TY - JOUR
T1 - Prognostic models predicting mortality in preterm infants: Systematic review and meta-analysis
AU - van Beek, Pauline E.
AU - Andriessen, Peter
AU - Onland, Wes
AU - Schuit, Ewoud
N1 - Funding Information: FUNDING: Dr van Beek was supported by an unrestricted grant from Stichting Tiny & Anny van Doorne Fonds. The funding source had no role in the design, conduct, analyses, or reporting of the study or in the decision to submit the manuscript for publication. The other authors received no external funding. Publisher Copyright: © 2021 by the American Academy of Pediatrics.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - CONTEXT: Prediction models can be a valuable tool in performing risk assessment of mortality in preterm infants. OBJECTIVE: Summarizing prognostic models for predicting mortality in very preterm infants and assessing their quality. DATA SOURCES: Medline was searched for all articles (up to June 2020). STUDY SELECTION: All developed or externally validated prognostic models for mortality prediction in liveborn infants born ,32 weeks' gestation and/or ,1500 g birth weight were included. DATA EXTRACTION: Data were extracted by 2 independent authors. Risk of bias (ROB) and applicability assessment was performed by 2 independent authors using Prediction model Risk of Bias Assessment Tool. RESULTS: One hundred forty-two models from 35 studies reporting on model development and 112 models from 33 studies reporting on external validation were included. ROB assessment revealed high ROB in the majority of the models, most often because of inadequate (reporting of) analysis. Internal and external validation was lacking in 41% and 96% of these models. Meta-analyses revealed an average C-statistic of 0.88 (95% confidence interval [CI]: 0.83-0.91) for the Clinical Risk Index for Babies score, 0.87 (95% CI: 0.81-0.92) for the Clinical Risk Index for Babies II score, and 0.86 (95% CI: 0.78-0.92) for the Score for Neonatal Acute Physiology Perinatal Extension II score. LIMITATIONS: Occasionally, an external validation study was included, but not the development study, because studies developed in the presurfactant era or general NICU population were excluded. CONCLUSIONS: Instead of developing additional mortality prediction models for preterm infants, the emphasis should be shifted toward external validation and consecutive adaption of the existing prediction models.
AB - CONTEXT: Prediction models can be a valuable tool in performing risk assessment of mortality in preterm infants. OBJECTIVE: Summarizing prognostic models for predicting mortality in very preterm infants and assessing their quality. DATA SOURCES: Medline was searched for all articles (up to June 2020). STUDY SELECTION: All developed or externally validated prognostic models for mortality prediction in liveborn infants born ,32 weeks' gestation and/or ,1500 g birth weight were included. DATA EXTRACTION: Data were extracted by 2 independent authors. Risk of bias (ROB) and applicability assessment was performed by 2 independent authors using Prediction model Risk of Bias Assessment Tool. RESULTS: One hundred forty-two models from 35 studies reporting on model development and 112 models from 33 studies reporting on external validation were included. ROB assessment revealed high ROB in the majority of the models, most often because of inadequate (reporting of) analysis. Internal and external validation was lacking in 41% and 96% of these models. Meta-analyses revealed an average C-statistic of 0.88 (95% confidence interval [CI]: 0.83-0.91) for the Clinical Risk Index for Babies score, 0.87 (95% CI: 0.81-0.92) for the Clinical Risk Index for Babies II score, and 0.86 (95% CI: 0.78-0.92) for the Score for Neonatal Acute Physiology Perinatal Extension II score. LIMITATIONS: Occasionally, an external validation study was included, but not the development study, because studies developed in the presurfactant era or general NICU population were excluded. CONCLUSIONS: Instead of developing additional mortality prediction models for preterm infants, the emphasis should be shifted toward external validation and consecutive adaption of the existing prediction models.
UR - http://www.scopus.com/inward/record.url?scp=85105261954&partnerID=8YFLogxK
U2 - https://doi.org/10.1542/peds.2020-020461
DO - https://doi.org/10.1542/peds.2020-020461
M3 - Review article
C2 - 33879518
SN - 0031-4005
VL - 147
JO - Pediatrics
JF - Pediatrics
IS - 5
M1 - e2020020461
ER -