Clinical trial NCT03345069: Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS)
Official title: Early Prediction of Cognitive and Motor Deficits Using Advanced MRI in Very Preterm Infants
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Premature Infant
Keywords: Premature
Magnetic resonance imaging (MRI)
Neurodevelopmental impairment
Cerebral Palsy
Cognitive Impairment
Diffusion MRI
Functional MRI
Infant
Magnetic resonance spectroscopy
Lead sponsor: Children's Hospital Medical Center, Cincinnati
Sponsor class: OTHER
Collaborators: name: University of Cincinnati
name: Good Samaritan Hospital, Ohio
name: Kettering Health Network
name: National Institute of Neurological Disorders and Stroke (NINDS)
name: National Institutes of Health (NIH)
First posted: 2017-11-17
Enrollment: 393
Sex: ALL
Maximum age (years): 3
Standard ages: CHILD
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: All very preterm infants (≤32 weeks completed gestational age) from a geographically-based population cared for in level III/IV NICUs in Cincinnati/Dayton are eligible. Those with known chromosomal or congenital anomalies affecting the central nervous system or with cyanotic heart disease will be excluded.

Brief summary: The Early Prediction Study is a longitudinal population-based cohort study for very preterm infants ≤32 weeks gestational age. Preterm infants recruited from three greater Cincinnati and two Dayton area neonatal intensive care units (NICUs) will undergo advanced MRIs at 41 weeks postmenstrual age and neurodevelopmental testing at the corrected ages of two and three years correct age. The goal of the Early Prediction Study is to accurately predict motor, cognitive, and behavioral deficits in individual very preterm infants using neuroimaging technologies and established epidemiologic approaches.

Detailed description: With every neonatal intensive care unit discharge, physicians and early intervention specialists face a critical challenge: How to counsel parents about their very preterm infant's risk of developing cognitive and motor deficits and accurately assign early intervention therapies. More than 100,000 babies are born very preterm at ≤32 weeks gestational age every year in the United States. Up to 35% of these preterm survivors develop cognitive deficits, and up to 20% develop motor impairment. This places them at high risk for poor educational, health, and social outcomes. Yet reliable diagnosis of cognitive and motor deficits cannot be made until early childhood. This long and unnecessary delay wastes early intervention resources, dilutes the effectiveness of infant stimulation programs, and disrupts parental adaptation. Attempts to address these gaps with conventional neuroimaging and other approaches have failed. Thus, a critical need exists before neonatal discharge, to accurately predict cognitive and motor deficits in individual very preterm infants with the use of novel neuroimaging technologies and established epidemiologic approaches.

The investigators long-term research goal is to elucidate the etiology, pathogenesis, and early prediction of cognitive and motor deficits in order to facilitate preventive early interventions in very preterm infants, resulting in better outcomes. The investigators objectives in this application therefore are to:

1. Identify the clinical antecedents of objectively diagnosed diffuse white matter abnormality (DWMA).
2. Associate DWMA with pathologic changes on neuroimaging.
3. Predict cognitive and behavioral deficits at 3 years of age using objectively diagnosed DWMA in a geographic cohort of very preterm infants.
4. To predict motor impairment, especially cerebral palsy at 24 months corrected age.

The investigators central hypothesis is that objectively quantified DWMA is pathologic, associated with inflammation-associated perinatal illnesses, and an independent predictor of cognitive deficits at 3 years corrected age in very preterm infants. The investigators rationale for this research is that new knowledge investigators expect to have generated will enhance parental counseling, facilitate accurate risk stratification for early intervention therapies, and guide biologically-based strategies for early prevention of DWMA and cognitive and motor deficits in very preterm infants.

Eligibility criteria: Inclusion Criteria:

* Hospitalized infants born at ≤32 weeks completed gestational age that are being cared for in all three level III/IV NICUs from the Greater Cincinnati area.

Exclusion Criteria:

* Infants with known chromosomal or congenital anomalies affecting the central nervous system or with cyanotic heart disease.

