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Automated movement analysis to predict motor impairment in preterm infants: a retrospective study

Journal of Perinatology volume 39, pages 1362–1369 (2019)Cite this article

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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.

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Acknowledgements

The authors gratefully acknowledge all the members of the PRISM laboratory at Holland Bloorview Kids Rehabilitation Hospital for providing us with guidance and equipment for the study. We also acknowledge all the members of the Neonatal Follow-up Clinic at Sunnybrook for assisting with this project. This Project is partially supported by Brain Canada Foundation through the Canada Brain Research Fund, with the financial support of Health Canada and the funding partners.

Funding

This project is partially supported by Brain Canada Foundation through the Canada Brain Research Fund, with the financial support of Health Canada and their funding partners.

Author information

Authors and Affiliations

  1. Division of Neonatology, Department of Pediatrics, University of Toronto, Toronto, ON, Canada

    Kamini Raghuram, Vibhuti Shah, Rudaina Banihani & Paige Church

  2. Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada

    Silvia Orlandi & Tom Chau

  3. Department of Paediatrics, Mount Sinai Hospital, Toronto, ON, Canada

    Vibhuti Shah

  4. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

    Vibhuti Shah

  5. Neonatology and Neonatal Follow-up, Women and Babies Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

    Maureen Luther, Rudaina Banihani & Paige Church

Authors
  1. Kamini Raghuram
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  2. Silvia Orlandi
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Correspondence to Paige Church.

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Raghuram, K., Orlandi, S., Shah, V. et al. Automated movement analysis to predict motor impairment in preterm infants: a retrospective study. J Perinatol 39, 1362–1369 (2019). https://doi.org/10.1038/s41372-019-0464-0

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