ArticleTranslational pediatrics2026
Development and internal validation of an exploratory clinical prediction model for MRI-defined white matter injury in moderate-to-late preterm infants: a single-center retrospective study.
Article in Translational pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Moderate-to-late preterm (MLPT) infants (32-36 Methods: This single-center retrospective study included infants born at 32-36 Results: Of the 201 infants included in the final analysis, 42 (20.9%) were diagnosed with WMI, and 159 (79.1%) were classified as non-WMI. RFECV selected 14 candidate predictors for model construction. Following bootstrap optimism correction, the logistic regression model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.62 (95% CI: 0.53-0.70). The model achieved an accuracy of 0.74 (95% CI: 0.68-0.80), sensitivity of 0.00 (95% CI: 0.00-0.09), specificity of 0.94 (95% CI: 0.89-0.98), PPV of 0.09 (95% CI: 0.00-0.41), NPV of 0.78 (95% CI: 0.72-0.83), and a Brier score of 0.18 (95% CI: 0.15-0.22). Calibration assessment showed a calibration intercept of 0.00 (95% CI: -0.42 to 0.44) and a calibration slope of 0.51 (95% CI: 0.27-0.81). Conclusions: In this study, we developed and internally validated an exploratory prediction model for MRI-defined WMI among MLPT infants using a complete-case cohort from a single center. The logistic regression model showed modest discrimination. However, because the model has only been internally validated within the derivation cohort and has not yet been evaluated in independent external cohorts, it should not currently be used as a stand-alone clinical screening tool. Future studies incorporating larger sample sizes, multicenter external validation, and further optimization of prediction thresholds are warranted to determine its potential clinical utility.
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