Performance of a cardiac lipid panel compared to four prognostic scores in chronic heart failure

The cardiac lipid panel (CLP) is a novel panel of metabolomic biomarkers that has previously shown to improve the diagnostic and prognostic value for CHF patients. Several prognostic scores have been developed for cardiovascular disease risk, but their use is limited to specific populations and precision is still inadequate. We compared a risk score using the CLP plus NT-proBNP to four commonly used risk scores: The Seattle Heart Failure Model (SHFM), Framingham risk score (FRS), Barcelona bio-HF (BCN Bio-HF) and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score. We included 280 elderly CHF patients from the Cardiac Insufficiency Bisoprolol Study in Elderly trial. Cox Regression and hierarchical cluster analysis was performed. Integrated area under the curves (IAUC) was used as criterium for comparison. The mean (SD) follow-up period was 81 (33) months, and 95 (34%) subjects met the primary endpoint. The IAUC for FRS was 0.53, SHFM 0.61, BCN Bio-HF 0.72, MAGGIC 0.68, and CLP 0.78. Subjects were partitioned into three risk clusters: low, moderate, high with the CLP score showing the best ability to group patients into their respective risk cluster. A risk score composed of a novel panel of metabolite biomarkers plus NT-proBNP outperformed other common prognostic scores in predicting 10-year cardiovascular death in elderly ambulatory CHF patients. This approach could improve the clinical risk assessment of CHF patients

Incremental prognostic value of a novel metabolite-based biomarker score in congestive heart failure patients

Aims: The Cardiac Lipid Panel (CLP) is a newly discovered panel of metabolite-based biomarkers that has shown to improve the diagnostic value of N terminal pro B type natriuretic peptide (NT-proBNP). However, little is known about its usefulness in predicting outcomes. In this study, we developed a risk score for 4-year cardiovascular death in elderly chronic heart failure (CHF) patients using the CLP. Methods and results: From the Cardiac Insufficiency Bisoprolol Study in Elderly trial, we included 280 patients with CHF aged \u3e65 years. A targeted metabolomic analysis of the CLP biomarkers was performed on baseline serum samples. Cox regression was used to determine the association of the biomarkers with the outcome after accounting for established risk factors. A risk score ranging from 0 to 4 was calculated by counting the number of biomarkers above the cut-offs, using Youden index. During the mean (standard deviation) follow-up period of 50 (8) months, 35 (18%) subjects met the primary endpoint of cardiovascular death. The area under the receiver operating curve for the model based on clinical variables was 0.84, the second model with NT-proBNP was 0.86, and the final model with the CLP was 0.90. The categorical net reclassification index was 0.25 using three risk categories: 0-60% (low), 60-85% (intermediate), and \u3e85% (high). The continuous net reclassification index was 0.772, and the integrated discrimination index was 0.104. Conclusions: In patients with CHF, incorporating a panel of three metabolite-based biomarkers into a risk score improved the prognostic utility of NT-proBNP by predicting long-term cardiovascular death more precisely. This novel approach holds promise to improve clinical risk assessment in CHF patients. Keywords: Biomarkers; Congestive heart failure; Metabolite profiling; Metabolomics; Prognosi

Stroke Genomics: Current Knowledge, Clinical Applications and Future Possibilities

The pathophysiology of stoke involves many complex pathways and risk factors. Though there are several ongoing studies on stroke, treatment options are limited, and the prevalence of stroke is continuing to increase. Understanding the genomic variants and biological pathways associated with stroke could offer novel therapeutic alternatives in terms of drug targets and receptor modulations for newer treatment methods. It is challenging to identify individual causative mutations in a single gene because many alleles are responsible for minor effects. Therefore, multiple factorial analyses using single nucleotide polymorphisms (SNPs) could be used to gain new insight by identifying potential genetic risk factors. There are many studies, such as Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) which have identified numerous independent loci associated with stroke, which could be instrumental in developing newer drug targets and novel therapies. Additionally, using analytical techniques, such as meta-analysis and Mendelian randomization could help in evaluating stroke risk factors and determining treatment priorities. Combining SNPs into polygenic risk scores and lifestyle risk factors could detect stroke risk at a very young age and help in administering preventive interventions

Obstetric outcomes during delivery hospitalizations among obese pregnant women in the United States

