BACKGROUND: Extremely-low-birth-weight (ELBW; ≤1,000 g) infants are at high risk for neurodevelopmental impairments. Conventional brain MRI at term-equivalent age is increasingly used for prediction of outcomes. However, optimal prediction models remain to be determined, especially for cognitive outcomes. OBJECTIVE: The aim was to evaluate the accuracy of a data-driven MRI scoring system to predict neurodevelopmental impairments. METHODS: 122 ELBW infants had a brain MRI performed at term-equivalent age. Conventional MRI findings were scored with a standardized algorithm and tested using a multivariable regression model to predict neurodevelopmental impairment, defined as one or more of the following at 18-24 months' corrected age: cerebral palsy, bilateral blindness, bilateral deafness requiring amplification, and/or cognitive/language delay. Results were compared with a commonly cited scoring system. RESULTS: In multivariable analyses, only moderate-to-severe gyral maturational delay was a significant predictor of overall neurodevelopmental impairment (OR: 12.6, 95% CI: 2.6, 62.0; p < 0.001). Moderate-to-severe gyral maturational delay also predicted cognitive delay, cognitive delay/death, and neurodevelopmental impairment/death. Diffuse cystic abnormality was a significant predictor of cerebral palsy (OR: 33.6, 95% CI: 4.9, 229.7; p < 0.001). These predictors exhibited high specificity (range: 94-99%) but low sensitivity (30-67%) for the above outcomes. White or gray matter scores, determined using a commonly cited scoring system, did not show significant association with neurodevelopmental impairment. CONCLUSIONS: In our cohort, conventional MRI at term-equivalent age exhibited high specificity in predicting neurodevelopmental outcomes. However, sensitivity was suboptimal, suggesting additional clinical factors and biomarkers are needed to enable accurate prognostication.
BACKGROUND: Extremely-low-birth-weight (ELBW; ≤1,000 g) infants are at high risk for neurodevelopmental impairments. Conventional brain MRI at term-equivalent age is increasingly used for prediction of outcomes. However, optimal prediction models remain to be determined, especially for cognitive outcomes. OBJECTIVE: The aim was to evaluate the accuracy of a data-driven MRI scoring system to predict neurodevelopmental impairments. METHODS: 122 ELBW infants had a brain MRI performed at term-equivalent age. Conventional MRI findings were scored with a standardized algorithm and tested using a multivariable regression model to predict neurodevelopmental impairment, defined as one or more of the following at 18-24 months' corrected age: cerebral palsy, bilateral blindness, bilateral deafness requiring amplification, and/or cognitive/language delay. Results were compared with a commonly cited scoring system. RESULTS: In multivariable analyses, only moderate-to-severe gyral maturational delay was a significant predictor of overall neurodevelopmental impairment (OR: 12.6, 95% CI: 2.6, 62.0; p < 0.001). Moderate-to-severe gyral maturational delay also predicted cognitive delay, cognitive delay/death, and neurodevelopmental impairment/death. Diffuse cystic abnormality was a significant predictor of cerebral palsy (OR: 33.6, 95% CI: 4.9, 229.7; p < 0.001). These predictors exhibited high specificity (range: 94-99%) but low sensitivity (30-67%) for the above outcomes. White or gray matter scores, determined using a commonly cited scoring system, did not show significant association with neurodevelopmental impairment. CONCLUSIONS: In our cohort, conventional MRI at term-equivalent age exhibited high specificity in predicting neurodevelopmental outcomes. However, sensitivity was suboptimal, suggesting additional clinical factors and biomarkers are needed to enable accurate prognostication.
Authors: Majid Mirmiran; Patrick D Barnes; Kathy Keller; Janet C Constantinou; Barry E Fleisher; Susan R Hintz; Ronald L Ariagno Journal: Pediatrics Date: 2004-10 Impact factor: 7.124
Authors: R Rathbone; S J Counsell; O Kapellou; L Dyet; N Kennea; J Hajnal; J M Allsop; F Cowan; A D Edwards Journal: Neurology Date: 2011-10-12 Impact factor: 9.910
Authors: Susan R Hintz; Douglas E Kendrick; Deanne E Wilson-Costello; Abhik Das; Edward F Bell; Betty R Vohr; Rosemary D Higgins Journal: Pediatrics Date: 2010-12-27 Impact factor: 7.124
Authors: Leigh E Dyet; Nigel Kennea; Serena J Counsell; Elia F Maalouf; Morenike Ajayi-Obe; Philip J Duggan; Michael Harrison; Joanna M Allsop; Joseph Hajnal; Amy H Herlihy; Bridget Edwards; Sabrina Laroche; Frances M Cowan; Mary A Rutherford; A David Edwards Journal: Pediatrics Date: 2006-08 Impact factor: 7.124
Authors: Susan R Hintz; Patrick D Barnes; Dorothy Bulas; Thomas L Slovis; Neil N Finer; Lisa A Wrage; Abhik Das; Jon E Tyson; David K Stevenson; Waldemar A Carlo; Michele C Walsh; Abbot R Laptook; Bradley A Yoder; Krisa P Van Meurs; Roger G Faix; Wade Rich; Nancy S Newman; Helen Cheng; Roy J Heyne; Betty R Vohr; Michael J Acarregui; Yvonne E Vaucher; Athina Pappas; Myriam Peralta-Carcelen; Deanne E Wilson-Costello; Patricia W Evans; Ricki F Goldstein; Gary J Myers; Brenda B Poindexter; Elisabeth C McGowan; Ira Adams-Chapman; Janell Fuller; Rosemary D Higgins Journal: Pediatrics Date: 2014-12-01 Impact factor: 7.124
Authors: Julia E Kline; Venkata Sita Priyanka Illapani; Lili He; Mekibib Altaye; John Wells Logan; Nehal A Parikh Journal: Arch Dis Child Fetal Neonatal Ed Date: 2019-11-08 Impact factor: 5.747
Authors: L van Eijk; M Seidel; K Pannek; J M George; S Fiori; A Guzzetta; A Coulthard; J Bursle; R S Ware; D Bradford; S Rose; P B Colditz; R N Boyd; J Fripp Journal: AJNR Am J Neuroradiol Date: 2021-08-19 Impact factor: 4.966
Authors: Venkata Sita Priyanka Illapani; David A Edmondson; Kim M Cecil; Mekibib Altaye; Manoj Kumar; Karen Harpster; Nehal A Parikh Journal: Pediatr Res Date: 2021-03-02 Impact factor: 3.953