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Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs

Pediatric Research volume 93, pages 1913–1921 (2023)Cite this article

Abstract

Background

Heart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO2) data contain signatures that improve sepsis risk prediction over HR or demographics alone.

Methods

We analyzed cardiorespiratory data from very low birth weight (VLBW, <1500 g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10 m: mean, standard deviation, skewness, kurtosis of HR and SpO2, and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO2 features alone for comparison with HR-SpO2 models.

Results

Performance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO2 model performed better than models using either HR or SpO2 alone. Demographics improved the discrimination of all physiologic data models but dampened dynamic performance.

Conclusions

Cardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction.

Impact

  • Heart rate characteristics aid early detection of late-onset sepsis, but respiratory data contain signatures of illness due to infection.

  • Predictive models using both heart rate and respiratory data may improve early sepsis detection.

  • A cardiorespiratory early warning score, analyzing heart rate from electrocardiogram or pulse oximetry with SpO2, predicts late-onset sepsis within 24 h across multiple NICUs and detects sepsis better than heart rate characteristics or demographics alone.

  • Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction.

  • The results increase understanding of physiologic signatures of neonatal sepsis.

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Fig. 1: Schematic overview of methods.
Fig. 2: Calibration plots.
Fig. 3: The average risk of sepsis.
Fig. 4: Variable importance plots.
Fig. 5: Evaluating the model sensitivity across a range of thresholds.

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Data availability

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

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Funding

We acknowledge the following grants for funding the work presented in this manuscript: K23 HD097254 [PI: B Sullivan]; R01 HD092071 [Co-PIs KD Fairchild & JR Moorman, Co-I DE Lake] K23NS111086 [PI: Z Vesoulis].

Author information

Authors and Affiliations

  1. Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA

    Sherry L. Kausch, Karen D. Fairchild & Brynne A. Sullivan

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

    Jackson G. Brandberg, Jiaxing Qiu, J. Randall Moorman & Douglas E. Lake

  3. Department of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, MO, USA

    Aneesha Panda, Alexandra Binai & Zachary A. Vesoulis

  4. Department of Pediatrics, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA

    Joseph Isler & Rakesh Sahni

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  1. Sherry L. Kausch
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Contributions

S.L.K., B.S., K.F., D.L., R.S., Z.V., and J.R.M. have made substantial contributions to the conception or design of the work; S.L.K., J.Q., J.B., A.P., A.B., and J.I. made substantial contributions to the acquisition, analysis, or interpretation of data; SK and BS drafted the work and all other authors have substantively revised it. All authors have approved the submitted version. All authors have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Corresponding author

Correspondence to Sherry L. Kausch.

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Competing interests

Some authors have financial conflicts of interest. J.R.M. and D.E.L. own stock in Medical Prediction Sciences Corporation. J.R.M. is a consultant for Nihon Kohden Digital Health Solutions. Z.A.V. is a consultant for Medtronic. All other authors have no financial conflicts to disclose. No authors have any non-financial conflicts of interest to disclose.

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Kausch, S.L., Brandberg, J.G., Qiu, J. et al. Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs. Pediatr Res 93, 1913–1921 (2023). https://doi.org/10.1038/s41390-022-02444-7

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