Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review
Introduction Neonatal sepsis causes significant morbidity and mortality worldwide but is difficult to diagnose clinically. Clinical prediction models (CPMs) could improve diagnostic accuracy, facilitating earlier treatment for cases and avoiding antibiotic overuse. Neonates in low-income and middle-...
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BMJ Publishing Group
2025-04-01
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| Series: | BMJ Global Health |
| Online Access: | https://gh.bmj.com/content/10/4/e017582.full |
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| author | Michelle Heys Mario Cortina-Borja Kirsty Le Doare Felicity Fitzgerald Samuel R Neal David Musorowegomo Hannah Gannon Gwendoline Chimhini Michele Zaman Sarah S Sturrock |
| author_facet | Michelle Heys Mario Cortina-Borja Kirsty Le Doare Felicity Fitzgerald Samuel R Neal David Musorowegomo Hannah Gannon Gwendoline Chimhini Michele Zaman Sarah S Sturrock |
| author_sort | Michelle Heys |
| collection | DOAJ |
| description | Introduction Neonatal sepsis causes significant morbidity and mortality worldwide but is difficult to diagnose clinically. Clinical prediction models (CPMs) could improve diagnostic accuracy, facilitating earlier treatment for cases and avoiding antibiotic overuse. Neonates in low-income and middle-income countries (LMICs) are disproportionately affected by sepsis, yet no review has comprehensively synthesised evidence for CPMs validated in this setting.Methods We performed a scoping review of CPMs to diagnose neonatal sepsis using Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, Global Index Medicus and the Cochrane Library. The most recent searches were performed on 16 June 2024. We included studies published in English or Spanish that validated a new or existing CPM for neonatal sepsis in any healthcare setting in an LMIC. Studies were excluded if they validated a prognostic model or where data for neonates could not be separated from a larger paediatric population. Studies were selected by two independent reviewers and summarised by narrative synthesis.Results From 4598 unique records, we included 82 studies validating 44 distinct models in 24 252 neonates. Most studies were set in neonatal intensive or special care units (n=64, 78%) in middle-income countries (n=81, 99%) and included neonates already suspected of sepsis (n=58, 71%). Only four studies (5%) were set in the WHO African region, and only one study included data from a low-income country. Two-thirds of CPMs (n=30) required laboratory parameters, and three-quarters (n=34) were only validated in one study.Conclusion Our review highlights several literature gaps, particularly a paucity of studies validating models in the lowest-income countries where neonatal sepsis is most prevalent, and models for the undifferentiated neonatal population that do not rely on laboratory tests. Furthermore, heterogeneity in study populations, definitions of sepsis and reporting of models inhibits meaningful comparison between studies and may hinder progress towards useful diagnostic tools. |
| format | Article |
| id | doaj-art-bb9e5558c5984840aa618edfe8d9754b |
| institution | OA Journals |
| issn | 2059-7908 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Global Health |
| spelling | doaj-art-bb9e5558c5984840aa618edfe8d9754b2025-08-20T02:16:50ZengBMJ Publishing GroupBMJ Global Health2059-79082025-04-0110410.1136/bmjgh-2024-017582Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping reviewMichelle Heys0Mario Cortina-Borja1Kirsty Le Doare2Felicity Fitzgerald3Samuel R Neal4David Musorowegomo5Hannah Gannon6Gwendoline Chimhini7Michele Zaman8Sarah S Sturrock9UCL GOS Institute of Child Health, London, UKUCL GOS Institute of Child Health, London, UKSt George’s University of London, London, UKImperial College London, London, UKUCL GOS Institute of Child Health, London, UKUniversity of Zimbabwe Faculty of Medicine and Health Sciences, Harare, ZimbabweUCL GOS Institute of Child Health, London, UKUniversity of Zimbabwe Faculty of Medicine and Health Sciences, Harare, ZimbabweQueen’s University School of Medicine, Kingston, Ontario, CanadaSt George’s University of London, London, UKIntroduction Neonatal sepsis causes significant morbidity and mortality worldwide but is difficult to diagnose clinically. Clinical prediction models (CPMs) could improve diagnostic accuracy, facilitating earlier treatment for cases and avoiding antibiotic overuse. Neonates in low-income and middle-income countries (LMICs) are disproportionately affected by sepsis, yet no review has comprehensively synthesised evidence for CPMs validated in this setting.Methods We performed a scoping review of CPMs to diagnose neonatal sepsis using Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, Global Index Medicus and the Cochrane Library. The most recent searches were performed on 16 June 2024. We included studies published in English or Spanish that validated a new or existing CPM for neonatal sepsis in any healthcare setting in an LMIC. Studies were excluded if they validated a prognostic model or where data for neonates could not be separated from a larger paediatric population. Studies were selected by two independent reviewers and summarised by narrative synthesis.Results From 4598 unique records, we included 82 studies validating 44 distinct models in 24 252 neonates. Most studies were set in neonatal intensive or special care units (n=64, 78%) in middle-income countries (n=81, 99%) and included neonates already suspected of sepsis (n=58, 71%). Only four studies (5%) were set in the WHO African region, and only one study included data from a low-income country. Two-thirds of CPMs (n=30) required laboratory parameters, and three-quarters (n=34) were only validated in one study.Conclusion Our review highlights several literature gaps, particularly a paucity of studies validating models in the lowest-income countries where neonatal sepsis is most prevalent, and models for the undifferentiated neonatal population that do not rely on laboratory tests. Furthermore, heterogeneity in study populations, definitions of sepsis and reporting of models inhibits meaningful comparison between studies and may hinder progress towards useful diagnostic tools.https://gh.bmj.com/content/10/4/e017582.full |
| spellingShingle | Michelle Heys Mario Cortina-Borja Kirsty Le Doare Felicity Fitzgerald Samuel R Neal David Musorowegomo Hannah Gannon Gwendoline Chimhini Michele Zaman Sarah S Sturrock Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review BMJ Global Health |
| title | Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review |
| title_full | Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review |
| title_fullStr | Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review |
| title_full_unstemmed | Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review |
| title_short | Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review |
| title_sort | clinical prediction models to diagnose neonatal sepsis in low income and middle income countries a scoping review |
| url | https://gh.bmj.com/content/10/4/e017582.full |
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