Dafne Viliani, Alberto Cecconi, Miguel Angel Spinola Tena, Alberto Vera, Alvaro Ximenez-Carrillo, Carmen Ramos, Pablo Martinez-Vives, Beatriz Lopez-Melgar, Alvaro Montes Muniz, Clara Aguirre, Jose Vivancos, Guillermo Ortega, Fernando Alfonso, Luis Jesus Jimenez-Borreguero
{"title":"计算机辅助心电图分析提高隐源性卒中潜在心房颤动的风险评估。","authors":"Dafne Viliani, Alberto Cecconi, Miguel Angel Spinola Tena, Alberto Vera, Alvaro Ximenez-Carrillo, Carmen Ramos, Pablo Martinez-Vives, Beatriz Lopez-Melgar, Alvaro Montes Muniz, Clara Aguirre, Jose Vivancos, Guillermo Ortega, Fernando Alfonso, Luis Jesus Jimenez-Borreguero","doi":"10.14740/cr2016","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The detection of underlying paroxysmal atrial fibrillation (AF) in patients with cryptogenic stroke (CS) can be challenging, and there is great interest in finding predictors of its hidden presence. The recent development of sophisticated software has enhanced the diagnostic and prognostic performance of the 12-lead electrocardiogram (ECG). Our aim was to assess the additional role of a computer-assisted ECG analysis in identifying predictors of AF in patients with CS.</p><p><strong>Methods: </strong>Sixty-seven patients with ischemic stroke or high-risk transient ischemic attack of unknown etiology were prospectively studied. Their 12-lead digitized ECG was analyzed with dedicated software, quantifying 468 morphological variables. The main clinical, biochemical, and echocardiographic variables were also collected. At discharge, patients were monitored with a wearable Holter for 15 days, and the primary outcome was the detection of AF.</p><p><strong>Results: </strong>The median age was 80 (interquartile range (IQR): 73 - 84) and AF was detected in 21 patients (31.3%). After preselecting significant ECG variables from the univariate analysis, a multivariate regression including other significant clinical, biochemical and echocardiographic predictors of AF was performed. Among the automatically analyzed ECG parameters, the amplitude of the R wave in V1 (V1_ramp) was significantly associated with the outcome. The best model to predict AF was composed of age, N-terminal B-type natriuretic peptide (NT-proBNP), left atrial reservoir strain (LASr) and V1_ramp. This model showed good discrimination capacity (corrected Somer's D<sub>xy</sub>: 0.907, Brier's B: 0.079, area under the curve (AUC): 0.941) and performed better than the same model without the ECG variable (Somer's D<sub>xy</sub>: 0.827, Brier's B: 0.119, AUC: 0.896).</p><p><strong>Conclusions: </strong>The addition of computer-assisted ECG analysis can help stratify the risk of AF in the challenging clinical setting of CS.</p>","PeriodicalId":9424,"journal":{"name":"Cardiology Research","volume":"16 2","pages":"120-129"},"PeriodicalIF":1.4000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11882229/pdf/","citationCount":"0","resultStr":"{\"title\":\"Computer-Assisted Electrocardiogram Analysis Improves Risk Assessment of Underlying Atrial Fibrillation in Cryptogenic Stroke.\",\"authors\":\"Dafne Viliani, Alberto Cecconi, Miguel Angel Spinola Tena, Alberto Vera, Alvaro Ximenez-Carrillo, Carmen Ramos, Pablo Martinez-Vives, Beatriz Lopez-Melgar, Alvaro Montes Muniz, Clara Aguirre, Jose Vivancos, Guillermo Ortega, Fernando Alfonso, Luis Jesus Jimenez-Borreguero\",\"doi\":\"10.14740/cr2016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The detection of underlying paroxysmal atrial fibrillation (AF) in patients with cryptogenic stroke (CS) can be challenging, and there is great interest in finding predictors of its hidden presence. The recent development of sophisticated software has enhanced the diagnostic and prognostic performance of the 12-lead electrocardiogram (ECG). Our aim was to assess the additional role of a computer-assisted ECG analysis in identifying predictors of AF in patients with CS.</p><p><strong>Methods: </strong>Sixty-seven patients with ischemic stroke or high-risk transient ischemic attack of unknown etiology were prospectively studied. Their 12-lead digitized ECG was analyzed with dedicated software, quantifying 468 morphological variables. The main clinical, biochemical, and echocardiographic variables were also collected. At discharge, patients were monitored with a wearable Holter for 15 days, and the primary outcome was the detection of AF.