Dataset Information

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.

ABSTRACT:

Objective

To identify perinatal clinical diseases and treatments that are associated with the development of objectively diagnosed diffuse white matter abnormality (DWMA) on structural magnetic resonance imaging (MRI) at term-equivalent age in infants born very preterm.

Study design

A prospective cohort of 392 infants born very preterm (≤32 weeks of gestational age) was enrolled from 5 level III/IV neonatal intensive care units between September 2016 and November 2019. MRIs of the brain were collected at 39 to 45 weeks of postmenstrual age to evaluate DWMA volume. A predefined list of pertinent maternal characteristics, pregnancy/delivery data, and neonatal intensive care unit data were collected for enrolled patients to identify antecedents of objectively diagnosed DWMA.

Results

Of the 392 infants in the cohort, 377 (96%) had high-quality MRI data. Their mean (SD) gestational age was 29.3 (2.5) weeks. In multivariable linear regression analyses, pneumothorax (P = .027), severe bronchopulmonary dysplasia (BPD) (P = .009), severe retinopathy of prematurity (P < .001), and male sex (P = .041) were associated with increasing volume of DWMA. The following factors were associated with decreased risk of DWMA: postnatal dexamethasone therapy for severe BPD (P = .004), duration of caffeine therapy for severe BPD (P = .009), and exclusive maternal milk diet at neonatal intensive care unit discharge (P = .049).

Conclusions

Severe retinopathy of prematurity and BPD exhibited the strongest adverse association with development of DWMA. We also identified treatments and nutritional factors that appear protective against the development of DWMA that also have implications for the clinical care of infants born very preterm.

SUBMITTER: Parikh NA

PROVIDER: S-EPMC8290900 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Publications

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.

Parikh Nehal A NA Sharma Puneet P He Lili L Li Hailong H Altaye Mekibib M Priyanka Illapani Venkata Sita VS

The Journal of pediatrics 20201128

<h4>Objective</h4>To identify perinatal clinical diseases and treatments that are associated with the development of objectively diagnosed diffuse white matter abnormality (DWMA) on structural magnetic resonance imaging (MRI) at term-equivalent age in infants born very preterm.<h4>Study design</h4>A prospective cohort of 392 infants born very preterm (≤32 weeks of gestational age) was enrolled from 5 level III/IV neonatal intensive care units between September 2016 and November 2019. MRIs of the ...[more]

PMID: 33259857

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Project description:Diffuse white matter abnormality (DWMA), or diffuse excessive high signal intensity is observed in 50-80% of very preterm infants at term-equivalent age. It is subjectively defined as higher than normal signal intensity in periventricular and subcortical white matter in comparison to normal unmyelinated white matter on T2-weighted MRI images. Despite the well-documented presence of DWMA, it remains debatable whether DWMA represents pathological tissue injury or a transient developmental phenomenon. Manual tracing of DWMA exhibits poor reliability and reproducibility and unduly increases image processing time. Thus, objective and ideally automatic assessment is critical to accurately elucidate the biologic nature of DWMA. We propose a deep learning approach to automatically identify DWMA regions on T2-weighted MRI images. Specifically, we formulated DWMA detection as an image voxel classification task; that is, the voxels on T2-weighted images are treated as samples and exclusively assigned as DWMA or normal white matter voxel classes. To utilize the spatial information of individual voxels, small image patches centered on the given voxels are retrieved. A deep convolutional neural networks (CNN) model was developed to differentiate DWMA and normal voxels. We tested our deep CNN in multiple validation experiments. First, we examined DWMA detection accuracy of our CNN model using computer simulations. This was followed by in vivo assessments in a cohort of very preterm infants (N = 95) using cross-validation and holdout validation. Finally, we tested our approach on an independent preterm cohort (N = 28) to externally validate our model. Our deep CNN model achieved Dice similarity index values ranging from 0.85 to 0.99 for DWMA detection in the aforementioned validation experiments. Our proposed deep CNN model exhibited significantly better performance than other popular machine learning models. We present an objective and automated approach for accurately identifying DWMA that may facilitate the clinical diagnosis of DWMA in very preterm infants.

