A prognostic model using 12 clinical variables improves prediction of cerebral palsy (CP) compared with clinical presentation with encephalopathy, according to a study published online Jan. 17 in JAMA Pediatrics.
Amira Rouabhi, from McGill University in Montreal, and colleagues conducted a case-control study using data from the Canadian Cerebral Palsy Registry for children with CP and the Alberta Pregnancy Outcomes and Nutrition study for controls to develop a CP prognostic tool that can be applied to all term neonates. A total of 3,250 infants were included.
The researchers found that encephalopathy was present in 28 percent of 1,184 infants with CP and 0 controls. The final prediction model included 12 variables and was able to classify 75 percent of infants correctly, with sensitivity and specificity of 56 and 82 percent, respectively, and a C-statistic of 0.74. Risk factors were additive.
At a proposed threshold for screening of a probability greater than 0.3, sensitivity and specificity were 65 and 71 percent, respectively. Overall, 2.4-fold more children with CP were identified with the prognostic tool than would have presented with encephalopathy (odds ratio, 13.8).
“This tool may be used at a population level for the early detection of CP and may allow for infants with uneventful pregnancies or deliveries to be identified for early interventions,” the authors write.
Amira Rouabhi et al, Development of a Bedside Tool to Predict the Diagnosis of Cerebral Palsy in Term-Born Neonates, JAMA Pediatrics (2023). DOI: 10.1001/jamapediatrics.2022.5177
Toohey Monica et al, New Screening Tool for Term-Born Infants Enables Update to the Clinical Practice Guideline for Early Diagnosis of Cerebral Palsy, JAMA Pediatrics (2023). DOI: 10.1001/jamapediatrics.2022.5189
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