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Background <p>Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Chronic hypoxia at high altitude may create a unique risk profile and potentially influence the onset and progression of ROP. However, predictive tools specifically designed for preterm infants in high-altitude settings remain limited. This study aimed to develop and validate machine learning (ML)-based models for individualized ROP risk prediction in this population.</p> Methods <p>We retrospectively collected clinical data from 2,138 preterm infants who underwent standardized ROP screening at Qinghai Red Cross Hospital between May 2014 and May 2025. The cohort was divided into a training set (<i>n</i> = 1,496) and an independent testing set (<i>n</i> = 642) in a 7:3 ratio. The prediction timepoint (t0) was defined as immediately before the first scheduled ROP fundus screening. Candidate predictors were initially screened using univariate analysis, followed by temporal plausibility assessment and LASSO logistic regression (λ_min), resulting in 24 predictors for subsequent modeling. Nine ML algorithms, logistic regression, decision tree, random forest, XGBoost, LightGBM, support vector machine (SVM), Gaussian Naive Bayes (GaussianNB), multilayer perceptron (MLP), and TabNet, as well as a soft-voting ensemble (Random Forest + XGBoost + LightGBM) were trained using five-fold cross-validation and tuned via Bayesian optimization. Model performance was then evaluated on the independent testing set.</p> Results <p>In the independent testing set, the random forest model achieved the highest discrimination (AUC = 0.850, 95% CI: 0.795–0.906; PR-AUC = 0.607). XGBoost and the soft-voting ensemble demonstrated similar AUCs (both 0.845), with XGBoost providing the best overall classification balance (F1-score = 0.622; MCC = 0.573).</p> Conclusions <p>ML models based on routinely available pre-screening clinical variables can effectively stratify ROP risk in preterm infants at high altitude. Anchoring predictions to the timepoint immediately before the first scheduled ROP examination enhances clinical interpretability and temporal validity, potentially enabling more efficient allocation of screening resources in this setting.</p>

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Machine learning-based individualized prediction: risk assessment of retinopathy in preterm infants at high altitude

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月7日

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  • Yang Yu,
  • Yunjie Zhang,
  • Yuanfang Xin,
  • Nancuo Suo,
  • Xueren Ma,
  • Yumei Guan,
  • Yingying Ma,
  • Hui Yu,
  • Qiuxia Dong,
  • Xinzhang Li

摘要

Background

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Chronic hypoxia at high altitude may create a unique risk profile and potentially influence the onset and progression of ROP. However, predictive tools specifically designed for preterm infants in high-altitude settings remain limited. This study aimed to develop and validate machine learning (ML)-based models for individualized ROP risk prediction in this population.

Methods

We retrospectively collected clinical data from 2,138 preterm infants who underwent standardized ROP screening at Qinghai Red Cross Hospital between May 2014 and May 2025. The cohort was divided into a training set (n = 1,496) and an independent testing set (n = 642) in a 7:3 ratio. The prediction timepoint (t0) was defined as immediately before the first scheduled ROP fundus screening. Candidate predictors were initially screened using univariate analysis, followed by temporal plausibility assessment and LASSO logistic regression (λ_min), resulting in 24 predictors for subsequent modeling. Nine ML algorithms, logistic regression, decision tree, random forest, XGBoost, LightGBM, support vector machine (SVM), Gaussian Naive Bayes (GaussianNB), multilayer perceptron (MLP), and TabNet, as well as a soft-voting ensemble (Random Forest + XGBoost + LightGBM) were trained using five-fold cross-validation and tuned via Bayesian optimization. Model performance was then evaluated on the independent testing set.

Results

In the independent testing set, the random forest model achieved the highest discrimination (AUC = 0.850, 95% CI: 0.795–0.906; PR-AUC = 0.607). XGBoost and the soft-voting ensemble demonstrated similar AUCs (both 0.845), with XGBoost providing the best overall classification balance (F1-score = 0.622; MCC = 0.573).

Conclusions

ML models based on routinely available pre-screening clinical variables can effectively stratify ROP risk in preterm infants at high altitude. Anchoring predictions to the timepoint immediately before the first scheduled ROP examination enhances clinical interpretability and temporal validity, potentially enabling more efficient allocation of screening resources in this setting.