Saba Maleki

Background: Data on transcatheter aortic valve replacement (TAVR) outcomes in the Middle East and North Africa region are limited. We synthesized available evidence on post‐TAVR clinical outcomes in the Middle East and North Africa region. Methods: We systematically searched PubMed, Embase, and Scopus from inception through May 2025 for studies reporting TAVR outcomes in this region. Outcomes included device success, all‐cause death, cardiovascular death, myocardial infarction, stroke, new‐onset atrial fibrillation, moderate to severe paravalvular leak, and permanent pacemaker implantation. Overall incidence rates were pooled via Poisson analysis and reported per 100 person‐years with 95% CIs. Results: Forty‐eight studies encompassing 7331 patients (mean age, 76.9±8.5 years; 52.4% women) were included. Pooled device success rate was 96.4% (95% CI, 95.8%–97.0%). At latest follow‐up, pooled incidence rates per 100 person‐years were 13.7 for all‐cause death (95% CI, 12.9–14.6), 4.8 for cardiovascular death (95% CI, 4.1–5.7), 1.0 for myocardial infarction (95% CI, 0.7–1.4), 1.6 for stroke (95% CI, 1.3–1.9), 2.7 for moderate to severe paravalvular leak (95% CI, 2.2–3.4), 6.5 for new‐onset atrial fibrillation (95% CI, 5.5–7.7), and 10.0 for permanent pacemaker implantation (95% CI, 9.2–10.9). Post hoc exploratory meta‐regression identified significant study‐level modifying effects, with higher mean age and greater use of first‐generation versus second‐generation valves associated with higher overall all‐cause mortality incidence following TAVR. Certainty of evidence was low or very low across all outcomes. Conclusions: TAVR in the Middle East and North Africa region was associated with high device success rate and relatively low cardiovascular death. Permanent pacemaker implantation was the most prevalent post‐TAVR complication. Further studies are warranted to optimize TAVR practice in this region.

Background: Post-transplant diabetes mellitus (PTDM) is a common complication following solid organ transplantation and is associated with increased morbidity and mortality. This study aimed to identify both generalizable and solid organ-specific predictors of PTDM with machine learning methods. Materials & Methods: In this study, we used 2002-2023 data from the Scientific Registry of Transplant Recipients (SRTR) to examine the risk of PTDM development across the liver (n = 128 016), kidney (n = 360 562), heart (n = 45 505), and lung (n = 36 260) transplant recipients. We examined various features at baseline using logistic regression and random forest models. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), Brier scores, sensitivity, and specificity. Shapley Additive exPlanations were used for model interpretation and to identify key relevant features. Results: The incidence of PTDM ranged from 15.0% in kidney to 20.2% in lung recipients. Compared to logistic regression, random forest demonstrated modestly improved discrimination (AUROC: 0.62-0.63) and calibration (Brier: 0.124-0.156). It was found across all models that age and BMI were consistent predictors. Prednisone and cyclosporine maintenance were often found to be associated with higher PTDM risk while mycophenolate mofetil was seen as protective. SHAP analysis demonstrated complex, nonlinear interactions and supported known risk patterns such as increased age and male sex. Conclusion: Our machine learning model identified risk factors common to post-transplant diabetes across organ transplant recipients, guiding more optimal patient survival post-transplant.

Background: Liver transplantation (LT) is offered as a life‐saving treatment to those with end‐stage liver disease. There has recently been much interest in considering the survival benefit of transplant when prioritizing patients for transplant. In this study, we aimed to measure the survival benefit of LT across various patient demographics by measuring the number of life‐years gained after LT. Method: In this study, 101,770 patients were included from the Scientific Registry of Transplant Recipients (SRTR) transplanted between 2003 and 2021. The survival benefit of LT was calculated using restricted mean survival time (RMST) in the next 16 years post‐transplant. Cox proportional hazard models and Weibull models were employed to quantify the impact of various predictors on survival benefit. Results: LT was found to provide survival gains of 4.93, 6.43, 6.30, and 3.67 years for the 18–29, 30–49, 50–69, and 70+ age groups, respectively. Survival benefit was highest at 6.62 years for patients with model for end‐stage liver disease (MELD) scores of 15–19, with survival benefits of 5.7, 5.4, and 5.85 years for the 6–14, 20–29, and 30–40 MELD score cohorts, respectively. Older age (HR: 1.13), male sex (HR: 1.32), diabetes (HR: 1.32), and higher MELD scores were predictors of lesser survival benefit. Protective factors included higher education levels (HR: 0.70) as well as diagnoses of fulminant liver failure and autoimmune biliary disease (HR: 0.65). Conclusion: This study underscores how survival benefit varies across patients with different demographic and clinical characteristics, highlighting the nuanced interplay of these characteristics with survival and emphasizing the need for tailored post‐transplant management strategies to optimize outcomes.