Primary outcomes:
measure: Motor development
timeFrame: 2 years of age, corrected for prematurity
measure: Cognitive development
timeFrame: 3 years of age, corrected for prematurity
measure: Behavioral development
timeFrame: 3 years of age, corrected for prematurity
Secondary outcomes:
measure: Cognitive and language deficits at 2years of age
timeFrame: 2 years of age, corrected for prematurity
measure: Executive function
timeFrame: 3 years of age, corrected for prematurity
measure: Behavioral development
timeFrame: 3 years of age, corrected for prematurity
measure: Pre-academic skills
timeFrame: 3 years of age, corrected for prematurity
Other outcomes:
Locations:
city: Cincinnati
country: United States
facility: Cincinnati Children's Hospital Medical Center
state: Ohio
Study officials:
affiliation: Children's Hospital Medical Center, Cincinnati
name: Nehal A Parikh, DO, MS
role: PRINCIPAL_INVESTIGATOR
References:
citation: Parikh NA. Advanced neuroimaging and its role in predicting neurodevelopmental outcomes in very preterm infants. Semin Perinatol. 2016 Dec;40(8):530-541. doi: 10.1053/j.semperi.2016.09.005. Epub 2016 Nov 15.
citation: He L, Li H, Wang J, Chen M, Gozdas E, Dillman JR, Parikh NA. A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants. Sci Rep. 2020 Sep 15;10(1):15072. doi: 10.1038/s41598-020-71914-x.
citation: Tamm L, Patel M, Peugh J, Kline-Fath BM, Parikh NA; Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS) Group. Early brain abnormalities in infants born very preterm predict under-reactive temperament. Early Hum Dev. 2020 May;144:104985. doi: 10.1016/j.earlhumdev.2020.104985. Epub 2020 Mar 9.
citation: Parikh NA, Sharma P, He L, Li H, Altaye M, Priyanka Illapani VS; Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS) Investigators. Perinatal Risk and Protective Factors in the Development of Diffuse White Matter Abnormality on Term-Equivalent Age Magnetic Resonance Imaging in Infants Born Very Preterm. J Pediatr. 2021 Jun;233:58-65.e3. doi: 10.1016/j.jpeds.2020.11.058. Epub 2020 Nov 28.
citation: Chandwani R, Kline JE, Harpster K, Tkach J, Parikh NA; Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS) Group. Early micro- and macrostructure of sensorimotor tracts and development of cerebral palsy in high risk infants. Hum Brain Mapp. 2021 Oct 1;42(14):4708-4721. doi: 10.1002/hbm.25579. Epub 2021 Jul 29.
citation: Chen M, Li H, Fan H, Dillman JR, Wang H, Altaye M, Zhang B, Parikh NA, He L. ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome. Med Phys. 2022 May;49(5):3171-3184. doi: 10.1002/mp.15545. Epub 2022 Mar 14.
citation: Lal SK, Nath LS. An atypical case of Vogt-Koyanagi syndrome. J Indian Med Assoc. 1986 Dec;84(12):382-3. No abstract available.
citation: Jain VG, Kline JE, He L, Kline-Fath BM, Altaye M, Muglia LJ, DeFranco EA, Ambalavanan N, Parikh NA; Cincinnati Infant Neurodevelopment Early Prediction Study Investigators. Acute histologic chorioamnionitis independently and directly increases the risk for brain abnormalities seen on magnetic resonance imaging in very preterm infants. Am J Obstet Gynecol. 2022 Oct;227(4):623.e1-623.e13. doi: 10.1016/j.ajog.2022.05.042. Epub 2022 May 26.
citation: Kline JE, Yuan W, Harpster K, Altaye M, Parikh NA. Association between brain structural network efficiency at term-equivalent age and early development of cerebral palsy in very preterm infants. Neuroimage. 2021 Dec 15;245:118688. doi: 10.1016/j.neuroimage.2021.118688. Epub 2021 Nov 7.
citation: He L, Li H, Chen M, Wang J, Altaye M, Dillman JR, Parikh NA. Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. Front Neurosci. 2021 Oct 5;15:753033. doi: 10.3389/fnins.2021.753033. eCollection 2021.
citation: Chandwani R, Harpster K, Kline JE, Mehta V, Wang H, Merhar SL, Schwartz TL, Parikh NA. Brain microstructural antecedents of visual difficulties in infants born very preterm. Neuroimage Clin. 2022;34:102987. doi: 10.1016/j.nicl.2022.102987. Epub 2022 Mar 9.
citation: Kline JE, Illapani VSP, Li H, He L, Yuan W, Parikh NA. Diffuse white matter abnormality in very preterm infants at term reflects reduced brain network efficiency. Neuroimage Clin. 2021;31:102739. doi: 10.1016/j.nicl.2021.102739. Epub 2021 Jun 25.
citation: Li H, Chen M, Wang J, Illapani VSP, Parikh NA, He L. Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants. Radiol Artif Intell. 2021 Feb 3;3(3):e200166. doi: 10.1148/ryai.2021200166. eCollection 2021 May.
citation: Yuan W, Tamm L, Harpster K, Altaye M, Illapani VSP, Parikh NA. Effects of intraventricular hemorrhage on white matter microstructural changes at term and early developmental outcomes in infants born very preterm. Neuroradiology. 2021 Sep;63(9):1549-1561. doi: 10.1007/s00234-021-02708-9. Epub 2021 Apr 8.
citation: Harpster K, Merhar S, Priyanka Illapani VS, Peyton C, Kline-Fath B, Parikh NA. Associations Between Early Structural Magnetic Resonance Imaging, Hammersmith Infant Neurological Examination, and General Movements Assessment in Infants Born Very Preterm. J Pediatr. 2021 May;232:80-86.e2. doi: 10.1016/j.jpeds.2020.12.056. Epub 2021 Jan 13.