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Percutaneous coronary intervention (PCI) with drug-eluting stents is the standard of care for treatment of native coronary artery stenoses, but optimum treatment strategies for bare metal stent and drug-eluting stent in-stent restenosis (ISR) have not been established. We aimed to compare and rank percutaneous treatment strategies for ISR. We did a network meta-analysis to synthesise both direct and indirect evidence from relevant trials. We searched PubMed, the Cochrane Library Central Register of Controlled Trials, and Embase for randomised controlled trials published up to Oct 31, 2014, of different PCI strategies for treatment of any type of coronary ISR. The primary outcome was percent diameter stenosis at angiographic follow-up. This study is registered with PROSPERO, number CRD42014014191. We deemed 27 trials eligible, including 5923 patients, with follow-up ranging from 6 months to 60 months after the index intervention. Angiographic follow-up was available for 4975 (84%) of 5923 patients 6–12 months after the intervention. PCI with everolimus-eluting stents was the most effective treatment for percent diameter stenosis, with a difference of −9·0% (95% CI −15·8 to −2·2) versus drug-coated balloons (DCB), −9·4% (–17·4 to −1·4) versus sirolimus-eluting stents, −10·2% (–18·4 to −2·0) versus paclitaxel-eluting stents, −19·2% (–28·2 to −10·4) versus vascular brachytherapy, −23·4% (–36·2 to −10·8) versus bare metal stents, −24·2% (–32·2 to −16·4) versus balloon angioplasty, and −31·8% (–44·8 to −18·6) versus rotablation. DCB were ranked as the second most effective treatment, but without significant differences from sirolimus-eluting (–0·2% [95% CI −6·2 to 5·6]) or paclitaxel-eluting (–1·2% [–6·4 to 4·2]) stents. These findings suggest that two strategies should be considered for treatment of any type of coronary ISR: PCI with everolimus-eluting stents because of the best angiographic and clinical outcomes, and DCB because of its ability to provide favourable results without adding a new stent layer. None.

Background Selective outcome reporting and publication bias threaten the validity of systematic reviews and meta-analyses and can affect clinical decision-making. A rigorous method to evaluate the impact of this bias on the results of network meta-analyses of interventions is lacking. We present a tool to assess the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN). Methods ROB-MEN first evaluates the risk of bias due to missing evidence for each of the possible pairwise comparison that can be made between the interventions in the network. This step considers possible bias due to the presence of studies with unavailable results ( within-study assessment of bias ) and the potential for unpublished studies ( across-study assessment of bias ). The second step combines the judgements about the risk of bias due to missing evidence in pairwise comparisons with (i) the contribution of direct comparisons to the network meta-analysis estimates, (ii) possible small-study effects evaluated by network meta-regression, and (iii) any bias from unobserved comparisons. Then, a level of “low risk”, “some concerns”, or “high risk” for the bias due to missing evidence is assigned to each estimate, which is our tool’s final output. Results We describe the methodology of ROB-MEN step-by-step using an illustrative example from a published NMA of non-diagnostic modalities for the detection of coronary artery disease in patients with low risk acute coronary syndrome. We also report a full application of the tool on a larger and more complex published network of 18 drugs from head-to-head studies for the acute treatment of adults with major depressive disorder. Conclusions ROB-MEN is the first tool for evaluating the risk of bias due to missing evidence in network meta-analysis and applies to networks of all sizes and geometry. The use of ROB-MEN is facilitated by an R Shiny web application that produces the Pairwise Comparisons and ROB-MEN Table and is incorporated in the reporting bias domain of the CINeMA framework and software.

The Non-adherence Academic Research Consortium (NARC) has recently developed a consensus-based standardized classification for medication non-adherence in cardiovascular clinical trials. We aimed to assess the prevalence of NARC-defined self-reported non-adherence to P2Y12 inhibitors and its impact on clinical outcomes in patients undergoing percutaneous coronary intervention (PCI). Using a standardized questionnaire administered at 1 year after PCI, we assessed the 4 NARC-defined non-adherence levels including type, decision-maker, reasons, and timing within the Bern PCI registry. The primary endpoint was the patient-oriented composite endpoint (POCE) defined as a composite of death, myocardial infarction, stroke, and any revascularization at 1 year. The recommended P2Y12 inhibitor duration was 12 months. Among 3,896 patients, P2Y12 inhibitor non-adherence was observed in 647 (17%) patients. Discontinuation was permanent in the majority of patients (84%). The decision was mainly driven by a physician (94%), and rarely by patients (6%). The most frequent reason was risk profile change (43%), followed by unlisted reasons (25%), surgery (17%), and adverse events (14%). Non-adherence occurred early (<30 days) in 21%, late (30-180 days) in 45%, and very late (>180 days) in 33%. The majority of POCE events (n = 421/502, 84%) occurred during adherence to the prescribed P2Y12 inhibitor. Permanent discontinuation, doctor-driven non-adherence, and risk profile change emerged as independent predictors for POCE. In real-world PCI population treated with 1-year DAPT, non-adherence was observed in nearly one-fifth of patients. Non-adherence to P2Y12 inhibitors was associated with worse clinical outcomes, while the risk was related to underlying contexts. NCT02241291.

Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build a deep learning-based predictive tool for TAVR-related CVE. Integrated clinical and imaging characteristics from consecutive patients enrolled into a prospective TAVR registry were analysed. CVE comprised any strokes and transient ischemic attacks. Predictive variables were selected by recursive feature reduction to train an autoencoder predictive model. Area under the curve (AUC) represented the model’s performance to predict 30-day CVE. Among 2279 patients included between 2007 and 2019, both clinical and imaging data were available in 1492 patients. Median age was 83 years and STS score was 4.6%. Acute (< 24 h) and subacute (day 2–30) CVE occurred in 19 (1.3%) and 36 (2.4%) patients, respectively. The occurrence of CVE was associated with an increased risk of death (HR [95% CI] 2.62 [1.82–3.78]). The constructed predictive model uses less than 107 clinical and imaging variables and has an AUC of 0.79 (0.65–0.93). TAVR-related CVE can be predicted using a deep learning-based predictive algorithm. The model is implemented online for broad usage.

To evaluate the effects of diagnostic testing on patient outcomes in a large sample of diagnostic randomized controlled trials (D-RCTs) and to examine whether the effects for patient outcomes correlate with the effects on management and with diagnostic accuracy. We considered D-RCTs that evaluated diagnostic interventions for any condition and reported effectiveness data on one or more patient outcomes. We calculated odds ratios for patient outcomes and outcomes pertaining to the use of further diagnostic and therapeutic interventions and the diagnostic odds ratio (DOR) for the accuracy of experimental tests. One hundred forty trials (153 comparisons) were eligible. Patient outcomes were significantly improved in 28 comparisons (18%). There was no concordance in significance and direction of effects between the patient outcome and outcomes for use of further diagnostic or therapeutic interventions (weighted κ 0.02 and 0.09, respectively). The effect size for the patient outcome did not correlate with the effect sizes for use of further diagnostic (r = 0.05; P = 0.78) or therapeutic interventions (r = 0.18; P = 0.08) or the experimental intervention DOR in the same trial (r = −0.24; P = 0.51). Few tests have well-documented benefits on patient outcomes. Diagnostic performance or the effects on management decisions are not necessarily indicative of patient benefits.

To evaluate how often newly developed risk prediction models undergo external validation and how well they perform in such validations. We reviewed derivation studies of newly proposed risk models and their subsequent external validations. Study characteristics, outcome(s), and models' discriminatory performance [area under the curve, (AUC)] in derivation and validation studies were extracted. We estimated the probability of having a validation, change in discriminatory performance with more stringent external validation by overlapping or different authors compared to the derivation estimates. We evaluated 127 new prediction models. Of those, for 32 models (25%), at least an external validation study was identified; in 22 models (17%), the validation had been done by entirely different authors. The probability of having an external validation by different authors within 5 years was 16%. AUC estimates significantly decreased during external validation vs. the derivation study [median AUC change: −0.05 (P < 0.001) overall; −0.04 (P = 0.009) for validation by overlapping authors; −0.05 (P < 0.001) for validation by different authors]. On external validation, AUC decreased by at least 0.03 in 19 models and never increased by at least 0.03 (P < 0.001). External independent validation of predictive models in different studies is uncommon. Predictive performance may worsen substantially on external validation.

Background—Percutaneous left atrial appendage (LAA) closure (LAAC) offers a valid alternative to oral anticoagulation in patients with atrial fibrillation (AF) at high risk of bleeding. However, its impact on AF burden and device function in patients with cardiac implantable electronic devices (CIEDs) remains largely unexplored. Methods—From our prospective LAAC registry (clinicaltrial.gov—NCT04628078), which includes all consecutive LAAC procedures performed at our institution, we identified patients with a CIED and retrospectively analyzed procedural and follow-up data. The primary endpoint was defined as a composite of death, TIA/stroke, systemic or pulmonary embolism and major bleeding (BARC 3-5) within 7 days of the procedure. The secondary endpoint was CIED lead dislodgement. Additionally, AF burden was compared before and after LAAC. Results—Of the 586 LAAC procedures performed between August 2015 and January 2023, 36 patients (6%) had a CIED. The median CHA2DS2-VASC and HAS-BLED scores were 4.0 and 3.0, respectively. The primary endpoint occurred in one (3%) patient, and no patient experienced CIED lead dislodgement. AF burden data before and after LAAC were available in 20 patients. The mean AF burden increased from 6% to 31% following LAAC (p = 0.064). Conclusions—A CIED was present in 6% of LAAC procedures, and LAAC appears feasible and safe in this patient population. Larger, prospective studies are warranted to further evaluate the impact of LAAC on AF burden.

ObjectiveGiven the complexities of testing the translational capability of new artificial intelligence (AI) tools, we aimed to map the pathways of training/validation/testing in development process and external validation of AI tools evaluated in dedicated randomised controlled trials (AI-RCTs).MethodsWe searched for peer-reviewed protocols and completed AI-RCTs evaluating the clinical effectiveness of AI tools and identified development and validation studies of AI tools. We collected detailed information, and evaluated patterns of development and external validation of AI tools.ResultsWe found 23 AI-RCTs evaluating the clinical impact of 18 unique AI tools (2009–2021). Standard-of-care interventions were used in the control arms in all but one AI-RCT. Investigators did not provide access to the software code of the AI tool in any of the studies. Considering the primary outcome, the results were in favour of the AI intervention in 82% of the completed AI-RCTs (14 out of 17). We identified significant variation in the patterns of development, external validation and clinical evaluation approaches among different AI tools. A published development study was found only for 10 of the 18 AI tools. Median time from the publication of a development study to the respective AI-RCT was 1.4 years (IQR 0.2–2.2).ConclusionsWe found significant variation in the patterns of development and validation for AI tools before their evaluation in dedicated AI-RCTs. Published peer-reviewed protocols and completed AI-RCTs were also heterogeneous in design and reporting. Upcoming guidelines providing guidance for the development and clinical translation process aim to improve these aspects.