Gabapentin as a novel adjunct for postoperative irritability after superior cavopulmonary connection operation in children.

OBJECTIVES: Describing our institution's off-label use of gabapentin to treat irritability after superior cavopulmonary connection surgery and its impact on subsequent opiate and benzodiazepine requirements. METHODS: This is a single-center retrospective cohort study including infants who underwent superior cavopulmonary connection operation between 2011 and 2019. RESULTS: Gabapentin was administered in 74 subjects (74/323, 22.9%) during the observation period, with a median (IQR) starting dose of 5.7 (3.3, 15.0) mg/kg/day and a maximum dose of 10.7 (5.5, 23.4) mg/kg/day. Infants who underwent surgery in 2015-19 were more likely to receive gabapentin compared with those who underwent surgery in 2011-14 (p < 0.0001). Infants prescribed gabapentin were younger at surgery (137 versus 146 days, p = 0.007) and had longer chest tube durations (1.8 versus 0.9 days, p < 0.001), as well as longer postoperative intensive care (5.8 versus 3.1 days, p < 0.0001) and hospital (11.5 versus 7.0 days, p < 0.0001) lengths of stays. The year of surgery was the only predisposing factor associated with gabapentin administration in multivariate analysis. In adjusted linear regression, infants prescribed gabapentin on postoperative day 0-4 (n = 64) had reduced benzodiazepine exposure in the following 3 days (-0.29 mg/kg, 95% CI -0.52 - -0.06, p = 0.01) compared with those not prescribed gabapentin, while no difference was seen in opioid exposure (p = 0.59). CONCLUSIONS: Gabapentin was used with increasing frequency during the study period. There was a modest reduction in benzodiazepine requirements associated with gabapentin administration and no reduction in opioid requirements. A randomised controlled trial could better assess gabapentin's benefits postoperatively in children with congenital heart disease.

Circulating microRNA-33b levels are associated with the presence and severity of coronary heart disease.

MicroRNA-33b (miR-33b) affected various biological pathways in regulating cholesterol homeostasis which may link to the pathogenesis of atherosclerotic lesions. However, whether this marker is associated with the presence and severity of coronary heart disease (CHD) is undetermined. We aim to explore the diagnostic value of circulating miR-33b level in the presence and severity of CHD. Altogether 320 patients were enrolled, including 240 patients diagnosed with CHD while 80 were classified as controls after CAG examination. Circulating miR-33b level was analyzed in all subjects, the Gensini score was calculated to assess the severity of stenotic lesions. The association between miR-33b and the presence and severity of CHD was analyzed, and the diagnostic potential of miR-33b of CHD was performed by the receiver operating characteristic (ROC) analysis. The CHD group had higher miR-33b levels (p < 0.001), and the miR-33b content significantly elevated following an increasing Gensini score (p for trend < 0.001). After adjustments for potential risk factors, such as several blood lipid markers, miR-33b remained a significant determinant for CHD (p < 0.001). ROC analysis disclosed that the AUC was 0.931. The optimal cutoff value of miR-33b was with a sensitivity of 81.3% and a specificity of 98.7% in differentiating CHD. It can prognosticate that the higher level of miR-33b was linked to increased severity of disease in CHD patients. Thus, the application of this marker might assist in the diagnosis and classification of CHD patients. Nevertheless, additional studies with larger sample sizes will be required to verify these results.

Factors Associated with Growth in a Cohort of Children with Complex Biventricular Congenital Heart Disease.

OBJECTIVES: To evaluate patterns and determinants of longitudinal growth among children requiring complex biventricular repair for congenital heart disease, as well as to assess for associations of growth with early feeding modality, comorbidities, postoperative complications, and socioeconomic characteristics. STUDY DESIGN: A single-institution retrospective cohort study was performed in children born February 1999 to March 2009 with complex congenital heart disease who underwent biventricular repair before age 4 years, defined by Risk Adjustment in Congenital Heart Surgery-1 category 3-5. Clinical characteristics, height, weight, and body mass index (BMI) from ages 2-12 years were collected by chart review. Neighborhood-level socioeconomic data were identified using a geographic information system approach. The adjusted association of covariates with growth outcomes was estimated using multivariable linear regression models using generalized estimating equations. RESULTS: Compared with population growth curves, the cohort (n = 150) trended toward early decrease in age-adjusted weight and height. Early tube feeding was significantly associated with decreased BMI before adolescence (-0.539; 95% CI -1.02, -0.054; P = .029). In addition, other clinical and perioperative characteristics had significant associations with growth, including low birth weight, preoperative tube feeds, need for multiple bypass runs, and diagnosis of feeding disorder. CONCLUSIONS: Early childhood growth in children with complex biventricular repair may be impaired. Early tube feeding was associated with decreased BMI over the course of early childhood, which may indicate a need for continued close nutrition follow-up and support even beyond the duration of tube feeds.

