Neonatal sepsis prediction through clinical decision support algorithms: A systematic reviewShow others and affiliations
2021 (English)In: Acta Paediatrica, ISSN 0803-5253, E-ISSN 1651-2227, Vol. 110, no 12, p. 3201-3226Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
Wiley , 2021. Vol. 110, no 12, p. 3201-3226
Keywords [en]
algorithm, clinical decision system, machine learning, neonatal sepsis, sepsis detection, C reactive protein, abdominal distension, antibiotic therapy, Apgar score, apnea, artificial ventilation, aspiration, blood culture, bloodstream infection, body temperature, bradycardia, breathing pattern, clinical assessment, clinical decision support system, clinical evaluation, clinical feature, clinical practice, coagulase negative Staphylococcus, cyanosis, data extraction, decision support system, diagnostic accuracy, endotracheal intubation, evidence based practice, funding, GRADE approach, heart rate, heart rate variability, human, hyperthermia, hypotension, hypothermia, inflammation, length of stay, lethargy, mortality rate, neonatal intensive care unit, newborn, newborn sepsis, outcome assessment, periodic breathing, pneumonia, prediction, predictive value, prematurity, prospective study, randomized controlled trial (topic), receiver operating characteristic, resuscitation, Review, risk assessment, risk factor, seizure, septicemia, systematic review, tachycardia, tachypnea, very low birth weight, vital sign, infant, sepsis, statistical bias, Algorithms, Bias, Decision Support Systems, Clinical, Humans, Infant, Newborn
National Category
Pediatrics
Identifiers
URN: urn:nbn:se:kth:diva-311644DOI: 10.1111/apa.16083ISI: 000695085200001PubMedID: 34432903Scopus ID: 2-s2.0-85114749059OAI: oai:DiVA.org:kth-311644DiVA, id: diva2:1655393
Note
QC 20220502
2022-05-022022-05-022022-06-25Bibliographically approved