Publications by authors named "T R Henry"
Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, non-iterative estimator for latent variable models. Associated with this estimator are equation specific tests of model misspecification. One issue with equation specific tests is that they lack specificity, in that they indicate that some instruments are problematic without revealing which specific ones.
View Article and Find Full Text PDFThe Revolution of STEMI Care: A Story of Resilience, Persistence, and Success.
J Soc Cardiovasc Angiogr Interv
November 2024
SCAI Expert Consensus Statement on the Management of Patients With STEMI Referred for Primary PCI.
J Soc Cardiovasc Angiogr Interv
November 2024
ST-elevation myocardial infarction (STEMI) remains a leading cause of morbidity and mortality in the United States. Timely reperfusion with primary percutaneous coronary intervention is associated with improved outcomes. The Society for Cardiovascular Angiography & Interventions puts forth this expert consensus document regarding best practices for cardiac catheterization laboratory team readiness, arterial access with an algorithm to help determine proper arterial access in STEMI, and diagnostic angiography.
View Article and Find Full Text PDFArticle Synopsis
- The study investigates how bone marrow-derived pro-inflammatory macrophages and embryo-derived reparative macrophages impact progressive heart failure with reduced ejection fraction (HFrEF) and explores the potential of mesenchymal precursor cells (MPCs) to improve patient outcomes.
- In the DREAM-HF trial, significant risk factors for cardiovascular death (CVD) were identified in patients with HFrEF, particularly inflammation and ischaemic aetiology, which increased the risk by 61% and 38%, respectively.
- The introduction of MPCs led to notable reductions in major adverse cardiovascular events (MACE) by 88% and 52% over a 30-month follow-up in patients with ischaemic HFrEF and inflammation.
Background: While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice.
Methods: In response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data.