Outcome of BMI2+: Motivational Interviewing to Reduce BMI Through Primary Care AAP PROS Practices.

BACKGROUND: Pediatric obesity rates in the United States remain at an all-time high. Pediatric primary care clinicians and registered dietitians can help treat childhood obesity, and motivational interviewing (MI) has shown promising effects in prior trials. METHODS: We randomized 18 pediatric primary care practices to receive the Brief Motivational Interviewing to Reduce BMI or BMI2+ intervention or continue with usual care (UC). Practices were recruited through the American Academy of Pediatrics Pediatric Research in Office Settings network. The intervention comprised 4 components1: in-person and telehealth MI counseling by pediatric clinicians; 4 recommended sessions,2 6 telephone MI counseling sessions from a registered dietitian,3 text message reminders and tailored motivational messages, and4 parent educational materials. The main outcome was the change in the percentage of the 95th percentile of BMI. The study was conducted 2017 through 2021. RESULTS: There was a significant treatment x time interaction (b = 0.017, 95% confidence interval: [0.0066-0.027]) for the main outcome, favoring the UC group, with youth in the intervention arm showing a greater relative increase in their percent of the 95th percentile. CONCLUSIONS: There was no overall benefit of the intervention and, contrary to expectations, youth in the intervention arm gained more weight, based on percent of the distance from the 95th percentile than matched youth from UC practices. The absolute excess weight gain among intervention relative to UC youth was small, approximately 0.5 BMI units and 1 kg over 2 years. We offer several potential explanations for these unexpected findings.

Causal Effects of Stochastic PrEP Interventions on HIV Incidence Among Men Who Have Sex With Men.

Antiretroviral preexposure prophylaxis (PrEP) is highly effective in preventing human immunodeficiency virus (HIV) infection, but uptake has been limited and inequitable. Although interventions to increase PrEP uptake are being evaluated in clinical trials among men who have sex with men (MSM), those trials cannot evaluate effects on HIV incidence. Estimates from observational studies of the causal effects of PrEP-uptake interventions on HIV incidence can inform decisions about intervention scale-up. We used longitudinal electronic health record data from HIV-negative MSM accessing care at Fenway Health, a community health center in Boston, Massachusetts, from January 2012 through February 2018, with 2 years of follow-up. We considered stochastic interventions that increased the chance of initiating PrEP in several high-priority subgroups. We estimated the effects of these interventions on population-level HIV incidence using a novel inverse-probability weighted estimator of the generalized g-formula, adjusting for baseline and time-varying confounders. Our results suggest that even modest increases in PrEP initiation in high-priority subgroups of MSM could meaningfully reduce HIV incidence in the overall population of MSM. Interventions tailored to Black and Latino MSM should be prioritized to maximize equity and impact.

Multiply robust causal inference of the restricted mean survival time difference.

The hazard ratio (HR) remains the most frequently employed metric in assessing treatment effects on survival times. However, the difference in restricted mean survival time (RMST) has become a popular alternative to the HR when the proportional hazards assumption is considered untenable. Moreover, independent of the proportional hazards assumption, many comparative effectiveness studies aim to base contrasts on survival probability rather than on the hazard function. Causal effects based on RMST are often estimated via inverse probability of treatment weighting (IPTW). However, this approach generally results in biased results when the assumed propensity score model is misspecified. Motivated by the need for more robust techniques, we propose an empirical likelihood-based weighting approach that allows for specifying a set of propensity score models. The resulting estimator is consistent when the postulated model set contains a correct model; this property has been termed multiple robustness. In this report, we derive and evaluate a multiply robust estimator of the causal between-treatment difference in RMST. Simulation results confirm its robustness. Compared with the IPTW estimator from a correct model, the proposed estimator tends to be less biased and more efficient in finite samples. Additional simulations reveal biased results from a direct application of machine learning estimation of propensity scores. Finally, we apply the proposed method to evaluate the impact of intrapartum group B streptococcus antibiotic prophylaxis on the risk of childhood allergic disorders using data derived from electronic medical records from the Children's Hospital of Philadelphia and census data from the American Community Survey.

