Bcharah, George

Background: Patients with spontaneous coronary artery dissection (SCAD) are at increased risk of cardiovascular events, including dissection in other arterial territories such as cervical arteries. Those with combined coronary and cervical arterial dissection (CACAD) may have more widespread arterial abnormalities and worse outcomes, underscoring the need for better risk stratification and personalized care. Objective: This study examined the prevalence of cervical artery dissection (CvAD) in patients diagnosed with SCAD, evaluated potential risk factors for developing CvAD, and assessed their impact on clinical outcomes, including all-cause mortality, SCAD recurrence, and stroke. The goal was to enhance risk stratification by identifying SCAD patients at increased risk for CvAD. Design: In this retrospective cohort study, patients diagnosed with SCAD between 2018 and 2024 across Mayo Clinic sites were identified using ICD-10 codes. Manual chart review confirmed CvAD diagnoses. Patients were stratified based on the presence or absence of CvAD. Chi-square and independent t-tests were used to compare risk factors and comorbidities. Univariate screening and multivariable logistic regression were used to identify predictors of CvAD. Results: Among 1380 patients with SCAD, 131 (9.5%) had CvAD. The median follow-up was 2.02 years (IQR: 1.1–5.2 years). All CvAD diagnoses were identified after the index SCAD event through systematic post-SCAD vascular imaging. The mean age was similar between groups (51.5 ± 11.3 years in CACAD vs. 50.9 ± 10.7 years in SCAD-only, p = 0.540), and the majority were female (93.9% vs. 91.9%, p = 0.529). Multivariable regression identified extremity arterial dissection (OR: 8.13, 95% CI: 3.41–19.36, p < 0.001), fibromuscular dysplasia (FMD) (OR: 7.52, 95% CI: 4.67–12.10, p < 0.001), connective tissue disorders (CTDs) (OR: 6.85, 95% CI: 2.35–19.98, p < 0.001), and anxiety (OR: 1.69, 95% CI: 1.01–2.81, p = 0.044) as significant predictors of CvAD. CvAD was a strong independent predictor of stroke (OR: 6.37, 95% CI: 2.93–13.87, p < 0.001). Mortality and SCAD recurrence did not differ significantly between groups. Conclusions: Patients with CACAD demonstrate a systemic vascular phenotype with significantly higher rates of FMD, connective tissue disease, extremity arterial dissection, and stroke. These findings highlight the importance of comprehensive vascular screening and individualized monitoring in at-risk SCAD patients.

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.