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Article Open Access

Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants

  • Authors:
    • Li Li
    • Yanlin Gao
    • Wei Chen
    • Mei Han
  • View Affiliations / Copyright

    Affiliations: Tianjin Key Laboratory of Ophthalmology and Vision Science, Tianjin Eye Hospital, Tianjin 300020, P.R. China
    Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 4.0].
  • Article Number: 195
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    Published online on: October 22, 2025
       https://doi.org/10.3892/br.2025.2073
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Abstract

Retinopathy of prematurity (ROP) is a proliferative vascular disease affecting preterm infants with incompletely developed retinal vasculature, characterized by abnormal vascular proliferation that can lead to retinal detachment and blindness. Given its impact on neonatal visual health, developing reliable risk prediction models for ROP has become crucial for optimizing clinical screening and intervention strategies. However, existing models exhibit substantial heterogeneity in methodology, validation, and performance, limiting their generalizability across diverse clinical settings. The present study aimed to evaluate and summarize the effectiveness of existing ROP risk prediction models in preterm infants through a systematic review and meta‑analysis, with the goal of providing reliable clinical screening tools based on effectiveness metrics. A systematic search was conducted across PubMed, Cochrane Library, Web of Science and Embase databases using a strategy that combined MeSH terms and free‑text words to identify literature associated with risk prediction models for ROP in preterm infants. The risk of bias was assessed using the PROBAST tool. Statistical analysis involved data synthesis, heterogeneity testing, subgroup and sensitivity analyses, and publication bias assessment. A total of 492 relevant articles were retrieved; following deduplication and screening, 28 articles involving ROP risk prediction models were included. The included studies were published between 2009 and 2025, with sample sizes ranging from 90 to 22,569 participants, and a total sample size of 72,991. A total of 16 studies did not specify the validation method, five conducted external validation, two performed both internal and external validation, and five performed only internal validation. PROBAST assessment revealed that all included models had a moderate risk of bias, primarily attributed to the retrospective nature of the study design, inconsistent variable measurement and inadequate control of confounding factors. Meta‑analysis showed that the pooled area under the receiver operating characteristic curve (AUC) was 0.87 (95% CI: 0.34; 0.99), indicating good discriminative ability of the models. However, significant heterogeneity was observed (I²=99.2%, P<0.05). Subgroup analysis by model type demonstrated significant heterogeneity in both traditional statistical (I²=92.2%) and machine learning models (I²=97.3%). Subgroup analysis by study region showed no significant heterogeneity in studies from South America (I²=0%), while high heterogeneity was found in studies from Asia and North America + Europe (I²=96.6 and 93.6%, respectively). This may be associated with cross‑regional differences in population characteristics (such as ethnicity and disease spectra) and variations in medical standards. Funnel plot and Peters' bias test indicated high reliability of the overall study conclusions, and the results of the sensitivity analysis were stable. However, some studies had small sample sizes and single‑center designs, leading to selection bias. Additionally, multiple studies lacked model validation, and samples were limited to specific regions, failing to cover diverse healthcare settings and ethnic groups. In conclusion, current ROP risk prediction models for preterm infants exhibit good clinical application potential, with certain discriminative and predictive abilities, which can provide references for clinical screening. However, the risk of bias and insufficient validation limit their generalization ability. Future studies should expand sample sizes through prospective designs, strengthen external validation and optimize model development to improve prediction accuracy and universality, addressing the identified risks of bias and limited generalizability.
View Figures

Figure 1

Flowchart of literature screening.
Initial retrieval from PubMed, Cochrane Library, Web of Science and
Embase yielded 492 records. After removing 163 duplicates, 329
records remained. Following the exclusion of 83 ineligible records
(meta-analyses, reviews, animal studies, conference abstracts), 246
full texts were assessed. Further exclusions (n=218) included
studies without prediction models. Ultimately, 28 studies were
included, encompassing 28 retinopathy of prematurity risk
prediction models with a total sample size of 72,991 infants.

Figure 2

Publication bias of the included
studies.

Figure 3

Specific assessment results of
PROBAST. Risk of bias evaluation for 28 included studies using the
PROBAST tool. Each study was assessed across 20 items in four
domains (participants, predictors, outcomes, analysis), with an
overall unclear risk of bias. PROBAST, Prediction Model Risk Of
Bias Assessment Tool.

Figure 4

Forest plot of retinopathy of
prematurity risk prediction model for premature infants. Individual
and pooled (0.87, 95% CI: 0.34; 0.99) AUC values with 95% CIs for
22 risk prediction models. Heterogeneity is marked by I²=99.2%
(P<0.05). AUC, area under the receiver operating characteristic
curve; CI, confidence interval.

Figure 5

Subgroup analysis of types of
retinopathy of prematurity risk prediction model for premature
infants. (A) Traditional statistical (n=17) and (B) machine
learning models (n=5). Each subgroup includes individual study AUCs
with 95% CIs and pooled effect sizes, with I² values of 92.2 and
97.3% respectively. AUC, area under the receiver operating
characteristic curve; CI, confidence interval.

Figure 6

Regional subgroup analysis of
retinopathy of prematurity risk in preterm infants. Forest plots of
the subgroup meta-analyses of 22 models by geographic region: South
America (n=2), Asia (n=15) and North America + Europe (n=6). Each
region includes individual study AUCs with 95% CIs and pooled
effect sizes, with I² values of 0.0, 96.6 and 93.6%, respectively.
AUC, area under the receiver operating characteristic curve; CI,
confidence interval.

Figure 7

Funnel plot of the retinopathy of
prematurity risk prediction model for premature infants.
Distribution of 22 studies, with each point representing AUC and
precision (1/standard error). The vertical line indicates the
pooled AUC (0.87). AUC, area under the receiver operating
characteristic curve.

Figure 8

Sensitivity analysis of the combined
retinopathy of prematurity risk prediction model for premature
infants. Pooled area under the receiver operating characteristic
curve after iteratively excluding each study is shown.

Figure 9

Forest plot of the external
validation of four retinopathy of prematurity risk prediction
models for premature infants. The diamond indicates the pooled
external validation area under the receiver operating
characteristic curve (0.90, 95% CI: 0.76; 0.96). CI, confidence
interval.
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Copy and paste a formatted citation
Spandidos Publications style
Li L, Gao Y, Chen W and Han M: Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants. Biomed Rep 23: 195, 2025.
APA
Li, L., Gao, Y., Chen, W., & Han, M. (2025). Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants. Biomedical Reports, 23, 195. https://doi.org/10.3892/br.2025.2073
MLA
Li, L., Gao, Y., Chen, W., Han, M."Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants". Biomedical Reports 23.6 (2025): 195.
Chicago
Li, L., Gao, Y., Chen, W., Han, M."Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants". Biomedical Reports 23, no. 6 (2025): 195. https://doi.org/10.3892/br.2025.2073
Copy and paste a formatted citation
x
Spandidos Publications style
Li L, Gao Y, Chen W and Han M: Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants. Biomed Rep 23: 195, 2025.
APA
Li, L., Gao, Y., Chen, W., & Han, M. (2025). Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants. Biomedical Reports, 23, 195. https://doi.org/10.3892/br.2025.2073
MLA
Li, L., Gao, Y., Chen, W., Han, M."Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants". Biomedical Reports 23.6 (2025): 195.
Chicago
Li, L., Gao, Y., Chen, W., Han, M."Systematic review and meta‑analysis of risk prediction models for retinopathy of prematurity in preterm infants". Biomedical Reports 23, no. 6 (2025): 195. https://doi.org/10.3892/br.2025.2073
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