Significant Covariates Research Articles

A Bayesian multistage spatio-temporally dependent model for spatial clustering and variable selection.

In spatio-temporal epidemiological analysis, it is of critical importance to identify the significant covariates and estimate the associated time-varying effects on the health outcome. Due to the heterogeneity of spatio-temporal data, the subsets of important covariates may vary across space and the temporal trends of covariate effects could be locally different. However, many spatial models neglected the potential local variation patterns, leading to inappropriate inference. Thus, this article proposes a flexible Bayesian hierarchical model to simultaneously identify spatial clusters of regression coefficients with common temporal trends, select significant covariates for each spatial group by introducing binary entry parameters and estimate spatio-temporally varying disease risks. A multistage strategy is employed to reduce the confounding bias caused by spatially structured random components. A simulation study demonstrates the outperformance of the proposed method, compared with several alternatives based on different assessment criteria. The methodology is motivated by two important case studies. The first concerns the low birth weight incidence data in 159 counties of Georgia, USA, for the years 2007 to 2018 and investigates the time-varying effects of potential contributing covariates in different cluster regions. The second concerns the circulatory disease risks across 323 local authorities in England over 10 years and explores the underlying spatial clusters and associated important risk factors.

Optical Coherence Tomography in Schizophrenia Spectrum Disorders: A Systematic Review and Meta-analysis

BackgroundInner retinal atrophy has been demonstrated in schizophrenia spectrum disorder (SSD) using optical coherence tomography (OCT). This systematic review and meta-analysis investigated the role of contemporary Fourier domain OCT devices in SSD. MethodsMEDLINE, PubMed, Scopus, Embase, PsycInfo, PYSNDEX, World Health Organization, and Cochrane databases were searched from inception until May 2022. All peer-reviewed adult SSD case-control studies using Fourier domain OCT were included. Ocular pathologies known to affect retinal OCT scans were excluded. Search, data appraisal, and summary data extraction were independently performed by 2 authors. ResultsThe review criteria was met by k = 36 studies, with k = 24 studies (1074 cases, 854 controls) suitable for meta-analysis. The SSD group exhibited a thinner global peripapillary retinal nerve fiber layer (−3.26 μm, 95% CI, −5.07 to −1.45, I2 = 64%, k = 21), thinner average macular layer (−7.88 μm, 95% CI, −12.73 to −3.04, I2 = 65%, k = 11), and thinner macular ganglion cell-inner plexiform sublayer (−2.44 μm, 95% CI, −4.13 to −0.76, I2 = 30%, k = 8) compared with the control group. Retinal nerve fiber layer findings remained significant after exclusion of metabolic disease, low quality, outlier, and influential studies. Studies involving eye examinations to exclude eye disease were associated with greater atrophy in SSD. Except for cardiometabolic disease, most studies did not report clinically significant covariate data known to influence retinal thickness. ConclusionsIndividuals with SSD generally exhibited retinal atrophy, possibly paralleling reduced brain volumes documented in clinical imaging. Prospective longitudinal studies that collect clinical data, including various illness phases, and control for confounders will be necessary to evaluate retinal atrophy as a biomarker in SSD.

Using population pharmacokinetics to optimize initial vancomycin dosing guidelines for neonates to treat sepsis caused by coagulase-negative staphylococcus.

Vancomycin dosing tailored for newborns is challenging due to the significant influence of maturation and organ function on pharmacokinetics. Population pharmacokinetic (popPK) models can be used to improve target attainment in neonates. The primary objective was to derive and evaluate a popPK model of intravenous vancomycin for neonates. Second, the predictive performance of this popPK model was compared with published popPK models. This is a retrospective cohort study of neonates admitted to the neonatal intensive care unit receiving intravenous vancomycin. A popPK model was derived with 70% of the dataset using a nonlinear mixed effects modeling method. The predictive performance of the current popPK model was validated and compared with 22 published popPK models using the remaining 30% of the dataset. Monte Carlo simulations (MCS) were performed to derive optimal dosing regimens to treat neonatal sepsis caused by coagulase-negative staphylococci (CoNS). Among 655 vancomycin courses from 448 neonates, 78% of vancomycin trough concentrations were outside target range (10-15 mg/L) for central nervous system infections and 43% were outside target range (5-12 mg/L) for other infections using the institution's vancomycin dosing. A one-compartment model best described the observed data with a mean clearance of 0.11 ± 0.03 L/kg/h and volume of distribution (V) of 1.02 ± 0.08 L/kg. Body weight (WT), postmenstrual age (PMA), and serum creatinine (SCr) were significant covariates associated with clearance (p < 0.001) and body WT was a significant covariate associated with V (p = 0.009). Our study's popPK model has similar or better accuracy and precision than other published models. MCS-derived vancomycin doses from the validated model achieved >90% target attainment for a steady state through target range of 10-15 mg/L in the majority of PMA and SCr categories (78%) to treat CoNS sepsis. A vancomycin dosing guideline derived from a validated popPK model in neonates with CoNS sepsis is recommended to improve target attainment.