Antibiotic Use Among Infants Admitted to Neonatal Intensive Care Units.

This cross-sectional study examines antibiotic exposure, days of therapy, types of antibiotics, and changes in use patterns among newborns in neonatal intensive care units (NICUs) across the US from 2009 to 2021.

Combining meta-analysis with multiple imputation for one-step, privacy-protecting estimation of causal treatment effects in multi-site studies.

Missing data complicates statistical analyses in multi-site studies, especially when it is not feasible to centrally pool individual-level data across sites. We combined meta-analysis with within-site multiple imputation for one-step estimation of the average causal effect (ACE) of a target population comprised of all individuals from all data-contributing sites within a multi-site distributed data network, without the need for sharing individual-level data to handle missing data. We considered two orders of combination and three choices of weights for meta-analysis, resulting in six approaches. The first three approaches, denoted as RR + metaF, RR + metaR and RR + std, first combined results from imputed data sets within each site using Rubin's rules and then meta-analyzed the combined results across sites using fixed-effect, random-effects and sample-standardization weights, respectively. The last three approaches, denoted as metaF + RR, metaR + RR and std + RR, first meta-analyzed results across sites separately for each imputation and then combined the meta-analysis results using Rubin's rules. Simulation results confirmed very good performance of RR + std and std + RR under various missing completely at random and missing at random settings. A direct application of the inverse-variance weighted meta-analysis based on site-specific ACEs can lead to biased results for the targeted network-wide ACE in the presence of treatment effect heterogeneity by site, demonstrating the need to clearly specify the target population and estimand and properly account for potential site heterogeneity in meta-analyses seeking to draw causal interpretations. An illustration using a large administrative claims database is presented.

Ulinastatin inhibits the metastasis of nasopharyngeal carcinoma by involving uPA/uPAR signaling.

Distant metastasis is the primary reason for treatment failure in patients with nasopharyngeal carcinoma (NPC). In this study, we investigated the effect of ulinastatin (UTI) on NPC metastasis and its underlying mechanism. Highly-metastatic NPC cell lines S18 and 58F were treated with UTI and the effect on cell proliferation, migration, and invasion were determined by MTS and Transwell assays. S18 cells with luciferase-expressing (S18-1C3) were injected into the left hind footpad of nude mice to establish a model of spontaneous metastasis from the footpad to popliteal lymph node (LN). The luciferase messenger RNA (mRNA) was measured by quantitative polymerase chain reaction (qPCR), and the metastasis inhibition rate was calculated. Key molecular members of the UTI-related uPA, uPAR, and JAT/STAT3 signaling pathways were detected by qPCR and immunoblotting. UTI suppressed the migration and infiltration of S18 and 5-8F cells and suppressed the metastasis of S18 cells in vivo without affecting cell proliferation. uPAR expression decreased from 24 to 48 h after UTI treatment. The antimetastatic effect of UTI is partly due to the suppression of uPA and uPAR. UTI partially suppresses NPC metastasis by downregulating the expression of uPA and uPAR.

Patterns in the Development of Pediatric Allergy.

