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Moyamoya angiopathy (MMA) lacks stage-specific comparative evidence for surgical strategy. Because many studies mix Suzuki grades, potential technique effects may be obscured. We compared direct revascularization (DR) with indirect revascularization (IR) in a stage-restricted cohort (Suzuki I-III), using propensity score weighting (PSW). We conducted a multicenter retrospective cohort study across 13 academic centers. Adults with confirmed MMA (Suzuki I-III) who underwent DR or IR were included. Patients < 16 years and combined procedures were excluded. Outcomes were symptomatic stroke, overall perioperative stroke, intraoperative complications, discharge NIHSS/mRS, length of stay, and follow-up stroke. PSW used Covariate Balancing Propensity Scores (CBPS) with absolute standardized mean difference (ASMD) diagnostics. Group differences were modeled with logistic/linear regression, and stroke-free survival was compared by Kaplan-Meier/log-rank. We analyzed 208 hemispheres (IR = 104; DR = 104). Baseline demographics and comorbidities were similar. Unadjusted analyses showed no significant differences in overall perioperative stroke (10.5
BACKGROUND:Obesity is considered as a risk factor for postoperative complications after ventral hernia repair (VHR). While promising results of robotic VHR in patients with obesity have been demonstrated, outcomes stratified across BMI categories in patients undergoing robotic transversus abdominis release (rTAR) remain limited. In this multicenter study, we compared perioperative and long-term outcomes after rTAR in patients without obesity (BMI < 30 kg/m2), patients with class I obesity (30-34.9 kg/m2), and patients with class II obesity (35-39.9 kg/m2). METHODS:We performed a retrospective cohort analysis of consecutive rTAR patients at five centers between February 2015 and July 2025. The primary outcome was overall complication rates; secondary outcomes included surgical site events (SSEs), including occurrences (SSOs), infections (SSIs), and hernia recurrences. Outcomes were analyzed using univariate tests. Multivariate logistic regression tests were run to identify independent risk factors. RESULTS:A total of 343 patients included in this study; 160 (46.6%) were patients without obesity, 117 (34.1%) patients had class I obesity, and 66 (19.2%) patients had class II obesity. Overall complication rates were comparable across groups (31.1% vs 27.6% vs 30.3%, p = 0.818). No significant differences observed in length of hospital stay, 30-day readmissions, Clavien-Dindo grades, Comprehensive Complication Index scores, or SSEs, including SSOs and SSIs. Hernia recurrence occurred in one patient per group. Multivariate analysis demonstrated that BMI category was not an independent predictor of complications. Independent risk factors for any postoperative complication included COPD (OR 3.45, 95% CI 1.58-7.11), prior wound infection (OR 2.22, 95% CI 1.19-4.11), non-use of the TEP approach (OR 1.94, 95% CI 1.10-3.43), bilateral TAR (OR 2.58, 95% CI 1.27-5.22), prolonged adhesiolysis > 30 min (OR 1.77, 95% CI 1.01-3.08), and lack of primary defect closure (OR 5.67, 95% CI 1.37-23.47). For SSEs specifically, COPD (OR 2.43, 95% CI 1.10-5.37), prior wound infection (OR 2.17, 95% CI 1.10-4.28), prolonged adhesiolysis (OR 1.96, 95% CI 1.09-3.52), and lack of primary defect closure (OR 4.78, 95% CI 1.36-16.78) were independent predictors. CONCLUSION:Comparable short- and long-term outcomes were observed with rTAR across patients without obesity, with class I obesity, and with class II obesity. Surgeons may need to consider comorbidities, particularly COPD and prior wound infection history, alongside operative factors such as TEP access, primary defect closure, and the careful application of bilateral TAR, in order to optimize patient outcomes.
Vascular surgery has evolved to include a large proportion of minimally invasive endovascular procedures. These procedures are better tolerated by patients, but can be taxing for the operator with less than adequate ergonomics and high effective radiation doses. Lead aprons do not protect the entire body and cause significant strain on the spine. Traditional lead shields are difficult to adequately position to optimize visualization and operator protection. Novel radiation protection systems provide stable protection for the entire team. Here we present a case using a novel protection system allowing the entire team to operate without traditional lead apron shielding.
Background Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with early signs that are frequently overlooked or attributed to other conditions. This study proposes a novel, externally validated artificial intelligence (AI) tool using ECG data (ECG‐AI) for the simultaneous detection of subtypes of LV dysfunction and HFpEF. Methods Two ECG‐AI models, using 12‐lead or single‐lead ECG, were developed using data from Atrium Health Wake Forest Baptist and University of Tennessee Health Science Center (UTHSC) to classify ECGs into 4 categories: reduced LV ejection fraction (rEF; EF<40), midrange EF (mEF; 40≤mEF<50), HFpEF, and controls (no LV dysfunction or HFpEF). Next, a boosting algorithm was used to incorporate clinical risk factors. Finally, the models were independently validated on pediatric data from UTHSC. Results The Atrium Health Wake Forest Baptist cohort included 1 078 198 digital ECGs from 165 243 patients (5% rEF, 8% mEF, 2% HFpEF), and the UTHSC external validation cohort comprised 72 832 ECGs from 42 880 patients (1% rEF, 1% mEF, 1% HFpEF). For the 12‐lead (Lead‐I) ECG‐AI model, areas under the curve for rEF/mEF/HFpEF were 0.90/0.81/0.80 (0.89/0.78/0.75) in the Atrium Health Wake Forest Baptist holdout data and 0.92/0.76/0.73 (0.90/0.75/0.74) in the UTHSC data. Clinical data‐only machine‐learning models lacked generalizability; clinical + ECG‐AI models did not show any significant improvement compared with ECG‐alone models. With 8418 ECGs (142 cases, 8276 controls) from UTHSC pediatric data, the 12‐lead model achieved areas under the curve of 0.97/0.71/0.64 for rEF/mEF/HFpEF, and the Lead I model had areas under the curve of 0.94/0.77/0.67. Conclusions ECG‐AI can accurately detect LV dysfunction and HFpEF even from single‐lead ECG, enabling potential low‐cost screening.