Population Pharmacokinetic Analysis of Follicle-Stimulating Hormone During Ovarian Stimulation: Relation with Weight, Prolactin and Gene Polymorphism in THADA and ADIPOQ.

Personalisation strategies of ovarian stimulation for in vitro fertilisation (IVF)/ intracytoplasmic sperm injection (ICSI) treatments using exogenous follicle-stimulating hormone (FSH) have been extensively studied over the past 20 years. This research aimed to develop a FSH population pharmacokinetic (PPK) model taking into account the contribution of gene polymorphisms in Chinese reproductive-age women. Data from 173 patients undergoing GnRH agonist down-regulation long protocols of IVF/ICSI treatment were collected. PPK analysis was subsequently conducted using the nonlinear mixed-effect model (NONMEM) software. Several covariates, including 18 single nucleotide polymorphisms, demographic factors and biological characteristics, were evaluated. The final PPK model was extensively validated using bootstrapping and normalised prediction error distribution, as well as external validation on an independent group of 35 patients. FSH PPK was accurately described by a one-compartment model with first-order absorption. The typical population value of apparent clearance was estimated to be 0.81 L/h [relative standard errors (RSE) 5.3%] with an inter-individual variability (IIV) of 16.0%. The typical apparent distribution volume was 8.36 L (RSE 9.7%, 59.7% IIV), and the absorption rate constant was estimated to be 0.0444 h-1 (RSE 9.1%). Body weight, basal prolactin concentration and the gene ADIPOQ (rs1501299) showed a significant covariate effect on the FSH clearance rate and exposure concentration. Genotypes of THADA (rs12478601) significantly influenced the distribution volume. Simulation results indicated that patients with the TT genotype of THADA (rs12478601) required a longer time to reach steady state and had less fluctuation in FSH levels. Model evaluations showed that the final model accurately and precisely described the observed data and demonstrated effective prediction performance. PPK models of FSH have been developed, which could potentially be used for FSH dosage individualisation in the clinical setting. This study has been registered with the Chinese Clinical Trials Registry (ChiCTR2100049142).

Soil disturbance, amelioration and rehabilitation affect forest growth, health, soil carbon and chemistry on five long-term soil productivity (LTSP) sites in southeastern British Columbia

Soil disturbance amelioration affected tree survival, growth, health, mineral soil chemistry, and their covariation on five Long-Term Soil Productivity (LTSP) sites in southeastern British Columbia. The two most different LTSP treatments (baseline disturbance of no compaction / all organic matter left, versus most severe of heavy compaction / forest floor removed) were compared to five amelioration treatments: rehabilitation of most severe LTSP treatment (soil tillage with or without incorporation of removed forest floor [Rehab + FF]), root disease (Armillaria ostoyae (Romagn.) Herink) amelioration treatments (stump removal or biological with Hypholoma fasiculare), and enhancement of soil organic matter (tilling extra forest floor into mineral soil). Treatment significantly (p < 0.05) affected 10-year mineral soil carbon, nitrogen, sulfur, mineralizable nitrogen, and potassium, which were lower on forest floor removal treatments and higher on forest floor addition treatments (27 to 97% of baseline versus 78 to 183% of baseline, respectively). Western white pine (Pinus monticola Dougl. ex D.Don) 15-year productivity (m3/ha) was significantly affected by treatment (Rehab + FF yielding a 10 fold increase in productivity versus the poorly growing baseline treatment), with treatment also significant when total disease was a covariate and for average and total volume when mineral soil Ca:Al ratio was a marginally significant (p < 0.1) covariate, and for survival when the incidence of total disease or white pine blister rust (Cronartium ribicola) were significant covariates; or with mineral soil C:N ratio as a significant covariate. The incidence of blister rust and total disease were significantly influenced by treatment when C:N was a significant covariate. For 20-year lodgepole pine (Pinus contorta Dougl. Ex Loud) there were no direct relationships apparent, but western gall rust (Endocronartium harknessii) had treatment significant when total sulfur was a significant covariate. Fifteen- and twenty-year Douglas-fir (Pseudotsuga menziesii var. glauca [Beissn.] Franco) had no significant relationships. Results demonstrate that restoring or adding extra organic matter (forest floor) into mineral soil increased soil carbon and related nutrients and can increase above ground carbon (forest productivity). Conversely, removing forest floor lowered mineral soil carbon and generally lowered tree volume. Varied effects due to growth limiting factors like frost and competing vegetation affected results on a few plots. Recommendations include leaving or enhancing organic matter onsite for soil carbon, nutrients, forest health and site productivity. Further analysis with the larger LTSP dataset and ongoing monitoring is encouraged to determine if the results persist in the long-term and to better understand the significant covariate relationships.