OBJECTIVES: Describe clinical and epidemiologic patterns of pediatric allergy using longitudinal electronic health records (EHRs) from a multistate consortium of US practices. METHODS: Using the multistate Comparative Effectiveness Research through Collaborative Electronic Reporting EHR database, we defined a cohort of 218 485 children (0-18 years) who were observed for ≥5 years between 1999 and 2020. Children with atopic dermatitis (AD), immunoglobulin E-mediated food allergy (IgE-FA), asthma, allergic rhinitis (AR), and eosinophilic esophagitis (EoE) were identified using a combination of diagnosis codes and medication prescriptions. We determined age at diagnosis, cumulative incidence, and allergic comorbidity. RESULTS: Allergic disease cumulative (and peak age of) incidence was 10.3% (4 months) for AD, 4.0% (13 months) for IgE-FA, 20.1% (13 months) for asthma, 19.7% (26 months) for AR, and 0.11% (35 months) for EoE. The most diagnosed IgE-FAs were peanut (1.9%), egg (0.8%), and shellfish (0.6%). A total of 13.4% of children had ≥2 allergic conditions, and respiratory allergies (ie, asthma, AR) were commonly comorbid with each other, and with other allergic conditions. CONCLUSIONS: We detail pediatric allergy patterns using longitudinal, health care provider-based data from EHR systems across multiple US states and varied pediatric practice types. Our results support the population-level allergic march progression and indicate high rates of comorbidity among children with food and respiratory allergies.

Off-brand: A 6-year study of medication brand and generic name usage in a multifacility academic healthcare system.

BACKGROUND: Usage of medication brand names in electronic health records may introduce conflicts of interest, perpetuate false perceptions of brand superiority, alter prescribing practices, and cause confusion leading to errors. OBJECTIVE: We sought to identify the frequency of brand name medication usage in clinical documentation, as well as factors associated with increased usage. DESIGNS, SETTINGS, AND PARTICIPANTS: We conducted a retrospective analysis of all clinical documentation written at our healthcare system (a multifacility academic urban healthcare system) between 2015 and 2020. MAIN OUTCOMES AND MEASURES: We used string-matching and regular expressions to identify medication mentions. We conducted bivariate analyses to identify associations between brand name usage and author-, note-, and medication-level factors, and a multivariate Poisson regression to clarify independent associations between individual factors and brand usage. RESULTS: A total of 104,456,653 notes from 37,285 unique authors were included in our analysis. A total of 162,906,009 medication mentions were identified, of which 36.0% were brand name mentions with a steady year-over-year decrease. Factors associated with the usage of a brand name include: author role, years since release, length and syllabic complexity of the generic name, service type, and encounter context. Over-the-counter availability did not affect usage. There was sizable individual variation between note writers.

Trends and Persistent Disparities in Child Obesity During the COVID-19 Pandemic.

The COVID-19 pandemic has been associated with increases in pediatric obesity and widening pre-existing disparities. To better understand the pandemic's long-term impacts, we evaluated trends in obesity across different demographic groups during the pandemic through December 2022. Using a retrospective cohort design, we analyzed electronic health record data from a large pediatric primary care network. Logistic regression models fit using generalized estimating equations estimated odds ratios (ORs) for changes in the level and trajectory of obesity across 2-year month-matched periods: prepandemic (June 2017 to December 2019) and pandemic (June 2020 to December 2022). Among a cohort of 153,667 patients with visits in each period, there was a significant increase in the level of obesity at the pandemic onset [OR: 1.229, 95% confidence interval (CI): 1.211-1.247] followed by a significant decrease in the trend for obesity (OR: 0.993, 95% CI: 0.992-0.993). By December 2022, obesity had returned to prepandemic levels. However, persistent sociodemographic disparities remain.

Revisiting sample size planning for receiver operating characteristic studies: A confidence interval approach with precision and assurance.

Estimation of areas under receiver operating characteristic curves and their differences is a key task in diagnostic studies. Here we develop closed-form sample size formulas for such studies with a focus on estimation rather than hypothesis testing, by explicitly incorporating pre-specified precision and assurance, with precision denoted by the lower limit of confidence interval and assurance denoted by the probability of achieving that lower limit. For sample size estimation purposes, we introduce a normality-based variance function for valid estimation allowing for unequal variances of observations in the disease and non-disease groups. Simulation results demonstrate that the proposed formulas produce empirical assurance probability close to the pre-specified assurance probability and empirical coverage probability close to the nominal level. Compared with a frequently used existing variance function, the proposed function provides more accurate and efficient sample size estimates. For an illustration of the proposed formulas, we present real-world worked examples. To facilitate implementation, we have developed an online calculator openly available at https://dishu.page/calculator/.

