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Neonatal sepsis prediction through clinical decision support algorithms: A systematic review
Author(s) -
Persad Emma,
Jost Kerstin,
Honoré Antoine,
Forsberg David,
Coste Karen,
Olsson Hanna,
Rautiainen Susanne,
Herlenius Eric
Publication year - 2021
Publication title -
acta paediatrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 115
eISSN - 1651-2227
pISSN - 0803-5253
DOI - 10.1111/apa.16083
Subject(s) - medicine , sepsis , demographics , intensive care medicine , medline , randomized controlled trial , clinical decision making , clinical trial , algorithm , emergency medicine , computer science , demography , sociology , political science , law
Aim To systematically summarise the current evidence of employing clinical decision support algorithms (CDSAs) using non‐invasive parameters for sepsis prediction in neonates. Methods A comprehensive search in PubMed, CENTRAL and EMBASE was conducted. Screening, data extraction and risk of bias were performed by two authors. The certainty of the evidence was assessed using GRADE. PROSPERO ID: CRD42020205143. Results After abstract and full‐text screening, 36 studies comprising 18,096 infants were included. Most CDSAs evaluated heart rate (HR)‐based parameters. Two publications derived from one randomised‐controlled trial assessing HR characteristics reported significant reduction in 30‐day septicaemia‐related mortality. Thirty‐four non‐randomised studies found promising yet inconclusive results. Conclusion Heart rate‐based parameters are reliable components of CDSAs for sepsis prediction, particularly in combination with additional vital signs and demographics. However, inconclusive evidence and limited standardisation restricts clinical implementation of CDSAs outside of a controlled research environment. Further experimentation and comparison of parameter combinations and testing of new CDSAs are warranted.