Implementation of a national PMS for standardized data collection and reporting across multiple clinical sites ultimately provided important and reliable information on utilization of services, patient outcomes, and survival rates on treatment. These data are used at the national level to monitor the efficacy of the HIV care and treatment program. Successful implementation requires early inclusion of all committed stakeholders and a dedicated human resource team to ensure sustainability of the system.
Background Cardiovascular disease (CVD) is a leading cause of premature mortality in the United States and the world. CVD comprises several complex and mostly heritable conditions, which range from myocardial infarction to congenital heart disease. The risk factors contributing to the development of CVD and response to therapy in an individual patient are highly variable. Here, we report our findings from an integrative analysis of gene expression, disease‐causing gene variants and associated phenotypes among CVD populations, with a focus on high‐risk heart failure (HF) patients. Methods We built a cohort using electronic health records of consented patients with available samples and then performed high‐throughput whole genome and RNA sequencing of key genes responsible for HF and other CVD pathologies. Our in‐depth gene expression analysis revealed differentially expressed genes associated with HF and other CVDs. We performed a variant analysis of whole genome sequence data of CVD patients and identified genes with altered gene expression with functional and non‐functional mutations in these genes. Results Our results highlight the importance of investigating the mechanisms of CVD progression through multi‐omics datasets. Next, we performed splice mutation and variant distribution analysis of genes associated with HF and other CVD. We implemented Jensen–Shannon divergence (JSD)‐based method and identified HBA1, FADD, ADRB2, NPPB, ADRB1, ADB and NPPC genes with the greatest variance based on their JSD scores. Our study provided evidence that applying integrative data analysis approach involving genomics and transcriptomics data will not only help understand the pathophysiology of CVD diseases but also reduce heterogeneity in disease subtypes.
Cardiovascular disease (CVD) is a leading cause of premature mortality in the US and the world. CVD comprises of several complex and mostly heritable conditions, which range from myocardial infarction to congenital heart disease. Here, we report our findings from an integrative analysis of gene expression, disease-causing gene variants, and associated phenotypes among CVD populations, with a focus on high-risk Heart Failure (HF) patients. We built a cohort using electronic health records (EHR) of consented patients with available samples, and then performed high-throughput whole-genome and RNA sequencing (RNA-seq) of key genes responsible for HF and other CVD pathologies. We also incorporated a translational aspect to our study by integrating genomics findings with patient medical records. This involved linking ICD-10 codes with our gene expression and variant data to identify associations with HF and other CVDs. Our in-depth gene expression analysis revealed differentially expressed genes associated with HF (41 genes) and other CVDs (23 genes). Furthermore, a variant analysis of whole-genome sequence data of CVD patients identified genes with altered gene expression (FLNA, CST3, LGALS3, and HBA1) with functional and nonfunctional mutations in these genes. Our study highlights the importance of an integrative approach that leverages gene expression, genetic mutations, and clinical data that will allow the prioritization of key driver genes for complex diseases to improve personalized healthcare.
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