Technical challenges related to implementation of a formula one real time data acquisition and analysis system in a paediatric intensive care unit

Most existing, expert monitoring systems do not provide the real time continuous analysis of the monitored physiological data that is necessary to detect transient or combined vital sign indicators nor do they provide long term storage of the data for retrospective analyses. In this paper we examine the feasibility of implementing a long term data storage system which has the ability to incorporate real-time data analytics, the system design, report the main technical issues encountered, the solutions implemented and the statistics of the data recorded. McLaren Electronic Systems expertise used to continually monitor and analyse the data from F1 racing cars in real time was utilised to implement a similar real-time data recording platform system adapted with real time analytics to suit the requirements of the intensive care environment. We encountered many technical (hardware and software) implementation challenges. However there were many advantages of the system once it was operational. They include: (1) The ability to store the data for long periods of time enabling access to historical physiological data. (2) The ability to alter the time axis to contract or expand periods of interest. (3) The ability to store and review ECG morphology retrospectively. (4) Detailed post event (cardiac/respiratory arrest or other clinically significant deteriorations in patients) data can be reviewed clinically as opposed to trend data providing valuable clinical insight. Informed mortality and morbidity reviews can be conducted. (5) Storage of waveform data capture to use for algorithm development for adaptive early warning systems. Recording data from bed-side monitors in intensive care/wards is feasible. It is possible to set up real time data recording and long term storage systems. These systems in future can be improved with additional patient specific metrics which predict the status of a patient thus paving the way for real time predictive monitoring

Escalation of care in children at high risk of clinical deterioration in a tertiary care children’s hospital using the Bedside Pediatric Early Warning System

Background: Escalation and de-escalation are a routine part of high-quality care that should be matched with clinical needs. The aim of this study was to describe escalation of care in relation to the occurrence and timing of Pediatric Intensive Care Unit (PICU) admission in a cohort of pediatric inpatients with acute worsening of their clinical condition. Methods: A monocentric, observational cohort study was performed from January to December 2018. Eligible patients were children: 1) admitted to one of the inpatient wards other than ICU; 2) under the age of 18 years at the time of admission; 3) with two or more Bedside-Paediatric-Early-Warning-System (BedsidePEWS) scores ≥ 7 recorded at a distance of at least one hour and for a period of 4 h during admission. The main outcome -the 24-h disposition – was defined as admission to PICU within 24-h of enrolment or staying in the inpatient ward. Escalation of care was measured using an eight-point scale—the Escalation Index (EI), developed by the authors. The EI was calculated every 6 h, starting from the moment the patient was considered eligible. Analyses used multivariate quantile and logistic regression models. Results: The 228 episodes included 574 EI calculated scores. The 24-h disposition was the ward in 129 (57%) and the PICU in 99 (43%) episodes. Patients who were admitted to PICU within 24-h had higher top EI scores [median (IQR) 6 (5–7) vs 4 (3–5), p < 0.001]; higher initial BedsidePEWS scores [median (IQR) 10(8–13) vs. 9 (8–11), p = 0.02], were less likely to have a chronic disease [n = 62 (63%) vs. n = 127 (98%), p < 0.0001], and were rated by physicians as being at a higher risk of having a cardiac arrest (p = 0.01) than patients remaining on the ward. The EI increased over 24 h before urgent admission to PICU or cardiac arrest by 0.53 every 6-h interval (CI 0.37–0.70, p < 0.001), while it decreased by 0.25 every 6-h interval (CI -0.36–0.15, p < 0.001) in patients who stayed on the wards. Conclusion: Escalation of care was related to temporal changes in severity of illness, patient background and environmental factors. The EI index can improve responses to evolving critical illness