Background: CAR T cells still face numerous obstacles in treating hematologic and solid malignancies. Although gene editing technologies have improved CAR T cell therapy, there are currently no systematic reviews to broadly address preclinical and clinical outcomes of gene-edited CAR T cells. Therefore, we aimed to systematically review the preclinical and clinical studies that evaluate the outcomes of knocked-out/knocked-down (KO/KD) CAR T cells. Methods: This study was submitted to international Prospective Register of Systematic Reviews (PROSPERO) with the ID CRD42022320541 and follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We searched Five databases (PubMed, EMBASE, Cochrane Library, Web of Science, and Clinicaltrials.gov) up to March 19th, 2022 for the keywords of “CAR T cell” and “knock-out/knock-down”. The retrieved records then underwent a two-step screening process based on the inclusion criteria, first title/abstract and then full-text screenings, and their data were used for qualitative synthesis. Results: Our search results yielded 3780 records. Finally, a total of 241 records, including 193 animal and 52 human studies (four concurrent in both groups) that reported KO/KD genes for 105 proteins were included. The positive effects of these 105 KO/KD were categorized into five groups: (1) enabling allogeneic CAR production while limiting GVHD, (2) increasing the efficacy of CAR T cells, (3) Decreasing their side effects, (4) limiting CAR T cell fratricide, and (5) enabling the use of concurrent therapies. In the human section, solid tumors had fewer studies with less favorable outcomes compared to hematologic malignancies. Conclusions: This systematic review emphasized the various mechanisms by which CAR T cell effects could be boosted. Future researchers can choose their desired genes out of the 105 mentioned candidates. We also encourage the researchers to increase their efforts on solid tumors to compensate for the lack of increased efficacy in this group.

Background/Objectives: Long-term survival among liver transplant (LT) recipients who live beyond one year has remained relatively stable over recent decades. However, reducing long-term morbidity is increasingly important, and metabolomics may enable risk-based, personalized immunosuppression. We aimed to evaluate and compare the serum metabolomic profiles of LT recipients treated with tacrolimus (TAC) versus sirolimus (SIR), to elucidate metabolic pathways associated with these regimens. Methods: Targeted metabolomic profiling of 894 metabolites was conducted on serum samples from 128 LT recipients using the Biocrates MxP^® Quant 500 kit. Data were analyzed with MetaboAnalyst 6.0, and multivariate analysis was performed using Partial Least Squares-Discriminant Analysis (PLS-DA). Metabolites with Variable Importance in Projection (VIP) scores > 1.5 underwent pathway enrichment in OmicsNet, incorporating Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG)-based network analysis. Results: Eighty-seven metabolites were significantly altered between groups. Phosphatidylcholines (PCs) and ceramides were elevated in TAC-treated patients, while di- and triacylglycerols were higher in the SIR group. Pathway enrichment implicated lipid metabolism, particularly glycerophospholipid, ether lipid, and sphingolipid pathways. Network analysis identified enriched modules related to metabolic regulation and immune response. Conclusions: Divergent metabolomic profiles distinguish TAC- and SIR-treated recipients, suggesting regimen-specific impacts on lipid metabolism with potential relevance to post-transplant complications.

Background Weight regain (WR) and insufficient weight loss (WL) occur in 20–25% of patients after bariatric surgery due to various factors. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have shown promise in promoting WL; however, evidence regarding their effectiveness in managing WL and preventing WR post-bariatric surgery remains limited. Objective This study aims to evaluate the role of GLP-1RAs in treating and preventing WR following bariatric surgery. Methods A systematic search was conducted across PubMed, Scopus, and Web of Science for studies assessing the impact of GLP-1RAs on WR after bariatric surgery. Results Our search identified 27 original studies, with 10 included in the meta-analysis involving 769 participants (392 treated with GLP-1RAs). The mean age was 44.05 years, with 30.47% male. The time interval from surgery to the initiation of GLP-1RAs treatment ranged from 1.5 to 86.7 months, with treatment durations between 4 and 12 months. The analysis showed significantly greater WL in the GLP-1RAs group compared to placebo (SMD = 0.82, 95% CI 0.23 to 1.42). Subgroup analysis for treatment durations ≤ 6 months indicated a higher WL in the GLP-1RAs group (SMD = 0.79, 95% CI 0.25 to 1.34). Adverse events were primarily gastrointestinal, with nausea significantly more frequently in the GLP-1RAs group (OR = 2.01, 95% CI 1.24 to 3.27). Conclusion GLP-1RAs effectively promote WL among participants experiencing WR after bariatric surgery. Initiating GLP-1RAs therapy shortly after surgery may help prevent WR. Further research is warranted to explore long-term outcomes and optimize treatment protocols for this patient population. Graphical Abstract

