MEDICA@MUSC (Medical University of South Carolina)
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A Needs Assessment Tool to Evaluate HIV Care in South Carolina: Utilizing a Telehealth Model During COVID-19
Get PDFA newly identified virus coronavirus, SARS-CoV-2 caused a worldwide pandemic named COVID-19. This pandemic caused an interruption in the way medical care was traditionally delivered to People Living With HIV/AIDS (PLWHA). To meet the ongoing medical needs of PLWHA, HIV medical care was expanded to telehealth (i.e., telephone calls, video chat). Telehealth was implemented to keep patients engaged in care and treatment adherent. This study explores patients’ feelings and attitudes around their HIV care during the COVID-19 pandemic. A one-time survey was administered to patients who received care at an outpatient Ryan White HIV/AIDS Program funded HIV clinic in the Southeastern part of the United States from March-August 2020. The survey items collected demographic information and explored patients’ thoughts and feelings toward telehealth. There were 137 participants that completed the survey with the median age of 52, 76% were male, 22% were female, and 48% identified as African American. For patients who utilized the telehealth option (n=99), 68% agreed that they would utilize telehealth option for their care if it was offered again in the future. The main benefit to using telehealth as reported by patients was the ability for telehealth to fit better with their schedules. There was no significant difference in age of telehealth users. The results of this study indicate that this telehealth model can be applied to the HIV care continuum moving forward with participants reporting overall satisfaction with their HIV care during COVID-19. Telehealth programs in HIV care can improve retention in care and medication adherence for PLWHA
Accuracy in Orienting Profile Photographs, Lateral Cephalographs, and Lateral CBCT Images to Natural Head Orientation (NHO)
Get PDFIntroduction: The purpose of this study was to (1) assess the ability of orthodontists and surgeons to accurately orient pretreatment lateral facial photographs, lateral cephalographs, and lateral CBCT facial images relative to a clinically determined natural head orientation (NHO) and (2) to assess any difference between orthodontists and oral surgeons in orienting images to clinically determined NHO and relative to their years in practice. Methods: Lateral facial photographs, lateral cephalographs, and lateral CBCT images of four(4) pretreatment patients were selected, and rotated in 1° increments from -3° to +4°. A total of 96 images were evaluated by 79 orthodontists and 43 oral surgeons via survey. Survey participants were asked to select which image in each image group best represented NHO. Results: Seventy-eight percent (78.1 %) of all respondents were able to identify and agree on a NHO across all types of images studied that also agrees with the clinical impression of NHO within± 2°; however, the entire range of images was selected as representing NHO within each image type. The results indicate there were statistical differences between CBCT & photographs (p\u3c0.05) and cephalographs(p\u3c0.05), but not photographs and cephalographs. The difference between CBCTs and both photographs and cephalographs was approximately 1.2°. A statistically significant difference was found but between orthodontists and oral surgeons (p\u3c0.05) for photographs, but the difference was 0.4°. There were some differences between certain groups by years of practice (p \u3c0.05), but those differences were less than 1°. There was a statistically significant difference by those respondents with 31 + years of experience, but, again, the difference was less than 1°. Conclusions: Most orthodontists and oral surgeons can reliably orient lateral facial photographs, lateral cephalographs, and lateral CBCT images within± 2° relative to a clinically determined NHO. There was significant difference in the ability to orient lateral CBCT images
BRCA1 and BRD4: Regulation of Chromatin Signaling in DNA Repair and Transcription
Get PDFThe eggs of Xenopus laevis frogs have been used extensively to study various aspects of chromatin biology. In this document, we characterize two novel systems to study transcription (Chapter 2) and DNA double strand break repair (Chapter 4) utilizing different egg extracts. Notably, we show that nucleoplasmic extract is the first established Xenopus egg extract to support RNA polymerase II-mediated transcription of plasmid-borne gene constructs. Using this cell-free transcription system, we provide the first evidence of a molecular connection between the tumor suppressor, BRCA1, and the major epigenetic regulator, BRD4 (Chapter 3). We show BRCA1, along with its constitutive binding partner BARD1, negatively regulates the DNA-binding of BRD4. This mechanism likely involves the acetylation of histone