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  • Clinical Research Article
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Heart rate patterns predicting cerebral palsy in preterm infants

Pediatric Research (2023)Cite this article

Abstract

Background

Heart rate (HR) patterns can inform on central nervous system dysfunction. We previously used highly comparative time series analysis (HCTSA) to identify HR patterns predicting mortality among patients in the neonatal intensive care unit (NICU) and now use this methodology to discover patterns predicting cerebral palsy (CP) in preterm infants.

Method

We studied NICU patients <37 weeks’ gestation with archived every-2-s HR data throughout the NICU stay and with or without later diagnosis of CP (n = 57 CP and 1119 no CP). We performed HCTSA of >2000 HR metrics and identified 24 metrics analyzed on HR data from two 7-day periods: week 1 and 37 weeks’ postmenstrual age (week 1, week 37). Multivariate modeling was used to optimize a parsimonious prediction model.

Results

Week 1 HR metrics with maximum AUC for CP prediction reflected low variability, including “RobustSD” (AUC 0.826; 0.772–0.870). At week 37, high values of a novel HR metric, “LongSD3,” the cubed value of the difference in HR values 100 s apart, were added to week 1 HR metrics for CP prediction. A combined birthweight + early and late HR model had AUC 0.853 (0.805–0.892).

Conclusions

Using HCTSA, we discovered novel HR metrics and created a parsimonious model for CP prediction in preterm NICU patients.

Impact

  • We discovered new heart rate characteristics predicting CP in preterm infants.

  • Using every-2-s HR from two 7-day periods and highly comparative time series analysis, we found a measure of low variability HR week 1 after birth and a pattern of recurrent acceleration in HR at term corrected age that predicted CP.

  • Combined clinical and early and late HR features had AUC 0.853 for CP prediction.

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Fig. 1: CP predictors in the final model.
Fig. 2: HR patterns illustrating RobustSD and LongSD3 HR metrics for CP prediction.

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Data availability

Anonymized HR HCTSA data on UVA NICU patients, with the evidence graph for the clustering, are openly available in the University of Virginia’s LibraData archive at https://doi.org/10.18130/V3/VJXODP.

Code availability

Python code used to process this data is archived in Zenodo at https://doi.org/10.5281/zenodo.4321332. This version and any future versions are also available on GitHub at https://github.com/fairscape/hctsa-py. Our code is licensed under terms of the MIT license (https://opensource.org/licenses/MIT), and is a reimplementation in Python of most of Ben Fulcher’s original MATLAB code, available here: https://github.com/benfulcher/hctsa. Software for clustering analysis and cross-implementation testing, together with the test data, may be found here: https://doi.org/10.5281/zenodo.4627625.

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Funding

L.L. is an iTHRIV Scholar. The iTHRIV Scholars Program is supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Numbers UL1TR003015 and KL2TR003016. L.L. also received funding from AACPDM Pedal with Pete for this project.

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Authors and Affiliations

  1. Department of Pediatrics, Neurodevelopmental and Behavioral Pediatrics, UVA Children’s Hospital, University of Virginia School of Medicine, Charlottesville, VA, USA

    Lisa Letzkus

  2. University of Virginia School of Medicine, Charlottesville, VA, USA

    Robin Picavia, Jackson Brandberg, Jiaxing Qiu & Sherry Kausch

  3. Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA

    Genevieve Lyons

  4. Department of Cardiovascular Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA

    Doug Lake

  5. Department of Pediatrics, Neonatology, UVA Children’s Hospital, University of Virginia School of Medicine, Charlottesville, VA, USA

    Karen Fairchild

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Contributions

L.L., G.L., J.B., D.L., and K.F. contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript. R.P. and S.K. contributed to the data collection and the writing of the manuscript.

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Correspondence to Lisa Letzkus.

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The authors declare no competing interests.

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Ethical approval was provided by the Institutional Review Board of University of Virginia in advance of implementation. A waiver of consent was approved. Patient consent was not required.

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Letzkus, L., Picavia, R., Lyons, G. et al. Heart rate patterns predicting cerebral palsy in preterm infants. Pediatr Res (2023). https://doi.org/10.1038/s41390-023-02853-2

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