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Hornby B, Paleg GS, Williams SA, Hidalgo-Robles Á, Livingstone RW, Montufar Wright PE, Taylor A, Shrader MW. Identifying Opportunities for Early Detection of Cerebral Palsy. CHILDREN (BASEL, SWITZERLAND) 2024; 11:515. [PMID: 38790510 PMCID: PMC11119564 DOI: 10.3390/children11050515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/12/2024] [Accepted: 04/21/2024] [Indexed: 05/26/2024]
Abstract
This study aimed to evaluate assessment and referral practices for the early detection and diagnosis of children at risk for or with cerebral palsy (CP) by health care and education providers in Maryland and Delaware. A secondary aim was to identify barriers for using early detection tools and identify opportunities for change to support early diagnosis and improve care. Seventy-two participants answered ≥ 50% of the survey questions. Most were occupational or physical therapists (86%) working in early intervention (61%). Eighty-eight percent indicated awareness that CP can be diagnosed by 12 months. Though 86% stated they typically suspect a diagnosis of CP between 0 and 12 months, only 19% reported that their patients received a CP diagnosis < 12 months. The Developmental Assessment of Young Children (73%) and the Peabody Developmental Motor Scales-2 (59%) were used most. Many respondents indicated never using magnetic resonance imaging (70%), the General Movements Assessment (87%), or the Hammersmith Infant Neurological Exam (69%). Participants identified clinical signs and symptoms prompting a referral for the diagnostic assessment of CP, most commonly stiffness in legs (95%), excessive head lag (93%), and persistent fisting (92%). Policy and organizational change, clinician education, and training are needed to support the implementation of CP early detection guidelines.
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Affiliation(s)
- Brittany Hornby
- Physical Therapy Department, Kennedy Krieger Institute, Baltimore, MD 21205, USA;
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | | | - Sîan A. Williams
- School of Allied Health, Curtin University, Perth, WA 6009, Australia;
- Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Álvaro Hidalgo-Robles
- Facultad de Educación, Universidad Internacional de La Rioja, 26006 Logroño, La Rioja, Spain;
| | - Roslyn W. Livingstone
- Occupational Science and Occupational Therapy, University of British Columbia, Vancouver, BC V6T 2B5, Canada;
| | | | - Alice Taylor
- Nemours Children’s Health, Wilmington, DE 19803, USA; (P.E.M.W.); (A.T.)
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Implementation of Early Detection and Intervention for Cerebral Palsy in High-Risk Infant Follow-Up Programs: U.S. and Global Considerations. Clin Perinatol 2023; 50:269-279. [PMID: 36868710 DOI: 10.1016/j.clp.2022.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Early detection and intervention for cerebral palsy is best practice for all high-risk infants according to international guidelines, consensus statements and research-supported evidence. It allows support for families and optimization of developmental trajectories into adulthood. All phases of implementation of CP early detection can be found across the world in high-risk infant follow-up programs, demonstrating feasibility and acceptability through standardized implementation science. The largest clinical network for CP early detection and intervention in the world has now sustained an average age at detection less than 12 months corrected age for more than 5 years. Targeted referrals and interventions for CP can now be offered to patients in optimal periods of neuroplasticity, and new therapies researched as the age of detection decreases. Implementation of guidelines and incorporation of rigorous CP research studies both allow high-risk infant follow-up programs to fulfill their mission of improving outcomes of those with the most vulnerable developmental trajectories from birth.