H4K8, a known substrate for BRD4-binding, as this mark is increased in the absence of BRCA1. Furthermore, we identify and characterize a novel system for double strand break repair in extract. Importantly, we show that in this system, broken DNA ends are repaired by both non-homologous end joining as well as homologous recombination, similar to the established literature. We then use this system to show that BRD4 plays a role in homology-directed repair that is independent to its established role in transcription regulation (Chapter 4). Notably, we establish a direct interaction between BRD4 and two major DNA repair proteins, CtIP and BRG1. Loss of BRD4 results in reduced recruitment of each of these proteins to damaged DNA, and ultimately reduces DNA end resection and subsequent homologous recombination repair
Practice Makes Perfect: The Volume-Outcome Association in Pediatric Stoma Closure Surgery
Get PDFChildren with anorectal malformations often receive a temporary colostomy or ileostomy before surgical repair of the anomaly to divert stool and allow their anatomy time to heal. Once their bodies have healed, the stoma is taken down, a standard procedure for pediatric general surgeons. However, for decades, stoma takedown surgery has been associated with a high risk of postoperative complications. This study seeks to determine if hospital prior-year stoma closure case volume influences pediatric patients\u27 quality outcome measures. Population. This study identified 340 pediatric patients having undergone stoma closure surgery during the study period at hospitals in a representative sample of seven states; Arkansas, Florida, Georgia, Maryland, Mississippi, New York, and Washington. Study Design. This study is a retrospective analysis of archival billing data for pediatric stoma closure patients. The billing data source is the 2016 - 2017 Agency for Healthcare Research and Quality\u27s (AHRQ) Healthcare Cost and Utilization Project (HCUP) database. Outcome Measures. This study uses generally accepted surrogate measures of a quality outcome. The quality outcome measures for this study are the rate of in-hospital mortality during the index admission, readmission to the hospital within 30 days of discharge, and length of stay (LOS) during the index admission. Results. One mortality occurred in the study population (.29%), while 39 patients were readmitted (11.5%). Logistic regression analysis found no significant volume-outcome association between volume and the outcome measures. However, when categorized into age groups, a statistically significant association exists between hospital prior-year volume and readmission (p \u3c .04) in the infant age group (Age \u3c 1). A similar association was found between hospital prior-year volume and LOS (p \u3c .002) in the infant group compared to the non-infant group. With each prior-year increase of 10 cases, the likelihood of readmission decreases by 52% and expected hospitalization days decreases by 25%. Conclusion. This study validates an inverse hospital volume-readmission association in infant stoma closure surgery and an inverse volume-LOS association among all pediatric patients, with the magnitude of the association being most significant in the infant population
Patient-Level Perspectives on the Use of Novel Psychotherapeutics for the Treatment of Substance Use Disorders by
Get PDFSubstance use disorders (SUDs) present a rapidly evolving public health crisis and many individuals with SUDs fail to maintain abstinence despite adherence to current standard of care treatment options. Prior research has demonstrated compounds with unique psychoactive properties may improve ability to maintain abstinence across a variety of SUDs; examples of such compounds include psilocybin, ketamine, and 3,4-Methylenedioxymethamphetamine (MDMA). However, target population support for mental health treatment using these medications is unknown. In this study, a cross-sectional survey (n=919) was administered to analyze patient-level perspectives on the use of these novel psychotherapeutics for the treatment of SUDs. We hypothesized that individuals with SUDs would demonstrate differential acceptance of these treatment modalities as a function of prior awareness of these medications. The results showed that the majority of survey participants supported medical trials being conducted with psilocybin (72.1%), ketamine (71.6%), and MDMA (68.1%) in the future. Furthermore, survey respondents with prior knowledge of ketamine as a potential treatment option were significantly more in support of clinical trial research with ketamine compared to individuals without such prior awareness (3.96 vs 3.79; p= .005). However, there was no statistically significant difference in support for future research into psilocybin or MDMA based on prior knowledge of these potential treatment modalities. These results can be used to direct future research recruitment efforts and provide insight into clinical considerations that should be made when using these treatments
