Originally published by 2 Minute Medicine® (view original article). Reused on AccessMedicine with permission.

1. In a case-control study, a diagnostic prediction tool was able to identify cerebral palsy with reasonable sensitivity and specificity using 12 common variables pertaining to pregnancy, delivery, and neonates.

2. The performance characteristics of the diagnostic tool were similar when used in a cohort of infants without encephalopathy, and identified 2.4-fold more cases than would be identified with encephalopathy alone.

Evidence Rating Level: 3 (Average)

Study Rundown:

Cerebral palsy (CP) is a common abnormality of motor development that can be challenging to diagnose in the neonatal period due to a slow evolution of tone abnormalities which is central to clinical diagnosis. Many CP treatments are more effective when initiated early, highlighting the importance of early identification tools. To address this challenge, this study utilized a national CP registry cohort (~2000 cases) to identify risk factors associated with CP from common variables pertaining to pregnancy, delivery, and neonates, to create a diagnostic prediction model. This model identified 12 variables that altered the risk of CP, with the largest contribution being from the following variables: 5-minute APGAR score, chorioamnionitis, birthweight, and maternal drug use. The diagnostic tool correctly classified 75% of cases, with a sensitivity of 56% and specificity of 82%. Similar sensitivity and specificity occurred when tested against infants without encephalopathy. At a probability threshold of 0.3, the tool identified 2.4 fold more CP infants than encephalopathy alone and may improve CP diagnosis compared with a clinical presentation with encephalopathy. The main limitation of this study is the lack of external validation in other cohorts and thus has unclear clinical utility. Overall, this diagnostic tool may be helpful in identifying neonates at risk for CP and is particularly useful in infants without encephalopathy.

Click here to read the article in JAMA Pediatrics

In-Depth [case-control study]:

This case-control study identified CP cases of term infants (>37 weeks’ gestation) in the national CP registry born between 2003 and March 2020 in Canada. Controls were infants without CP diagnosis with normal motor development at age 3 years. The final analysis was performed with 1265 children with CP and 1985 controls. The final prediction model included 12 variables. There were 7 binary variables including tobacco use (OR 2.3, 95% CI 1.7-3.0), drug use (OR 10.4 95% CI 16.1-18.0), diabetes (OR 2.1, 95% CI 1.5-3.0), preeclampsia (OR 4.0, 95% CI 2.0-8.0), chorioamnionitis (OR 15.4, 95% CI 6.9-39.1), prolonged rupture of membranes (OR 0.5, 95% CI 0.4-0.7), and male sex (OR 1.2, 95% CI 1.0-1.5). In addition, there were 5 continuous variables including number of pregnancies (OR 1.4, 95% CI 1.3-1.5), number of miscarriages (OR 0.8, 95% CI 0.6-0.9), 5-minute Apgar score (OR 0.6, 95% CI 0.6-0.7), and birthweight (OR 1.3, 95% CI 1.04-1.66). The risk factors were found to be additive. When evaluating model fit, the tool correctly classified 75% of infants, with a sensitivity of 56% (95% CI 52-60) and specificity of 82% (95% CI 81-84). The authors suggest a probability threshold of 0.3 as the threshold for CP screening. The model fit was retested in patients without encephalopathy, with a sensitivity of 57% (95% CI 53-62), and a specificity of 73% (95% CI 71-75). The tool identified 2.4-fold more children with CP than would be identified with encephalopathy symptoms alone when using a probability threshold of 0.3.

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