Citations

Abstract Background aneurysmal subarachnoid hemorrhage (aSAH) is neurological emergency associated with substantial mortality and disability. Current grading systems such as the modified Fisher Scale (mFS) and World Federation of Neurological Societies (WFNS) score, rely on semiquantitative and examination based assessments. Hence, they demonstrate limited predictive precision. The enhanced subarachnoid hemorrhage (eSAH) score is a simplified quantitative model integrating age, Glasgow Coma Scale (GCS), and cisternal subarachnoid hemorrhage volume (SAHV) to predict clinical outcomes after aSAH. Methods We performed a retrospective multicenter cohort study that included 1088 patients across three tertiary-care centers the United States. Predictive performance for unfavorable functional outcome, in-hospital mortality and delayed cerebral ischemia (DCI) was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). Comparative analyses were performed and compared to the WFNS and mFS grading systems. Results the eSAH score demonstrated excellent discrimination for unfavorable functional outcome at discharge ( AUC 0.89 ) and in-hospital mortality (AUC 0.87). The DCI subscore demonstrated good discriminatory performance for predicting DCI (AUC 0.77). Compared with conventional grading systems, this was superior to both the WFNS (AUC 0.75) and the mFS ( AUC 0.70). increasing eSAH scores were additionally associated with progressively higher rates of mortality and unfavorable functional outcomes. Conclusion the eSAH score demonstrates strong external validity, reproducibility and superior predictive performance compared with conventional grading systems in a large multicenter cohort. These findings support the clinical utility of quantitative hemorrhage burden integration for early risk stratification in patients with aSAH.

Abstract Objective Shunt-dependent hydrocephalus is a common and costly complication of aneurysmal subarachnoid hemorrhage (aSAH), affecting up to 28% of survivors. Existing prediction tools, including the Chronic Hydrocephalus Ensuing from SAH Score (CHESS), have limited discriminative accuracy. We developed the CHECKMATE score, a clinically practical tool to improve prediction of ventriculoperitoneal shunt dependency after aSAH. Methods In this multicenter retrospective cohort of 486 patients with aSAH from Mayo Clinic (January 1, 2006-December 31, 2021), we used multivariable logistic regression and machine learning to identify independent predictors of ventriculoperitoneal shunt placement. The CHECKMATE score was derived from 5 weighted variables: symptomatic hydrocephalus (10 points), intraventricular hemorrhage (5 points), SAH volume greater than 10 mL (3 points), neutrophil-to-lymphocyte ratio greater than 12 (2 points), and 10-year incremental age thresholds starting at older than 60 years (1 point each). Results Of 486 patients (mean age, 56.3 years; 64.6% female), 137 (28.2%) required ventriculoperitoneal shunt placement. The CHECKMATE score achieved an area under the curve of 0.808 (compared to 0.737 for CHESS), with a sensitivity of 0.85, specificity of 0.67, and negative predictive value of 0.92 at the optimal cutoff of 14 points. Conclusions The CHECKMATE score outperforms CHESS for predicting ventriculoperitoneal shunt dependency after aSAH and is easily used at the bedside. Its high negative predictive value helps identify low-risk patients who may benefit from earlier external ventricular drain weaning and shorter hospital stays.

Background: Pancreatic resection (Pancreatoduodenectomy, PD, distal pancreatectomy, DP, or total pancreatectomy, TP) is the standard of care for resectable pancreatic adenocarcinoma (PDAC). Despite advances in multimodal therapy, recurrence rates remain high, approaching 80%, and continue to drive poor overall survival. Objective: This review evaluates the burden and clinical significance of localized recurrence in PDAC and critically examines the potential role of intraoperative radiation therapy (IORT) in improving locoregional disease control. Results: Distant recurrence remains the predominant pattern of failure, occurring in 55–75% of patients, most commonly involving the liver, lungs, and peritoneum. However, isolated local recurrence, observed in approximately 17–32% of patients, represents a clinically meaningful subset associated with significant morbidity and potential for subsequent metastatic progression. IORT, delivered as a single high-dose radiation treatment to the tumor bed at the time of surgery, enables precise targeting of areas at highest risk for residual microscopic disease while minimizing radiation exposure to adjacent radiosensitive structures. Retrospective and meta-analytic data, while limited, suggest that IORT is associated with improved local control and modest survival benefit without a significant increase in perioperative morbidity, though interpretation is limited by study heterogeneity and lack of randomized control trials. Conclusion: IORT represents a rational adjunct in the multimodal management of PDAC, particularly for patients at high risk of locoregional failure, including those with borderline resectable or locally advanced disease, nodal involvement, perineural invasion, or concern for margin positivity. Prospective studies are needed to better define optimal patient selection and to clarify the role of IORT in the modern treatment paradigm.

