Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis

Abstract

Background Current approaches to early detection of clinical deterioration in children have relied on intermittent track-and-trigger warning scores such as the Pediatric Early Warning Score (PEWS) that rely on periodic assessment and vital sign entry. There are limited data on the utility of these scores prior to events of decompensation leading to pediatric intensive care unit (PICU) transfer. Objective The purpose of our study was to determine the accuracy of recorded PEWS scores, assess clinical reasons for transfer, and describe the monitoring practices prior to PICU transfer involving acute decompensation. Methods We conducted a retrospective cohort study of patients ≤21 years of age transferred emergently from the acute care pediatric floor to the PICU due to clinical deterioration over an 8-year period. Clinical charts were abstracted to (1) determine the clinical reason for transfer, (2) quantify the frequency of physiological monitoring prior to transfer, and (3) assess the timing and accuracy of the PEWS scores 24 hours prior to transfer. Results During the 8-year period, 72 children and adolescents had an emergent PICU transfer due to clinical deterioration, most often due to acute respiratory distress. Only 35% (25/72) of the sample was on continuous telemetry or pulse oximetry monitoring prior to the transfer event, and 47% (34/72) had at least one incorrectly documented PEWS score in the 24 hours prior to the event, with a score underreporting the actual severity of illness. Conclusions This analysis provides support for the routine assessment of clinical deterioration and advocates for more research focused on the use and utility of continuous cardiorespiratory monitoring for patients at risk for emergent transfer.

Requirement Analysis for an Intelligent Warning System to Alarm the Rapid Response Team Prior to Patient Deterioration

“…Biosign which is a system working based on vital sign monitoring for CD detection is another choice that has limitations (2). Early Warning Scores (EWS) have limitations of not conveniently being used in practice (13) and the problem of nurse acceptance and adherence (14,15). Although the CD prediction methods using machine learning IOT*: Internet of Things, DL**: Deep Learning, ML***: Machine learning, KB****: Knowledge Base algorithms outperformed the EWS in an academic setting, they were poor to respond acute clinical changes in real clinical status (1); that is, these systems resulted in low certainly evidence indicating little or no difference in-hospital mortality, unplanned ICU admission, length of hospital stay or adverse event (8).…”

Akut symptomatische Anfälle und die Abgrenzung zum Status epilepticus auf der Intensivstation bei Kindern und Erwachsenen

“…Kreislaufunterstützung waren signifikante Risikofaktoren für schlechtes Outcome [3]. Bei Anwendung des PEWS werden 0 bis 3 Punkte in den Domänen "Verhalten", "kardiovaskulär" und "respiratorisch" vergeben, wobei eine höhere Punktezahl ein erhöhtes Risiko ausdrückt [18,19]. ASA betrugen 53,3 % in einer PICU in der Türkei [20].…”

Under-triage: A New Trigger to Drive Quality Improvement in the Emergency Department

“…Commonly used triggers, such as ED revisits with admission, and transfers to intensive care units (ICUs) after inpatient admission, are sensitive but not specific and lack positive predictive value (PPV) in identifying medical errors. 9 , 10 For example, the PPV of ED revisits with admission is approximately 4%; thus, reviewing 24 charts may detect one medical error. 11 The ED lacks an efficient, high-yield trigger for identifying medical errors.…”