Early detection of cerebral palsy among a high-risk cohort in Bangladesh.

OBJECTIVE: To evaluate the predictive validity of best practice early detection tools for cerebral palsy (CP) in a high-risk cohort. STUDY DESIGN: Prospective longitudinal cohort study. SETTING: Neonatal intensive care unit of a regional tertiary hospital in Bangladesh. PARTICIPANTS: Neonates with risk factors for CP admitted to Mymensingh Medical College Hospital Neonatal Intensive Care Unit between November 2019 and March 2020. OUTCOME MEASURES: General Movements Assessment (GMA) at writhing and fidgety periods; Hammersmith Infant Neurological Examination (HINE) and Peabody Developmental Motor Scales Second Edition (PDMS-2) conducted in person at 3, 12 and 24 months. The Developmental Assessment of Young Children (DAYC-2), Ages and Stages Questionnaire (ASQ-3) and Developmental Milestones Chart (DMC) were administered remotely at 6, 9, 12, 18 and 24 months. Due to the impact of COVID-19, a proportion of the cohort was not able to have GMA fidgety videos completed and the first HINE assessment was delayed. RESULTS: A total of 227 infants were enrolled. Of the surviving infants assessed at 24 months, 36 (29%) had a confirmed diagnosis of CP. The most accurate combination of tools for early detection was GMA and HINE at 3 months (sensitivity 0.91; specificity 1.00). The PDMS-2 Total Motor Quotient, with an optimised cut-off of 59, showed high accuracy at 24 months (sensitivity 0.94; specificity 0.99). Among the tools administered remotely, the DAYC-2 PD, DMC (Gross and Fine Motor domains) and ASQ-3 (Gross and Fine Motor domains) demonstrated strong predictive validity-both individually and in combination-at 9, 12, 18 and 24 months, supporting their use as practical alternatives when in-person assessments are not feasible. CONCLUSIONS: Despite pandemic-related disruptions, an accurate diagnosis was possible as early as 3 months of age using the best practice tools. Our findings support the practicability of scalable early detection models integrating in-person and remote assessments to improve access to timely diagnosis.