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. 2020 Oct;7(10):1779-1787.
doi: 10.1002/acn3.51157. Epub 2020 Aug 30.

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

Sonja Gröschel  1 , Björn Lange  2 , Katrin Wasser  3 , Marianne Hahn  1 , Rolf Wachter  4   5   6 , Klaus Gröschel  1 , Timo Uphaus  1
Affiliations

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

Sonja Gröschel et al. Ann Clin Transl Neurol. 2020 Oct.

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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Conflict of interest statement

TU reports personal fees from Merck Serono and Pfizer, grants from Else Kröner‐Fresenius Stiftung. RW reports having been an investigator or consultant for, or received fees from Bayer, Berlin Chemie, Bristol‐Myers‐ Squibb, Boehringer Ingelheim, CVRx, Daiichi Sankyo, Gilead, Johnson & Johnson, Medtronic, Novartis, Pfizer, Sanofi, Servier since 2003. He received research grants from Boehringer Ingelheim, European Union and Bundesministerium für Bildung und Forschung (BMBF). KG reports personal fees and/or non‐financial support from Bayer, Boehringer Ingelheim, Bristol‐Meyers Squibb, Daiichi Sankyo and Pfizer. SG, BL, KW and MH report no disclosures.

Figures

Figure 1
Figure 1
Selection of patients included in the study with regard to availability of automated analysis (AA). Abbreviations: AF atrial fibrillation; pAF paroxysmal atrial fibrillation; AA automated algorithm.
Figure 2
Figure 2
Receiver operating characteristic (ROC) curve to predict detection of paroxysmal atrial fibrillation during 72 hours of Holter ECG for AS5F and risk stratification by AA exhibited an area under the curve (AUC) of 0.751 (95% CI 0.724‐0.778, P < 0.001) and 0.645 (95% CI 0.615‐0.674, P < 0.001), respectively. The combined use of risk stratification by AA and AS5F resulted in a significant improvement of AUC to 0.789 (95% CI 0.763‐0.814, P < 0.001, p for difference between the AUC 0.022).
See this image and copyright information in PMC

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