Motor outcome after perinatal stroke and early prediction of unilateral spastic cerebral palsy

Pascal, Aurelie; Govaert, Paul; Ortibus, Els; Naulaers, Gunnar; Adde, Lars; Fjørtoft, Torill; Oostra, Anna; Zecic, Alexandra; Cools, Filip; Cloet, Eva; Casaer, Alexandra; Cornette, Luc; Laroche, Sabrina; Samijn, Bieke; Broeck, Christine Van den

doi:10.1016/j.ejpn.2020.09.002

Cited by 13 publications

(13 citation statements)

References 34 publications

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“…The sample comprised 557 infants with a high risk of perinatal brain injury who were prospectively enrolled in previous studies 27 , 31 , 32 , 33 of CP risk prediction between September 10, 2001, and October 25, 2018. Statistical analysis was performed between February 11, 2020, and September 23, 2021.…”

Section: Methodsmentioning

confidence: 99%

“…Data on infants excluded because of missing video recordings, GMA classification, or CP status are reported elsewhere. 27 , 31 , 32 , 33 The sample size was determined by the number of infants from the previous studies 27 , 31 , 32 , 33 who had available data.…”

Section: Methodsmentioning

confidence: 99%

“…To achieve representative samples for method development (ie, training and internal validation) and external validation, all infants at high risk were stratified into classes based on the study in which they were originally enrolled, 27 , 31 , 32 , 33 the country of the center in which the study was conducted (Belgium, India, Norway, or the US) (step 1 in Figure 1 ), and their CP subtype (spastic bilateral CP, spastic unilateral CP, or no CP) (step 2 in Figure 1 ). Data on race and ethnicity were not reported because the previous studies (from which the infant samples were derived) used different study protocols with inconsistent collection of these data.…”

Section: Methodsmentioning

confidence: 99%

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Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

et al. 2022

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Key Points Question What is the external validity of a deep learning–based method to predict cerebral palsy (CP) based on infants’ spontaneous movements at 9 to 18 weeks’ corrected age? Findings In this prognostic study of 557 infants with a high risk of perinatal brain injury, a deep learning–based method for early prediction of CP had sensitivity of 71%, specificity of 94%, positive predictive value of 68%, and negative predictive value of 95%. Prognosis of CP based on the deep learning–based method was associated with later functional level and CP subtype in children with CP. Meaning This study’s findings suggest that deep learning–based assessments could support early detection of CP in infants at high risk.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

et al. 2022

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“… 37 The hand use and grasp sensor system also showed potential to provide quantitative assessment of hand use symmetry during the developmental process, which would be atypical in unilateral brain injury or anomalies of commissural axonal tracks. 38 …”

Section: Discussionmentioning

confidence: 99%

Hand Use and Grasp Sensor System in Monitoring Infant Fine Motor Development

Kuo

Wang

Schladen

et al. 2022

Archives of Rehabilitation Research and Clinical Translation

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“…According to the recommended guidelines for early diagnosis by Novak et al., 4 standardized motor assessment tools with strong psychometric properties, like the HAI, should be used in combination with magnetic resonance imaging. By doing so, it is possible to set a preliminary diagnosis (risk of) unilateral CP as early as 3.5 to 4.5 months of age 5,6 . Thus, the HAI has already been shown to be a useful tool for diagnostic purposes, for the evaluation of treatments, and for describing early development in children at high risk of unilateral CP 5–9 .…”

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confidence: 99%

Interrater and test–retest reliability of the Hand Assessment for Infants

Ullenhag

Eliasson

et al. 2021

Develop Med Child Neuro

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Aim To evaluate the interrater and test–retest reliability, standard error of measurement (SEM), and the smallest detectable difference (SDD) of the Hand Assessment for Infants (HAI). Method HAI assessments of 55 infants (26 females, 29 males), 25 with clinical signs of unilateral cerebral palsy (CP) and 30 typically developing (mean [SD] age 6.8mo [2.4], range 3–11mo), were scored individually by three therapists. Three clinically experienced occupational therapists (OT 1–OT 3) with extensive experience in using the HAI, independently scored the video recorded HAI play sessions. Analysis of the combined group of infants and just the infants with clinical signs of unilateral CP (12 females, 13 males; mean age 7.6mo [2.1]) were conducted. Intraclass correlation coefficients (ICC, 2.1), Bland–Altman plots, SEM, and SDD were calculated. Results Interrater and test–retest reliability were excellent for the Both Hands Measure (BoHM) and the Each Hand Sum score (EaHS), with ICCs of 0.96 to 0.99. For individual items, the interrater and test–retest reliability was good to excellent (ICC 0.81–0.99). The SDD for the EaHS was 2 points, and for the BoHM the SDD it was 3 HAI units for infants with signs of unilateral CP. Interpretation The HAI results showed good to excellent reliability. The SDDs were low, indicating that results beyond these levels exceed the measurement error and, thus, can be considered true changes. The Hand Assessment for Infants (HAI) shows excellent reliability. A change of ≥3 HAI units is considered a true change. The HAI yields reliable measures for research and clinical practice.

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Motor outcome after perinatal stroke and early prediction of unilateral spastic cerebral palsy

Cited by 13 publications

References 34 publications

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

Hand Use and Grasp Sensor System in Monitoring Infant Fine Motor Development

Interrater and test–retest reliability of the Hand Assessment for Infants

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