Correlation between disease activity and markers of coagulation cascade in patients of chronic spontaneous urticaria: a hospital based cross-sectional study

Background: Both extrinsic as well as intrinsic coagulation cascade are involved in the pathogenesis of Chronic spontaneous urticaria. This study was done to correlate the disease activity and markers of coagulation cascade in patients of CSU. Methods: A hospital-based, cross-sectional, descriptive study was carried out in 100 patients of chronic urticaria. Baseline D-Dimer, prothrombin time, activated partial thromboplastin time was performed. Disease activity was assessed using Urticaria activity score over 7 days. Results: Of total 100 patients, d-dimer was raised in 72 (72%) patients. Correlation between D-dimer and UAS-7 days showed significant covariance between D-dimer and UAS-7 (p&lt;0.001, r=0.93). Prothrombin time was raised in 55 (55%) patients. Significant correlation was found between PT and UAS-7 (p&lt;0.001, r=0.76). Raised APTT was observed in 61(61%) patients. Correlation between APTT and UAS-7 showed that there was a significant covariance between APTT and UAS-7 (p&lt;0.001, r=0.59). Conclusions: A significant correlation between markers of coagulation cascade and the disease activity in patients of CSU was seen. These markers can help predict the disease severity and possibly monitor therapeutic response.

Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques

Urmia Lake in Iran has been gradually drying up since the 2000 s, revealing soil that was once underwater. There is limited research on how this drying process affects the distribution of soil chemical ripening in the lakebed sediments. To investigate this, 131 surface soil samples were collected in July 2017 from six zones based on their distance from the lake's shoreline. Elemental concentrations in the samples were measured using a portable X-ray fluorescence (XRF) spectrometer. Five geochemical indices (GCIs) were calculated: Titanium/Zirconium, Barium/Strontium, Silicon/Aluminium, Silicon/R2O3, and the Darmody index. A remote sensing image (Landsat-8 OLI) from the same sampling period was used as environmental covariates to explain the spatial distribution of GCIs. Random forests (RF) and cubist tree models were employed to model and map these GCIs. The bootstrapping method was used to assess the uncertainty of the maps. Results indicated that the RF model performed better than the cubist model based on the concordance values in the validation dataset. Remotely sensed clay minerals ratio and normalized burn ratio were identified as the most significant covariates in making predictions. The study found that the distribution of all geochemical indices was directly related to their distance from the shoreline. Ba/Sr emerged as the best index for detecting soil chemical ripening in the study area. The index displayed the most significant differences between the drying zones. This research demonstrated that combining satellite imagery with GCIs using the digital soil mapping (DSM) technique can effectively detect soil ripening progression.

A semimechanistic pharmacokinetic/pharmacodynamic model for alanine aminotransferase-based hepatotoxicity of methotrexate in paediatric patients with acute lymphoid leukaemia.