Seasonal variations, source apportionment and dry deposition of chemical species of total suspended particulate in Pengjia Yu Island, East China Sea.

Total of 172 total suspended particulate (TSP) samples and its chemical compositions were collected and analyzed from January to December 2010 in Pengjia Yu Island, an open region in East China Sea (ECS). Despite the predominance of sea-salt major ions (Na+, Cl-), the presence of non-sea-salt SO42- (nss-SO42-) and NO3- as well as combustion-derived trace metals clearly establishes the impact of anthropogenic sources over ECS. The annual contributions of coal, heavy-fuel oil and traffic to the measured chemical species were 21.0 %, 15.0 % and 15.5 %, respectively. Especially in spring, the contributions of crustal minerals to measured chemical species during dust period (33.6 %) were higher than that (13.2 %) during non-dust period. The calculated annual average dry deposition fluxes for trace metals and total inorganic nitrogen were 246.1 ± 345.8 µg/m2/d and 2950.4 ± 2245.0 µg/m2/d, suggesting that atmospheric deposition is an important source of nutrient elements for the south of ECS.

A rank-based approach to design and analysis of pretest-posttest randomized trials, with application to COVID-19 ordinal scale data.

Randomized controlled trials with a pretest-posttest design frequently yield ordered categorical outcome data. Focusing on the estimation of the win probability that a treated participant would have a better score than (or win over) a control participant, we developed methods for analysis and sample size planning for such trials. We exploited the analysis of covariance framework with the dependent variable being individual participants' win fractions at posttest and the covariate being the win fractions at pretest. The win fractions were obtained using the mid-ranks of the ordinal data. Simulation evaluation based on a recent randomized trial on COVID-19 suggests that the methods perform very well. A sample SAS code for data analysis is presented.

Robust causal inference of drug-drug interactions.

There is growing interest in developing causal inference methods for multi-valued treatments with a focus on pairwise average treatment effects. Here we focus on a clinically important, yet less-studied estimand: causal drug-drug interactions (DDIs), which quantifies the degree to which the causal effect of drug A is altered by the presence versus the absence of drug B. Confounding adjustment when studying the effects of DDIs can be accomplished via inverse probability of treatment weighting (IPTW), a standard approach originally developed for binary treatments and later generalized to multi-valued treatments. However, this approach generally results in biased results when the propensity score model is misspecified. Motivated by the need for more robust techniques, we propose two empirical likelihood-based weighting approaches that allow for specifying a set of propensity score models, with the second method balancing user-specified covariates directly, by incorporating additional, nonparametric constraints. The resulting estimators from both methods are consistent when the postulated set of propensity score models contains a correct one; this property has been termed multiple robustness. In this paper, we derive two multiply-robust estimators of the causal DDI, and develop inference procedures. We then evaluate their finite sample performance through simulation. The results demonstrate that the proposed estimators outperform the standard IPTW method in terms of both robustness and efficiency. Finally, we apply the proposed methods to evaluate the impact of renin-angiotensin system inhibitors (RAS-I) on the comparative nephrotoxicity of nonsteroidal anti-inflammatory drugs (NSAID) and opioids, using data derived from electronic medical records from a large multi-hospital health system.

Distributed Cox proportional hazards regression using summary-level information.

Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general distributed methodology to fit Cox proportional hazards models without sharing individual-level data in multi-site studies. We make inferences on the log hazard ratios based on an approximated partial likelihood score function that uses only summary-level statistics. This approach can be applied to both stratified and unstratified models, accommodate both discrete and continuous exposure variables, and permit the adjustment of multiple covariates. In particular, the fitting of stratified Cox models can be carried out with only one file transfer of summary-level information. We derive the asymptotic properties of the proposed estimators and compare the proposed estimators with the maximum partial likelihood estimators using pooled individual-level data and meta-analysis methods through simulation studies. We apply the proposed method to a real-world data set to examine the effect of sleeve gastrectomy versus Roux-en-Y gastric bypass on the time to first postoperative readmission.

Meta-analysis with sample-standardization in multi-site studies.

