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Published on: August 25, 2022
Prediction models for intraventricular hemorrhage in very preterm infants: a systematic review
Ping Xiong1, Yonggang Wei1, Lei Li1
1Department of Neonatology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China.
Insights
This review critically appraises prediction models for Intraventricular hemorrhage (IVH) in very preterm infants. Many models show high risk of bias, necessitating larger sample sizes and improved data handling for future development.
Area of Science:
- Neonatal Medicine
- Clinical Prediction Modeling
- Perinatal Epidemiology
Background:
- Intraventricular hemorrhage (IVH) is a significant concern in very preterm infants.
- Accurate prediction models are crucial for early intervention and improved outcomes.
Purpose of the Study:
- To provide an overview and critical appraisal of existing prediction models for IVH in very preterm infants.
- To identify key predictors and assess the quality of current models.
Main Methods:
- A comprehensive literature search was conducted across major databases up to February 2025.
- Studies developing or validating IVH prediction models in infants born at <32 weeks were included.
- Risk of bias and applicability were assessed using standardized tools (TRIPOD-SRMA, PREDOA).
Main Results:
- 30 models from 11 development studies and 2 from 2 validation studies were analyzed.
- Common predictors included gestational age, sex, antenatal corticosteroids, and blood pressure.
- The median C-statistic for model development was 0.83, but most studies had a high risk of bias.
Conclusions:
- Existing IVH prediction models for preterm infants often suffer from high risk of bias.
- Future research should focus on augmenting sample sizes, improving data handling, and optimizing statistical analysis for better generalizability.
Objective:
To provide an overview and critical appraisal of prediction models for Intraventricular hemorrhage (IVH) in very preterm infants.
Methods:
Our comprehensive literature search encompassed PubMed (MEDLINE), Embase, Web of Science, the Cochrane Library along with targeted searches of the Chinese Medical Association's online journal platform (up to 8 February 2025). We examined relevant citations during full-text review and thoroughly evaluated them for inclusion. We included studies that reported the development and/or validation of predictive models for IVH in preterm infants born at <32 weeks. We extracted the data independently based on the TRIPOD-SRMA checklist. We checked for risk of bias and applicability independently using the Prediction model Risk Of Bias Assessment.
Results:
A total of 30 prediction models from 11 studies reporting on model development and 2 models from 2 studies reporting on external validation were included in the analysis. The most frequently reported outcome in both model development studies (54.5%) and model validation studies (50%) was IVH I-IV. The most frequently used predictors in the models were gestational age (43.33%), followed by sex (36.67%), antenatal corticosteroids (33.33%), diastolic blood pressure (33.33%), birth weight (30%), and mean airway pressure (30%). The median C-statistic reported at model development was 0.83 (range 0.74-0.99). The majority of the included studies had a high risk of bias, mainly due to suboptimal analysis and mishandling of missing data. Furthermore, small sample sizes and insufficient numbers of event patients were observed in both types of studies. No meta-analysis was performed because no two studies validated the same model in comparable populations. We summarized performance metrics (e.g., C-statistic) descriptively.
Conclusion:
The included studies may still be flawed to a certain extent. It is recommended that future studies augment the sample size and number of events, whilst ensuring that any missing data is addressed in a rational manner. Furthermore, the statistical analysis should be optimised, and the study made transparent for the purpose of model generalisation.
