The Predictive Accuracy of the General Movement Assessment for Cerebral Palsy: A Prospective, Observational Study of

Ragnhild Støen1,2, Lynn Boswell3, Raye-Ann de Regnier3,4

  • 1Department of Neonatology, St. Olavs hospital, Trondheim University Hospital, 7006 Trondheim, Norway.

Insights

General Movement Assessment (GMA) shows moderate accuracy in predicting cerebral palsy (CP) in high-risk infants. Combining GMA with neonatal imaging significantly improves early CP prediction accuracy.

Area of Science:

  • Neurology
  • Developmental Pediatrics
  • Medical Imaging

Background:

  • Early prediction of cerebral palsy (CP) is crucial for timely intervention in high-risk infants.
  • General Movement Assessment (GMA) during the fidgety movements (FM) period is recommended as standard care.
  • The study aimed to evaluate GMA's accuracy alone and with neonatal imaging for CP prediction.

Purpose of the Study:

  • To determine the predictive accuracy of GMA for cerebral palsy (CP) in high-risk infants.
  • To assess if combining GMA with neonatal imaging enhances CP prediction.
  • To investigate the role of sporadic fidgety movements in CP prediction.

Main Methods:

  • Prospective, multi-center observational study of 450 high-risk infants (2009-2014).
  • Fidgety movements (FM) classified by blinded GMA observers; abnormal GMA defined as absent or sporadic FM.
  • CP status determined by clinicians unaware of GMA results; follow-up to 18-24 months.

Main Results:

  • Of 405 infants with data, 42 (10.4%) developed CP.
  • Absent/sporadic FM showed 76.2% sensitivity, 82.4% specificity, and 81.7% accuracy for CP.
  • Combining absent FM with abnormal neonatal imaging yielded the highest accuracy (95.3%).
  • Sporadic FM in 37 infants resulted in CP in only 3 (8.1%).

Conclusions:

  • GMA's early CP prediction accuracy was lower than previously reported but improved with neonatal imaging.
  • Sporadic FM, as assessed by GMA, did not reliably predict CP in this cohort.
  • Combined GMA and neonatal imaging offer a more accurate approach for early CP detection in at-risk infants.
Abstract

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