The assessment of general movements in term and late-preterm infants diagnosed with neonatal encephalopathy, as a predictive tool of cerebral palsy by 2 years of age-a scoping review

The General Movement Optimality Score-Revised (GMOS-R) with Socioeconomically Stratified Percentile Ranks

Background: The general movement optimality score (GMOS) quantifies the details of general movements (GMs). We recently conducted psychometric analyses of the GMOS and developed a revised scoresheet. Consequently, the GMOS-Revised (GMOS-R) instrument necessitated validation using new percentile ranks. This study aimed to provide these percentile ranks for the GMOS-R and to investigate whether sex, preterm birth, or the infant's country of birth and residence affected the GMOS-R distribution. Methods: We applied the GMOS-R to an international sample of 1983 infants (32% female, 44% male, and 24% not disclosed), assessed in the extremely and very preterm period (10%), moderate (12%) and late (22%) preterm periods, at term (25%), and post-term age (31%). Data were grouped according to the World Bank's classification into lower- and upper-middle-income countries (LMICs and UMICs; 26%) or high-income countries (HICs; 74%), respectively. Results: We found that sex and preterm or term birth did not affect either GM classification or the GMOS-R, but the country of residence did. A lower median GMOS-R for infants with normal or poor-repertoire GMs from LMICs and UMICs compared with HICs suggests the use of specific percentile ranks for LMICs and UMICs vs. HICs. Conclusion: For clinical and scientific use, we provide a freely available GMOS-R scoring sheet, with percentile ranks reflecting socioeconomic stratification.

Predictive Value of Fidgety Movement Assessment and Magnetic Resonance Imaging for Cerebral Palsy in Infants

BACKGROUND

The early prediction of cerebral palsy (CP) could enable the follow-up of high-risk infants during the neuroplasticity period. This study aimed to explore the predictive value of fidgety movement assessment (FMA) and brain magnetic resonance imaging (MRI) for the development of CP in clinic rehabilitation setting.

METHODS

This retrospective observational study included infants who underwent FMA and brain MRI at age nine to 20 weeks at Children's Hospital, Zhejiang University School of Medicine, between March 2018 and September 2019. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of FMA and MRI for predicting the development of CP were assessed.

RESULTS

A total of 258 infants (169 males, gestational age 37.4 ± 3.0 weeks, birth weight 2987.9 ± 757.1 g) were included. Fifteen children had CP after age two years. The diagnostic value of FMA and brain MRI combination showed 86.7% sensitivity (95% confidence interval [CI]: 58.4% to 97.7%), 98.4% specificity (95% CI: 95.6% to 99.5%), and 97.7% accuracy (95% CI: 95.0% to 99.1%); the combination diagnostic value also showed a significantly higher AUC for predicting CP after age two years than FMA alone (AUC: 0.981 vs 0.893, P = 0.013).

CONCLUSIONS

The diagnostic value of FMA and brain MRI combination during infancy showed a high predictive value for CP development in clinical rehabilitation setting.

Machine learning approaches to evaluate infants' general movements in the writhing stage-a pilot study

The goals of this study are to describe machine learning techniques employing computer-vision movement algorithms to automatically evaluate infants' general movements (GMs) in the writhing stage. This is a retrospective study of infants admitted 07/2019 to 11/2021 to a level IV neonatal intensive care unit (NICU). Infant GMs, classified by certified expert, were analyzed in two-steps (1) determination of anatomic key point location using a NICU-trained pose estimation model [accuracy determined using object key point similarity (OKS)]; (2) development of a preliminary movement model to distinguish normal versus cramped-synchronized (CS) GMs using cosine similarity and autocorrelation of major joints. GMs were analyzed using 85 videos from 74 infants; gestational age at birth 28.9 ± 4.1 weeks and postmenstrual age (PMA) at time of video 35.9 ± 4.6 weeks The NICU-trained pose estimation model was more accurate (0.91 ± 0.008 OKS) than a generic model (0.83 ± 0.032 OKS, p < 0.001). Autocorrelation values in the lower limbs were significantly different between normal (5 videos) and CS GMs (5 videos, p < 0.05). These data indicate that automated pose estimation of anatomical key points is feasible in NICU patients and that a NICU-trained model can distinguish between normal and CS GMs. These preliminary data indicate that machine learning techniques may represent a promising tool for earlier CP risk assessment in the writhing stage and prior to hospital discharge.

Feeding dysfunction in NICU patients with cramped synchronized movements

Patients admitted to the neonatal intensive care unit (NICU) have higher association for neurodevelopment deficits, specifically cerebral palsy (CP). We identified patients with risk for CP using abnormal Pretchl's General Movement Assessment (GMA) and sub-category of cramped synchronized movements (CSM) and reported their feeding outcomes at discharge. Over 75 % of these patients required either nasogastric (NGT) or gastrostomy tube (GT) at discharge. Of these, 57 % weaned off their NGT or GT at home and 43 % of patients still needed a GT one year after discharge. Of those that could not wean off their NGT or GT, these patients had longer hospital stay, took lower percentage by mouth, and an older post-menstrual age at discharge. We did not find a difference in NGT or GT use between patients with IVH, ELBW, nor between their birthweight or gestation age at birth. This study provides further clinical characteristics in NICU patients who have higher risk of CP, and supports the need for skilled feeding therapy and resources both during and after NICU admission.

Predictors of Risk for Cerebral Palsy: A Review

PURPOSE

To identify the earliest predictors of risk for diagnosis of cerebral palsy (CP).

METHODS

A comprehensive literature search was conducted using various databases. The publications were reviewed to identify risk factors for CP from conception to early infancy. Studies were critically appraised with Joanna Briggs Institute guidelines for quality appraisal and evaluated for risk of bias using the Agency for Health Care Research and Quality guidelines.

RESULTS

The initial search yielded 129 studies and 20 studies were included. Forty-seven risk factors for CP were extracted of which several were duplicate terms. The significant risk factors found to be indicative of CP were low birth weight (<1500 g), birth at less than 28 weeks of gestational age, periventricular leukomalacia, grade 3 or 4 intraventricular hemorrhage, preeclampsia, prematurity, an Apgar score of less than 4 at the first minute, birth asphyxia, preterm premature rupture of membrane, and absent fidgety movements.

CONCLUSION

Twenty-three factors were consistently reported as predictors of CP.

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study

The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks’ gestational age or birthweight ≤ 1500 g evaluated at 3−5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7−292/7 weeks) and 960 g (range 769−1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18−24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.

Healthcare applications of single camera markerless motion capture: a scoping review

Background

Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data.

Survey Methodology

Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population.

Results

A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking.

Conclusions

Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.