figure). Conclusion: A liaison-mediated CR referral is associated with higher odds of post-PCI CR participation and may mitigate delays in CR enrollment. This may be an effective referral strategy to improve CR participation rates and patient outcomes after PCI.

Abstract 13402: Outcomes of Cardiac Resynchronization Therapy in Ischemic versus Non-Ischemic Cardiomyopathy: A Patient-Level Meta-Analysis of 7 Randomized Clinical Trials

Background: Data on whether CRT results in better clinical and echocardiographic outcomes in patients with NICM versus patients with ICM are conflicting. Objective: To determine if there are differences in clinical and echocardiographic outcomes of CRT in patients with ICM versus NICM. Methods: We analyzed patient-level data from 7 CRT trials (MIRACLE, MIRACLE-ICD, MIRACLE-ICD II, COMPANION, RAFT, MADIT-CRT, REVERSE) using Bayesian Hierarchical Weibull survival regression modeling, adjusted for baseline characteristics and presence of an ICD, to determine the effect of etiology of cardiac dysfunction on time to all-cause mortality alone and in combination with heart failure hospitalization (HFH). Kaplan-Meier (KM) survival curves were developed with unadjusted frequentist analyses. Linear regression was used to assess the effect of etiology of cardiomyopathy on echocardiographic measurements. Results: Of the 6252 patients included, 1535 (25%) were women, the median age was 66 (IQR 58-73), 3704 (59%) had ICM, and 3778 (60%) received CRT. Of those who received CRT, 2207 (58%) patients had ICM. Overall, CRT increased the time to HFH or death (hazard ratio (HR) 0.67, 95% credible interval (CrI) 0.56-0.82, p&lt;0.001) with no difference by etiology (HR ratio 1.06, CrI 0.87-1.29, p=0.57). CRT increased the time to death (HR 0.71, CrI 0.55-0.93, p=0.019) with no difference by etiology (HR ratio 1.06, CrI 0.80-1.43, p=0.70), although overall event rate was higher in ICM. CRT in ICM increased the median survival and time free of HFH (70 versus 50 months, p=0.0015) and had borderline effect in NICM (p=0.05) (Figure). Echocardiographic data that were available for 2430 (39%) patients showed that CRT improvements in LVEF, LVEDD, and LVESD were larger for patients with NICM. Conclusion: CRT increased the time to HFH or death independent of etiology of cardiac dysfunction. However, CRT led to greater increases in LVEF and reductions in ventricular dimensions for patients with NICM.

Cognitive Disengagement Syndrome But Not ADHD Symptoms Increases Risk for Child Body Mass Index: Examination in a Sample of Clinically Referred Youth.

Given the substantial increase in pediatric obesity rates in recent decades, its long-term stability, and its pervasive negative outcomes, continuous efforts to identify factors that may place children at increased risk for overweight or obesity (OW/OB) are essential. As such, the primary aim of the present investigation was to examine the extent to which symptoms of Cognitive Disengagement Syndrome (CDS; i.e., Sluggish Cognitive Tempo) relates to child body mass index (BMI) independent of Attention-Deficit/Hyperactivity Disorder (ADHD). The study is the first to examine whether CDS subdomains of slowed thinking, hypoarousal, and daydreamy is associated with BMI. Analyses included data from 72 clinically-referred children (46 males, 26 females) aged 4-12years old (M = 8.41, SD = 2.48). CDS and ADHD were assessed using standardized parent-report rating scales, and children's BMI was collected at the time of encounter. Bayesian hierarchical regression models revealed no evidence that overall CDS symptoms or ADHD symptoms (overall and subdomain) predicted child BMI. However, models did provide moderate evidence that hypoarousal and daydream subdomains jointly predicted BMI independent of ADHD (BF10 = 19.28-21.87). The present study suggests that CDS is a risk factor for obesity in young children and future research is needed to inform clinical interventions and to provide further understanding of the relatively nuanced association between CDS symptoms and obesity.

