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.

BACKGROUND: Moderate-to-late preterm (MLPT) infants (32-36+6 weeks of gestation) account for approximately 84% of preterm births. Although white matter injury (WMI) may lack obvious early neurological manifestations, it has been associated with adverse neurodevelopmental outcomes. In this cohort, magnetic resonance imaging (MRI)-detected WMI occurred in 20.9% of MLPT infants. Brain MRI is important for WMI detection, but its use for early screening is limited by accessibility and examination timing. Previous WMI prediction models have mainly focused on extremely preterm infants, with limitations in population heterogeneity, sample size, outcome definitions, calibration, and validation. This study aimed to identify clinical predictors of MRI-defined WMI and develop and internally validate a clinical risk prediction model for early risk stratification in MLPT infants.

METHODS: This single-center retrospective study included infants born at 32-36+6 weeks who underwent 3.0-T brain MRI at 7-14 days of life between June 2023 and May 2025 at Jinjiang Municipal Hospital (Shanghai Sixth People's Hospital, Fujian). Of 208 eligible infants, 7 were excluded because of missing key predictors, resulting in 201 infants. MRI-defined binary WMI was assessed independently by two blinded radiologists according to the Miller grading system. Maternal, delivery-related, and neonatal variables available before MRI were evaluated as candidate predictors. Synthetic Minority Over-sampling Technique (SMOTE) was used to address class imbalance, and recursive feature elimination with cross-validation (RFECV) was applied for feature selection. Multivariable logistic regression and classification and regression tree (CART) models were developed and internally validated using 1,000 bootstrap resampling iterations with optimism correction. Model performance was assessed using discrimination, calibration, Brier score, and classification metrics.

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.