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Maitre NL, Byrne R, Duncan A, Dusing S, Gaebler-Spira D, Rosenbaum P, Winter S. "High-risk for cerebral palsy" designation: A clinical consensus statement. J Pediatr Rehabil Med 2022; 15:165-174. [PMID: 35275579 DOI: 10.3233/prm-220030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Nathalie L Maitre
- Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, Georgia, USA
| | | | | | - Stacey Dusing
- University of Southern California, Los Angeles, CA, USA
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McCay KD, Hu P, Shum HPH, Woo WL, Marcroft C, Embleton ND, Munteanu A, Ho ESL. A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants. IEEE Trans Neural Syst Rehabil Eng 2021; 30:8-19. [PMID: 34941512 DOI: 10.1109/tnsre.2021.3138185] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve accessibility of the assessment and also enhance the understanding of movement development of infants. Previous works have established the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a series of new and improved features, and a feature fusion pipeline for this classification task. We also introduce the RVI-38 dataset, a series of videos captured as part of routine clinical care. By utilising this challenging dataset we establish the robustness of several motion features for classification, subsequently informing the design of our proposed feature fusion framework based upon the GMA. We evaluate our proposed framework's classification performance using both the RVI-38 dataset and the publicly available MINI-RGBD dataset. We also implement several other methods from the literature for direct comparison using these two independent datasets. Our experimental results and feature analysis show that our proposed pose-based method performs well across both datasets. The proposed features afford us the opportunity to include finer detail than previous methods, and further model GMA specific body movements. These new features also allow us to take advantage of additional body-part specific information as a means of improving the overall classification performance, whilst retaining GMA relevant, interpretable, and shareable features.
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Maitre NL, Burton VJ, Duncan AF, Iyer S, Ostrander B, Winter S, Ayala L, Burkhardt S, Gerner G, Getachew R, Jiang K, Lesher L, Perez CM, Moore-Clingenpeel M, Lam R, Lewandowski DJ, Byrne R. Network Implementation of Guideline for Early Detection Decreases Age at Cerebral Palsy Diagnosis. Pediatrics 2020; 145:e20192126. [PMID: 32269135 PMCID: PMC7193973 DOI: 10.1542/peds.2019-2126] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/13/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Early diagnosis of cerebral palsy (CP) is critical in obtaining evidence-based interventions when plasticity is greatest. In 2017, international guidelines for early detection of CP were published on the basis of a systematic review of evidence. Our study aim was to reduce the age at CP diagnosis throughout a network of 5 diverse US high-risk infant follow-up programs through consistent implementation of these guidelines. METHODS The study leveraged plan-do-study-act and Lean methodologies. The primary outcome was age at CP diagnosis. Data were acquired during the corresponding 9-month baseline and quarterly throughout study. Balancing measures were clinic no-show rates and parent perception of the diagnosis visit. Clinic teams conducted strengths, weaknesses, opportunities, and threats analyses, process flow evaluations, standardized assessments training, and parent questionnaires. Performance of a 3- to 4-month clinic visit was a critical process step because it included a Hammersmith Infant Neurologic Examination, a General Movements Assessment, and standardized assessments of motor function. RESULTS The age at CP diagnosis decreased from a weighted average of 19.5 (95% confidence interval 16.2 to 22.8) to 9.5 months (95% confidence interval 4.5 to 14.6), with P = .008; 3- to 4-month visits per site increased from the median (interquartile range) 14 (5.2-73.7) to 54 (34.5-152.0), with P < .001; and no-show rates were not different. Parent questionnaires revealed positive provider perception with improvement opportunities for information content and understandability. CONCLUSIONS Large-scale implementation of international guidelines for early detection of CP is feasible in diverse high-risk infant follow-up clinics. The initiative was received positively by families and without adversely affecting clinic operational flow. Additional parent support and education are necessary.
