The Montreal Heart Institute (MHI) (French: Institut de Cardiologie de Montréal), in Montreal, Quebec, is a specialty hospital dedicated to the development of cardiology, which is affiliated with the Université de Montréal. The MHI is founded in 1954 by Paul David, and it is considered one of the largest cardiology institutes in the world, which is the first educational hospital on cardiovascular diseases in Canada, and is ranked as one of the largest preventive medicine centre in Quebec.Prominent Montreal businessman J. Louis Levesque was a major benefactor whose initial $1 million donation in 1969 doubled the research department's budget. Levesque supported the Institute for more than a quarter of a century, donating more than $10 million.The MHI was ranked number one research hospital in Canada for researcher intensity, and research expenditure per researcher, on the 2015 list of "Canada's Top 40 Research Hospitals" published by Research Infosource.
Background The intimal flap at the distal aortic anastomosis after standard aortic dissection repair creates distal anastomotic new entry, leading to false lumen (FL) pressurization and true lumen (TL) collapse and resulting in increased mortality, malperfusion, aortic growth, and reinterventions. The Ascyrus Medical Dissection Stent (AMDS; Ascyrus Medical, Boca Raton, FL) is a hybrid prosthesis that seals and depressurizes the FL at the distal anastomosis while expanding and pressurizing the TL. Methods The Dissected Aorta Repair Through Stent Implantation trial is a prospective, nonrandomized, international type A dissection trial where patients with acute DeBakey I dissections were enrolled between March 2017 and January 2019. Forty-seven patients were enrolled (median age, 62.5; 67.4% men) with a median follow-up of 631 days. Results All patients underwent emergent surgical repair with successful AMDS implantation. One patient was excluded because of use in iatrogenic dissection. Overall mortality at 30 days and 1 year was 13.0% (6/46) and 19.6% (9/46), whereas new strokes occurred in 6.5% (3/46). Over 95% of vessel malperfusions resolved because of AMDS-induced TL expansion, including 3 patients with preoperative paralysis. Positive remodeling of the aortic arch occurred in 100% of cases with complete obliteration or thrombosis of the FL in 74%. In the proximal descending thoracic aorta positive remodeling occurred in 77% and complete obliteration or FL thrombosis in 53% of cases. Conclusions AMDS facilitates single-stage management of malperfusion and induces positive remodeling of the aortic arch through effective sealing of the distal anastomotic FL, depressurization of the FL with expansion, and pressurization of the TL. Importantly the use of AMDS is safe and reproducible.
With the advancement of AI capabilities, AI reviewers are beginning to be deployed in scientific peer review, yet their capability and credibility remain in question: many scientists simply view them as probabilistic systems without the expertise to evaluate research, while other researchers are more optimistic about their readiness without concrete evidence. Understanding what AI reviewers do well, where they fall short, and what challenges remain is essential. However, existing evaluations of AI reviewers have focused on whether their verdicts match human verdicts (e.g., score alignment, acceptance prediction), which is insufficient to characterize their capabilities and limits. In this paper, we close this gap through a large-scale expert annotation study, in which 45 domain scientists in Physical, Biological, and Health Sciences spent 469 hours rating 2,960 individual criticisms (each targeting one specific aspect of a paper) from human-written and AI-generated reviews of 82 Nature-family papers on correctness, significance, and sufficiency of evidence. On a composite of all three dimensions, a reviewing agent powered by GPT-5.2 scores above each paper's top-rated human reviewer (60.0