Variable Importance Performance when Multicollinearity Is Present
No full textAdvances in high-throughput technologies and the increasing availability of large- scale patient electronic health record (EHR) data provide unique opportunities to develop prediction algorithms for personalized medicine. These opportunities depend on the integration of the most relevant subset of features that enhance predictive ability by reducing the amount of random noise caused by unimportant features that increase the model’s chances of overfitting and computational costs. Identifying relevant features may be challenging when prediction models are difficult to optimize due to large numbers of potential features that are potentially collinear. To address this challenge, both traditional regression methods and machine learning methods can model a binary outcome and provide quantitative or semi-quantitative measures of feature importance. In this study, we evaluated how available feature importance algorithms for different prediction models performed in the presence of multicollinearity to provide guidance for selecting from among these approaches given strength of correlation between features and the dimensionality of the data. We conducted an extensive simulation study to examine the impact of multicollinearity and dimensionality on the ability of different feature importance algorithms to correctly identify features as important or arbitrary. Our results indicate that for linear and non-linear relationships between features and the outcome, LASSO and elastic net provide the most consistent ranking in low-dimensional data where the number of observations is far greater than the number of features. However, as dimensionality increases such that the number of features increases relative to the number of observations, feature importance algorithms in machine learning approaches such as support vector ma- chines (SVMs), artificial neural networks (ANNs), and random forests (RF) become more ideal approaches
Advancing Colorectal Cancer Screening Adherence in a Community Hospital: Consultative Report
Get PDFColorectal cancer (CRC) is one of the top three leading causes of death in both men and women, as well as the second leading cause of cancer-related deaths in both men and women in the United States. Screening, on the other hand, can aid in the detection and prevention of CRC. Early reports suggested that colorectal screening compliance could significantly reduce the risk of mortality associated with CRC in people aged 50 to 75 years. Colonoscopy cancellations have been identified as a significant contributor to non-compliance with CRC screening in subsequent studies. However, research indicates that patient navigators are used in the most effective CRC screening compliance programs, particularly in underserved communities. Some of these limitations stem from the studies’ use of a single center or site model, which raises concerns about its generalizability to other settings. This consultative report examines the literature to address several factors that contribute to low colorectal cancer screening rates, such as patient cancellations, and recommends interventions to achieve and sustain the national goal of achieving and maintaining an 80 percent colorectal screening rate in the population. Following that, we look at the patient demographics associated with colonoscopy screening. In addition, identify the significant factors linked to colonoscopy screening cancellations. Finally, we discuss various interventions for increasing CRC screening
Statistical Methods for Integrative Analysis, Subgroup Identification, and Variable Selection Using Cancer Genomic Data
Get PDFIn recent years, comprehensive cancer genomics platform, such as The Cancer Genome Atlas (TCGA), provides access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alteration, DNA methylation, and somatic mutation. Currently most existing analysis approaches focused only on gene-level analysis and suffered from limited interpretability and low reproducibility of findings. Additionally, with increasing availability of the modern compositional data including immune cellular fraction data and high-dimensional zero-inflated microbiome data, variable selection techniques for compositional data became of great interest because they allow inference of key immune cell types (immunology data) and key microbial species (microbiome data) associated with development and progression of various diseases. In the first dissertation aim, we address these challenges by developing a Bayesian sparse latent factor model for pathway-guided integrative genomic data analysis. Specifically, we constructed a unified framework to simultaneously identify cancer patient subgroups (clustering) and key molecular markers (variable selection) based on