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Biomedical subjects

Benjamin Brennan

Publications and source records attributed to Benjamin Brennan.

2 recordsLinked to original sources

Impact of subthreshold troponin levels and temporal trends on short term adverse cardiovascular outcomes in patients discharged from the emergency department: a RACE-IT trial substudy.

BACKGROUND: High-sensitivity cardiac troponin I assays enable early exclusion of myocardial infarction in the emergency department. However, the clinical implications of detectable troponin values below the 99th percentile upper reference limit (4-18 ng/L) remain unclear. OBJECTIVE: To assess the association between subthreshold troponin levels and 30-day outcomes in patients from the RACE-IT trial, using exact troponin values when available. METHODS: This post-hoc analysis of the RACE-IT stepped-wedge randomized controlled trial included patients with troponin ≤ 18 ng/L across nine EDs. Patients were stratified by initial troponin, peak value, absolute change, and percent change. The primary outcome was a 30-day composite of all-cause death, acute MI, percutaneous coronary intervention, and coronary artery bypass grafting. Logistic regression analysis after adjusting for age, sex, race, and coronary artery disease was performed. RESULTS: Among 19,194 patients with troponin ≤ 18 ng/L, 117 (0.6%) experienced the composite outcome. Higher troponin levels were associated with increased event rates in unadjusted analyses. Adjusted analyses showed no independent associations overall, though patients whose highest troponin values fell within the ≥ 11- ≤ 18 ng/L range continued to demonstrate significantly worse outcomes than those with lower peak levels. Elevated troponin values correlated with older age, male sex, and greater comorbidity burden. CONCLUSION: In this post-hoc analysis of patients with troponin values below the 99th percentile URL, absolute levels and temporal changes were not independently associated with 30-day adverse outcomes. These findings support the use of subthreshold troponin values in rapid rule-out protocols, emphasizing the need to consider clinical context and comorbidities in risk assessment.

Humans

Asymmetric integration of various cancer datasets for identifying risk-associated variants and genes.

MOTIVATION: Cancer genomic research provides an opportunity to identify cancer risk-associated genes, but often suffers from undesirable low statistical power due to a limited sample size. Integrated analysis with different cancers has the potential to enhance statistical power for identifying pan-cancer risk genes. However, substantial heterogeneity across various cancers makes this challenging. RESULTS: Recently, a novel asymmetric integration method was developed that can deal with data heterogeneity and exclude unhelpful datasets from the analysis. We adapted and applied this method to integrate genotype datasets with matched case and control individuals from the Michigan Genomics Initiative, using each cancer as the primary dataset of interest and the other cancers as auxiliary datasets, respectively. Conditional logistic regression models were coupled with the asymmetric integrated framework to handle the matched case-control study design and permutation tests were performed to control for false discovery rates (FDRs). At the same FDR level, the integrated analysis found more potential genetic variants and genes that are associated with the risks of various cancers, showcasing the promise of the proposed approach for integrated analysis of cancer datasets. AVAILABILITY AND IMPLEMENTATION: Our method is available as source code at https://github.com/rxxwang/integrate_cancer.

Journal Article