Evaluation of Analgesic Practice Changes Following the Nuss Procedure in Pediatric Patients.

INTRODUCTION: Pectus excavatum repair by the Nuss procedure results in severe postoperative pain. Regional blocks and intercostal nerve cryoablation (INC) have emerged as potential strategies to manage analgesia. This study compares pain-related outcomes following these perioperative interventions. METHODS: We reviewed charts of patients <18 y who underwent the Nuss procedure at Duke Children's Hospital from July 2018 to June 2022. Patients were divided into three groups by analgesic strategy: no block, regional catheters, or INC, representing the chronologic change in our practice. The primary outcome was total and daily in-hospital opioid utilization measured by oral morphine equivalents (OMEs). Secondary outcomes included average daily pain scores, length of stay, opioid refills after discharge, and complications. RESULTS: Twenty-one patients were included and analyzed: no block (n = 6), regional catheters (n = 7), and INC (n = 8). INC-treated patients required significantly lower total postoperative, in-hospital OMEs (64 ± 47 [mean ± standard deviation]) than those with no block (270 ± 217, P = 0.04) or those with regional catheters (273 ± 176, P = 0.03). INC was associated with longer average operative times (161 ± 36 min) than no block (105 ± 21 min, P = 0.005) or regional catheters (90 ± 11 min, P < 0.001). INC-treated patients had shorter hospital length of stays (median 68 h) than those with regional catheters (median 74 h, P = 0.006). CONCLUSIONS: INC was associated with longer operative times but decreased in-hospital OMEs when compared to bilateral regional block catheters and multimodal analgesia alone.

Coagulation Abnormalities and Clinical Complications in Children With SARS-CoV-2: A Systematic Review of 48,322 Patients.

Given the limited information on the coagulation abnormalities of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in pediatric patients, we designed a systematic review to evaluate this topic. A comprehensive literature search was conducted for "SARS-CoV-2," "coagulopathy," and "pediatrics." Two authors independently screened the articles that the search returned for bleeding, thrombosis, anticoagulant and/or antiplatelet usage, and abnormal laboratory markers in pediatric patients with SARS-CoV-2, and the authors then extracted the relevant data. One hundred twenty-six publications were included. Thirty-four (27%) studies reported thrombotic complications in 504 patients. Thirty-one (25%) studies reported bleeding complications in 410 patients. Ninety-eight (78%) studies reported abnormal laboratory values in 6580 patients. Finally, 56 (44%) studies reported anticoagulant and/or antiplatelet usage in 3124 patients. The variety of laboratory abnormalities and coagulation complications associated with SARS-CoV-2 presented in this review highlights the complexity and variability of the disease presentation in infants and children.

Donor-Recipient Height Mismatch Is Associated With Decreased Survival in Pediatric-to-Adult Liver Transplant Recipients.

Liver grafts from pediatric donors represent a small fraction of grafts transplanted into adult recipients, and their use in adults requires special consideration of donor size to prevent perioperative complications. In the past, graft weight or volume ratios have been adopted from the living donor liver transplant literature to guide clinicians; however, these metrics are not regularly available to surgeons accepting deceased donor organs. In this study, we evaluated all pediatric-to-adult liver transplants in the United Network for Organ Sharing Standard Transplant Analysis and Research database from 1987 to 2019, stratified by donor age and donor-recipient height mismatch ratio (HMR; defined as donor height/recipient height). On multivariable regression controlling for cold ischemia time, age, and transplantation era, the use of donors from ages 0 to 4 and 5 to 9 had increased risk of graft failure (hazard ratio [HR], 1.81 [P < 0.01] and HR, 1.16 [P < 0.01], respectively) compared with donors aged 15 to 17. On Kaplan-Meier survival analysis, a HMR < 0.8 was associated with inferior graft survival (mean, 11.8 versus 14.6 years; log-rank P < 0.001) and inferior patient survival (mean, 13.5 versus 14.9 years; log-rank P < 0.01) when compared with pairs with similar height (HMR, 0.95-1.05; ie, donors within 5% of recipient height). This study demonstrates that both young donor age and low HMR confer additional risk in adult recipients of pediatric liver grafts.

Challenging 30-day mortality as a site-specific quality metric in non-small cell lung cancer.

