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Sample entropy correlates with intraventricular hemorrhage and mortality in premature infants early in life

Pediatric Research (2024)Cite this article

Abstract

Background

Mortality and intraventricular hemorrhage (IVH) are common adverse outcomes in preterm infants and are challenging to predict clinically. Sample entropy (SE), a measure of heart rate variability (HRV), has shown predictive power for sepsis and other morbidities in neonates. We evaluated associations between SE and mortality and IVH in the first week of life.

Methods

Participants were 389 infants born before 32 weeks of gestation for whom bedside monitor data were available. A total of 29 infants had IVH grade 3 or 4 and 31 infants died within 2 weeks of life. SE was calculated with the PhysioNet open-source benchmark. Logistic regressions assessed associations between SE and IVH and/or mortality with and without common clinical covariates over various hour of life (HOL) censor points.

Results

Lower SE was associated with mortality by 4 HOL, but higher SE was very strongly associated with IVH and mortality at 24–96 HOL. Bootstrap testing confirmed SE significantly improved prediction using clinical variables at 96 HOL.

Conclusion

SE is a significant predictor of IVH and mortality in premature infants. Given IVH typically occurs in the first 24–72 HOL, affected infants may initially have low SE followed by a sustained period of high SE.

Impact

  • SE correlates with IVH and mortality in preterm infants early in life.

  • SE combined with clinical factors yielded ROC AUCs well above 0.8 and significantly outperformed the clinical model at 96 h of life. Previous studies had not shown predictive power over clinical models.

  • First study using the PhysioNet Cardiovascular Toolbox benchmark in young infants.

  • Relative to the generally accepted timing of IVH in premature infants, we saw lower SE before or around the time of hemorrhage and a sustained period of higher SE after. Higher SE after acute events has not been reported previously.

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Fig. 1: Mean SE by hour of life grouped according to clinical outcomes.
Fig. 2: Distribution of SE by hour of life grouped according to clinical outcomes depicting only the cases and gestational age-matched controls.
Fig. 3: Additive impact of SE on logistic models at different HOL censor points measured by ROC AUC mean and 95% confidence intervals estimated from bootstrap analysis, 500 iterations on random 80% samples of the training data.

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Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research used data or services provided by STARR, “STAnford medicine Research data Repository,” a clinical data warehouse containing live Epic data from Stanford Health Care, the Stanford Children’s Hospital, the University Healthcare Alliance and Packard Children’s Health Alliance clinics and other auxiliary data from Hospital applications such as radiology PACS. STARR platform is developed and operated by the Stanford Medicine Research Technology team and is made possible by the Stanford School of Medicine Research Office. This work was supported by a grant from the Stanford Maternal & Child Health Research Institute (MCHRI, https://med.stanford.edu/mchri.html) with Melissa Scala as the principal investigator. Additional support included a faculty scholar award from MCHRI and resources generated from NIH/NICHD (R00 HD084749-01A1) to Katherine Travis (PI).

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Authors and Affiliations

  1. Neonatal and Developmental Medicine, Stanford University School of Medicine, Palo Alto, CA, USA

    Michael D. Scahill, Valerie Chock & Melissa Scala

  2. Developmental Behavioral Medicine, Stanford University School of Medicine, Palo Alto, CA, USA

    Katherine Travis & Molly Lazarus

  3. Advanced Algorithm Research Center, Hospital Patient Monitoring, Philips Healthcare, Sunnyvale, CA, USA

    Eric Helfenbein

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Contributions

M.D.S., V.C., K.T. and M.S. were responsible for the study design. M.D.S. and M.S. were responsible for the literature search and manuscript drafting. All authors were responsible for the critical revision of the manuscript, contributed to the article and approved the submitted version.

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Correspondence to Michael D. Scahill.

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Competing interests

E.H. is a full-time employee of Philips Healthcare and owns stock in the company. M.D.S., V.C., K.T., M.L., and M.S. declare no competing interests.

Ethical approval

The Stanford University Institutional Review Board approved this study (protocol number 50602). All data were gathered in the course of routine clinical care and anonymized, and so no specific consent was indicated.

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Scahill, M.D., Chock, V., Travis, K. et al. Sample entropy correlates with intraventricular hemorrhage and mortality in premature infants early in life. Pediatr Res (2024). https://doi.org/10.1038/s41390-024-03075-w

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