Bcharah, Hend

Background/Objectives: Cesarean section (C-section) is a common surgical procedure associated with an increased risk of 30-day postpartum hospital readmissions. This study utilized machine learning (ML) to predict readmissions using a nationwide database. Methods: A retrospective analysis of the National Surgical Quality Improvement Project (2012–2022) included 54,593 patients who underwent C-sections. Random Forests (RF) and Extreme Gradient Boosting (XGBoost) models were developed and compared to logistic regression (LR) using demographic, preoperative, and perioperative data. Results: Of the cohort, 1306 (2.39%) patients were readmitted. Readmitted patients had higher rates of being of African American race (17.99% vs. 9.83%), diabetes (11.03% vs. 8.19%), and hypertension (11.49% vs. 4.68%) (p < 0.001). RF achieved the highest performance (AUC = 0.737, sensitivity = 72.03%, specificity: 61.33%), and a preoperative-only RF model achieved a sensitivity of 83.14%. Key predictors included age, BMI, operative time, white blood cell count, and hematocrit. Conclusions: ML effectively predicts C-section readmissions, supporting early identification and interventions to improve patient outcomes and reduce healthcare costs.

Thoracic aortic aneurysm and dissection (TAAD) encompasses a clinically heterogeneous group of diseases characterized by high morbidity and mortality. Genetic studies over the past two decades have significantly expanded our understanding of the molecular landscape underlying heritable TAAD, revealing contributions from multiple interconnected biological pathways. This review systematically summarizes more than 75 genes implicated in TAAD pathogenesis, categorizing them according to major mechanistic roles including TGF-β signaling, extracellular matrix remodeling, smooth muscle cell contractility and cytoskeletal regulation, cell–matrix and cell–cell adhesion, metabolic processes, ion transport, and transcriptional regulation. Special emphasis is placed on emerging genes with variable or overlapping clinical phenotypes, dual-mechanism candidates, and their implications for personalized clinical management, including surveillance and surgical intervention thresholds. The integration of molecular insights into clinical practice, along with cautious consideration of genes of uncertain significance, promises to enhance diagnostic precision and risk stratification in individuals and families affected by heritable aortic disease.

Bicuspid aortic valve (BAV) and thoracic aortic aneurysms and dissections (TAAD) are recognized in syndromic connective tissue diseases (CTD), but most cases occur sporadically. The extent to which non-syndromic BAV or TAAD predisposes to additional arteriopathies, particularly in younger individuals, remains unclear. We retrospectively analyzed 1438 patients (mean age = 48.0, 67.7% female), excluding those with CTDs. Participants were ≤60 years old and categorized by the presence of BAV and/or TAAD. We examined co-existing arterial pathologies, including fibromuscular dysplasia, spontaneous coronary artery dissection, abdominal aortic aneurysms (AAA), mesenteric, peripheral extremity, and carotid/cerebral arteriopathies. Overall, 44.6% had either BAV or TAAD, and 27.2% had multiple arteriopathies. While vascular diseases were frequently noted, odds ratios demonstrated no significantly increased risk of extra-aortic arteriopathies in the BAV or TAAD cohorts. AAA exhibited a non-significant trend toward higher prevalence in TAAD patients. These findings support current guidelines recommending targeted imaging (transthoracic echocardiography of the aortic root and ascending aorta) over comprehensive “head-to-pelvis” screening for non-syndromic BAV or TAAD patients without additional risk factors. Ongoing genetic analyses may elucidate whether particular variants predispose to multi-site aneurysms or dissections. Consequently, targeted surveillance remains appropriate, with broader imaging reserved for patients with genetic or clinical indicators of higher risk.