Prestroke CHA2DS2-VASc Score and Severity of Acute Stroke in Patients with Atrial Fibrillation: Findings from RAF Study

Acciarresi, Monica; Paciaroni, Maurizio; Agnelli, Giancarlo; Falocci, Nicola; Caso, Valeria; Becattini, Cecilia; Marcheselli, Simona; Rueckert, Christina; Pezzini, Alessandro; Morotti, Andrea; Costa, Paolo; Padovani, Alessandro; Csiba, László; Szabó, Lilla; Sohn, Sung Il; Tassinari, Tiziana; Abdul-Rahim, Azmil H; Michel, Patrik; Cordier, M; Vanacker, Peter; Rémillard, Suzette; Alberti, Andrea; Venti, Michele; D’Amore, Cataldo; Scoditti, Umberto; Denti, Licia; Orlandi, Giovanni; Chiti, Alberto; Gialdini, Gino; Bovi, Paolo; Carletti, Monica; Rigatelli, Alberto; Putaala, Jukka; Tatlısumak, Turgut; Masotti, Luca; Lorenzini, Gianni; Tassi, Rossana; Guideri, Francesca; Martini, Giuseppe; Tsivgoulis, Georgios; Vadikolias, Kostantinos; Liantinioti, Chrissoula; Corea, Francesco; Sette, Massimo Del; Ageno, Walter; Lodovici, Maria Luisa De; Bono, Giorgio; Baldi, Antonio; D'Anna, Sebastiano; Sacco, Simona; Carolei, Antonio; Tiseo, Cindy; Imberti, Davide; Zabzuni, Dorjan; Доронин, Б. М.; Volodina, Vera; Consoli, Domenico; Galati, Franco; Pieroni, Alessio; Toni, Danilo; Monaco, Serena; Baronello, Mario Maimone; Barlinn, Kristian; Pallesen, Lars Peder; Kepplinger, Jessica; Bodechtel, Ulf; Gerber, Johannes; Deleu, Dirk; Melikyan, Gayane; Ibrahim, F.; Akhtar, Naveed; Mosconi, Maria Giulia; Lees, Kennedy R.

doi:10.1016/j.jstrokecerebrovasdis.2017.02.011

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…to predict vascular events, mortality, and functional outcomes in patients experiencing AF-related stroke events. Although originally used for thromboembolic risk assessment in AF outpatients, the CHADS 2 score and its updated version, CHA 2 DS 2 VASc, can be used to effectively predict the prognosis of an AF-related stroke event (2)(3)(4)(5)(6). However, these models are simplified and do not incorporate clinical and laboratory parameters such as the severity of stroke symptoms at admission, serum inflammatory markers, and image-based features derived from the early stages of stroke.…”

Section: Introductionmentioning

confidence: 99%

Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning

Jeon,

Jung,

Yeo

et al. 2023

Front. Neurol.

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BackgroundPrognostic prediction and the identification of prognostic factors are critical during the early period of atrial-fibrillation (AF)-related strokes as AF is associated with poor outcomes in stroke patients.MethodsTwo independent datasets, namely, the Korean Atrial Fibrillation Evaluation Registry in Ischemic Stroke Patients (K-ATTENTION) and the Korea University Stroke Registry (KUSR), were used for internal and external validation, respectively. These datasets include common variables such as demographic, laboratory, and imaging findings during early hospitalization. Outcomes were unfavorable functional status with modified Rankin scores of 3 or higher and mortality at 3 months. We developed two machine learning models, namely, a tree-based model and a multi-layer perceptron (MLP), along with a baseline logistic regression model. The area under the receiver operating characteristic curve (AUROC) was used as the outcome metric. The Shapley additive explanation (SHAP) method was used to evaluate the contributions of variables.ResultsMachine learning models outperformed logistic regression in predicting both outcomes. For 3-month unfavorable outcomes, MLP exhibited significantly higher AUROC values of 0.890 and 0.859 in internal and external validation sets, respectively, than those of logistic regression. For 3-month mortality, both machine learning models exhibited significantly higher AUROC values than the logistic regression for internal validation but not for external validation. The most significant predictor for both outcomes was the initial National Institute of Health and Stroke Scale.ConclusionThe explainable machine learning model can reliably predict short-term outcomes and identify high-risk patients with AF-related strokes.

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Section: Introductionmentioning

confidence: 99%

Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning

Jeon,

Jung,

Yeo

et al. 2023

Front. Neurol.

