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Prognostic prediction models for adverse birth outcomes: A systematic review
Achenef Asmamaw Muche1,2, Likelesh Lemma Baruda1,3, Clara Pons-Duran4
1Health System and Reproductive Health Research Directorate, Ethiopian Public Health Institute, Addis Ababa, Ethiopia.
Insights
This review found varied factors and high risk of bias in models predicting adverse birth outcomes like preterm birth and low birth weight. Consistent factors and external validation are recommended for future prediction models.
Area of Science:
- Maternal and child health
- Clinical prediction modeling
- Global public health
Background:
- Adverse birth outcomes remain a significant global health challenge despite progress in reducing maternal and child mortality.
- Developing accurate prediction models is crucial for early risk detection and implementing preventive strategies.
- This systematic review focuses on the performance of existing prediction models for adverse birth outcomes.
Conclusions:
- Heterogeneity in prognostic factors and study methodologies complicates the prediction of adverse birth outcomes.
- There is a need for prediction models that utilize consistent prognostic factors and undergo external validation.
- Future models should be adapted for diverse settings to improve the prediction of adverse birth outcomes globally.
Background:
Despite progress in reducing maternal and child mortality worldwide, adverse birth outcomes such as preterm birth, low birth weight (LBW), small for gestational age (SGA), and stillbirth continue to be a major global health challenge. Developing a prediction model for adverse birth outcomes allows for early risk detection and prevention strategies. In this systematic review, we aimed to assess the performance of existing prediction models for adverse birth outcomes and provide a comprehensive summary of their findings.
Methods:
We used the Population, Index prediction model, Comparator, Outcome, Timing, and Setting (PICOTS) approach to retrieve published studies from PubMed/MEDLINE, Scopus, CINAHL, Web of Science, African Journals Online, EMBASE, and Cochrane Library. We used WorldCat, Google, and Google Scholar to find the grey literature. We retrieved data before 1 March 2022. Data were extracted using CHecklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. We assessed the risk of bias with the Prediction Model Risk of Bias Assessment tool. We descriptively reported the results in tables and graphs.
Results:
We included 115 prediction models with the following outcomes: composite adverse birth outcomes (n = 6), LBW (n = 17), SGA (n = 23), preterm birth (n = 71), and stillbirth (n = 9). The sample sizes ranged from composite adverse birth outcomes (n = 32-549), LBW (n = 97-27 233), SGA (n = 41-116 070), preterm birth (n = 31-15 883 784), and stillbirth (n = 180-76 629). Only nine studies were conducted on low- and middle-income countries. 10 studies were externally validated. Risk of bias varied across studies, in which high risk of bias was reported on prediction models for SGA (26.1%), stillbirth (77.8%), preterm birth (31%), LBW (23.5%), and composite adverse birth outcome (33.3%). The area under the receiver operating characteristics curve (AUROC) was the most used metric to describe model performance. The AUROC ranged from 0.51 to 0.83 in studies that reported predictive performance for preterm birth. The AUROC for predicting SGA, LBW, and stillbirth varied from 0.54 to 0.81, 0.60 to 0.84, and 0.65 to 0.72, respectively. Maternal clinical features were the most utilised prognostic markers for preterm and LBW prediction, while uterine artery pulsatility index was used for stillbirth and SGA prediction.
Conclusions:
A varied prognostic factors and heterogeneity between studies were found to predict adverse birth outcomes. Prediction models using consistent prognostic factors, external validation, and adaptation of future risk prediction models for adverse birth outcomes was recommended at different settings.
Registration:
PROSPERO CRD42021281725.
