On-treatment follow-up in real-world studies of direct oral anticoagulants in atrial fibrillation: Association with treatment effects.

Background Numerous observational studies support the safety and effectiveness of the direct oral anticoagulants (DOAC) for stroke prevention in atrial fibrillation (AF), but these data are often limited to short duration of follow-up. We aimed to assess the length of on-treatment follow-up in the accumulated real-world evidence and the relationship between follow-up duration and estimates of DOAC effectiveness and safety. Methods We searched the literature for observational studies reporting comparative effectiveness and safety outcomes of DOACs versus warfarin. In random-effects meta-analyses, we assessed associations of specific DOACs vs warfarin for stroke/systematic embolism (SE) and major bleeding. In meta-regression analyses, we assessed the correlation between the reported on-treatment follow-up with the effect sizes for stroke/SE and major bleeding outcomes. Results In 45 eligible observational studies, the average on-treatment follow-up was <1 year for all DOACs. In meta-analyses, all DOACs showed significantly lower risks of stroke/SE, but only dabigatran and apixaban showed lower risks for major bleeding compared to warfarin. There was no correlation between follow-up duration and magnitude of stroke/SE reduction for any of the DOACs. Longer follow-up correlated with greater major bleeding reduction for dabigatran (p = 0.006) and rivaroxaban (p = 0.033) as compared to warfarin, but it correlated with smaller major bleeding reduction for apixaban (p = 0.004). Conclusions The numerous studies of DOAC effectiveness and safety in the routine AF practice pertain to short treatment follow-up. Study follow-up correlates significantly with DOAC-specific vs warfarin associations for major bleeding

Invasive electrophysiological testing to predict and guide permanent pacemaker implantation after transcatheter aortic valve implantation: A meta-analysis.

BACKGROUND Atrioventricular conduction abnormalities after transcatheter aortic valve implantation (TAVI) are common. The value of electrophysiological study (EPS) for risk stratification of high-grade atrioventricular block (HG-AVB) and guidance of permanent pacemaker (PPM) implantation is poorly defined. OBJECTIVE The purpose of this study was to identify EPS parameters associated with HG-AVB and determine the value of EPS-guided PPM implantation after TAVI. METHODS We performed a systematic review and meta-analysis of studies investigating the value of EPS parameters for risk stratification of TAVI-related HG-AVB and for guidance of PPM implantation among patients with equivocal PPM indications after TAVI. RESULTS Eighteen studies (1230 patients) were eligible. In 7 studies, EPS was performed only after TAVI, whereas in 11 studies EPS was performed both before and after TAVI. Overall PPM implantation rate for HG-AVB was 16%. AV conduction intervals prolonged after TAVI, with the AH and HV intervals showing the largest magnitude of changes. Pre-TAVI HV >70 ms and the absolute value of the post-TAVI HV interval were associated with subsequent HG-AVB and PPM implantation with odds ratios of 2.53 (95% confidence interval [CI] 1.11-5.81; P = .04) and 1.10 (95% CI 1.03-1.17; P = .02; per 1-ms increase), respectively. In 10 studies, PPM was also implanted due to abnormal EPS findings in patients with equivocal PPM indications post-TAVI (typically new left bundle branch block or transient HG-AVB). Among them, the rate of long-term PPM dependency was 57%. CONCLUSION Selective EPS testing may assist in the risk stratification of post-TAVI HG-AVB and in the guidance of PPM implantation, especially in patients with equivocal PPM indications post-TAVI

Self-reported non-adherence to P2Y12 inhibitors in patients undergoing percutaneous coronary intervention: Application of the medication non-adherence academic research consortium classification.

AIMS 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). METHODS AND RESULTS 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 (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. CONCLUSIONS 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. CLINICALTRIALS.GOV IDENTIFIER NCT02241291

Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study

Aims: Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results: A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion: The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation

Effect of Galectin 3 on Aldosterone-Associated Risk of Cardiovascular Mortality in Patients Undergoing Coronary Angiography

