A predictive model based on maternal immune-inflammatory markers for ARDS identification in mechanically ventilated late-preterm and term neonates.

Objective: To develop and internally evaluate a model that uses maternal blood markers obtained before delivery to differentiate neonatal acute respiratory distress syndrome (ARDS) from other causes of respiratory distress in late-preterm and term neonates requiring mechanical ventilation. Methods: This retrospective study enrolled 227 mother-infant pairs from two tertiary hospitals between June 2024 and January 2026. Neonates born at 34-40 weeks' gestation with ventilation-dependent respiratory distress were divided into a training cohort and an internal test cohort. Predictors were screened using elastic-net regression with 1,000 bootstrap resamples. Four feature sets and six algorithms were then compared by 20 repeats of stratified five-fold cross-validation, and a six-predictor random forest (RF) was ultimately selected. Discrimination, calibration and net benefit were assessed, with 500-resample bootstrap optimism correction and Shapley additive explanation (SHAP) interpretation. Results: Maternal systemic immune-inflammation index (SII) and related inflammatory ratios were higher, and antenatal infection was more frequent, in the ARDS group, whereas recorded antenatal corticosteroid (ACS) use was less frequent (P < 0.001). Six predictors were retained in the final model: SII, antenatal infection, maternal age, gestational hypothyroidism, gestational diabetes mellitus and ACS use. The principal internal performance estimate, the mean training-set cross-validated area under the curve (AUC) was 0.729 ± 0.080, the fixed-pipeline optimism-corrected AUC was 0.793 and the internal test-set AUC was 0.827 (95% CI, 0.712-0.923), with an average precision of 0.828 and a Brier score of 0.175. These three values are not interchangeable and are reported as one principal and two secondary estimates, and none represents fully independent performance. At the training-derived threshold of 0.424, sensitivity was 78.8% and specificity 77.8%. SHAP identified SII as the largest contributor, without implying a causal effect for any predictor. Logistic regression using the same six predictors reached a numerically higher internal test-set AUC (0.838 versus 0.827, ΔAUC = -0.011, P = 0.744) and a cross-validated AUC of 0.706 ± 0.082 versus 0.729 ± 0.080, the RF therefore conferred no discriminative advantage. Conclusions: In mechanical ventilation needed neonates born at 34-40 weeks' gestation with respiratory distress, the six-predictor RF showed moderate discrimination, whereas evidence of adequate calibration was limited. The model remains preliminary and requires external validation, recalibration and prospective clinical-impact evaluation before clinical use.