Background: Cerebral palsy (CP) causes developmental delays, affecting quality of life. Many risk factors are theorized however, no total risks summary exists, nor a CP prediction score for newborns. The result is under surveillance, treatment delays, and non-rectifiable complications.
Objective: To establish total risk factors and create a prediction score for assessing CP neonatal risk before discharge. A prediction score has great utility for medical professionals and parents in screening high-risk patients and developing adequate monitoring systems.
Materials and Methods: Using a case-controlled retrospect of children aged 0 to 2 years, born at Thammasat University Hospital, Thailand between 2005 and 2014, prenatal, perinatal, and postnatal risks were compared between children without CP as control, and those diagnosed with CP as case, by multivariable logistic regression. Predictors were assessed with area under the receiver operating characteristic (AuROC), odds ratio (OR), 95% confidence interval (CI), p-value, and clinical predisposition. Logistic regression was applied, including calibration, validation, and categorization of risk.
Results: Cerebral and non-cerebral malformations, multi-fetal gestation, low birthweight, and neonatal sepsis were found as potential predictors, scoring 3, 1.5, 1, 2, 2.5, respectively, AuROC being 0.86 (95% CI 0.79 to 0.92). Low, moderate, and high-risk groups were set with scores of less than 1, 1.5 to 3, and more than 3.5, respectively.
Conclusion: The present predictive CP risks and scoring system shows excellent discrimination power. If newborns were categorized in the highrisk group, close monitoring and surveillance are needed.
Keywords: Cerebral palsy, Risk score, Prenatal, Perinatal, Postnatal
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