The rates of both maternal and fetal adverse outcomes increase significantly with higher body mass index. The aim of this study was to calculate national estimates of adverse maternal and fetal outcomes and associated hospitalization cost among obese pregnant women using a national database. This study was a retrospective analysis of data retrieved from Nationwide Inpatient Sample database, collected during 2010–2014. The primary outcomes of this study were adverse maternal and fetal outcomes, hospital length of stay, and hospitalization cost. There was a total of 18,687,217 delivery-related hospitalizations, of which 1,048,323 were among obese women. Obese women were more likely to have cesarean deliveries (aOR 1.70, 95% CI 1.62–1.79) and labor inductions (aOR 1.51, 95% CI 1.42–1.60), greater length of stay after cesarean deliveries (aOR 1.14, 95% CI 1.08–1.36) and vaginal deliveries (aOR 1.48, 95% CI 1.23–1.77). They were also more likely to have pregnancy-related hypertension, preeclampsia, gestational diabetes, premature rupture of membranes, chorioamnionitis, venous thromboembolism, excessive fetal growth, and fetal distress. Obese pregnant women had significantly greater risk for adverse obstetrical outcomes, which substantially increased the hospital and economic burden. Risk stratification of pregnant patients based on obesity could also help obstetricians to make better clinical decisions and improve patient outcomes

Trends in quality of primary care in the United States, 2007–2016

During the past decade, many reforms were proposed and implemented for improving primary care in the US. This study assessed improvements in quality of primary care, using a nationally representative database. We conducted a retrospective trend analysis of National Inpatient Sample data (2007–2016). The quality of primary care was assessed using Prevention Quality Indicators (PQIs), which consist of 13 sets of preventable hospitalization conditions. PQI hospitalization decreased from 154,565 to 151,168 per million hospitalizations during the study period (relative decrease, 2.2%; P = 0.041). Age-adjusted hospitalization rate increased for diabetes short-term complications (relative increase, 46.9%; P \u3c 0.001) and lower-extremity amputations (relative increase, 15.1%; P = 0.035). Age stratified trends showed that hospitalization rates decreased significantly in all age-groups for diabetes short-term complications. For lower-extremity amputations, hospitalization rates increased significantly in younger age groups and decreased significantly in the older age groups. All other PQIs showed either decreasing or no change in trends. Adults aged 18–64 years should be the focus for future prevention attempts for diabetes complications. Identifying and acting on the factors responsible for these changes could help in reversing the concerning trends observed in this study. Existing strategies should focus on improving access to diabetes care and self-management

Stroke Genomics: Current Knowledge, Clinical Applications and Future Possibilities

The pathophysiology of stoke involves many complex pathways and risk factors. Though there are several ongoing studies on stroke, treatment options are limited, and the prevalence of stroke is continuing to increase. Understanding the genomic variants and biological pathways associated with stroke could offer novel therapeutic alternatives in terms of drug targets and receptor modulations for newer treatment methods. It is challenging to identify individual causative mutations in a single gene because many alleles are responsible for minor effects. Therefore, multiple factorial analyses using single nucleotide polymorphisms (SNPs) could be used to gain new insight by identifying potential genetic risk factors. There are many studies, such as Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) which have identified numerous independent loci associated with stroke, which could be instrumental in developing newer drug targets and novel therapies. Additionally, using analytical techniques, such as meta-analysis and Mendelian randomization could help in evaluating stroke risk factors and determining treatment priorities. Combining SNPs into polygenic risk scores and lifestyle risk factors could detect stroke risk at a very young age and help in administering preventive interventions

An Integrated Oncology Data Warehouse for Clinical Decision Support and Complex Patient Cohort Identification in a Hybrid Cancer Center

BACKGROUND: A data warehouse is a repository that centralizes and integrates data from disparate systems to provide the ability to easily access historical, consistent data. Integration of disparate source systems into one centralized location can enable rapid identification of more robust research cohorts and enable data-driven decision making. The objective of the Miami Cancer Institute (MCI) Oncology Data Warehouse (ODW) is to collect and organize data from clinical records, research, and administrative systems to support information retrieval, business intelligence, and analytics for high-level decision making for oncology patients. The design, architecture, and implementation aligns with industry best practices which includes Data Governance, Enterprise Data Modeling, and Metadata Management. METHODS: We integrated structured and unstructured data from disparate sources into one centralized data model optimized for querying known as the ODW. The ODW is modeled as a star schema, with fact tables and conformed dimension tables, and expands to a galaxy schema with constellation facts and dimensions that can snowflake to other data models as needed. Each fact table represents a subject area (i.e. pathology), that is directly related to the conformed dimension tables using surrogate and foreign keys. Conformed dimensions represent the attributes associated to the subject area (i.e. date of encounter). The source data is extracted, transformed and loaded (ETL) automatically from different databases into a set of tables. The ETL code performs incremental loads at regular prescribed intervals into two parallel storage areas, a relational database management system (RDMS) as well as a Big Data file storage system. RESULTS: An interdisciplinary team of physicians, engineers, scientists, and subject matter experts at the Miami Cancer Institute of Baptist Health South Florida, has designed, developed, and implemented the ODW with information originating from different data sources which include: Electronic Medical Record (EMR) systems, Financial Systems, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Pathology synoptic reports and archives, and Next Generation Sequencing services. Structurally it is a subject-oriented, integrated collection of data leveraging conformed dimensions. The ODW is capable of connecting most business intelligence (i.e. Tableau) or statistical (i.e. SAS) tools for automated or static report development. CONCLUSION: The growing ODW enables physicians, clinical management teams, and medical analysts to systematically mine and review the molecular, genomic, and associated clinical or administrative information of patients, and identify patterns that may influence treatment decisions and potential outcomes. By implementing an innovative combination of technology tools and methods, we were able to organize enterprise information about oncology patients which can be utilized for clinical decision support and precision medicine use cases

