Huo, Zhiqiang;
Booth, John;
Monks, Thomas;
Knight, Philip;
Watson, Liam;
Peters, Mark;
Pagel, Christina;
... Li, Kezhi; + view all
(2023)
Distribution and trajectory of vital signs from high-frequency continuous monitoring during pediatric critical care transport.
Intensive Care Medicine – Paediatric and Neonatal
, 1
, Article 18. 10.1007/s44253-023-00018-x.
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Abstract
Objective: To describe comprehensively the distribution and progression of high-frequency continuous vital signs monitoring data for children during critical care transport and explore associations with patient age, diagnosis, and severity of illness. // Design: Retrospective cohort study using prospectively collected vital signs monitoring data linked to patient demographic and transport data. // Setting: A regional pediatric critical care transport team based in London, England. // Patients: Critically ill children (age ≤ 18 years) transported by the Children’s Acute Transport Service (CATS) at Great Ormond Street Hospital (GOSH) between January 2016 and May 2021 with available high-frequency vital signs monitoring data. // Interventions: None. // Main results: Numeric values of heart rate (HR), blood pressure (BP), respiratory rate (RR), oxygen saturations (SpO2), and end-tidal carbon dioxide in ventilated children (etCO2) were extracted at a frequency of one value per second totalling over 40 million data points. Age-varying vital signs (HR, BP, and RR) were standardized using Z scores. The distribution of vital signs measured in the first 10 min of monitoring during transport, and their progression through the transport, were analyzed by age group, diagnosis group and severity of illness group. A complete dataset comprising linked vital signs, patient and transport data was extracted from 1711 patients (27.7% of all transported patients). The study cohort consisted predominantly of infants (median age of 6 months, IQR 0–51), and respiratory illness (36.0%) was the most frequent diagnosis group. Most patients were invasively ventilated (70.7%). The Infection group had the highest average (+ 2.5) and range (− 5 to + 9) of HR Z scores, particularly in septic children. Infants and pre-school children demonstrated a greater reduction in the HR Z score from the beginning to the end of transport compared to older children. // Conclusions: Marked differences in the distribution and progression of vital signs between age groups, diagnosis groups, and severity of illness groups were observed by analyzing the high-frequency data collected during paediatric critical care transport.
| Type: | Article |
|---|---|
| Title: | Distribution and trajectory of vital signs from high-frequency continuous monitoring during pediatric critical care transport |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s44253-023-00018-x |
| Publisher version: | https://doi.org/10.1007/s44253-023-00018-x |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2023. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Critical care transport; Predictive analytics; Physiological monitoring; Data science |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10180738 |
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