Decision Trade-Offs in Ecological Momentary Assessments and Digital Wearables Uptake: Protocol for a Discrete Choice Experiment.

Ecological momentary assessments (EMAs) and digital wearables (DW) are commonly used remote monitoring technologies that capture real-time data in people's natural environments. Real-time data are core to personalized medical care and intensively adaptive health interventions. The utility of such personalized care is contingent on user uptake and continued use of EMA and DW. Consequently, it is critical to understand user preferences that may increase the uptake of EMA and DW. The study aims to quantify users' preferences of EMA and DW, examine variations in users' preferences across demographic and behavioral subgroups, and assess the association between users' preferences and intentions to use EMA and DW. We will administer 2 discrete choice experiments (DCEs) paired with self-report surveys on the internet to a total of 3260 US adults through Qualtrics. The first DCE will assess participants' EMA preferences using a choice-based conjoint design that will ask participants to compare the relative importance of prompt frequency, number of questions per prompt, prompt type, health topic, and assessment duration. The second DCE will measure participants' DW preferences using a maximum difference scaling design that will quantify the relative importance of device characteristics, effort expectancy, social influence, and facilitating technical, health care, and market factors. Hierarchical Bayesian multinomial logistic regression models will be used to generate subject-specific preference utilities. Preference utilities will be compared across demographic (ie, sex, age, race, and ethnicity) and behavioral (ie, substance use, physical activity, dietary behavior, and sleep duration) subgroups. Regression models will determine whether specific utilities are associated with attitudes toward or intentions to use EMA and DW. Mixture models will determine the associations of attitudes toward and intentions to use EMA and DW with latent profiles of user preferences. The institutional review board approved the study on December 19, 2022. Data collection started on January 20, 2023, and concluded on May 4, 2023. Data analysis is currently underway. The study will provide evidence on users' preferences of EMA and DW features that can improve initial uptake and potentially continued use of these remote monitoring tools. The sample size and composition allow for subgroup analysis by demographics and health behaviors and will provide evidence on associations between users' preferences and intentions to uptake EMA and DW. Limitations include the cross-sectional nature of the study, which limits our ability to measure direct behavior. Rather, we capture behavioral intentions for EMA and DW uptake. The nonprobability sample limits the generalizability of the results and introduces self-selection bias related to the demographic and behavioral characteristics of participants who belong to web-based survey panels. DERR1-10.2196/47567.

Comorbidities and clinical response to cardiac resynchronization therapy: patient-level meta-analysis from eight clinical trials.

Patients with heart failure usually have several other medical conditions that might alter the effects of interventions. We investigated whether the burden of comorbidity modified the clinical response to cardiac resynchronization therapy (CRT). Original patient-level data from eight randomized trials exploring the effects of CRT versus no CRT were pooled (BLOCK-HF, MIRACLE, MIRACLE-ICD, MIRACLE-ICD II, RAFT, COMPANION, MADIT-CRT and REVERSE). A prior history of the following comorbidities was considered: episodic or persistent atrial fibrillation (n = 920), coronary artery disease (n = 3732), diabetes (n = 2171), and hypertension (n = 3353). Patients were classified into three groups based on the number of comorbidities: 0, 1-2, or ≥3. The outcomes of interest were time to all-cause mortality and time to the composite outcome of heart failure hospitalization (HFH) or all-cause mortality. Outcomes were evaluated within each comorbidity group using a Bayesian hierarchical Weibull survival regression model. Of 6324 patients, 970 (15%) had no comorbidities, 4052 (64%) had 1-2 and 1302 (21%) had ≥3 comorbidities. The adjusted hazard ratio (aHR) for CRT versus no CRT for all-cause mortality in the overall cohort was 0.79 (95% credible interval [CI] 0.68-0.93) (p = 0.010); for no comorbidities the aHR was 0.54 (95% CI 0.34-0.86), for 1-2 comorbidities was 0.81 (95% CI 0.67-0.97) and for ≥3 comorbidities was 0.83 (95% CI 0.64-1.07) (no significant interaction between CRT and comorbidity burden: p = 0.13). For the endpoint of HFH or all-cause mortality, the aHR for the overall cohort was 0.74 (95% CI 0.65-0.84) (p = 0.001), for no comorbidities was 0.69 (95% CI 0.50-0.94), for 1-2 comorbidities was 0.77 (95% CI 0.66-0.90) and for ≥3 comorbidities was 0.68 (95% CI 0.55-0.82) (no significant interaction between CRT and comorbidity burden: p = 0.081). In a meta-analysis of patient-level data from eight major trials, the totality of evidence suggests that CRT reduces HFH and/or all-cause mortality even when several comorbid diseases are present. NCT00271154, NCT00251251, NCT00267098, NCT00180271.

