Pediatric neurology · 2026
Early Identification of Risk for Cerebral Palsy in a Cohort of Preterm Infants: Development of a Propensity Score Model Using Neonatal Risk Factors.
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Scheidt Milene, Silveira Rita C, Procianoy Renato S, Valentini Nadia Cristina
Аннотация
BACKGROUND: To develop a preliminary propensity score model to predict cerebral palsy (CP) based on neonatal risk factors in preterm infants.
METHODS: This case-control study included preterm infants followed for neurodevelopment and growth. Five controls were selected per case, totaling 108 participants (90 without CP and 18 with CP). Logistic regression was used to identify significant risk factors and estimate propensity scores.
RESULTS: Of the 19 risk factors analyzed, 10 were significant; five were retained as independent predictors in the final model as follows: abnormal magnetic resonance imaging, seizures, peri-intraventricular hemorrhage, periventricular leukomalacia, and duration of invasive mechanical ventilation. The following two cutoff points were evaluated: 0.5 (50%), yielded 72.2% of sensitivity and 97.8% specificity, and 0.2 yielding higher sensitivity (83.3%) but lower specificity (91.1%) (20%).
CONCLUSIONS: The model effectively discriminates CP risk in preterm infants. A higher cutoff (0.5) improves specificity and reduces false positives, whereas a lower cutoff (0.2) increases sensitivity and identifies more cases. Early identification may facilitate timely interventions and improve neurodevelopmental outcomes. Larger studies are needed to validate and refine the model.
METHODS: This case-control study included preterm infants followed for neurodevelopment and growth. Five controls were selected per case, totaling 108 participants (90 without CP and 18 with CP). Logistic regression was used to identify significant risk factors and estimate propensity scores.
RESULTS: Of the 19 risk factors analyzed, 10 were significant; five were retained as independent predictors in the final model as follows: abnormal magnetic resonance imaging, seizures, peri-intraventricular hemorrhage, periventricular leukomalacia, and duration of invasive mechanical ventilation. The following two cutoff points were evaluated: 0.5 (50%), yielded 72.2% of sensitivity and 97.8% specificity, and 0.2 yielding higher sensitivity (83.3%) but lower specificity (91.1%) (20%).
CONCLUSIONS: The model effectively discriminates CP risk in preterm infants. A higher cutoff (0.5) improves specificity and reduces false positives, whereas a lower cutoff (0.2) increases sensitivity and identifies more cases. Early identification may facilitate timely interventions and improve neurodevelopmental outcomes. Larger studies are needed to validate and refine the model.