Are stentless valves hemodynamically superior to stented valves? Long-term follow-up of a randomized trial comparing Carpentier–Edwards pericardial valve with the Toronto Stentless Porcine Valve

ObjectiveThe benefit of stentless valves remains in question. In 1999, a randomized trial comparing stentless and stented valves was unable to demonstrate any hemodynamic or clinical benefits at 1 year after implantation. This study reviews long-term outcomes of patients randomized in the aforementioned trial.MethodsBetween 1996 and 1999, 99 patients undergoing aortic valve replacement were randomized to receive either a stented Carpentier–Edwards pericardial valve (CE) (Edwards Lifesciences, Irvine, Calif) or a Toronto Stentless Porcine Valve (SPV) (St Jude Medical, Minneapolis, Minn). Among these, 38 patients were available for late echocardiographic follow-up (CE, n = 17; SPV, n = 21). Echocardiographic analysis was undertaken both at rest and with dobutamine stress, and functional status (Duke Activity Status Index) was compared at a mean of 9.3 years postoperatively (range, 7.5–11.1 years). Clinical follow-up was 82% complete at a mean of 10.3 years postoperatively (range, 7.5–12.2 years).ResultsPreoperative characteristics were similar between groups. Effective orifice areas increased in both groups over time. Although there were no differences in effective orifice areas at 1 year, at 9 years, effective orifice areas were significantly greater in the SPV group (CE, 1.49 ± 0.59 cm2; SPV, 2.00 ± 0.53 cm2; P = .011). Similarly, mean and peak gradients decreased in both groups over time; however, at 9 years, gradients were lower in the SPV group (mean: CE, 10.8 ± 3.8 mm Hg; SPV, 7.8 ± 4.8 mm Hg; P = .011; peak: CE, 20.4 ± 6.5 mm Hg; SPV, 14.6 ± 7.1 mm Hg; P = .022). Such differences were magnified with dobutamine stress (mean: CE, 22.7 ± 6.1 mm Hg; SPV, 15.3 ± 8.4 mm Hg; P = .008; peak: CE, 48.1 ± 11.8 mm Hg; SPV, 30.8 ± 17.7 mm Hg; P = .001). Ventricular mass regression occurred in both groups; however, no differences were demonstrated between groups either on echocardiographic, magnetic resonance imaging, or biochemical (plasma B-type [brain] natriuretic peptide) assessment (P = .74). Similarly, Duke Activity Status Index scores of functional status improved in both groups over time; however, no differences were noted between groups (CE, 27.5 ± 19.1; SPV, 19.9 ± 12.0; P = .69). Freedom from reoperation at 12 years was 92% ± 5% in patients with CEs and 75% ± 5% in patients with SPVs (P = .65). Freedom from valve-related morbidity at 12 years was 82% ± 7% in patients with CEs and 55% ± 7% in patients with SPVs (P = .05). Finally, 12-year actuarial survival was 35% ± 7% in patients with CEs and 52% ± 7% in patients with SPVs (P = .37).ConclusionAlthough offering improved hemodynamic outcomes, the SPV did not afford superior mass regression or improved clinical outcomes up to 12 years after implantation

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

Abstract Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. Research Significance This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI’s significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. Materials and Methods The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. Results Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. Conclusion AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare

Incidence and Clinical Outcomes of New-Onset Atrial Fibrillation in Critically Ill Patients with COVID-19: A Multicenter Cohort Study

Atrial fibrillation (Afib) can contribute to a significant increase in mortality and morbidity in critically ill patients. Thus, our study aims to investigate the incidence and clinical outcomes associated with the new-onset Afib in critically ill patients with COVID-19. A multicenter, retrospective cohort study includes critically ill adult patients with COVID-19 admitted to the intensive care units (ICUs) from March, 2020 to July, 2021. Patients were categorized into two groups (new-onset Afib vs control). The primary outcome was the in-hospital mortality. Other outcomes were secondary, such as mechanical ventilation (MV) duration, 30-day mortality, ICU length of stay (LOS), hospital LOS, and complications during stay. After propensity score matching (3:1 ratio), 400 patients were included in the final analysis. Patients who developed new-onset Afib had higher odds of in-hospital mortality (OR 2.76; 95% CI: 1.49-5.11, P  =   .001). However, there was no significant differences in the 30-day mortality. The MV duration, ICU LOS, and hospital LOS were longer in patients who developed new-onset Afib (beta coefficient 0.52; 95% CI: 0.28-0.77; P  < .0001,beta coefficient 0.29; 95% CI: 0.12-0.46; P  < .001, and beta coefficient 0.35; 95% CI: 0.18-0.52; P  < .0001; respectively). Moreover, the control group had significantly lower odds of major bleeding, liver injury, and respiratory failure that required MV. New-onset Afib is a common complication among critically ill patients with COVID-19 that might be associated with poor clinical outcomes; further studies are needed to confirm these findings