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“…Epidemiological investigations have reported that the prevalence of AF dramatically increases with age ( Bapat et al, 2018 ). AF causes a high risk of disability and death ( Acciarresi et al, 2017 ), which places a heavy burden on individuals and the healthcare system. It has been reported that the underlying mechanisms of the occurrence and maintenance of AF are regulated by genetic factors, particularly single nucleotide polymorphisms (SNPs) ( He et al, 2018 ; Lin et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Atrial Fibrillation-Associated Genes ERBB2 and MYPN Using Genome-Wide Association and Transcriptome Expression Profile Data on Left–Right Atrial Appendages

et al. 2021

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More reliable methods are needed to uncover novel biomarkers associated with atrial fibrillation (AF). Our objective is to identify significant network modules and newly AF-associated genes by integrative genetic analysis approaches. The single nucleotide polymorphisms with nominal relevance significance from the AF-associated genome-wide association study (GWAS) data were converted into the GWAS discovery set using ProxyGeneLD, followed by merging with significant network modules constructed by weighted gene coexpression network analysis (WGCNA) from one expression profile data set, composed of left and right atrial appendages (LAA and RAA). In LAA, two distinct network modules were identified (blue: p = 0.0076; yellow: p = 0.023). Five AF-associated biomarkers were identified (ERBB2, HERC4, MYH7, MYPN, and PBXIP1), combined with the GWAS test set. In RAA, three distinct network modules were identified and only one AF-associated gene LOXL1 was determined. Using human LAA tissues by real-time quantitative polymerase chain reaction, the differentially expressive results of ERBB2, MYH7, and MYPN were observed (p < 0.05). This study first demonstrated the feasibility of fusing GWAS with expression profile data by ProxyGeneLD and WGCNA to explore AF-associated genes. In particular, two newly identified genes ERBB2 and MYPN via this approach contribute to further understanding the occurrence and development of AF, thereby offering preliminary data for subsequent studies.

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CHA2DS2–VASc score predicts short- and long-term outcomes in patients with acute ischemic stroke treated with intravenous thrombolysis

Merlino

Rana

Naliato

et al. 2017

J Thromb Thrombolysis

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The CHADS-VASc score is a validated tool to assess the thromboembolic risk in patients with atrial fibrillation. Pre-stroke CHADS-VASc score may predict outcome in patients with acute ischemic stroke (AIS) without atrial fibrillation. The aim of this study was to investigate if the pre-stroke CHADS-VASc score is able to predict short- and long-term outcomes in AIS patients treated with intravenous thrombolysis (IVT). The study group consisted of 256 consecutive patients admitted to the Udine University Hospital with AIS and underwent IVT between January 2015 to March 2017. The pre-stroke CHADS-VASc score for each patient was calculated from the collected baseline data. Patients were classified into three groups according to their pre-stroke CHADS-VASc score: a score of 0 of 1, a score of 2 or 3 and a score above 3. Primary outcome measures were: rate of favorable outcome at 90-days and at 1-year, and mortality at 90-days and at 1-year. Data on functional outcome and mortality 1 year after stroke were collected in 165 patients (65% of the entire sample). Favorable outcome was defined as a modified Rankin Scale score ≤ 2. Compared with the CHADS-VASc score 0-1 group, patients with higher CHADS-VASc scores had a worse outcome and a higher mortality 3 months and 1 year after stroke. The diagnostic performance of the CHADS-VASc score as judged with AUC-ROC was 0.70 (95% CI, 0.64-0.76; p < 0.001) for favorable outcome at 90-days, 0.78 (95% CI, 0.71-0.85; p < 0.001) for favorable outcome at 1-year, 0.71 (95% CI 0.61-0.79) for mortality at 90-days, 0.73 (95% CI 0.64-0.80; p < 0.001) for mortality at 1-year. Pre-stroke CHADSVASc score represents a good predictor for short- and long-term outcomes in AIS patients treated with IVT.

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Prestroke CHA2DS2-VASc Score and Severity of Acute Stroke in Patients with Atrial Fibrillation: Findings from RAF Study

Cited by 8 publications

References 14 publications

Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning

Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning

Identification of Atrial Fibrillation-Associated Genes ERBB2 and MYPN Using Genome-Wide Association and Transcriptome Expression Profile Data on Left–Right Atrial Appendages

CHA2DS2–VASc score predicts short- and long-term outcomes in patients with acute ischemic stroke treated with intravenous thrombolysis

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