BACKGROUND:Cardiac resynchronisation therapy (CRT) is a key treatment for heart failure (HF) in acquired heart disease, but its benefits in adults with congenital heart disease and a systemic right ventricle (sRV) remain unclear. This study aimed to assess whether CRT improves outcomes in patients with sRV. METHODS:This is an international, retrospective study including patients >18 years from 33 centres with transposition of the great arteries (TGA) following atrial switch operation and congenitally corrected TGA. The primary endpoint included overall survival and survival free from HF. The secondary endpoint was a composite of death, hospitalisation for HF, heart transplant, mechanical support and ventricular tachycardia/implantable cardioverter-defibrillator therapies. RESULTS:We identified 105 out of 1721 patients (3.5%) who underwent CRT. Median follow-up after CRT implant was 4.6 (1.6-8) years. QRS improvement was limited to those with previous pacing (167±35 vs 154±28 ms; p=0.002). Following CRT, there was no significant change in B-type natriuretic peptide values, peak VO2 and tricuspid regurgitation severity by echocardiography. CRT complications occurred in 10 (9.5%), though they were usually minor. Patients with CRT were propensity-matched to controls according to age, sex, anatomy, presence of complex disease, previous HF and sRV dysfunction at baseline. At univariable analysis, CRT (HR 4.39-95%, CI 1.6 to 11.9; p=0.003), older age and moderate-to-severe sRV dysfunction at baseline were predictive of death, while CRT (HR 3-95%, CI 1.3 to 7; p=0.01) and sRV dysfunction were associated with HF admission. By multivariable analysis, CRT (HR 8.8-95%, CI 2.9 to 26.6; p=0.0001) and age (HR 1.1%-95%, CI 1.01 to 1.15; p<0.0001) were independently associated with poorer outcome. CONCLUSION:In this retrospective study in the largest population thus far described with an sRV, CRT implant was not associated with improved survival, even after controlling for key confounders.
Right ventricular (RV) injury (including RV dilatation/dysfunction/limitation/failure) and pulmonary vascular dysfunction are common in patients with acute respiratory distress syndrome (ARDS). Despite increasing recognition, RV injury is associated with increased mortality in patients with ARDS, and implementation of multimodal monitoring and timely RV-targeted interventions may therefore confer outcome benefit. The aim of this narrative review is to explore the clinical applications of diagnostic modalities for the RV and pulmonary circulation in invasively ventilated patients with ARDS, including the complementary roles of invasive hemodynamics, echocardiography, and pulmonary monitoring. We discuss the physiologic basis and utility of RV and pulmonary monitoring to guide the bedside intensivist in personalizing therapies aimed at protecting the RV. Building on previous work that focused on the principles and terminology of abnormal RV biomechanics in critical illness, this review is centered on monitoring of RV pathophysiology in ARDS and implications for bedside management.
Aims:Right ventricular systolic function (RVSF) is a critical determinant of cardiovascular outcomes, yet assessment during coronary angiography remains challenging without prior imaging. We developed and validated DeepRV, a deep learning model predicting RVSF from routine coronary angiograms. Methods and results:DeepRV, a video-based deep neural network, was developed using 8053 coronary angiography studies from 6923 patients at Montreal Heart Institute (2017-23), with RVSF determined by echocardiography. The model was externally validated at the University of California, San Francisco, and prospectively deployed during primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI). In the internal test set (n = 1586; 10.5% reduced RVSF), DeepRV achieved area under the receiver operating characteristic curve (AUROC) 0.80 [95% confidence interval (CI): 0.76-0.84], sensitivity 70.5%, specificity 78.5%, and negative predictive value 95.8%. External validation demonstrated AUROC 0.75 (95% CI: 0.72-0.77) on the UCSF dataset (n = 2247 studies; 30% reduced RVSF). Prospective deployment of DeepRV during STEMI cases at our institution (n = 82) achieved AUROC 0.83 (95% CI: 0.71-0.93) using post-PCI angiogram with a median 5.1 s inference time. In a human performance evaluation (n = 200), artificial intelligence (AI) assistance improved accuracy of identifying RVSF for cardiologists (72.1-77.6%) and medical students (43.5-64.0%). Artificial intelligence alone achieved the highest accuracy (79.5%) and sensitivity (70.0%), while cardiologists with AI achieved the highest specificity (84.6%). Conclusion:DeepRV enables automated RVSF assessment from routine coronary angiograms and enhances diagnostic accuracy across experience levels. Real-time inference and open-weight availability support its potential as a point-of-care tool for risk stratification during coronary angiography.