OBJECTIVE: The objective of this project was to assess the best measure for postoperative outcomes by comparing 30-day and 90-day mortality rates after surgery for non-small cell lung cancer using the National Cancer Database. Secondarily, hospital performance was examined at multiple postoperative intervals to assess changes in ranking based on mortality up to 1 year after surgery. METHODS: Patients who had undergone surgery for non-small cell lung cancer between 2004 and 2013 were identified in the National Cancer Database. Mortality rates at 30 days and 90 days were compared after adjusting for several patient characteristics, tumor variables, and hospital procedural volume using generalized logistic mixed models. Subsequently, mixed model logistic regression models were employed to evaluate hospital performance based on calculated mortality at prespecified time points. RESULTS: A total of 303,579 patients with non-small cell lung cancer were included for analysis. The 90-day mortality was almost double the 30-day mortality (3.0% vs 5.7%). Several patient characteristics, tumor features, and hospital volume were significantly associated with mortality at both 30 days and 90 days. Hospital rankings fluctuate appreciably between early mortality time points, which is additional evidence that quality metrics need to be based on later mortality time points. CONCLUSIONS: Thirty-day mortality is the commonly accepted quality measure for thoracic surgeons; however, hospital rankings may be inaccurate if based on this variable alone. Mortality after 90 days appears to be a threshold after which there is less variability in hospital ranking and should be considered as an alternative quality metric in lung cancer surgery.

Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals.

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). RESULTS: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. CONCLUSIONS: These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.

Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR).

BACKGROUND: Despite heritability estimates of 40-70 % for obesity, less than 2 % of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. METHODS: Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. RESULTS: We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 % increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. CONCLUSION: This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.

Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols.

Background. Phenome-Wide Association Studies (PheWAS) identify genetic associations across multiple phenotypes. Clinical trials offer opportunities for PheWAS to identify pharmacogenomic associations. We describe the first PheWAS to use genome-wide genotypic data and to utilize human immunodeficiency virus (HIV) clinical trials data. As proof-of-concept, we focused on baseline laboratory phenotypes from antiretroviral therapy-naive individuals. Methods. Data from 4 AIDS Clinical Trials Group (ACTG) studies were split into 2 datasets: Dataset I (1181 individuals from protocol A5202) and Dataset II (1366 from protocols A5095, ACTG 384, and A5142). Final analyses involved 2547 individuals and 5 954 294 imputed polymorphisms. We calculated comprehensive associations between these polymorphisms and 27 baseline laboratory phenotypes. Results. A total of 10 584 (0.17%) polymorphisms had associations with P < .01 in both datasets and with the same direction of association. Twenty polymorphisms replicated associations with identical or related phenotypes reported in the Catalog of Published Genome-Wide Association Studies, including several not previously reported in HIV-positive cohorts. We also identified several possibly novel associations. Conclusions. These analyses define PheWAS properties and principles with baseline laboratory data from HIV clinical trials. This approach may be useful for evaluating on-treatment HIV clinical trials data for associations with various clinical phenotypes.

Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using biofilter, and gene-environment interactions using the Phenx Toolkit*.

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, cataract cases and controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 527,953 and 527,936 single nucleotide polymorphisms (SNPs) for gene-gene and gene-environment analyses, respectively, with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 13 statistically significant SNP-SNP models with an interaction with p-value < 1 × 10(-4), as well as an overall model with p-value < 0.01 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use;these environmental factors have been previously associated with the formation of cataracts. We found a total of 782 gene-environment models that exhibit an interaction with a p-value < 1 × 10(-4) associatedwith cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

BioBin: a bioinformatics tool for automating the binning of rare variants using publicly available biological knowledge.

