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Risk prediction models for emergence delirium in paediatric general anaesthesia: a systematic review
Maria-Alexandra Petre1, Bibek Saha2, Shugo Kasuya3
1Department of Pediatric Anesthesia, Montreal Children's Hospital, McGill University, Montreal, Quebec, Canada.
Insights
Emergence delirium (ED) prediction in children is crucial. The Emergence Agitation Risk Scale (EARS) shows good discrimination but has low usability due to high bias, necessitating new predictive models.
Area of Science:
- Anesthesiology
- Pediatric Medicine
- Medical Informatics
Background:
- Emergence delirium (ED) affects approximately 25% of pediatric general anesthetics, leading to adverse effects.
- Predictive models for ED are needed to improve patient outcomes and anesthetic management.
Purpose of the Study:
- To systematically review existing literature on predictive models for pediatric ED following general anesthesia.
- To determine the usability and performance characteristics of identified predictive models.
Main Methods:
- A systematic review was conducted using the PROBAST framework.
- Searched multiple databases (Medline, PubMed, Embase, Cochrane, PsycINFO, Scopus, Web of Science) and clinical trial registries.
- Included randomized controlled trials and cohort studies investigating predictive models for pediatric ED.
Main Results:
- Only one study, developing the Emergence Agitation Risk Scale (EARS), met inclusion criteria.
- EARS demonstrated good discrimination (c-index 0.81) and calibration (p=0.97).
- Despite low applicability concern, high risk of bias compromised EARS' overall usability.
Conclusions:
- The EARS model shows good discrimination but limited usability for predicting ED in pediatric patients.
- Further research is essential to develop novel, reliable predictive models for pediatric anesthesia.
- Improved prediction of ED can enhance patient safety and anesthetic care.
Objectives:
Emergence delirium (ED) occurs in approximately 25% of paediatric general anaesthetics and has significant adverse effects. The goal of the current systematic review was to identify the existing literature investigating performance of predictive models for the development of paediatric ED following general anaesthesia and to determine their usability.
Design:
Systematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.
Data Sources:
Medline (Ovid), PubMed, Embase (Ovid), Cochrane Database of Systematic Reviews (Ovid), Cochrane CENTRAL (Ovid), PsycINFO (Ovid), Scopus (Elsevier) and Web of Science (Clarivate Analytics), ClinicalTrials.gov, International Clinical Trials Registry Platform and ProQuest Digital Dissertations and Theses International through 17 November 2020.
Eligibility Criteria For Selecting Studies:
All randomised controlled trials and cohort studies investigating predictive models for the development of ED in children undergoing general anaesthesia.
Data Extraction And Synthesis:
Following title, abstract and full-text screening by two reviewers, data were extracted from all eligible studies, including demographic parameters, details of anaesthetics and performance characteristics of the predictive scores for ED. Evidence quality and predictive score usability were assessed according to the PROBAST framework.
Results:
The current systematic review yielded 9242 abstracts, of which only one study detailing the development and validation of the Emergence Agitation Risk Scale (EARS) met the inclusion criteria. EARS had good discrimination with c-index of 0.81 (95% CI 0.72 to 0.89). Calibration showed a non-significant Homer-Lemeshow goodness-of-fit test (p=0.97). Although the EARS demonstrated low concern of applicability, the high risk of bias compromised the overall usability of this model.
Conclusions:
The current systematic review concluded that EARS has good discrimination performance but low usability to predict ED in a paediatric population. Further research is warranted to develop novel models for the prediction of ED in paediatric anaesthesia.
Prospero Registration Number:
CRD42019141950.
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