Discriminative performance of pancreatic stone protein in predicting ICU mortality and infection severity in adult patients with infection: a systematic review and individual patient level meta-analysis

BackgroundSeveral studies suggested pancreatic stone protein (PSP) as a promising biomarker to predict mortality among patients with severe infection. The objective of the study was to evaluate the performance of PSP in predicting intensive care unit (ICU) mortality and infection severity among critically ill adults admitted to the hospital for infection.MethodsA systematic search across Cochrane Central Register of Controlled Trials and MEDLINE databases (1966 to February 2022) for studies on PSP published in English using ‘pancreatic stone protein’, ‘PSP’, ‘regenerative protein’, ‘lithostatin’ combined with ‘infection’ and ‘sepsis’ found 46 records. The search was restricted to the five trials that measured PSP using the enzyme-linked immunosorbent assay technique (ELISA). We used Bayesian hierarchical regression models for pooled estimates and to predict mortality or disease severity using PSP, C-Reactive Protein (CRP) and procalcitonin (PCT) as main predictor. We used statistical discriminative measures, such as the area under the receiver operating characteristic curve (AUC) and classification plots.ResultsAmong the 678 patients included, the pooled ICU mortality was 17.8% (95% prediction interval 4.1% to 54.6%) with a between-study heterogeneity (I-squared 87%). PSP was strongly associated with ICU mortality (OR = 2.7, 95% credible interval (CrI) [1.3–6.0] per one standard deviation increase; age, gender and sepsis severity adjusted OR = 1.5, 95% CrI [0.98–2.8]). The AUC was 0.69 for PSP 95% confidence interval (CI) [0.64–0.74], 0.61 [0.56–0.66] for PCT and 0.52 [0.47–0.57] for CRP. The sensitivity was 0.96, 0.52, 0.30 for risk thresholds 0.1, 0.2 and 0.3; respective false positive rate values were 0.84, 0.25, 0.10.ConclusionsWe found that PSP showed a very good discriminative ability for both investigated study endpoints ICU mortality and infection severity; better in comparison to CRP, similar to PCT. Combinations of biomarkers did not improve their predictive ability.

Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models

Effective spatial fisheries management requires a proper understanding of the spatial distribution of both target species and discards. Also, spatial modelling of fishery-dependent data is an effective tool to capture uncertainties in data-limited situations. This study analyses the drivers behind discarding by comparing the standardising properties of three different components: Total Discards, Discards Per Unit of Effort and Total Discard Ratio. These metrics were analysed by means of Bayesian hierarchical spatio-temporal Gamma regression models to correctly to identify areas with high discards values that are characterized as discards hotspots. Our results showed that Total Discards is the component which better quantified the aggregated ecological impact of discarding practices, whereas Total Discard Ratio and Discards Per Unit of Effort identify complementary issues of benefits versus loss of biomass. Spatial maps obtained by combining these three approaches are a powerful tool for the spatial management of discards.