Methotrexate (MTX) is recognized for its potential to induce hepatotoxicity, commonly manifested by elevated alanine aminotransferase (ALT) levels. However, the quantitative relationship between the pharmacokinetics (PK) of MTX and ALT-based hepatotoxicity remains unclear. This study aimed to develop a semimechanistic PK/pharmacodynamic (PD) model to characterize the MTX-induced hepatotoxicity based on ALT in paediatric patients with acute lymphoid leukaemia. A retrospective study was conducted on paediatric patients who received high-dose (3-5g/m2 ) MTX treatment. MTX concentrations were assessed at 24-h intervals until the concentration dropped below 0.1μmol/L. ALT concentrations were measured both before and after MTX administration. A population PK model was initially developed, which was later connected to a semimechanistic hepatotoxicity model. The PK model was developed using 354 MTX concentrations obtained from 51 patients, while the PD model was constructed using 379 ALT concentrations collected from 48 patients. The optimal PK model for MTX consisted of a 2-compartment structure, where body surface area served as a covariate for clearance and central volume of distribution. An indirect response model coupled to a liver injury signal transduction model was developed to describe the dynamics of ALT after MTX administration. The drug effect was adequately described by a linear model, exhibiting considerable interoccasion variability for each treatment session. No significant covariates were identified to have an impact on the PD parameters. A semimechanistic model was developed to describe ALT-based hepatotoxicity of MTX, and it has the potential to serve as a valuable tool for characterizing drug-induced hepatotoxicity.

Population pharmacokinetics of topiramate in Chinese children with epilepsy.

Topiramate, a broad-spectrum antiepileptic drug, exhibits substantial inter-individual variability in both its pharmacokinetics and therapeutic response. The aim of this study was to investigate the influence of patient characteristics and genetic variants on topiramate clearance using population pharmacokinetic (PPK) models in a cohort of Chinese pediatric patients with epilepsy. The PPK model was constructed using a nonlinear mixed-effects modeling approach, utilizing a dataset comprising 236 plasma concentrations of topiramate obtained from 181 pediatric patients with epilepsy. A one-compartment model combined with a proportional residual model was employed to characterize the pharmacokinetics of topiramate. Covariate analysis was performed using forward addition and backward elimination to assess the influence of covariates on the model parameters. The model was thoroughly evaluated through goodness-of-fit analysis, bootstrap, visual predictive checks, and normalized prediction distribution errors. Monte Carlo simulations were utilized to devise topiramate dosing strategies. In the final PPK models of topiramate, body weight, co-administration with oxcarbazepine, and a combined genotype of GKIR1-UGT (GRIK1 rs2832407, UGT2B7 rs7439366, and UGT1A1 rs4148324) were identified as significant covariates affecting the clearance (CL). The clearance was estimated using the formulas CL (L/h) = 0.44 × (BW⁄11.7)0.82 × eOXC for the model without genetic variants and CL (L/h) = 0.49 × (BW⁄11.7)0.81 × eOXC × eGRIK1-UGT for the model incorporating genetic variants. The volume of distribution (Vd) was estimated using the formulas Vd (L) = 6.6 × (BW⁄11.7). The precision of all estimated parameters was acceptable. Furthermore, the model demonstrated good predictability, exhibiting stability and effectiveness in describing the pharmacokinetics of topiramate. The clearance of topiramate in pediatric patients with epilepsy may be subject to the influence of factors such as body weight, co-administration with oxcarbazepine, and genetic polymorphism. In this study, PPK models were developed to better understand and account for these factors, thereby improving the precision and individualization of topiramate therapy in children with epilepsy.

Genetic Correlations Among Corneal Biophysical Parameters and Anthropometric Traits.

The genetic architecture of corneal dysfunction remains poorly understood. Epidemiological and clinical evidence suggests a relationship between corneal structural features and anthropometric measures. We used global and local genetic similarity analysis to identify genomic features that may underlie structural corneal dysfunction. We assembled genome-wide association study summary statistics for corneal features (central corneal thickness, corneal hysteresis [CH], corneal resistance factor [CRF], and the 3 mm index of keratometry) and anthropometric traits (body mass index, weight, and height) in Europeans. We calculated global genetic correlations (rg) between traits using linkage disequilibrium (LD) score regression and local genetic covariance using ρ-HESS, which partitions the genome and performs regression with LD regions. Finally, we identified genes located within regions of significant genetic covariance and analyzed patterns of tissue expression and pathway enrichment. Global LD score regression revealed significant negative correlations between height and both CH (rg = -0.12; P = 2.0 × 10-7) and CRF (rg = -0.11; P = 6.9 × 10-7). Local analysis revealed 68 genomic regions exhibiting significant local genetic covariance between CRF and height, containing 2874 unique genes. Pathway analysis of genes in regions with significant local rg revealed enrichment among signaling pathways with known keratoconus associations, including cadherin and Wnt signaling, as well as enrichment of genes modulated by copper and zinc ions. Corneal biophysical parameters and height share a common genomic architecture, which may facilitate identification of disease-associated genes and therapies for corneal ectasias. Local genetic covariance analysis enables the identification of associated genes and therapeutic targets for corneal ectatic disease.