Abstract Background New-onset atrial fibrillation (AF) is common in patients who are hospitalized with acute illness and is associated with overall poor outcomes. Current clinical and demographic predictors of new-onset AF have limitations. Electrocardiogram (ECG) waveforms, combined with these factors as part of multimodal approach could help improve prediction of new-onset AF. Purpose The purpose of our study was to develop a multimodal, deep-learning system that combines ECG waveforms with clinical data for prediction of new-onset AF in hospitalized patients. Methods We developed and compared two separate 1-dimensional models with a fusion model for prediction of new-onset AF. (a): one-dimensional convolutional neural network (1D-CNN) that learns patterns from the raw ECG waveforms. (b): a Multi-Layer Perceptron (MLP), unsupervised machine learning model which utilizes data from structured variables such as: age, heart rate, routinely obtained laboratory data and medications. The two modules each produce latent feature vectors, which are then placed end-to-end and fed into a final Multi- MLP head that outputs the probability of New-onset AF during hospitalization. We then compared this fused MLP model with the two simpler unimodal models based solely on ECG waveforms and clinical data respectively. Results On the held-out test set data, the feature-level fused model achieved an Area Under the Curve (AUC) of 0.81. This significantly outperformed both the ECG-alone model (0.77) and the clinical-alone model (0.74). By adjusting the threshold, we can fine-tune the tool at a threshold of 0.5, it flags 83% of patients who later develop AF (207 of 248) with a precision of 10%, a setting that could be useful for wider screening. By adjusting the threshold of 0.98 for a high-precision alert configuration, it has the ability to predict 46% of future occurrence of AF cases (113 of 248) while reducing false alarms by 81% (from 1,797 to 328), increasing precision to 26% and achieving an overall accuracy of 89%, a setting useful when resources for follow-up are limited. Conclusions Combining ECG signals with clinical data significantly enhances the model’s ability to detect new-onset AF in hospitalized patients as compared to a uni-dimensional CNN model. The dynamic nature of ML model also allows for adjustment of its decision threshold which can aid in broader screening and improving adverse outcomes related to occurrence of AF.ROC Curve for Fusion model

Abstract Rationale Chronic thromboembolic disease (CTED), which is the result of persistent pulmonary vascular obstruction despite 3 months of anticoagulation, can present with significant functional limitations and exercise intolerance despite normal resting hemodynamics. These patients may experience functional impairment due to exercise-induced pulmonary hypertension, impaired stroke volume augmentation with exercise, ventilatory inefficiency, and/or preload insufficiency. Such exercise-induced pathophysiological abnormalities can significantly impair quality of life and contribute to poor long-term outcomes. Although balloon pulmonary angioplasty (BPA) has shown benefit in patients with chronic thromboembolic pulmonary hypertension (CTEPH), its role in CTED patients with normal resting hemodynamics remains uncertain. This systematic review and meta-analysis evaluated the efficacy of BPA in this population. Methods A systematic search of PubMed, Cochrane, Embase, and Scopus was performed from inception through March 1, 2025. A total of 763 studies were screened, of which 42 full-text articles were assessed: six met the inclusion criteria (four retrospective, two prospective), comprising 98 patients. Results Across the included studies, baseline mean values were 20.81 +/- 3.62 mmHg for mean pulmonary arterial pressure (mPAP), 3.49 +/- 1.42 Wood units for pulmonary vascular resistance (PVR), and 405.72 +/- 108.40 m for the 6-minute walk distance (6MWD). Balloon Pulmonary Angioplasty (BPA) was consistently associated with significant improvements in hemodynamic and functional outcomes; BPA reduced mPAP by 3.40 mmHg and PVR by 0.90 WU, while increasing 6MWD by 31.90 m. These changes were also statistically robust across studies, with pooled standardized mean changes (SMC) of -0.88 for mPAP (95% CI, -1.10 to -0.67; P < .05), -0.68 for PVR (95% CI, -0.87 to -0.49; P < .05), and 0.29 for 6MWD (95% CI, 0.13 to 0.45; P < .05). Study-level effect sizes for 6MWD ranged from 0.19 to 0.54 (Figure 1A). Functional improvements were further demonstrated by shifts in NYHA class distribution (Figure 1B). Conclusion BPA was associated with meaningful reductions in mPAP and PVR and significant improvements in functional capacity among patients with CTED. These results indicate that BPA could be an effective treatment for certain patients with CTED, though more research is necessary. This abstract is funded by: None