Association between vitamin D deficiency and hypothyroidism: results from the National Health and Nutrition Examination Survey (NHANES) 2007–2012

Purpose: Many smaller studies have previously shown a significant association between thyroid autoantibody induced hypothyroidism and lower serum vitamin D levels. However, these finding have not been confirmed by large-scale studies. In this study, we evaluated the relationship between hypothyroidism and vitamin D levels using a large population-based data. Methods: For this study, we used National Health and Nutrition Examination Survey (NHANES) during the years 2007–2012. We categorized participants into three clinically relevant categories based on vitamin D levels: optimal, intermediate and deficient. Participants were also split into hypothyroid and hyperthyroid. Weighted multivariable logistic regression analyses were used to calculate the odds of being hypothyroid based on vitamin D status. Results: A total of 7943 participants were included in this study, of which 614 (7.7%) were having hypothyroidism. Nearly 25.6% of hypothyroid patients had vitamin D deficiency, compared to 20.6% among normal controls. Adjusted logistic regression analyses showed that the odds of developing hypothyroidism were significantly higher among patients with intermediate (adjusted odds ratio [aOR], 1.7, 95% CI: 1.5–1.8) and deficient levels of vitamin D (aOR, 1.6, 95% CI: 1.4–1.9). Conclusion: Low vitamin D levels are associated with autoimmune hypothyroidism. Healthcare initiatives such as mass vitamin D deficiency screening among at-risk population could significantly decrease the risk for hypothyroidism in the long-term

Emerging Evidence on the Effects of Dietary Factors on the Gut Microbiome in Colorectal Cancer

Dietary factors play an important role in shaping the gut microbiome which, in turn, regulates the molecular events in colonic mucosa. The composition and resulting metabolism of the gut microbiome have been implicated in the development of colorectal cancer (CRC). Diets low in dietary fibers and phytomolecules as well as other lifestyle-related factors may predispose to CRC. Emerging evidence demonstrates that the predominance of microbes, such as Fusobacterium nucleatum, can predispose the colonic mucosa to malignant transformation. Dietary and lifestyle modifications have been demonstrated to restrict the growth of potentially harmful opportunistic organisms. In this study, we aim to present evidence regarding the relationship of dietary factors to the gut microbiome and development of CRC. Keywords: dietary fibers; short chain fatty acid; gut microbiota; colorectal cancer prevention; epigenetic

A Brief Overview of Adaptive Designs for Phase I Cancer Trials

Phase I studies are used to estimate the dose-toxicity profile of the drugs and to select appropriate doses for successive studies. However, literature on statistical methods used for phase I studies are extensive. The objective of this review is to provide a concise summary of existing and emerging techniques for selecting dosages that are appropriate for phase I cancer trials. Many advanced statistical studies have proposed novel and robust methods for adaptive designs that have shown significant advantages over conventional dose finding methods. An increasing number of phase I cancer trials use adaptive designs, particularly during the early phases of the study. In this review, we described nonparametric and algorithm-based designs such as traditional 3 + 3, accelerated titration, Bayesian algorithm-based design, up-and-down design, and isotonic design. In addition, we also described parametric model-based designs such as continual reassessment method, escalation with overdose control, and Bayesian decision theoretic and optimal design. Ongoing studies have been continuously focusing on improving and refining the existing models as well as developing newer methods. This study would help readers to assimilate core concepts and compare different phase I statistical methods under one banner. Nevertheless, other evolving methods require future reviews