Background/Objectives: Transcatheter aortic valve replacement (TAVR) has been introduced as an optimal treatment for patients with severe aortic stenosis, offering a minimally invasive alternative to surgical aortic valve replacement. Predicting these outcomes following TAVR is crucial. Artificial intelligence (AI) has emerged as a promising tool for improving post-TAVR outcome prediction. In this systematic review and meta-analysis, we aim to summarize the current evidence on utilizing AI in predicting post-TAVR outcomes. Methods: A comprehensive search was conducted to evaluate the studies focused on TAVR that applied AI methods for risk stratification. We assessed various ML algorithms, including random forests, neural networks, extreme gradient boosting, and support vector machines. Model performance metrics—recall, area under the curve (AUC), and accuracy—were collected with 95% confidence intervals (CIs). A random-effects meta-analysis was conducted to pool effect estimates. Results: We included 43 studies evaluating 366,269 patients (mean age 80 ± 8.25; 52.9% men) following TAVR. Meta-analyses for AI model performances demonstrated the following results: all-cause mortality (AUC = 0.78 (0.74–0.82), accuracy = 0.81 (0.69–0.89), and recall = 0.90 (0.70–0.97); permanent pacemaker implantation or new left bundle branch block (AUC = 0.75 (0.68–0.82), accuracy = 0.73 (0.59–0.84), and recall = 0.87 (0.50–0.98)); valve-related dysfunction (AUC = 0.73 (0.62–0.84), accuracy = 0.79 (0.57–0.91), and recall = 0.54 (0.26–0.80)); and major adverse cardiovascular events (AUC = 0.79 (0.67–0.92)). Subgroup analyses based on the model development approaches indicated that models incorporating baseline clinical data, imaging, and biomarker information enhanced predictive performance. Conclusions: AI-based risk prediction for TAVR complications has demonstrated promising performance. However, it is necessary to evaluate the efficiency of the aforementioned models in external validation datasets.

Background The latest guidelines advocate for catheter ablation (CA) over standard medical therapy (SMT) for managing atrial fibrillation (AF) in patients with heart failure with reduced ejection fraction (HFrEF). However, significant knowledge gaps exist regarding the effectiveness of CA vs. SMT in patients with heart failure with preserved ejection fraction (HFpEF). Methods PubMed, Scopus, and Embase until February 2024 were systematically searched. Given the limited number of randomized studies, propensity score-matched observational studies comparing CA with SMT in AF patients with HFpEF were also included. The primary outcome was a composite endpoint of all-cause mortality and HF hospitalization. Results Eight studies that enrolled 17,717 SMT and 2537 CA patients were included. CA was associated with a significantly lower risk of the composite endpoint of all-cause mortality and HF hospitalization (HR 0.61; 95% CI, 0.43–0.85). The risk of HF hospitalization (HR 0.44; 95% CI, 0.23–0.83), cardiovascular mortality (HR 0.43; 95% CI, 0.22–0.84), and AF recurrence (HR 0.53; 95% CI, 0.39–0.73) were also lower in the CA group. Conclusion CA demonstrated significant cardiovascular morbidity and mortality benefits compared to SMT in the HFpEF population.

Chimeric antigen receptor (CAR) T-cell therapy is a new revolutionary method for treating refractory or relapsed hematologic malignancies, CAR T-cell therapy has been associated with cytokine release syndrome (CRS) and cardiotoxicity. We directed a systematic review and meta-analysis to determine the incidence and predictors of cardiovascular events (CVE) with CAR T-cell therapy. We investigated PubMed, Embase, Cochrane Library, and ClinicalTrials.gov for studies reporting cardiovascular outcomes in CAR-T cell recipients. The study protocol was listed in the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42023478602). Twenty-three studies were included in this study. The pooled incidence of CVE was 54% for arrhythmias, 30% for heart failure, 20% for cardiomyopathy, 10% for acute coronary syndrome, and 7% for cardiac arrest. Patients with CVE had a higher incidence of cytokine release syndrome grade ≥ 2 (RR 2.36, 95% CI 1.86–2.99). The incidence of cardiac mortality in our meta-analysis was 2% (95% CI: 1%–3%). Left ventricular ejection fraction decline was greater in the CVE group (-9.4% versus -1.5%, p < 0.001). Cardiac biomarkers like BNP, CRP, creatinine, and ferritin were also elevated. CAR T-cell therapy commonly leads to cardiotoxicity, mediated by cytokine release syndrome. Vigilant monitoring and tailored treatments are crucial to mitigate these effects. Importantly, there's no significant difference in cardiac mortality between groups, suggesting insights for optimizing preventive interventions and reducing risks after CAR T-cell therapy.