Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants
An Ambispective Cohort Study
Bibliographic Data
| ID | 15719702 |
|---|---|
| Authors | Kamini Raghuram (0000-0003-1765-8549, University of Toronto), Silvia Orlandi (0000-0003-2733-8450, Laboratori Guglielmo Marconi (Italy)), Paige Church (0000-0001-5844-3953, Sunnybrook Health Science Centre), Maureen Luther (Health Sciences Centre), Alex Kiss (Institute for Clinical Evaluative Sciences), Vibhuti Shah (0000-0001-8685-2447, Mount Sinai Hospital, corresponding author) |
| Year | 2022 |
| Volume | 9 |
| Issue | 6 |
| Pages | 843-843 |
| Publication date | 2022-06-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Children (JOURNAL) |
| Journal identifiers | ISSN: 2227-9067 • E-ISSN: 2227-9067 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/children9060843 |
| PMID | 35740780 |
| OpenAlex | W4283445680 |
| Language | EN |
| References cited | 54 |
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
Cerebral palsy · Cohort · Cohort study · Gestational age · Obstetrics · Physical medicine and rehabilitation · Positive predicative value · Predictive value · Predictive value of tests · Pregnancy · Retrospective cohort study · Statistic · Statistics · Cerebral Palsy and Movement Disorders · Infant Development and Preterm Care · Mathematics · Medicine · Neonatal and fetal brain pathology · Internal Medicine · Pediatrics
| Highly cited | No |
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