Bayesian Joint Modeling Analysis of Longitudinal Proportional and Survival Data

This paper focuses on a joint model to analyze longitudinal proportional and survival data. We utilize a logit transformation on the longitudinal proportional data and employ a partially linear mixed-effect model. With this model, we estimate the unknown function of time using the B-splines technique. Additionally, we introduce a centered Dirichlet process mixture model (CDPMM) to capture the random effects, allowing for a flexible distribution. The survival data are assumed using a Cox proportional hazard model, and the sharing random effects joint model is developed for the two types of data. We develop a Bayesian Lasso (BLasso) approach that combines the Gibbs sampler and the Metropolis–Hastings algorithm. The proposed method allows for the estimation of unknown parameters and the selection of significant covariates simultaneously. We evaluate the performance of our proposed methods through simulation studies and also provide an illustration of our methodologies using an example from the MA.5 research experiment.

Trajectories of physical activity during COVID-19: A longitudinal analysis of UK young adults.

Research has demonstrated a disproportionate reduction in the moderate-vigorous physical activity (MVPA) of young UK adults during the initial months of COVID-19. However, previous research has not examined the trajectory of MVPA for this demographic over subsequent phases of the pandemic. The present study investigated the trajectory of MVPA from April 2020 to January 2021 in young UK adults. Data were drawn from 18-29-year-old participants of the Understanding Society COVID-19 Survey (212 males, 542 females). Weekly MVPA was self-reported at three time points (April and September 2020; January 2021) using the International Physical Activity Questionnaire. After controlling for significant covariates, growth curve modelling revealed no variation in weekly MVPA, which remained higher than the UK Physical Activity Guidelines. Female gender; Asian, Black and Mixed ethnicity; lower income,; living with a partner; and no access to a private garden or other outdoor space were associated with lower MVPA in April 2020. Gender, however, was a significant moderator of the trajectory. Males' MVPA increased between April 2020 to August 2020, followed by a sharp decline; whereas females showed a steady rate of decline from April 2020 to January 2021. Despite the recurrent lockdowns, this study shows that young UK adults, on average, continued to engage in MVPA, above the recommendated amount. Nevertheless, significant variation associated with gender, ethnicity and income highlights the importance of providing accessible spaces for young adults to exercise, especially those with limited access to private gardens.

Sociodemographics and health-literacy as predictors of cervical cancer screening practices among Haitian women: A secondary data analysis of 2016-17 DHS surveys.

Cervical cancer screening rates in Haiti are concerningly low. Access to health-related information and health literacy may be important determinants of engagement in cervical cancer screening. This study explored the relationship between sociodemographics,literacy, and sources of information on cervical cancer screening among Haitian women. A secondary data analysis was conducted using USAID Demographics and Health Survey Haiti household data from 2016-2017. Univariate logistic regressions identified significant predictor covariates measuring sociodemographics and sources of information in cervical cancer screening uptake.Two multivariate logistic regression models with adjusted odds ratios were developed using the significant predictor variables from the univariate analysis. N = 610 women responded to questions pertaining to cervical cancer screening. The first multivariate model evaluating sociodemographics demonstrated an economic background of poorer (aOR = 4.06, 95% CI [1.16,14.27]) and richest (aOR = 19.10 , 95% CI[2.58,141.57]), higher education levels (aOR 7.58 , 95% CI [1.64,34.97]), and having insurance (aOR = 16.40, [95% CI 2.65, 101.42]) were significant predictors of cervical cancer screening. The second model evaluating literacy and sources of information indicated that access to a television (aOR = 4.28, 95% CI [1.21,9.34]), mobile phone ownership (aOR = 4.44, 95% CI [1.00,5.59]), and reading the newspaper (aOR = 3.57, [95% CI 1.10,11.59]) were significant predictors of cervical cancer screening. Diverse health communication initiatives that are adapted for literacy level and that incorporate multimedia components may effective in raising women's cervical cancer knowledge and awareness , and increasing intention and uptake of cervical cancer screening in Haiti.

