TY - JOUR
T1 - Predicting Clinical Deterioration and Mortality at Differing Stages During Hospitalization
T2 - A Systematic Review of Risk Prediction Models in Children in Low- and Middle-Income Countries
AU - van den Brink, Deborah A.
AU - de Vries, Isabelle S. A.
AU - Datema, Myrthe
AU - Perot, Lyric
AU - Sommers, Ruby
AU - Daams, Joost
AU - Calis, Job C. J.
AU - Brals, Daniella
AU - Voskuijl, Wieger
N1 - Funding Information: Dr van den Brink participated in the title and abstract review, performed the updated search, full-text screen, and data abstraction; drafted the initial manuscript, and revised the manuscript (DV). Dr de Vries defined the review scope, participated in the title and abstract review, full-text screen, and data abstraction; drafted sections of the initial manuscript, and revised the manuscript (IV). Dr Voskuijl defined the review scope, title and abstract review, full-text screen, drafted sections of the initial manuscript, senior project supervisor, and revised the manuscript (WV). Dr Brals defined the review scope, co-supervised the work, and revised the manuscript. Dr Calis co-supervised the work, revised the manuscript. Ms Summers and Ms Perot participated in title and abstract review, and full screen text (RS and LP). Ms Datema participated in title and abstract review, and full screen text screen, and data abstraction (MD). Mr Daams conducted and supervised literature search (JD). All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Publisher Copyright: © 2023 The Authors
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Objective: To determine which risk prediction model best predicts clinical deterioration in children at different stages of hospital admission in low- and middle-income countries. Methods: For this systematic review, Embase and MEDLINE databases were searched, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The key search terms were “development or validation study with risk-prediction model” AND “deterioration or mortality” AND “age 0-18 years” AND “hospital-setting: emergency department (ED), pediatric ward (PW), or pediatric intensive care unit (PICU)” AND “low- and middle-income countries.” The Prediction Model Risk of Bias Assessment Tool was used by two independent authors. Forest plots were used to plot area under the curve according to hospital setting. Risk prediction models used in two or more studies were included in a meta-analysis. Results: We screened 9486 articles and selected 78 publications, including 67 unique predictive models comprising 1.5 million children. The best performing models individually were signs of inflammation in children that can kill (SICK) (ED), pediatric early warning signs resource limited settings (PEWS-RL) (PW), and Pediatric Index of Mortality (PIM) 3 as well as pediatric sequential organ failure assessment (pSOFA) (PICU). Best performing models after meta-analysis were SICK (ED), pSOFA and Pediatric Early Death Index for Africa (PEDIA)-immediate score (PW), and pediatric logistic organ dysfunction (PELOD) (PICU). There was a high risk of bias in all studies. Conclusions: We identified risk prediction models that best estimate deterioration, although these risk prediction models are not routinely used in low- and middle-income countries. Future studies should focus on large scale external validation with strict methodological criteria of multiple risk prediction models as well as study the barriers in the way of implementation. Trial registration: PROSPERO International Prospective Register of Systematic Reviews: Prospero ID: CRD42021210489.
AB - Objective: To determine which risk prediction model best predicts clinical deterioration in children at different stages of hospital admission in low- and middle-income countries. Methods: For this systematic review, Embase and MEDLINE databases were searched, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The key search terms were “development or validation study with risk-prediction model” AND “deterioration or mortality” AND “age 0-18 years” AND “hospital-setting: emergency department (ED), pediatric ward (PW), or pediatric intensive care unit (PICU)” AND “low- and middle-income countries.” The Prediction Model Risk of Bias Assessment Tool was used by two independent authors. Forest plots were used to plot area under the curve according to hospital setting. Risk prediction models used in two or more studies were included in a meta-analysis. Results: We screened 9486 articles and selected 78 publications, including 67 unique predictive models comprising 1.5 million children. The best performing models individually were signs of inflammation in children that can kill (SICK) (ED), pediatric early warning signs resource limited settings (PEWS-RL) (PW), and Pediatric Index of Mortality (PIM) 3 as well as pediatric sequential organ failure assessment (pSOFA) (PICU). Best performing models after meta-analysis were SICK (ED), pSOFA and Pediatric Early Death Index for Africa (PEDIA)-immediate score (PW), and pediatric logistic organ dysfunction (PELOD) (PICU). There was a high risk of bias in all studies. Conclusions: We identified risk prediction models that best estimate deterioration, although these risk prediction models are not routinely used in low- and middle-income countries. Future studies should focus on large scale external validation with strict methodological criteria of multiple risk prediction models as well as study the barriers in the way of implementation. Trial registration: PROSPERO International Prospective Register of Systematic Reviews: Prospero ID: CRD42021210489.
UR - http://www.scopus.com/inward/record.url?scp=85164696491&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.jpeds.2023.113448
DO - https://doi.org/10.1016/j.jpeds.2023.113448
M3 - Article
C2 - 37121311
SN - 0022-3476
VL - 260
JO - Journal of pediatrics
JF - Journal of pediatrics
M1 - 113448
ER -