Objective We conducted a meta-analysis to estimate the risk of adverse events, life expectancy, and event-free life expectancy after the Ross procedure in adults. Methods We searched databases for... Show moreObjective We conducted a meta-analysis to estimate the risk of adverse events, life expectancy, and event-free life expectancy after the Ross procedure in adults. Methods We searched databases for reports evaluating the Ross procedure in patients aged more than or equal to 16 years of age. A microsimulation model was used to evaluate age- and gender-specific life expectancy for patients undergoing the Ross procedure. Results Data were pooled from 63 articles totaling 19 155 patients from 20 countries. Perioperative mortality was 2.5% (95% confidence interval [CI]: 1.9-3.1; N = 9978). We found a mortality risk of 5.9% (95% CI: 4.8-7.2) at a mean follow-up of 7.2 years (N = 7573). The rate of perioperative clinically significant bleeding was 1.0% (95% CI: 0.1-3.0); re-exploration for bleeding 4.6% (95% CI: 3.1-6.3); postoperative clinically significant bleeding from 30 days until a mean of 7.1 years was 0.5% (95% CI: 0.2-1.0). At a mean of 6.9 years of follow-up, reintervention rate of any operated valve was 7.9% (95% CI: 5.7-10.3). The risk of valve thrombosis was 0.3% (95% CI: 0.2-0.5) at 7.6 years; peripheral embolism 0.3% (95% CI: 0.2-0.4) at 6.4 years; stroke 0.9% (95% CI: 0.7-1.2) at 6.5 years; and endocarditis 2.1% (95% CI: 1.6-2.6) at 8.0 years. Microsimulation reported a 40-year-old undergoing the Ross procedure to have a life expectancy of 35.4 years and event-free life expectancy of 26.6 years. Conclusions Ross procedure in nonelderly adults is associated with low mortality and low risk of adverse events both at short- and long-term follow-up. The surgical community must prioritize a large, expertize-based randomized controlled trial to definitively address the risks and benefits of the Ross procedure compared to conventional aortic valve replacement. Show less
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of... Show moreResponse inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide twelve easy-to-implement consensus recommendations and point out the problems that can arise when these are not followed. Furthermore we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis. Show less
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of... Show moreResponse inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis. Show less