Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke

Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospec...
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Autor*in:	Sonja Gröschel [verfasserIn] Björn Lange [verfasserIn] Katrin Wasser [verfasserIn] Marianne Hahn [verfasserIn] Rolf Wachter [verfasserIn] Klaus Gröschel [verfasserIn] Timo Uphaus [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Übergeordnetes Werk:	In: Annals of Clinical and Translational Neurology - Wiley, 2015, 7(2020), 10, Seite 1779-1787
Übergeordnetes Werk:	volume:7 ; year:2020 ; number:10 ; pages:1779-1787

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DOI / URN:	10.1002/acn3.51157

Katalog-ID:	DOAJ053650972

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520			\|a Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.
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allfields	10.1002/acn3.51157 doi (DE-627)DOAJ053650972 (DE-599)DOAJ8247c9cab6d948ccbbb5691c15de5406 DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Sonja Gröschel verfasserin aut Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Björn Lange verfasserin aut Katrin Wasser verfasserin aut Marianne Hahn verfasserin aut Rolf Wachter verfasserin aut Klaus Gröschel verfasserin aut Timo Uphaus verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 7(2020), 10, Seite 1779-1787 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:7 year:2020 number:10 pages:1779-1787 https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/article/8247c9cab6d948ccbbb5691c15de5406 kostenfrei https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 10 1779-1787
spelling	10.1002/acn3.51157 doi (DE-627)DOAJ053650972 (DE-599)DOAJ8247c9cab6d948ccbbb5691c15de5406 DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Sonja Gröschel verfasserin aut Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Björn Lange verfasserin aut Katrin Wasser verfasserin aut Marianne Hahn verfasserin aut Rolf Wachter verfasserin aut Klaus Gröschel verfasserin aut Timo Uphaus verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 7(2020), 10, Seite 1779-1787 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:7 year:2020 number:10 pages:1779-1787 https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/article/8247c9cab6d948ccbbb5691c15de5406 kostenfrei https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 10 1779-1787
allfields_unstemmed	10.1002/acn3.51157 doi (DE-627)DOAJ053650972 (DE-599)DOAJ8247c9cab6d948ccbbb5691c15de5406 DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Sonja Gröschel verfasserin aut Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Björn Lange verfasserin aut Katrin Wasser verfasserin aut Marianne Hahn verfasserin aut Rolf Wachter verfasserin aut Klaus Gröschel verfasserin aut Timo Uphaus verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 7(2020), 10, Seite 1779-1787 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:7 year:2020 number:10 pages:1779-1787 https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/article/8247c9cab6d948ccbbb5691c15de5406 kostenfrei https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 10 1779-1787
allfieldsGer	10.1002/acn3.51157 doi (DE-627)DOAJ053650972 (DE-599)DOAJ8247c9cab6d948ccbbb5691c15de5406 DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Sonja Gröschel verfasserin aut Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Björn Lange verfasserin aut Katrin Wasser verfasserin aut Marianne Hahn verfasserin aut Rolf Wachter verfasserin aut Klaus Gröschel verfasserin aut Timo Uphaus verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 7(2020), 10, Seite 1779-1787 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:7 year:2020 number:10 pages:1779-1787 https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/article/8247c9cab6d948ccbbb5691c15de5406 kostenfrei https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 10 1779-1787
allfieldsSound	10.1002/acn3.51157 doi (DE-627)DOAJ053650972 (DE-599)DOAJ8247c9cab6d948ccbbb5691c15de5406 DE-627 ger DE-627 rakwb eng RC321-571 RC346-429 Sonja Gröschel verfasserin aut Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction. Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system Björn Lange verfasserin aut Katrin Wasser verfasserin aut Marianne Hahn verfasserin aut Rolf Wachter verfasserin aut Klaus Gröschel verfasserin aut Timo Uphaus verfasserin aut In Annals of Clinical and Translational Neurology Wiley, 2015 7(2020), 10, Seite 1779-1787 (DE-627)77139649X (DE-600)2740696-9 23289503 nnns volume:7 year:2020 number:10 pages:1779-1787 https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/article/8247c9cab6d948ccbbb5691c15de5406 kostenfrei https://doi.org/10.1002/acn3.51157 kostenfrei https://doaj.org/toc/2328-9503 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 10 1779-1787
language	English
source	In Annals of Clinical and Translational Neurology 7(2020), 10, Seite 1779-1787 volume:7 year:2020 number:10 pages:1779-1787
sourceStr	In Annals of Clinical and Translational Neurology 7(2020), 10, Seite 1779-1787 volume:7 year:2020 number:10 pages:1779-1787
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Neurosciences. Biological psychiatry. Neuropsychiatry Neurology. Diseases of the nervous system
isfreeaccess_bool	true
container_title	Annals of Clinical and Translational Neurology
authorswithroles_txt_mv	Sonja Gröschel @@aut@@ Björn Lange @@aut@@ Katrin Wasser @@aut@@ Marianne Hahn @@aut@@ Rolf Wachter @@aut@@ Klaus Gröschel @@aut@@ Timo Uphaus @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	77139649X
id	DOAJ053650972
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ053650972</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308174922.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/acn3.51157</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ053650972</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ8247c9cab6d948ccbbb5691c15de5406</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC321-571</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC346-429</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Sonja Gröschel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurosciences. Biological psychiatry. Neuropsychiatry</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurology. 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author	Sonja Gröschel
spellingShingle	Sonja Gröschel misc RC321-571 misc RC346-429 misc Neurosciences. Biological psychiatry. Neuropsychiatry misc Neurology. Diseases of the nervous system Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke
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topic_title	RC321-571 RC346-429 Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke
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title	Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke
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title_full	Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke
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author_browse	Sonja Gröschel Björn Lange Katrin Wasser Marianne Hahn Rolf Wachter Klaus Gröschel Timo Uphaus
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title_sort	software‐based analysis of 1‐hour holter ecg to select for prolonged ecg monitoring after stroke
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title_auth	Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke
abstract	Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.
abstractGer	Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.
abstract_unstemmed	Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.
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title_short	Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke
url	https://doi.org/10.1002/acn3.51157 https://doaj.org/article/8247c9cab6d948ccbbb5691c15de5406 https://doaj.org/toc/2328-9503
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doi_str	10.1002/acn3.51157
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up_date	2024-07-03T18:49:39.709Z
_version_	1803584882308284416
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ053650972</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308174922.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/acn3.51157</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ053650972</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ8247c9cab6d948ccbbb5691c15de5406</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC321-571</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC346-429</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Sonja Gröschel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Objective Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG. Results pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022). Interpretation Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurosciences. Biological psychiatry. Neuropsychiatry</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurology. 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Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke

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