BACKGROUND: With the recent decreasing cost of genome sequence data, there has been increasing interest in rare variants and methods to detect their association to disease. We developed BioBin, a flexible collapsing method inspired by biological knowledge that can be used to automate the binning of low frequency variants for association testing. We also built the Library of Knowledge Integration (LOKI), a repository of data assembled from public databases, which contains resources such as: dbSNP and gene Entrez database information from the National Center for Biotechnology (NCBI), pathway information from Gene Ontology (GO), Protein families database (Pfam), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, NetPath - signal transduction pathways, Open Regulatory Annotation Database (ORegAnno), Biological General Repository for Interaction Datasets (BioGrid), Pharmacogenomics Knowledge Base (PharmGKB), Molecular INTeraction database (MINT), and evolutionary conserved regions (ECRs) from UCSC Genome Browser. The novelty of BioBin is access to comprehensive knowledge-guided multi-level binning. For example, bin boundaries can be formed using genomic locations from: functional regions, evolutionary conserved regions, genes, and/or pathways. METHODS: We tested BioBin using simulated data and 1000 Genomes Project low coverage data to test our method with simulated causative variants and a pairwise comparison of rare variant (MAF < 0.03) burden differences between Yoruba individuals (YRI) and individuals of European descent (CEU). Lastly, we analyzed the NHLBI GO Exome Sequencing Project Kabuki dataset, a congenital disorder affecting multiple organs and often intellectual disability, contrasted with Complete Genomics data as controls. RESULTS: The results from our simulation studies indicate type I error rate is controlled, however, power falls quickly for small sample sizes using variants with modest effect sizes. Using BioBin, we were able to find simulated variants in genes with less than 20 loci, but found the sensitivity to be much less in large bins. We also highlighted the scale of population stratification between two 1000 Genomes Project data, CEU and YRI populations. Lastly, we were able to apply BioBin to natural biological data from dbGaP and identify an interesting candidate gene for further study. CONCLUSIONS: We have established that BioBin will be a very practical and flexible tool to analyze sequence data and potentially uncover novel associations between low frequency variants and complex disease.

Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofilter, and gene-environment interactions using the PhenX Toolkit.

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, 2580 cataract cases and 1367 controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) Biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 529,431 single nucleotide polymorphisms (SNPs) with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using the Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 5 statistically significant models with an interaction term with p-value < 0.05, as well as an overall model with p-value < 0.05 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use; these environmental factors have been previously associated with the formation of cataracts. We found a total of 288 models that exhibit an interaction term with a p-value ≤ 1×10(-4) associated with cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

Using BioBin to explore rare variant population stratification.

Rare variants (RVs) will likely explain additional heritability of many common complex diseases; however, the natural frequencies of rare variation across and between human populations are largely unknown. We have developed a powerful, flexible collapsing method called BioBin that utilizes prior biological knowledge using multiple publicly available database sources to direct analyses. Variants can be collapsed according to functional regions, evolutionary conserved regions, regulatory regions, genes, and/or pathways without the need for external files. We conducted an extensive comparison of rare variant burden differences (MAF < 0.03) between two ancestry groups from 1000 Genomes Project data, Yoruba (YRI) and European descent (CEU) individuals. We found that 56.86% of gene bins, 72.73% of intergenic bins, 69.45% of pathway bins, 32.36% of ORegAnno annotated bins, and 9.10% of evolutionary conserved regions (shared with primates) have statistically significant differences in RV burden. Ongoing efforts include examining additional regional characteristics using regulatory regions and protein binding domains. Our results show interesting variant differences between two ancestral populations and demonstrate that population stratification is a pervasive concern for sequence analyses.

Low frequency variants, collapsed based on biological knowledge, uncover complexity of population stratification in 1000 genomes project data.

Analyses investigating low frequency variants have the potential for explaining additional genetic heritability of many complex human traits. However, the natural frequencies of rare variation between human populations strongly confound genetic analyses. We have applied a novel collapsing method to identify biological features with low frequency variant burden differences in thirteen populations sequenced by the 1000 Genomes Project. Our flexible collapsing tool utilizes expert biological knowledge from multiple publicly available database sources to direct feature selection. Variants were collapsed according to genetically driven features, such as evolutionary conserved regions, regulatory regions genes, and pathways. We have conducted an extensive comparison of low frequency variant burden differences (MAF<0.03) between populations from 1000 Genomes Project Phase I data. We found that on average 26.87% of gene bins, 35.47% of intergenic bins, 42.85% of pathway bins, 14.86% of ORegAnno regulatory bins, and 5.97% of evolutionary conserved regions show statistically significant differences in low frequency variant burden across populations from the 1000 Genomes Project. The proportion of bins with significant differences in low frequency burden depends on the ancestral similarity of the two populations compared and types of features tested. Even closely related populations had notable differences in low frequency burden, but fewer differences than populations from different continents. Furthermore, conserved or functionally relevant regions had fewer significant differences in low frequency burden than regions under less evolutionary constraint. This degree of low frequency variant differentiation across diverse populations and feature elements highlights the critical importance of considering population stratification in the new era of DNA sequencing and low frequency variant genomic analyses.