Abstract Rationale Chronic thromboembolic disease (CTED) is characterized by persistent pulmonary vascular obstruction due to unresolved thromboemboli despite at least three months of anticoagulation. Although resting hemodynamics often remain normal, affected patients frequently develop functional limitation due to exercise-induced pulmonary hypertension, ventilatory inefficiency, limited stroke volume augmentation, or preload insufficiency. Such exercise-induced pathophysiological dysregulation can significantly impair quality of life and contribute to poor long-term outcomes. Pulmonary endarterectomy (PEA) is an established treatment for Chronic Thromboembolic Pulmonary Hypertension (CTEPH), offering significant improvements in hemodynamics and survival. Its role in CTED, however, remains unclear. While CTED has traditionally been managed with anticoagulation and supportive therapy, emerging evidence suggests PEA may provide symptomatic and functional benefit in selected CTED patients. Given the ongoing uncertainty regarding the role of PEA in CTED, we performed a systematic review and meta-analysis to define its safety profile and clinical efficacy in this patient subset. Methods A systematic search of PubMed, Cochrane, Embase, Scopus, Web of Science, ClinicalTrials.gov, and Google Scholar was conducted from database inception to March 1, 2025. Of 2,244 records screened, 34 full-text articles were assessed, and seven met the inclusion criteria. Results Across the included studies, the pooled baseline mean values were 20.48 ± 3.03 mm Hg for mean pulmonary arterial pressure (mPAP), 2.83 ± 1.11 Wood units for pulmonary vascular resistance (PVR), and 397 ± 100.08 m for the 6-minute walk distance (6MWD). Pulmonary Endarterectomy (PEA) was consistently associated with significant improvements in hemodynamic and functional outcomes; PEA reduced mPAP by 3.58 mmHg (pooled Standardized Mean Change (SMC)=-1.03; 95% CI, -1.23 to -0.83; P < .05), PVR by 0.86 Woods units (pooled SMC=-0.86; 95% CI, -1.05 to -0.67; P < .05), while, conversely, increased 6MWD by 37.49 m (pooled SMC, 0.49; 95% CI, 0.32 to 0.67; P < .05). Study-level effect sizes for 6MWD ranged from 0.21 to 1.47 (Figure 1A). This clinical improvement is further reflected in the enhancement of the NYHA functional class, as illustrated in Figure 1B. Conclusion PEA was associated with significant reductions in mPAP and PVR, as well as a considerable improvement in functional capacity in patients with CTED, with robust results across multiple tests for bias, influence, and sensitivity analyses. These findings support PEA as a viable therapeutic option for selected patients with CTED, although further research is warranted. This abstract is funded by: None

Abstract Context Most of the patients included in the clinical trials and real-world studies with osilodrostat only include patients with adrenocorticotropic hormone dependent Cushing syndrome (CS), while data on the efficacy and safety of osilodrostat in patients with adrenal CS is scarce. Objective To assess the efficacy and safety of osilodrostat in adrenal CS. Methods International study of patients with adrenal CS: patients treated with osilodrostat at any time were enrolled in the safety evaluation and those treated for longer than 4 weeks, in the efficacy evaluation. Patients were classified as responders if they experienced a reduction in urinary free cortisol (UFC) >50% (complete responders when UFC levels were below the upper limit of normal [ULN] and partial responders if there was a reduction >50% but not normalization). Results Twenty-eight patients with adrenal CS were enrolled: 16 with adrenocortical carcinoma and 12 with benign disease. Osilodrostat was used in monotherapy in 22 patients and in combination with metyrapone in 6 cases. In those patients treated for longer than 4 weeks (n = 21), 66.7% were classified as responders (28.6% with complete response and 38.1% with partial response), and for those treated for longer than 12 weeks, the rate of response increased to 87.5% The use of osilodrostat as a nonfirst-line therapy (odds ratio 15.0, P = .010) was a predictor of response. Osilodrostat led to a significant decrease in systolic blood pressure and body weight (P < .05). Nine patients developed one or more adverse events and in 56% (n = 5) led to osilodrostat discontinuation. Conclusion Osilodrostat controls hypercortisolism in 66.7% of patients with adrenal CS treated for longer than 4 weeks and in 87.5% of cases treated for longer than 12 weeks, with a positive impact on blood pressure and body weight. Patients who received osilodrostat after other previous steroidogenesis inhibitors have a higher probability of response.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.