Association Between Symptom Cluster Endorsement at Initiation of a Graduated Return-to-Activity Protocol and Time to Return to Unrestricted Activity After Concussion in United States Service Academy Cadets.

The endorsement of symptoms upon initiation of a graduated return-to-activity (GRTA) protocol has been associated with prolonged protocols. It is unclear whether there are specific symptom clusters affecting protocol durations. To describe the endorsement of specific concussion symptom clusters at GRTA protocol initiation and examine the association between symptom cluster endorsement and GRTA protocol duration. Cohort study; Level of evidence, 2. This study was conducted among cadets enrolled at 3 US service academies. Participants completed an evaluation upon GRTA protocol initiation. Participants endorsing symptoms were binarized based on 6 symptom clusters (cognitive, emotional, insomnia, physical, sensitivity, and ungrouped). The primary outcome of interest was GRTA protocol duration based on symptom cluster endorsement severity. Prevalence rates were calculated to describe symptom cluster endorsement. Kaplan-Meier survival estimates and univariate and multivariable Cox proportional hazards regression models were calculated for all 6 symptom clusters to estimate GRTA protocol duration while controlling for significant covariates. Data from 961 concussed participants were analyzed. Of these, 636 participants were asymptomatic upon GRTA protocol initiation. Among the 325 symptomatic participants, the physical symptom cluster (80%) was most endorsed, followed by the cognitive (29%), insomnia (23%), ungrouped (19%), sensitivity (15%), and emotional (9%) clusters. Univariate results revealed a significant association between endorsing cognitive (hazard ratio [HR], 0.79; p = .001), physical (HR, 0.84; p < .001), insomnia (HR, 0.83; p = .013), sensitivity (HR, 0.70; p < .001), and ungrouped (HR, 0.75; p = .005) symptom clusters and GRTA protocol duration. Endorsing physical (HR, 0.84; p < .001) and sensitivity (HR, 0.81; p = .036) clusters maintained a significant association with GRTA protocol duration in the multivariable models. Participants endorsing physical or sensitivity symptom clusters displayed GRTA protocols prolonged by 16% to 19% compared with participants not endorsing that respective cluster after controlling for significant covariates.

Possible genetic biomarker associated with antipsychotic-induced amenorrhea in female patients with schizophrenia.

This study explored the association of pharmacogenomics with antipsychotic-induced amenorrhea in female patients with schizophrenia. A total of 89 female schizophrenia patients aged 18-40 receiving consistent antipsychotics at a consistent dose for more than 3 months were enrolled in this study. Amenorrhea was defined as the absence of menstrual period for 3 months or three periods in a row. Serum levels of prolactin, estradiol, follicle-stimulating hormone, luteinizing hormone, and thyroid-stimulating hormone were measured and Cytochrome P450 2D6, dopamine receptor D2 ( DRD2 ) and estrogen receptor 1 were genotyped. Twenty-two patients with amenorrhea had higher prolactin levels and lower estradiol levels than those without amenorrhea (94.1 vs. 71.5 ng/ml for prolactin; P = 0.044 and 27.0 vs. 46.7 pg/ml for estradiol; P = 0.007, respectively). Multiple logistic regression analysis identified DRD2 -141C deletion [odds ratio (OR) = 1.71, 95% confidence interval (CI) = 1.01-4.17; P = 0.049] and drugs increasing prolactin levels (OR = 6.17, 95% CI = 1.28-29.64; P = 0.023) as significant covariates for antipsychotic-induced amenorrhea. This study suggests that DRD2 -141C deletion is associated with antipsychotic-induced amenorrhea although further studies are needed.