PURPOSE: To conceptualize a particular target population and estimand for multi-site pharmacoepidemiologic studies within data networks and to analytically examine sample-standardization as a meta-analytic method compared with inverse-variance weighted meta-analyses. METHODS: The target population of interest is all and only all individuals from the data-contributing sites. Standardization, a general conditioning technique frequently employed for confounding control, was adopted to estimate the network-wide causal treatment effect. Specifically, the proposed sample-standardization yields a meta-analysis estimator, that is, a weighted summation of site-specific results, where the weight for a site is the proportion of its size in the entire network. This sample-standardization estimator was evaluated analytically in comparison to estimators from inverse-variance weighted fixed-effect and random-effects meta-analyses in terms of statistical consistency. RESULTS: A proof is reported to justify the consistency of the sample-standardization estimator with and without treatment effect heterogeneity by site. Both inverse-variance weighted fixed-effect and random-effects meta-analyses were found to generally result in inconsistent estimators in the presence of treatment effect heterogeneity by site for this particular target population and estimand. CONCLUSIONS: Sample-standardization is a valid approach to generate causal inference in multi-site studies when the target population comprises all and only all individuals within the network, even in the presence of heterogeneity of treatment effect by site. Multi-site studies should clearly specify the target population and estimand to help select the most appropriate meta-analytic methods.

Research progress on Astragalus active ingredient in treatment of hepatic fibrosis based on cell signaling pathway / 中国药理学通报

Hepatic fibrosis is present in most chronic liver disease processes, and there are no ideal anti-fibrotic drugs available. Astragalus has a long history of medicinal use, and its anti-fibrotic effects have been confirmed by modern studies. In this study we have searched the literature to identify the signaling pathways and mechanisms of action of Astragalus and its active ingredients on hepatic fibrosis in recent years, so as to provide the basis and ideas for the development of anti-fibrotic drugs and mechanisms of Astragalus. It is showed that the active ingredients of Astragalus act through regulating p38MAPK, TGF-pl/Smads,NF-

A multifunctional non-viral vector for the delivery of MTH1-targeted CRISPR/Cas9 system for non-small cell lung cancer therapy.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system adapted from bacteria is a programmable nuclease-based genome editing tool. The long-lasting effect of gene silencing or correction is beneficial in cancer treatment. Considering the need to broaden the practical application of this technology, highly efficient non-viral vectors are urgently required. We prepared a multifunctional non-viral vector that could actively target tumor cells and deliver CRISPR/Cas9 plasmids into nuclei of cancer cells. Protamine sulfate (PS) which contains nuclear localization sequence was utilized to condense plasmid DNA and facilitate nuclei-targeted delivery. Liposome-coated protein/DNA complex avoided the degradation of nuclease in blood circulation. The obtained PS@Lip/pCas9 was further modified with distearoyl phosphoethanolamine-polyethylene glycol-hyaluronic acid (HA) to endow the vector ability to actively target tumor cell. Results suggested that PS@HA-Lip could deliver CRISPR/Cas9 plasmids into nuclei of tumor cells and induce genome editing effect. With the disruption of MTH1 (mutT homolog1) gene, the growth of non-small cell lung cancer was inhibited. Moreover, cell apoptosis in tumor tissue was promoted, and liver metastasis of non-small cell lung cancer (NSCLC) was reduced. Our study has provided a therapeutic strategy targeting MTH1 gene for NSCLC therapy. STATEMENT OF SIGNIFICANCE: CRISPR/Cas9 as a powerful tool for genome editing has drawn much attention. The long-lasting effect possesses unique advantage in cancer treatment. Non-viral vectors have high loading capacity, high safety and low immunogenicity, playing an important role in CRISPR/Cas9 delivery. In our study, a multifunctional non-viral vector for the efficient delivery of CRISPR/Cas9 plasmid was constructed. With the active targeting ligand and nuclei-targeting component, the cargo was efficiently delivered into cell nuclei and exerted genome editing effect. By using this vector, we successfully inhibited the growth and induced the apoptosis of non-small cell lung cancer by disrupting MTH1 expression with good safety. Our work provided an efficient non-vial vector for CRISPR/Cas9 delivery and explored the possibility for cancer treatment.