Publications by authors named "M C Mehra"
Introduction: Infection control in intensive care units (ICUs) is crucial due to the high risk of healthcare-associated infections (HAIs), which can increase patient morbidity, mortality, and costs. Effective measures such as hand hygiene, use of personal protective equipment (PPE), patient isolation, and environmental cleaning are vital to minimize these risks. The integration of artificial intelligence (AI) offers new opportunities to enhance infection control, from predicting outbreaks to optimizing antimicrobial use, ultimately improving patient safety and care in ICUs.
View Article and Find Full Text PDFImportance: The Aspirin and Hemocompatibility Events With a Left Ventricular Assist Device in Advanced Heart Failure (ARIES-HM3) study demonstrated that aspirin may be safely eliminated from the antithrombotic regimen after HeartMate 3 (HM3 [Abbott Cardiovascular]) left ventricular assist device (LVAD) implantation. This prespecified analysis explored whether conditions requiring aspirin (prior percutaneous coronary intervention [PCI], coronary artery bypass grafting [CABG], stroke, or peripheral vascular disease [PVD]) would influence outcomes differentially with aspirin avoidance.
Objective: To analyze aspirin avoidance on hemocompatibility-related adverse events (HRAEs) at 1 year after implant in patients with a history of CABG, PCI, stroke, or PVD.
Background: Prior analyses have suggested that a smaller left ventricular end-diastolic diameter (LVEDD) is associated with reduced survival following HeartMate 3 left ventricular assist device implantation.
Objectives: In this trial-based comprehensive analysis, the authors sought to examine clinical characteristics and association with the outcome of this specific relationship.
Methods: The authors analyzed the presence of LVEDD <55 mm among 1,921 analyzable HeartMate 3 patients within the MOMENTUM 3 (Multicenter Study of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy With HeartMate 3) trial portfolio, on endpoints of overall survival and adverse events at 2 years.
"Surveillance without scars": The quest for noninvasive detection of cardiac allograft rejection.
J Heart Lung Transplant
December 2024
Article Synopsis
- The study aims to use machine learning clustering to categorize distinct patient types among those with ischaemic heart failure and reduced ejection fraction (HFrEF) to improve personalized treatment approaches.
- Analysis of 8,591 HFrEF patients revealed five unique clusters based on clinical and biological traits, with varying risks for hospitalization and death, highlighting the need for tailored management strategies.
- The findings indicate that specific clusters correlate with different outcomes, suggesting that treatments like mineralocorticoid receptor antagonists may be more beneficial for certain patient groups, ultimately enhancing patient care in the future.