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Affiliation(s)
- Nathalie L Maitre
- Center for Perinatal Research and
- Department of Pediatrics, Nationwide Children's Hospital, Columbus, Ohio
| | - Vera J Burton
- Division of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Pediatrics and Neurosciences Intensive Care Nursery, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Andrea F Duncan
- Department of Pediatrics, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Sai Iyer
- Program of Developmental Behavioral Pediatrics, Department of Pediatrics, Mattel Children's Hospital, and University of California, Los Angeles, Los Angeles, California
| | - Betsy Ostrander
- Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah; and
| | - Sarah Winter
- Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah; and
| | - Lauren Ayala
- Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah; and
| | | | - Gwendolyn Gerner
- Division of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Pediatrics and Neurosciences Intensive Care Nursery, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Ruth Getachew
- Division of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland
| | - Kelsey Jiang
- Program of Developmental Behavioral Pediatrics, Department of Pediatrics, Mattel Children's Hospital, and University of California, Los Angeles, Los Angeles, California
| | - Laurie Lesher
- Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah; and
| | - Carrie M Perez
- Department of Pediatrics, The University of Texas Health Science Center at Houston, Houston, Texas
| | | | - Rebecca Lam
- Cerebral Palsy Foundation, New York, New York
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Ihlen EAF, Støen R, Boswell L, de Regnier RA, Fjørtoft T, Gaebler-Spira D, Labori C, Loennecken MC, Msall ME, Möinichen UI, Peyton C, Schreiber MD, Silberg IE, Songstad NT, Vågen RT, Øberg GK, Adde L. Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study. J Clin Med 2019; 9:E5. [PMID: 31861380 PMCID: PMC7019773 DOI: 10.3390/jcm9010005] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 11/29/2019] [Accepted: 12/16/2019] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings. METHODS The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time-frequency decomposition of the movement trajectories of the infant's body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9-15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement assessment (GMA) and neonatal imaging. RESULTS The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%). CONCLUSION The CIMA model may be a clinically feasible alternative to observational GMA.
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Affiliation(s)
- Espen A. F. Ihlen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway;
| | - Ragnhild Støen
- Department of Neonatology, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway;
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway;
| | - Lynn Boswell
- Ann and Robert H Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA; (L.B.); (R.-A.d.R.)
| | - Raye-Ann de Regnier
- Ann and Robert H Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA; (L.B.); (R.-A.d.R.)
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.G.-S.); (C.P.)
| | - Toril Fjørtoft
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway;
- Clinic of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway;
| | - Deborah Gaebler-Spira
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.G.-S.); (C.P.)
- Shirley Ryan AbilityLab, Chicago, IL 60611, USA
| | - Cathrine Labori
- Department of Clinical Therapeutic Services, University Hospital of North Norway, 9038 Tromsø, Norway; (C.L.); (G.K.Ø.)
| | - Marianne C. Loennecken
- Department of Pediatrics, Division of Paediatric and Adolescent Medicine, Oslo University Hospital, 0372 Oslo, Norway; (M.C.L.); (U.I.M.); (I.E.S.)
| | - Michael E. Msall
- University of Chicago Medicine, Comer Children’s Hospital, Section of Developmental and Behavioral Pediatrics, Chicago, IL 60637, USA; (M.E.M.); (M.D.S.)
- University of Chicago, Kennedy Research Center on Intellectual and Neurodevelopmental Disabilities, Chicago, IL 60637, USA
| | - Unn I. Möinichen
- Department of Pediatrics, Division of Paediatric and Adolescent Medicine, Oslo University Hospital, 0372 Oslo, Norway; (M.C.L.); (U.I.M.); (I.E.S.)
| | - Colleen Peyton
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.G.-S.); (C.P.)
- Department of Pediatrics, Comer Children’s Hospital, Department of Physical Therapy and Human Movement Science, Chicago, IL 60637, USA
| | - Michael D. Schreiber
- University of Chicago Medicine, Comer Children’s Hospital, Section of Developmental and Behavioral Pediatrics, Chicago, IL 60637, USA; (M.E.M.); (M.D.S.)
| | - Inger E. Silberg
- Department of Pediatrics, Division of Paediatric and Adolescent Medicine, Oslo University Hospital, 0372 Oslo, Norway; (M.C.L.); (U.I.M.); (I.E.S.)
| | - Nils T. Songstad
- Department of Pediatrics and Adolescent Medicine, University Hospital of North Norway, 9038 Tromsø, Norway;
| | - Randi T. Vågen
- Clinic of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway;
| | - Gunn K. Øberg
- Department of Clinical Therapeutic Services, University Hospital of North Norway, 9038 Tromsø, Norway; (C.L.); (G.K.Ø.)
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT- The Arctic University of Norway, 9019 Tromsø, Norway
| | - Lars Adde
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway;
- Clinic of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway;
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"STEP", an early developmental screening tool that predicts one-year outcomes. J Perinatol 2019; 39:153-155. [PMID: 30546059 DOI: 10.1038/s41372-018-0293-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 11/19/2018] [Accepted: 11/27/2018] [Indexed: 11/08/2022]
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