Prognostic Models Predicting Mortality in Preterm Infants: Systematic Review and Meta-analysis

Abstract

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.