the joint analysis of continuous, binary and count data. In addition, we applied Polya-Gamma mixtures of normal for binary and count data to promote an exact and fully automatic posterior sampling. Moreover, pathway information was used to improve accuracy and robustness in identification of cancer patient subgroups and key molecular features. In the second dissertation aim, we developed the R package InGRiD , a comprehensive software for pathway-guided integrative genomic data analysis. We further implemented the statistical model developed in Aim 1 and provide it as a part of this software. The third dissertation aim exploits variable selection in compositional data analysis with application to immunology data and microbiome data. Specifically, we identified key immune cell types by applying a stepwise pairwise log-ratio procedure to the immune cellular fractions data, while selecting key species in the microbiome data by using zero-inflated Wilcoxon rank sum test. These approaches consider key components specific to these data types, such as compositionality (i.e., sum-to-one), zero inflation, and high dimensionality, among others. The proposed methods were developed and evaluated on: 1) large scale, high dimensional, and multi-modal datasets from the TCGA database, including gene expression, DNA copy number alteration, and somatic mutation data (Aim 1); 2) cellular fraction data induced from Colorectal Adenocarcinoma TCGA Pan-Cancer study (Aim 3); 3) high dimensional zero-inflated microbiome data from studies of colorectal cancer (Aim 3)
Statistical Approaches for Functional Annotation Tree Guided Prioritization of Genome-wide Association Studies (GWAS) Results
Get PDFGenome-wide association studies (GWAS) have successfully identified over two hundred thousand trait risk-associated genetic variants; however, several challenges remain. First, a complex trait is associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes that are hard to detect with limited sample size due to a phenomenon called polygenicity. Additionally, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with complex traits. In the first dissertation aim, we address these challenges by proposing a statistical approach called GPA-Tree. GPA-Tree integratesGWAS summary statistics and functional annotation information for a single trait within a unified framework. Specifically, by combining a decision tree algorithm with a hierarchical modeling framework, GPA-Tree simultaneously implements association mapping and identifies key combinations of functional annotations related to the trait risk-associated SNPs. We evaluate the proposed GPA-Tree approach using simulation studies and demonstrate that, in most scenarios, GPA-Tree shows greater area under the curve (AUC) and power relative to existing statistical approaches in detecting risk-associated SNPs and greater accuracy in identifying the true combinations of functional annotations. We applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells. The second dissertation aim exploits the phenomenon called pleiotropy, shared genetic basis among multiple traits, to improve statistical power to detect SNPs associated with one or more traits. We extend GPA-Tree to develop Multi-GPA-Tree so that GWAS summary statistics for multiple traits and functional annotation information can be integrated within a unified framework. Specifically, by combining a multivariate decision tree algorithm with a hierarchical modeling framework, Multi-GPA-Tree simultaneously implements association mapping and identifies key combinations of functional annotations related to the SNPs associated with one or more traits. We evaluate the proposed Multi-GPA-Tree approach using simulation studies and demonstrate that, in most scenarios, Multi-GPA-Tree outperforms existing statistical approaches in detecting SNPs associated with one or more traits and identifying the true combinations of functional annotations with high accuracy. We utilize Multi-GPA-Tree to integrate GWAS from two rheumatic diseases, SLE and Rheumatoid Arthritis (RA), and GWAS from two inflammatory bowel diseases, Crohn’s trait (CD) and ulcerative colitis (UC), with GenoSkyline and GenoSkylinePlus annotations. The results from Multi-GPA-Tree highlight the dysregulation of blood immune cells for both joint analysis, including dysregulation of primary B cells for SLE and RA, and dysregulation of primary T regulatory cells for UC and CD. In the third dissertation aim, we develop the R package GPATree and the R Shiny app ShinyGPATree. The R package and Shiny app facilitate users’ convenience and make the GPA-Tree and Multi-GPA-Tree approach easily accessible. The package includes an example data and a vignette to facilitate seamless step-by-step implementation of the proposed methods. In addition, the Shiny app allows interactive and dynamic investigation of association mapping results and functional annotation trees