Stability of the Motor Optimality Score Revised (MOS-R) in medically complex infants.

BACKGROUND: The Motor Optimality Score-Revised (MOS-R) is a clinical test of infant spontaneous movement at 3-5 months of age and has been associated with neurodevelopmental outcomes in infants with medical complexity. However the stability of the MOS-R tested at different developmental ages is not yet known. AIM: To determine if the repeated scoring of the MOS-R remained consistent when tested at two developmental ages in young infants. STUDY DESIGN: In this prospective cohort study, infants were tested twice with the MOS-R between 12 and 13 weeks corrected age (CA) and 14-16 weeks CA. Bland Altman Plots were used to calculate agreement between the scores. Infants were grouped as having higher or lower medical complexity. MOS-R threshold scores were analyzed to assess changes over time within each group using Fisher's exact test. SUBJECTS: 85 infants with history of hospitalization in a neonatal intensive care unit (NICU). RESULTS: The MOS-R scores had a high agreement with negligible bias (0.058) between timepoints (95 % CI -1.10, 1.22). Using a MOS-R cut point of 19, infants with higher medical complexity were more likely to change groups between timepoints than infants with lower medical complexity (p = 0.008), but this was not significantly different using cut points of 20 or 21. CONCLUSION: The MOS-R scores were stable when measured repeatedly in infants who were hospitalized in a NICU. Infants with high medical complexity had less stable MOS-R scores using certain cut points than infants with lower medical complexity.

Baby Observational Selective Control AppRaisal (BabyOSCAR): Convergent and discriminant validity and reliability in infants with and without spastic cerebral palsy.

AIM: To describe the development of an observational measure of spontaneous independent joint motion in infants with spastic cerebral palsy (CP), the Baby Observational Selective Control AppRaisal (BabyOSCAR), and to test its convergent validity and reliability. METHOD: A retrospective sample of 75 infants (45 with spastic CP and 30 without CP) at 3 months of age were scored with the BabyOSCAR and compared with diagnosis of spastic CP, limbs affected, and Gross Motor Function Classification level at 2 years of age or later for convergent validity using t-tests, Kruskal-Wallis tests, and Spearman's rank correlation coefficients. BabyOSCAR interrater and test-retest reliability was also evaluated using intraclass correlation coefficients. RESULTS: Infants with spastic CP had significantly lower BabyOSCAR scores than children without CP (p < 0.001) and scores were significantly correlated with Gross Motor Function Classification System levels (p < 0.001). Children with unilateral CP had significantly higher asymmetry scores than children with bilateral CP or no CP (p < 0.01). Interrater and test-retest reliabilities were good to excellent. INTERPRETATION: Reductions in independent joint control measured in infancy are a hallmark of eventual diagnosis of spastic CP, and influence gross motor function later in childhood (with or without a diagnosis of CP).

Baby Observational Selective Control AppRaisal (BabyOSCAR): Scores at 3 months predict functional ability, spastic cerebral palsy distribution, and diagnosis at 2 years.

AIM: To assess the predictive capabilities of the Baby Observational Selective Control AppRaisal (BabyOSCAR) tool, administered at 3 months corrected age, in determining spastic cerebral palsy (CP) outcome, functional abilities, and body topography at 2 years of age or later. METHOD: Independent joint motions were measured at age 10 to 16 weeks from video recordings of spontaneous movement using BabyOSCAR in a sample of 75 infants. All included infants had known 2-year outcomes (45 with spastic CP and 30 without CP) including Gross Motor Functional Classification System (GMFCS) levels and CP body distribution. Receiver operating characteristic curves and cut points indicating greatest sensitivity and specificity were generated for predictive performance. RESULTS: Total BabyOSCAR score was a strong predictor of future outcome of spastic CP (cut score of 22.5, sensitivity = 98%, specificity = 100%, area under the curve = 0.99), and was able to distinguish children classified in GMFCS levels I and II from those in III to V (cut score of 13.5, sensitivity = 92%, specificity = 89%, area under the curve = 0.94). Having an (absolute) asymmetry score on the BabyOSCAR of more than 5 was a predictor of having unilateral CP at age 2 years (sensitivity = 56%, specificity = 100%, area under the curve = 0.86). INTERPRETATION: BabyOSCAR scores are predictors of diagnosis, body distribution, and future gross motor function in infants with spastic CP at 2 years of age or later.

Assessing Neonatal Intensive Care Unit (NICU) Graduates Across Varied Settings: A Study on the Feasibility of the Baby Moves App.

We assessed the feasibility of obtaining parent-collected General Movement Assessment videos using the Baby Moves app. Among 261 participants from 4 Chicago NICUs, 70% submitted videos. Families living in higher areas of childhood opportunity used the app more than those from areas of lower opportunity.