Time From Authorization by the US Food and Drug Administration to Medicare Coverage for Novel Technologies

A wide variety of novel medical diagnostics and devices are determined safe and effective by the US Food and Drug Administration (FDA) each year, but to our knowledge the literature lacks evidence documenting how long it takes to establish new Medicare coverage for these technologies. To measure time from FDA authorization to at least nominal Medicare coverage for technologies requiring a new reimbursement pathway. In this cross-sectional study, public databases were used to associate each technology to billing codes, determine the effective date of each code and Medicare coverage decisions, and stratify by the maturity of the Medicare coverage. At least nominal coverage was defined as achievement of explicit coverage milestones through a national coverage determination, local coverage determinations by Medicare administrative contractors, or by implicit coverage aligned to a new billing code. Characterization by product type (acute treatment, chronic or ongoing treatment, diagnostic assay, and diagnostic device), manufacturer size, and evidence level were assessed for association with coverage achievement. The study included new product applications authorized by the FDA through the premarket approval pathway, the de novo pathway, or with breakthrough designation in the 510(k) pathway from January 1, 2016, to December 31, 2019. Data analysis took place between May 1, 2022, and December 31, 2022. Time from FDA authorization to the first coverage milestone. Among 281 identified technologies in the total sample, 64 (23%) were deemed novel technologies based on the absence of coverage determinations and/or the use of temporary or miscellaneous billing codes. Twenty-eight of 64 technologies (44%) successfully achieved explicit or implicit coverage following FDA authorization. The median time to at least nominal coverage for the analysis cohort was 5.7 years (90% CI, 4.4-NA years). Analysis of time-to-coverage data highlighted company size (log-rank P<.001) and product type (log-rank P = .01) as significant covariates associated with coverage achievement. No association was observed for technologies with level 1 evidence at FDA authorization and subsequent coverage milestone achievement (log-rank P = .40). In this cross-sectional study of 64 novel technologies, only 28 (44%) achieved coverage milestones over the study timeline. The several-year period observed to establish at least nominal coverage suggests existing coverage processes may affect timely reimbursement of new technologies.

Longitudinal efficacy and safety modeling and simulation framework to aid dose selection of belantamab mafodotin for patients with multiple myeloma.

Belantamab mafodotin, a monomethyl auristatin F (MMAF)-containing monoclonal antibody-drug conjugate (ADC), demonstrated deep and durable responses in the DRiving Excellence in Approaches to Multiple Myeloma (DREAMM)-1 and pivotal DREAMM-2 studies in patients with relapsed/refractory multiple myeloma. As with other MMAF-containing ADCs, ocular adverse events were observed. To predict the effects of belantamab mafodotin dosing regimens and dose-modification strategies on efficacy and ocular safety end points, DREAMM-1 and DREAMM-2 data across a range of doses were used to develop an integrated simulation framework incorporating two separate longitudinal models and the published population pharmacokinetic model. A concentration-driven tumor growth inhibition model described the time course of serum M-protein concentration, a measure of treatment response, whereas a discrete time Markov model described the time course of ocular events graded with the GSK Keratopathy and Visual Acuity scale. Significant covariates included baseline β2 -microglobulin on growth rate, baseline M-protein on kill rate, extramedullary disease on the effect compartment rate constant, and baseline soluble B cell maturation antigen on maximal effect. Efficacy and safety end points were simulated for various doses with dosing intervals of 1, 3, 6, and 9 weeks and various event-driven dose-modification strategies. Simulations predicted that lower doses and longer dosing intervals were associated with lower probability and lower overall time with Grade 3+ and Grade 2+ ocular events compared with the reference regimen (2.5 mg/kg every 3 weeks), with a less-than-proportional reduction in efficacy. The predicted improved benefit-risk profiles of certain dosing schedules and dose modifications from this integrated framework has informed trial designs for belantamab mafodotin, supporting dose-optimization strategies.

Performance of pre- and post-pembrolizumab vesical imaging–reporting and data system (VI-RADS) to predict the pathological response and efficacy outcomes in muscle-invasive urothelial bladder cancer (MIBC): Full data analysis from the PURE-01 trial.

