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Goodwin A, Mazwi ML, Somer J, et al. Blood Pressure in Critically Ill Children: Exploratory Analyses of Concurrent Invasive and Noninvasive Measurements. Crit Care Explor. 2021;3(12):e0586doi: 10.1097/CCE.0000000000000586.
Goodwin, A., Mazwi, M. L., Somer, J., Schwartz, S. M., McEwan, A., & Eytan, D. (2021). Blood Pressure in Critically Ill Children: Exploratory Analyses of Concurrent Invasive and Noninvasive Measurements. Critical care explorations, 3(12), e0586. https://doi.org/10.1097/CCE.0000000000000586
Goodwin, Andrew, et al. "Blood Pressure in Critically Ill Children: Exploratory Analyses of Concurrent Invasive and Noninvasive Measurements." Critical care explorations vol. 3,12 (2021): e0586. doi: https://doi.org/10.1097/CCE.0000000000000586
Goodwin A, Mazwi ML, Somer J, Schwartz SM, McEwan A, Eytan D. Blood Pressure in Critically Ill Children: Exploratory Analyses of Concurrent Invasive and Noninvasive Measurements. Crit Care Explor. 2021 Dec 02;3(12):e0586. doi: 10.1097/CCE.0000000000000586. eCollection 2021 Dec. PMID: 34984339; PMCID: PMC8718171.
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Crit Care Explor. 2021 Dec 02;3(12):e0586. doi: 10.1097/CCE.0000000000000586. eCollection 2021 Dec.

Blood Pressure in Critically Ill Children: Exploratory Analyses of Concurrent Invasive and Noninvasive Measurements.

Critical care explorations

Andrew Goodwin, Mjaye L Mazwi, Jonathan Somer, Steven M Schwartz, Alistair McEwan, Danny Eytan

Affiliations

  1. Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.
  2. School of Biomedical Engineering, University of Sydney, Sydney, NSW, Australia.
  3. Faculty of Medicine, Department of Physiology, Technion, Haifa, Israel.
  4. Department of Pediatric Critical Care, Rambam Medical Center, Haifa, Israel.

PMID: 34984339 PMCID: PMC8718171 DOI: 10.1097/CCE.0000000000000586

Abstract

OBJECTIVES: Differences and biases between directly measured intra-arterial blood pressure and intermittingly measured noninvasive blood pressure using an oscillometric cuff method have been reported in adults and children. At the bedside, clinicians are required to assign a confidence to a specific blood pressure measurement before acting upon it, and this is challenging when there is discordance between measurement techniques. We hypothesized that big data could define and quantify the relationship between noninvasive blood pressure and intra-arterial blood pressure measurements and how they can be influenced by patient characteristics, thereby aiding bedside decision-making.

DESIGN: A retrospective analysis of cuff blood pressure readings with associated concurrent invasive arterial blood pressure measurements (452,195 noninvasive blood pressure measurements).

SETTING: Critical care unit at The Hospital for Sick Children, Toronto.

PATIENTS: Six-thousand two-hundred ninety-seven patients less than or equal to 18 years old, hospitalized in a critical care unit with an indwelling arterial line.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Two-dimensional distributions of intra-arterial blood pressure and noninvasive blood pressure were generated and the conditional distributions of intra-arterial blood pressure examined as a function of the noninvasive systolic, diastolic, or mean blood pressure. Modification of these distributions according to age and gender were examined using a multilevel mixed-effects model. For any given combination of patient age and noninvasive blood pressure, the expected distribution of intra-arterial blood pressure readings exhibited marked variability at the population level and a bias that significantly depended on the noninvasive blood pressure value and age. We developed an online tool that allows exploration of the relationship between noninvasive blood pressure and intra-arterial blood pressure and the conditional probability distributions according to age.

CONCLUSIONS: A large physiologic dataset provides clinically applicable insights into the relationship between noninvasive blood pressure and intra-arterial blood pressure measurements that can help guide decision-making at the patient bedside.

Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.

Keywords: big data; blood pressure; critical care

Conflict of interest statement

The authors have disclosed that they do not have any potential conflicts of interest.

References

  1. IEEE Rev Biomed Eng. 2015;8:44-63 - PubMed
  2. J Am Coll Cardiol. 2017 Aug 1;70(5):572-586 - PubMed
  3. Intensive Care Med. 2007 Apr;33(4):575-90 - PubMed
  4. Pediatr Crit Care Med. 2018 Feb;19(2):115-124 - PubMed
  5. Front Pediatr. 2017 Mar 17;5:52 - PubMed
  6. Ann Biomed Eng. 1982;10(6):271-80 - PubMed
  7. Hypertension. 2018 Mar;71(3):368-374 - PubMed
  8. Intensive Care Med. 2017 Oct;43(10):1540-1541 - PubMed
  9. Physiol Meas. 2020 Apr 20;41(3):035008 - PubMed
  10. Sci Rep. 2020 Feb 25;10(1):3382 - PubMed
  11. IEEE Trans Biomed Eng. 1996 Aug;43(8):761-78 - PubMed
  12. J Biomech Eng. 1980 Feb;102(1):28-33 - PubMed
  13. Anesthesiology. 2011 Nov;115(5):973-8 - PubMed
  14. Crit Care. 2016 Jun 08;20(1):177 - PubMed
  15. PLoS One. 2018 Dec 28;13(12):e0209831 - PubMed
  16. Anesth Essays Res. 2017 Jan-Mar;11(1):169-173 - PubMed
  17. J Hypertens. 2011 Mar;29(3):425-9 - PubMed

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