Recent experimental studies have suggested that galectin-3 has an interaction with aldosterone, and modifies its adverse effects. We therefore aimed to elucidate whether the relationship between plasma aldosterone concentrations (PACs) and long-term fatal cardiovascular (CV) events would depend on plasma galectin-3 levels. A total of 2,457 patients (median age: 63.5 [interquartile range (IQR) = 56.3 to 70.6] years, 30.1% women) from the LUdwigshafen RIsk and Cardiovascular Health study, with a median follow-up of 9.9 (IQR = 8.5 to 10.7) years, were included. We tested the interaction between aldosterone and galectin-3 for CV-mortality using a multivariate Cox proportional hazard model, reporting hazard ratios (HRs) with 95% confidence intervals (95%CIs). Adjustments for multiple CV risk factors as well as medication use were included. Mean PAC was 79.0 (IQR = 48.0 to 124.0) pg/ml and there were 558 (16.8%) CV deaths. There was a significant interaction between PAC and galectin-3 (p = 0.021). When stratifying patients by the median galectin-3, there was a significant association between aldosterone and CV-mortality for those above (HR per 1 standard deviation = 1.14; 95%CI [1.01 to 1.30], p = 0.023), but not below the cut-off value (HR per 1 standard deviation = 1.00; 95%CI [0.87 to 1.15], p = 0.185). In conclusion, the current study demonstrates for the first time a modifying effect of galectin-3 on the association between aldosterone and CV-mortality risk in humans. These findings indicate that galectin-3 is an intermediate between aldosterone and adverse outcomes

ROB-MEN: a tool to assess risk of bias due to missing evidence in network meta-analysis

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

Assessment of New Onset Arrhythmias After Transcatheter Aortic Valve Implantation Using an Implantable Cardiac Monitor.

Background Transcatheter aortic valve implantation (TAVI) is associated with new onset brady- and tachyarrhythmias which may impact clinical outcome. Aims To investigate the true incidence of new onset arrhythmias within 12 months after TAVI using an implantable cardiac monitor (ICM). Methods One hundred patients undergoing TAVI received an ICM within 3 months before or up to 5 days after TAVI. Patients were followed-up for 12 months after discharge from TAVI for the occurrence of atrial fibrillation (AF), bradycardia (≤30 bpm), advanced atrioventricular (AV) block, sustained ventricular and supraventricular tachycardia. Results A previously undiagnosed arrhythmia was observed in 31 patients (31%) and comprised AF in 19 patients (19%), advanced AV block in 3 patients (3%), and sustained supraventricular and ventricular tachycardia in 10 (10%) and 2 patients (2%), respectively. Three patients had a clinical diagnosis of sick-sinus-syndrome. A permanent pacemaker (PPM) was implanted in six patients (6%). The prevalence of pre-existing AF was 28%, and 47% of the patients had AF at the end of the study period. AF burden was significantly higher in patients with pre-existing [26.7% (IQR 0.3%; 100%)] compared to patients with new-onset AF [0.0% (IQR 0.0%; 0.06%); p = 0.001]. Three patients died after TAVI without evidence of an arrhythmic cause according to the available ICM recordings. Conclusions Rhythm monitoring for 12 months after TAVI revealed new arrhythmias, mainly AF, in almost one third of patients. Atrial fibrillation burden was higher in patients with prevalent compared to incident AF. Selected patients may benefit from short-term remote monitoring. Trial Registration https://clinicaltrials.gov/: NCT02559011

Selection and Presentation of Imaging Figures in the Medical Literature

Background: Images are important for conveying information, but there is no empirical evidence on whether imaging figures are properly selected and presented in the published medical literature. We therefore evaluated the selection and presentation of radiological imaging figures in major medical journals. Methodology/Principal Findings: We analyzed articles published in 2005 in 12 major general and specialty medical journals that had radiological imaging figures. For each figure, we recorded information on selection, study population, provision of quantitative measurements, color scales and contrast use. Overall, 417 images from 212 articles were analyzed. Any comment/hint on image selection was made in 44 (11%) images (range 0–50% across the 12 journals) and another 37 (9%) (range 0–60%) showed both a normal and abnormal appearance. In 108 images (26%) (range 0–43%) it was unclear whether the image came from the presented study population. Eighty-three images (20%) (range 0–60%) had any quantitative or ordered categorical value on a measure of interest. Information on the distribution of the measure of interest in the study population was given in 59 cases. For 43 images (range 0–40%), a quantitative measurement was provided for the depicted case and the distribution of values in the study population was also available; in those 43 cases there was no over-representation of extreme than average cases (p = 0.37). Significance: The selection and presentation of images in the medical literature is often insufficiently documented; quantitative data are sparse and difficult to place in context

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]