Mechanical Circulatory Assist Devices Stroke Subtype Classification: A Novel Stroke Classification System in Patients with Ventricular Assist Devices
Pinto, C. B.; Owens, C. D.; Alvarado-Dyer, R.; Loggini, A.; Ortiz Garcia, J. G.; Heldner, M. R.; Prabhakaran, S.; Saleh Velez, F. G.
Show abstract
BackgroundLeft ventricular assist devices (LVADs) have emerged as the standard of care for bridging-destination therapy in refractory heart failure, however, due to their inherent characteristics, they carry a higher risk of cerebrovascular events. Despite the increased incidence of associated cerebrovascular events in this population, the underlying pathophysiological mechanisms remain poorly understood, hindering effective management and prognosis. We aimed to (1) develop and apply a novel etiological classification system for mechanical circulatory assist device (MCAD)-related strokes, and (2) evaluate the interrater reliability of this classification when applied to cerebrovascular events in LVAD recipients. MethodsThe MCAD stroke classification divides LVAD-related strokes in four categories: 1. Management-related (M); 2. Clinically related (C); 3. Acquired (A); 4. Device-related (D). We evaluated the interrater agreement between two independent board-certified vascular neurologists who classified strokes in LVAD patients based on the proposed MCAD classification. ResultsWe retrospectively reviewed 192 LVAD patients at the University of Chicago Medical Center and identified 32 cerebrovascular events from 2010 to 2020. Among those, 59.4% were ischemic and 40.6% hemorrhagic strokes. The interrater agreement was extremely high (intraclass correlation coefficient = 0.96 {+/-} 0.03). The raters agreed in the primary classification of 32 subjects with the following distribution of mechanisms: 62.6% M, 15.6% A, 15.6% D, 6.2% C. Four subjects fit into more than one category. ConclusionWe propose an MCAD-focused stroke classification that integrates etiology with therapeutic and prognostic decision making. Although evaluated in LVAD patients, this classification can potentially be extrapolated to other mechanical assist devices.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Treatment of Slow-flow After Primary Percutaneous Coronary Intervention With Flow-mediated Hyperemia. The Randomized RAIN-FLOW Study 94%
- Prognostic Value of Patient-Reported Outcomes in Predicting Long-term Mortality after Transcatheter Aortic Valve Replacement (TAVR) 94%
- Smartwatch Facilitated Remote Health Care for Patients Undergoing Transcatheter Aortic Valve Replacement Amid COVID-19 Pandemic 94%
Similar papers in this journal
- Effects of low versus high inspired oxygen fraction on myocardial injury after transcatheter aortic valve implantation: A randomized clinical trial 95%
- Predicting factors for long-term survival in patients with out-of-hospital cardiac arrest - a propensity score-matched analysis 95%
- The effect of coronary revascularization treatment timing on mortality in patients with stable ischemic heart disease in British Columbia 94%
Similar papers in this journal
- Large Core Thrombectomy: Feasibility Of Simplified Protocol In Resource-Limited Settings 93%
- Prehospital triage of intracranial hemorrhage and anterior large vessel occlusion ischemic stroke: the value of the rapid arterial occlusion evalution 93%
- Long term stability of patients undergoing endovascular parent artery occlusion of their intracranial artery 93%
Similar papers in this journal
- Is Catheter Ablation Associated with Preservation of Cognitive Function? An Analysis From the SAGE-AF Observational Cohort Study 93%
- Endovascular thrombectomy: an effective and safe therapy for perioperative ischemic stroke 93%
- Workflow Intervals andOutcomesof Endovascular Treatment for Acute Large-Vessel Occlusion During On- Versus Off-Hours in China The ANGEL-ACT Registry 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.