</p><p><strong>Results: </strong>The median age was 80 (interquartile range (IQR): 73 - 84) and AF was detected in 21 patients (31.3%). After preselecting significant ECG variables from the univariate analysis, a multivariate regression including other significant clinical, biochemical and echocardiographic predictors of AF was performed. Among the automatically analyzed ECG parameters, the amplitude of the R wave in V1 (V1_ramp) was significantly associated with the outcome. The best model to predict AF was composed of age, N-terminal B-type natriuretic peptide (NT-proBNP), left atrial reservoir strain (LASr) and V1_ramp. This model showed good discrimination capacity (corrected Somer's D<sub>xy</sub>: 0.907, Brier's B: 0.079, area under the curve (AUC): 0.941) and performed better than the same model without the ECG variable (Somer's D<sub>xy</sub>: 0.827, Brier's B: 0.119, AUC: 0.896).</p><p><strong>Conclusions: </strong>The addition of computer-assisted ECG analysis can help stratify the risk of AF in the challenging clinical setting of CS.</p>\",\"PeriodicalId\":9424,\"journal\":{\"name\":\"Cardiology Research\",\"volume\":\"16 2\",\"pages\":\"120-129\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11882229/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cardiology Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14740/cr2016\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cardiology Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14740/cr2016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/6 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Computer-Assisted Electrocardiogram Analysis Improves Risk Assessment of Underlying Atrial Fibrillation in Cryptogenic Stroke.
Background: The detection of underlying paroxysmal atrial fibrillation (AF) in patients with cryptogenic stroke (CS) can be challenging, and there is great interest in finding predictors of its hidden presence. The recent development of sophisticated software has enhanced the diagnostic and prognostic performance of the 12-lead electrocardiogram (ECG). Our aim was to assess the additional role of a computer-assisted ECG analysis in identifying predictors of AF in patients with CS.
Methods: Sixty-seven patients with ischemic stroke or high-risk transient ischemic attack of unknown etiology were prospectively studied. Their 12-lead digitized ECG was analyzed with dedicated software, quantifying 468 morphological variables. The main clinical, biochemical, and echocardiographic variables were also collected. At discharge, patients were monitored with a wearable Holter for 15 days, and the primary outcome was the detection of AF.
Results: The median age was 80 (interquartile range (IQR): 73 - 84) and AF was detected in 21 patients (31.3%). After preselecting significant ECG variables from the univariate analysis, a multivariate regression including other significant clinical, biochemical and echocardiographic predictors of AF was performed. Among the automatically analyzed ECG parameters, the amplitude of the R wave in V1 (V1_ramp) was significantly associated with the outcome. The best model to predict AF was composed of age, N-terminal B-type natriuretic peptide (NT-proBNP), left atrial reservoir strain (LASr) and V1_ramp. This model showed good discrimination capacity (corrected Somer's Dxy: 0.907, Brier's B: 0.079, area under the curve (AUC): 0.941) and performed better than the same model without the ECG variable (Somer's Dxy: 0.827, Brier's B: 0.119, AUC: 0.896).
Conclusions: The addition of computer-assisted ECG analysis can help stratify the risk of AF in the challenging clinical setting of CS.
期刊介绍：
Cardiology Research is an open access, peer-reviewed, international journal. All submissions relating to basic research and clinical practice of cardiology and cardiovascular medicine are in this journal''s scope. This journal focuses on publishing original research and observations in all cardiovascular medicine aspects. Manuscript types include original article, review, case report, short communication, book review, letter to the editor.
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