Emma Persad1,2,3,4, Kerstin Jost1,2, Antoine Honoré1,2,5, David Forsberg1,2, Karen Coste1,6, Hanna Olsson1, Susanne Rautiainen1,2,7, Eric Herlenius1,2. 1. Department of Women's & Children's Health, Karolinska Institutet, Stockholm, Sweden. 2. Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden. 3. Karl Landsteiner University of Health Sciences, Krems, Austria. 4. Department of Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria. 5. Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden. 6. CNRS, INSERM, GReD, Université Clermont Auvergne, Clermont-Ferrand, France. 7. Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
Abstract
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.
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.
Authors: Melissa L Arvay; Nong Shang; Shamim A Qazi; Gary L Darmstadt; Mohammad Shahidul Islam; Daniel E Roth; Anran Liu; Nicholas E Connor; Belal Hossain; Qazi Sadeq-Ur Rahman; Shams El Arifeen; Luke C Mullany; Anita K M Zaidi; Zulfiqar A Bhutta; Sajid B Soofi; Yasir Shafiq; Abdullah H Baqui; Dipak K Mitra; Pinaki Panigrahi; Kalpana Panigrahi; Anuradha Bose; Rita Isaac; Daniel Westreich; Steven R Meshnick; Samir K Saha; Stephanie J Schrag Journal: Lancet Glob Health Date: 2022-09 Impact factor: 38.927