Comparison of downsizing strategy (HANGZHOU Solution) and standard annulus sizing strategy in type 0 bicuspid aortic stenosis patients undergoing transcatheter aortic valve replacement: Rationale and design of a randomized clinical trial.

BACKGROUND: There has not been a consensus on the prothesis sizing strategy in type 0 bicuspid aortic stenosis (AS) patients undergoing transcatheter aortic valve replacement (TAVR). Modifications to standard annular sizing strategies might be required due to the distinct anatomical characteristics. We have devised a downsizing strategy for TAVR using a self-expanding valve specifically for patients with type 0 bicuspid AS. The primary aim of this study is to compare the safety and efficacy of downsizing strategy with the Standard Annulus Sizing Strategy in TAVR for patients with type 0 bicuspid AS. TRIAL DESIGN: It is a prospective, multi-center, superiority, single-blinded, randomized controlled trial comparing the Down Sizing and Standard Annulus Sizing Strategy in patients with type 0 bicuspid aortic stenosis undergoing transcatheter aortic valve replacement. Eligible participants will include patients with severe type 0 bicuspid AS, as defined by criteria such as mean gradient across aortic valve ≥40 mmHg, peak aortic jet velocity ≥4.0 m/s, aortic valve area (AVA) ≤1.0 cm², or AVA index ≤0.6 cm2/m2. These patients will be randomly assigned, in a 1:1 ratio, to either the Down Sizing Strategy group or the Standard Sizing Strategy group. In the Down Sizing Strategy group, a valve one size smaller will be implanted if the "waist sign" manifests along with less than mild regurgitation during balloon pre-dilatation. The primary end point of the study is a composite of VARC-3 defined device success, absence of both permanent pacemaker implantation due to high-degree atrioventricular block and new-onset complete left bundle branch block. CONCLUSION: This study will compare the safety and efficacy of Down Sizing Strategy with the Standard Annulus Sizing Strategy and provide valuable insights into the optimal approach for sizing in TAVR patients with type 0 bicuspid AS. We hypothesize that the Down Sizing Strategy will demonstrate superiority when compared to the Standard Annulus Sizing Strategy. (Down Sizing Strategy (HANGZHOU Solution) vs Standard Sizing Strategy TAVR in Bicuspid Aortic Stenosis (Type 0) (TAILOR-TAVR), NCT05511792).

Technical Success after Transcatheter Aortic Valve Replacement for Bicuspid versus Tricuspid Aortic Stenosis.

Background: Comparative data of the Valve Academic Research Consortium (VARC-3)-defined technical success between bicuspid versus tricuspid aortic stenosis (AS) remain lacking. Aims: We sought to compare the technical success and other clinical outcomes between patients with bicuspid and tricuspid AS receiving transcatheter aortic valve replacement. Methods: A registration-based analysis was performed for 402 patients (211 and 191 cases of bicuspid and tricuspid AS, respectively). The primary outcome was VARC-3-defined technical success. Additional analysis was performed to assess outcomes for up to one year between the two groups. Results: Bicuspid AS patients tended to be younger (74 years vs. 77 years; p < 0.001) with a lower Society of Thoracic Surgeons score (4.4% vs. 5.4%; p = 0.003). Bicuspid AS patients showed a lower prevalence of hypertension and peripheral vascular diseases. Technical failure was encountered in 17.7% of these patients, driven primarily by the high incidence of second valve implantation. The technical success rates were comparable between the bicuspid and tricuspid AS groups (82.5% vs. 82.2%, p = 0.944). Chronic kidney disease (CKD) and larger sinotubular junctional diameter (STJ) were identified as predictors of technical failure, whereas CKD, impaired left ventricular ejection fraction (LVEF), along with larger STJ, were predictors of cardiac technical failure. Technical failure was associated with an increased risk of all-cause mortality at 30 days and 1 year, as evidenced by the Cox multivariable analysis. Conclusions: No significant differences were observed in the technical success rates and most clinical outcomes between the bicuspid and tricuspid AS groups. Technical failure conferred an increased risk for both 30-day and 1-year all-cause mortalities.

Gut Microbiome Signatures in the Progression of Hepatitis B Virus-Induced Liver Disease.

The gut microbiome is associated with hepatitis B virus (HBV)-induced liver disease, which progresses from chronic hepatitis B, to liver cirrhosis, and eventually to hepatocellular carcinoma. Studies have analyzed the gut microbiome at each stage of HBV-induced liver diseases, but a consensus has not been reached on the microbial signatures across these stages. Here, we conducted by a systematic meta-analysis of 486 fecal samples from publicly available 16S rRNA gene datasets across all disease stages, and validated the results by a gut microbiome characterization on an independent cohort of 15 controls, 23 chronic hepatitis B, 20 liver cirrhosis, and 22 hepatocellular carcinoma patients. The integrative analyses revealed 13 genera consistently altered at each of the disease stages both in public and validation datasets, suggesting highly robust microbiome signatures. Specifically, Colidextribacter and Monoglobus were enriched in healthy controls. An unclassified Lachnospiraceae genus was specifically elevated in chronic hepatitis B, whereas Bilophia was depleted. Prevotella and Oscillibacter were depleted in liver cirrhosis. And Coprococcus and Faecalibacterium were depleted in hepatocellular carcinoma. Classifiers established using these 13 genera showed diagnostic power across all disease stages in a cross-validation between public and validation datasets (AUC = 0.65-0.832). The identified microbial taxonomy serves as non-invasive biomarkers for monitoring the progression of HBV-induced liver disease, and may contribute to microbiome-based therapies.

A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure.

BACKGROUND: Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units. METHODS: Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation. RESULTS: The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820-0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805-0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk. CONCLUSION: Using readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.