Objective: To apply automated movement analysis to the general movements assessment (GMA) to build a predictive model for motor impairment (MI). Study design: A retrospective cohort study including infants ≤306/7 weeks GA or BW ≤1500 g seen at 3–5 months was conducted. Automated video analysis was used to develop a multivariable model to identify MI, defined as Bayley motor composite score <85 or cerebral palsy (CP). Results: One hundred and fifty two videos were analyzed. Median GA and BW were 275/7 weeks and 955 g, respectively. MI and CP rates were 22% (N = 33) and 14% (N = 22). Minimum, mean, and mean vertical velocity of the infant’s silhouette correlated significantly with MI. Sensitivity, specificity, positive and negative predictive values, and accuracy of automated GMA were 79%, 63%, 37%, 91%, and 66%, respectively. C-statistic indicated good fit (C = 0.77). Conclusions: Automated movement analysis predicts MI in preterm infants. Further refinement of this technology is required for clinical application.
Raghuram K., Orlandi S., Shah V., Chau T., Luther M., Banihani R., et al. (2019). Automated movement analysis to predict motor impairment in preterm infants: a retrospective study. JOURNAL OF PERINATOLOGY, 39(10), 1362-1369 [10.1038/s41372-019-0464-0].
Automated movement analysis to predict motor impairment in preterm infants: a retrospective study
Orlandi S.Secondo
Methodology
;
2019
Abstract
Objective: To apply automated movement analysis to the general movements assessment (GMA) to build a predictive model for motor impairment (MI). Study design: A retrospective cohort study including infants ≤306/7 weeks GA or BW ≤1500 g seen at 3–5 months was conducted. Automated video analysis was used to develop a multivariable model to identify MI, defined as Bayley motor composite score <85 or cerebral palsy (CP). Results: One hundred and fifty two videos were analyzed. Median GA and BW were 275/7 weeks and 955 g, respectively. MI and CP rates were 22% (N = 33) and 14% (N = 22). Minimum, mean, and mean vertical velocity of the infant’s silhouette correlated significantly with MI. Sensitivity, specificity, positive and negative predictive values, and accuracy of automated GMA were 79%, 63%, 37%, 91%, and 66%, respectively. C-statistic indicated good fit (C = 0.77). Conclusions: Automated movement analysis predicts MI in preterm infants. Further refinement of this technology is required for clinical application.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
