Determining predictors of sepsis at triage among children under 5 years of age in resource-limited settings: a modified Delphi process

Autor: J. Mark Ansermino, Niranjan Kissoon, Matthew O. Wiens, Jollee S. T. Fung, Samuel Akech, Mike English
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Male
Critical Care and Emergency Medicine
Pulmonology
Databases
Factual
Delphi Technique
Delphi method
Pathology and Laboratory Medicine
Global Health
Pediatrics
Mathematical and Statistical Techniques
0302 clinical medicine
Health care
Medicine and Health Sciences
Public and Occupational Health
030212 general & internal medicine
Cause of death
Multidisciplinary
Neonatal sepsis
Statistics
Child Health
3. Good health
Systematic review
Child
Preschool
Physical Sciences
Medicine
Female
Medical emergency
Neonatal Sepsis
Pediatric Infections
Research Article
Science
030231 tropical medicine
MEDLINE
Research and Analysis Methods
03 medical and health sciences
Signs and Symptoms
Diagnostic Medicine
Predictive Value of Tests
Sepsis
medicine
Humans
Statistical Methods
business.industry
Infant
Guideline
medicine.disease
Triage
Respiratory Infections
business
Mathematics
Forecasting
Zdroj: PLoS ONE, Vol 14, Iss 1, p e0211274 (2019)
PLoS ONE
Popis: Sepsis is a life-threatening dysfunction of the immune system leading to multiorgan failure that is precipitated by infectious diseases and is a leading cause of death in children under 5 years of age. It is necessary to be able to identify a sick child at risk of developing sepsis at the earliest point of presentation to a healthcare facility so that appropriate care can be provided as soon as possible. Our study objective was to generate a list of consensus-driven predictor variables for the derivation of a prediction model that will be incorporated into a mobile device and operated by low-skilled healthcare workers at triage. By conducting a systematic literature review and examination of global guideline documents, a list of 72 initial candidate predictor variables was generated. A two-round modified Delphi process involving 26 experts from both resource-rich and resource-limited settings, who were also encouraged to suggest new variables, yielded a final list of 45 predictor variables after evaluating each variable based on three domains: predictive potential, measurement reliability, and level of training and resources required. The final list of predictor variables will be used to collect data and contribute to the derivation of a prediction model.
Databáze: OpenAIRE
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