Project description:Infants born very preterm (VPT) are at risk of later visual problems. Although neonatal screening can identify ophthalmologic abnormalities, subtle perinatal brain injury and/or delayed brain maturation may be significant contributors to complex visual-behavioral problems. Our aim was to assess the micro and macrostructural antecedents of early visual-behavioral difficulties in VPT infants by using diffusion MRI (dMRI) at term-equivalent age. We prospectively recruited a cohort of 262 VPT infants (≤32 weeks gestational age [GA]) from five neonatal intensive care units. We obtained structural and diffusion MRI at term-equivalent age and administered the Preverbal Visual Assessment (PreViAs) questionnaire to parents at 3-4 months corrected age. We used constrained spherical deconvolution to reconstruct nine white matter tracts of the visual pathways with high reliability and performed fixel-based analysis to derive fiber density (FD), fiber-bundle cross-section (FC), and combined fiber density and cross-section (FDC). In multiple logistic regression analyses, we related these tract metrics to visual-behavioral function. Of 262 infants, 191 had both high-quality dMRI and completed PreViAs, constituting the final cohort: mean (SD) GA was 29.3 (2.4) weeks, 90 (47.1%) were males, and postmenstrual age (PMA) at MRI was 42.8 (1.3) weeks. FD and FC of several tracts were altered in infants with (N = 59) versus those without retinopathy of prematurity (N = 132). FDC of the left posterior thalamic radiations (PTR), left inferior longitudinal fasciculus (ILF), right superior longitudinal fasciculus (SLF), and left inferior fronto-occipital fasciculus (IFOF) were significantly associated with visual attention scores, prior to adjusting for confounders. After adjustment for PMA at MRI, GA, severe retinopathy of prematurity, and total brain volume, FDC of the left PTR, left ILF, and left IFOF remained significantly associated with visual attention. Early visual-behavioral difficulties in VPT infants are preceded by micro and macrostructural abnormalities in several major visual pathways at term-equivalent age.

Project description:AimThis study aimed to identify early clinical biomarkers from birth to 16 weeks corrected age to predict typical outcome and developmental delay in infants born very preterm or with very low birthweight.MethodA prospective cohort of infants on the Sunshine Coast, Australia, was assessed using the Premie-Neuro Examination, the General Movement Assessment (GMA), the Alberta Infant Motor Scale, and the Infant Sensory Profile 2. At 24 months corrected age, delay was identified using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) and Neurosensory Motor Developmental Assessment (NSMDA).ResultsOne hundred and four infants were recruited; 79 completed outcome assessments (43 females, 36 males; mean gestational age 30 weeks [SD 1 week 6 days], mean birthweight 1346 g [SD 323]). The incidence of developmental delay (motor or cognitive) was 6.3%. Suboptimal quality of fidgety general movements (temporal organization) at 16 weeks corrected age demonstrated the best predictive accuracy (Bayley-III motor: sensitivity 100% [95% confidence interval {CI} 3-100], specificity 75% [95% CI 63-84], area under the curve [AUC] 0.87); Bayley-III cognitive: sensitivity 100% [95% CI 3-100], specificity 75% [95% CI 64-84], AUC 0.88); NSMDA motor: sensitivity 100% [95% CI 40-100], specificity 81% [95% CI 70-90], AUC 0.91 [95% CI 0.86-0.95]). GMA trajectories that combined abnormal writhing general movements at 4 to 5 weeks corrected age with suboptimal quality of fidgety movement at 16 weeks corrected age were strongly predictive of developmental delay, superior to all other clinical tools, and perinatal and demographic variables investigated (p = 0.01, Akaike information criterion method 18.79 [score corrected for small sample size], accounting for 93% of the cumulative weight).InterpretationOnly the GMA had sufficient predictive validity to act as a biomarker for both conditions: typical outcome and developmental delay (motor or cognitive). GMA trajectories that assessed both writhing general movements at 4 to 5 weeks corrected age and quality of fidgety movement at 16 weeks corrected age predicted adverse neurodevelopmental outcome, accurately differentiating between infants with typical outcomes and those at increased risk for motor or cognitive delay.

Dataset Information

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.

Objective

Study design

Results

Conclusions

Publications

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.

Similar Datasets

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