Geisinger Health System (GHS) is a regional health care provider to central, south-central and northeastern Pennsylvania. Headquartered in Danville, Pennsylvania, Geisinger services over 3 million patients in 45 counties.
Background The Monopoint reperfusion system (Monopoint; Route 92 Medical, San Mateo, California, USA) is a large bore (0.088 or 0.070 inch inner diameter) aspiration thrombectomy platform designed to minimize ledge effect and improve neurovascular navigation and embolectomy. We aimed to describe a multicenter, real world experience of the safety and performance of the Monopoint system in first line aspiration thrombectomy for large vessel occlusions (LVOs), outside of the recently completed SUMMIT MAX (A Randomized, Controlled Trial to Evaluate the Safety and Effectiveness of the Route 92 Medical Reperfusion System) clinical trial.Methods Adults with acute anterior circulation LVO stroke between January 2019 and December 2024 consecutively treated with first line aspiration thrombectomy using the Monopoint at 10 centers were retrospectively reviewed. The primary outcome was first pass effect (FPE, modified Thrombolysis in Cerebral Infarction (mTICI) 2C/3 on first pass) and modified FPE (mFPE, mTICI 2B/2C/3 on first pass). The primary safety outcome was the rate of intraprocedural complications attributed to the Monopoint system.Results In 193 included patients, median age was 67 years (IQR 67-78), and 46.6% (90/193) were women. Successful delivery of the aspiration catheter to the clot site occurred in 96.2% (185/193) of patients. FPE was achieved in 57.5% (111/193) and mFPE was achieved in 68.4% (132/193) of patients. Of 10 (5.2%) total complications, most were vasospasm treated with intra-arterial verapamil (8/193, 4.1%); major complications included one dissection (1/193, 0.5%) and one perforation (1/193, 0.5%).Conclusion This multicenter study of the Monopoint reperfusion system for LVO thrombectomy outside of the SUMMIT MAX trial demonstrated a high FPE rate and a low rate of major complications.
BACKGROUND:Large-bore aspiration catheters are integral to mechanical thrombectomy (MT) for large vessel occlusions (LVOs), offering potential for improved first pass success and faster recanalization. OBJECTIVE:To assess the clinical performance, efficacy, and safety profile of the Broadway 8 system as a primary aspiration device in MT of LVOs. METHODS:This is a multicenter observational study conducted across 8 US comprehensive stroke centers. Efficacy outcomes included first pass effect (FPE), defined as a single-pass modified Treatment In Cerebral Infarction (mTICI) score ≥2c, and successful reperfusion (final mTICI ≥2b). Safety outcomes included device-related complications, symptomatic intracranial hemorrhage (sICH), and inpatient mortality. Functional outcomes included modified Rankin Scale (mRS) at discharge and delta National Institutes of Health Stroke Scale (NIHSS) score. Logistic regression was used to assess predictors of thrombus access and intermediate catheter use. RESULTS:49 patients were included. The Broadway 8 system reached the thrombus in 44 (89.8%). Median puncture-to-thrombus and puncture-to-reperfusion times were 11 min (IQR 9-18) and 20 min (14-31), respectively. FPE was achieved in 20 (40.8%). Successful reperfusion was achieved in 46/49 (93.8%), with 35 (71.4%) using Broadway 8. sICH occurred in 2 patients (4.1%). Median mRS at discharge was 3.0 (IQR 1.0-4.0); delta NIHSS was 8 (IQR 5-12). Regression analysis showed faster reperfusion when Broadway 8 was used without an intermediate catheter. CONCLUSION:The Broadway 8 system appears to be a safe and effective frontline aspiration device, demonstrating a safety and efficacy profile comparable to other large-bore aspiration systems that incorporate delivery-assist technology.
The atherogenic index of plasma (AIP), defined as log [triglycerides (TG)/high-density lipoprotein cholesterol], an emerging lipid-based biomarker reflecting circulating TG and high-density lipoprotein cholesterol levels, has been associated with metabolic syndrome, coronary heart disease, and atherosclerosis. Its role in cardiovascular disease has been well established, yet there is growing interest in its application in cerebrovascular conditions, particularly stroke. Stroke is one of the leading causes of death and disability worldwide; hence, there is a need for integrative biomarkers to help improve risk prediction and accuracy of prognostication. Recent studies suggest that elevated AIP is independently associated with stroke incidence, especially among individuals with diabetes, prediabetes, and metabolic syndrome. Higher AIP levels have been associated with worse stroke severity at presentation, a higher risk of early neurological deterioration, and worse short-term outcomes. This is likely a consequence of AIP indicating vascular inflammation, endothelial dysfunction, and intracranial atherosclerosis. In observational studies, AIP has demonstrated comparable or stronger associations than other markers of insulin resistance, such as the TG-glucose index and the Chinese Visceral Adiposity Index, in specific metabolic populations. It is a low-cost and easily available biomarker, making it useful in primary prevention clinics, stroke units, and for managing metabolic syndrome. Given the increasing number of observational studies and population-based data, a comprehensive synthesis is needed to evaluate AIP’s diagnostic, prognostic, and pathophysiological significance in stroke. This narrative review consolidates current findings on AIP’s relevance in ischemic stroke and explores its potential integration into stroke risk stratification. Existing evidence is largely observational in nature, limiting causal interpretation.
Background and Objective:Artificial intelligence (AI) has revolutionized the field of gastroenterology, leading to significant improvements in the diagnosis, management, and prognosis of several gastrointestinal (GI) disorders. With this context in mind, this brief review examines a wide range of subjects, including the history of AI in medicine and the state of AI in gastroenterology today, with a particular emphasis on its application in radiographic diagnosis, endoscopic procedures, disease detection, and clinical decision-making. Methods:A narrative review of the literature was conducted, encompassing studies published in English across major databases. The review covers historical developments of AI in medicine, contemporary AI applications in gastroenterology, and emerging trends. Key Content and Findings:AI techniques, including machine learning and deep learning, have demonstrated high accuracy in detecting GI pathologies such as polyps, neoplasms, inflammatory bowel disease, and other conditions. AI applications in endoscopy, video capsule endoscopy, and colonoscopy enable rapid analysis of large datasets, aiding early diagnosis and clinical decision-making. Challenges identified include data quality, model interpretability, ethical concerns, and liability associated with AI-assisted clinical decisions. Despite these challenges, AI continues to enhance gastroenterology practice and shows promise for broader clinical adoption. Conclusions:AI has significant potential to improve patient care in gastroenterology. Future advancements will require collaboration among AI developers, clinicians, and patients to address implementation barriers, optimize clinical utility, and inform policy and research directions.