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

Importance: Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity. Objective: To develop and assess the external validity of a novel deep learning-based method to predict CP based on videos of infants' spontaneous movements at 9 to 18 weeks' corrected age. Design, Setting, and Participants: This prognostic study of a deep learning-based method to predict CP at a corrected age of 12 to 89 months involved 557 infants with a high risk of perinatal brain injury who were enrolled in previous studies conducted at 13 hospitals in Belgium, India, Norway, and the US between September 10, 2001, and October 25, 2018. Analysis was performed between February 11, 2020, and September 23, 2021. Included infants had available video recorded during the fidgety movement period from 9 to 18 weeks' corrected age, available classifications of fidgety movements ascertained by the general movement assessment (GMA) tool, and available data on CP status at 12 months' corrected age or older. A total of 418 infants (75.0%) were randomly assigned to the model development (training and internal validation) sample, and 139 (25.0%) were randomly assigned to the external validation sample (1 test set). Exposure: Video recording of spontaneous movements. Main Outcomes and Measures: The primary outcome was prediction of CP. Deep learning-based prediction of CP was performed automatically from a single video. Secondary outcomes included prediction of associated functional level and CP subtype. Sensitivity, specificity, positive and negative predictive values, and accuracy were assessed. Results: Among 557 infants (310 [55.7%] male), the median (IQR) corrected age was 12 (11-13) weeks at assessment, and 84 infants (15.1%) were diagnosed with CP at a mean (SD) age of 3.4 (1.7) years. Data on race and ethnicity were not reported because previous studies (from which the infant samples were derived) used different study protocols with inconsistent collection of these data. On external validation, the deep learning-based CP prediction method had sensitivity of 71.4% (95% CI, 47.8%-88.7%), specificity of 94.1% (95% CI, 88.2%-97.6%), positive predictive value of 68.2% (95% CI, 45.1%-86.1%), and negative predictive value of 94.9% (95% CI, 89.2%-98.1%). In comparison, the GMA tool had sensitivity of 70.0% (95% CI, 45.7%-88.1%), specificity of 88.7% (95% CI, 81.5%-93.8%), positive predictive value of 51.9% (95% CI, 32.0%-71.3%), and negative predictive value of 94.4% (95% CI, 88.3%-97.9%). The deep learning method achieved higher accuracy than the conventional machine learning method (90.6% [95% CI, 84.5%-94.9%] vs 72.7% [95% CI, 64.5%-79.9%]; P < .001), but no significant improvement in accuracy was observed compared with the GMA tool (85.9%; 95% CI, 78.9%-91.3%; P = .11). The deep learning prediction model had higher sensitivity among infants with nonambulatory CP (100%; 95% CI, 63.1%-100%) vs ambulatory CP (58.3%; 95% CI, 27.7%-84.8%; P = .02) and spastic bilateral CP (92.3%; 95% CI, 64.0%-99.8%) vs spastic unilateral CP (42.9%; 95% CI, 9.9%-81.6%; P < .001). Conclusions and Relevance: In this prognostic study, a deep learning-based method for predicting CP at 9 to 18 weeks' corrected age had predictive accuracy on external validation, which suggests possible avenues for using deep learning-based software to provide objective early detection of CP in clinical settings.

The Predictive Accuracy of the General Movement Assessment for Cerebral Palsy: A Prospective, Observational Study of High-Risk Infants in a Clinical Follow-Up Setting.

BACKGROUND: Early prediction of cerebral palsy (CP) using the General Movement Assessment (GMA) during the fidgety movements (FM) period has been recommended as standard of care in high-risk infants. The aim of this study was to determine the accuracy of GMA, alone or in combination with neonatal imaging, in predicting cerebral palsy (CP). METHODS: Infants with increased risk of perinatal brain injury were prospectively enrolled from 2009-2014 in this multi-center, observational study. FM were classified by two certified GMA observers blinded to the clinical history. Abnormal GMA was defined as absent or sporadic FM. CP-status was determined by clinicians unaware of GMA results. RESULTS: Of 450 infants enrolled, 405 had scorable video and follow-up data until at least 18-24 months. CP was confirmed in 42 (10.4%) children at mean age 3 years 1 month. Sensitivity, specificity, positive and negative predictive values, and accuracy of absent/sporadic FM for CP were 76.2, 82.4, 33.3, 96.8, and 81.7%, respectively. Only three (8.1%) of 37 infants with sporadic FM developed CP. The highest accuracy (95.3%) was achieved by a combination of absent FM and abnormal neonatal imaging. CONCLUSION: In infants with a broad range of neonatal risk factors, accuracy of early CP prediction was lower for GMA than previously reported but increased when combined with neonatal imaging. Sporadic FM did not predict CP in this study.