65 Background: The VI-RADS is a standardized reporting system that uses multiparametric magnetic resonance imaging (mpMRI) parameters to differentiate non muscle-invasive bladder cancer (NMIBC) from MIBC. Limited data on clinical response assessment to neoadjuvant therapy are currently available. In this study, we aimed to analyze the performance of VI-RADS to predict the pT0 or pT≤1 response post-neoadjuvant immunotherapy in MIBC. Methods: In PURE-01 study (NCT02736266) patients (pts) were staged with mpMRI before and after treatment (3 cycles of Pembrolizumab) prior to radical cystectomy (RC). Logistic regression models analyzed pre- and post-pembro VI-RADS, with clinical and tumor-related variables, against ypT≤1N0 (primary endpoint) and ypT0N0 (secondary endpoint). VI-RADS scores were dichotomized between 0-3 (0 = no evidence of disease) and 4-5 (5 = extension to extravescical fat). Event-free survival (EFS) and overall survival (OS) analyses based on VI-RADS were performed. Comprehensive genomic profiling and transcriptome-wide expression profiling data were matched with VI-RADS scores. Model performance was also tested in the ongoing NureCombo (nivolumab+nab-paclitaxel; NCT04876313) and SURE-01 (sacituzumab govitecan; NCT05226117) neoadjuvant studies. Results: In total, 110 patients treated with neoadjuvant pembrolizumab between 02/2017 and 07/2020 had centrally reviewed scans (N=220 mpMRI). Pre-pembrolizumab, 21 patients (19.1%) had no measurable disease (VI-RADS=0), 34 (30.9%) had a VI-RADS 1-3 score, and 55 (50%) had a VI-RADS 4-5 score. Both pre-pembrolizumab and post-pembrolizumab VI-RADS 0-3 scores were the only significant covariates that predicted the ypT≤1N0 endpoint on multivariable analyses (MVA): the strongest effect was seen with post-pembrolizumab VI-RADS 0-3 predicting the ypT≤1N0 response (odds ratio [OR]: 23.4, 95%CI: 7-95.3, p&lt;0.0001). The area-under-the-curve (AUC) of this model was 0.90. Post-pembrolizumab VI-RADS 0-3 also predicted longer EFS (p&lt;0.001) and OS (p=0.044). The scores of several gene signatures from baseline tumors differed between pre-pembrolizumab VI-RADS 0-3 and 4-5 categories. RAF1 mutations were enriched in pre-pembrolizumab VI-RADS 0-3 group (p=0.045). Conclusions: VI-RADS scores post-pembrolizumab revealed a robust association with pathological downstaging and survival. VI-RADS scores were also characterized by distinct biomarker features. These results indicate that VI-RADS is emerging as an important tool to design next-generation trials in MIBC. Clinical trial information: NCT02736266 .

Possible contribution of better maternal psychological well-being to the acquisition of sleeping through the night in infants during the early postpartum period

The objective of this study was to elucidate whether better maternal psychological well-being contributes to the acquisition of “sleeping through the night” (STN) in infants during the early postpartum period. Fifty-two primiparous mothers completed the General Health Questionnaire-28 (GHQ-28) in the third trimester (prenatal) and when the conceptional ages of their babies reached 8–9 weeks (hereafter, 2 months), 12–13 weeks (3 months), and 16–17 weeks (4 months). They also recorded babies’ nocturnal sleep patterns in a timetable for 5 consecutive days each month postpartum. “Regular STN” was defined when the mean of longest nocturnal sleep duration for 5 consecutive days was > 8 h or between 6 and 8 h with < 1.0 nocturnal awakenings. According to these criteria, a total of 14 infants (27 %) acquired regular STN at 4 months (referred to as “STN infants”), with STN infants showing a marked increase in longest nocturnal sleep duration and a decrease in nocturnal awakenings from 2 to 3 months of age. The mothers of STN infants demonstrated steady reductions in postnatal GHQ-28 scores and had significantly lower prenatal GHQ-28 scores compared with the mothers of non-STN infants (3.7 ± 3.0 vs. 6.4 ± 4.1, p = 0.027). In random forest models for binomial classification, both prenatal and postnatal (at 4 months) GHQ-28 scores were identified as significant covariates for distinguishing STN infants, and other important covariates, including weeks of delivery, stepfamily, birth weight of the infant, and maternal co-sleeping at bedtime, were selected. Among these covariates, maternal co-sleeping at bedtime had relatively stronger correlations with both STN infants (r = − 0.440) and prenatal maternal GHQ-28 scores (r = 0.377). In conclusion, because prenatal maternal psychological well-being was thought to predict the acquisition of STN in infants, infants born from mothers with better psychological well-being appear to have some advantages in acquiring STN. These cross-lagged correlations suggest that the pathway from mothers to infants may be mediated by certain parenting behaviors, such as maternal co-sleeping at bedtime.

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R2 reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming.