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Open Access 20-04-2023 | Stroke | Original Communication

Data lake-driven analytics identify nocturnal non-dipping of heart rate as predictor of unfavorable stroke outcome at discharge

Authors: Alexander Nelde, Markus G. Klammer, Christian H. Nolte, Helena Stengl, Michael Krämer, Regina von Rennenberg, Andreas Meisel, Franziska Scheibe, Matthias Endres, Jan F. Scheitz, Christian Meisel

Published in: Journal of Neurology | Issue 8/2023

Abstract

Background

Post-stroke heart rate (HR) and heart rate variability (HRV) changes have been proposed as outcome predictors after stroke. We used data lake-enabled continuous electrocardiograms to assess post-stroke HR and HRV, and to determine the utility of HR and HRV to improve machine learning-based predictions of stroke outcome.

Methods

In this observational cohort study, we included stroke patients admitted to two stroke units in Berlin, Germany, between October 2020 and December 2021 with final diagnosis of acute ischemic stroke or acute intracranial hemorrhage and collected continuous ECG data through data warehousing. We created circadian profiles of several continuously recorded ECG parameters including HR and HRV parameters. The pre-defined primary outcome was short-term unfavorable functional outcome after stroke indicated through modified Rankin Scale (mRS) score of > 2.

Results

We included 625 stroke patients, 287 stroke patients remained after matching for age and National Institute of Health Stroke Scale (NIHSS; mean age 74.5 years, 45.6% female, 88.9% ischemic, median NIHSS 5). Both higher HR and nocturnal non-dipping of HR were associated with unfavorable functional outcome (p < 0.01). The examined HRV parameters were not associated with the outcome of interest. Nocturnal non-dipping of HR ranked highly in feature importance of various machine learning models.

Conclusions

Our data suggest that a lack of circadian HR modulation, specifically nocturnal non-dipping, is associated with short-term unfavorable functional outcome after stroke, and that including HR into machine learning-based prediction models may lead to improved stroke outcome prediction.

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Title: Data lake-driven analytics identify nocturnal non-dipping of heart rate as predictor of unfavorable stroke outcome at discharge
Authors: Alexander Nelde
Markus G. Klammer
Christian H. Nolte
Helena Stengl
Michael Krämer
Regina von Rennenberg
Andreas Meisel
Franziska Scheibe
Matthias Endres
Jan F. Scheitz
Christian Meisel
Publication date: 20-04-2023
Publisher: Springer Berlin Heidelberg
Keywords: Stroke
Electrocardiography
Electrocardiography
Published in: Journal of Neurology / Issue 8/2023
Print ISSN: 0340-5354
Electronic ISSN: 1432-1459
DOI: https://doi.org/10.1007/s00415-023-11718-x

2024 ASCO Annual Meeting

Springer Medicine

Data lake-driven analytics identify nocturnal non-dipping of heart rate as predictor of unfavorable stroke outcome at discharge

Abstract

Background

Methods

Results

Conclusions

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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