Evidence map›Paper›PMID 32147220›Full record

ArticleThe Journal of pediatrics2020

Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm.

Nehal A Parikh, Lili He, Venkata Sita Priyanka Illapani, Mekibib Altaye, Alonzo T Folger, Keith O Yeates

Open access · greenAbstract readMulticenter StudyValidation Study
In one paragraph

Article in The Journal of pediatrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.9field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 25 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Nehal A ParikhPerinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Center for Perinatal Research, The Research Institute at Nationwide Children's Hospital, Columbus, OH; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH. Electronic address: Nehal.Parikh@cchmc.org.
Lili HePerinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
Venkata Sita Priyanka IllapaniPerinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH.
Mekibib AltayeDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Division of Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
Alonzo T FolgerDepartment of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Division of Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
Keith O YeatesDepartment of Psychology, Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
Cincinnati Children's Hospital Medical Center · USUniversity of Cincinnati Medical Center · USNationwide Children's Hospital · USUniversity of Calgary · CA

Funding

A New Model to Identify Preterm Neonates at High-Risk for Cognitive Impairments and School ReadinessR01NS094200 · NINDS · CINCINNATI CHILDRENS HOSP MED CTR · PI NEHAL A. PARIKH · 2016 to 2026
$6.7M
Early Prediction of Cerebral Palsy in Premature Infants using Advanced MRI BiomarkersR01NS096037 · NINDS · CINCINNATI CHILDRENS HOSP MED CTR · PI PARIKH, NEHAL A. · 2016 to 2020
$2.2M
Early Prediction of Cognitive Deficits in Very Preterm Infants using Machine Learning and Brain ConnectomeR21HD094085 · NICHD · CINCINNATI CHILDRENS HOSP MED CTR · PI HE, LILI · 2018 to 2019
$437k
NICHD NIH HHS R21 HD094085NINDS NIH HHS R01 NS094200NINDS NIH HHS R01 NS096037
6 · The paper itself

Abstract

objectiveTo externally validate the independent value of objectively diagnosed diffuse white matter abnormality (DWMA; also known as diffuse excessive high signal intensity) volume to predict neurodevelopmental outcomes in very preterm infants (≤31 weeks of gestational age). STUDY

designA prospective, multicenter, regional population-based cohort study in 98 very preterm infants without severe brain injury on magnetic resonance imaging (MRI). DWMA volume was diagnosed objectively on structural MRI at term-equivalent age using our published algorithm. Multivariable linear regression was used to assess the value of DWMA volume to predict cognitive and language scores on the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) at 2 years corrected age.

resultsOf the infants who returned for follow-up (n = 74), the mean (SD) gestational age was 28.2 (2.4) weeks, and 42 (56.8%) were boys. In bivariable analyses, DWMA volume was a significant predictor of Bayley-III cognitive and language scores. In multivariable analyses, controlling for known predictors of Bayley-III scores (ie, socioeconomic status, gestational age, sex, and global brain abnormality score), DWMA volume remained a significant predictor of cognitive (P < .001) and language (P = .04) scores at 2 years. When dichotomized, objectively diagnosed severe DWMA was a significant predictor of cognitive and language impairments, whereas visual qualitative diagnosis of DWMA was a poor predictor.

conclusionsIn this multicenter, prospective cohort study, we externally validated our previous findings that objectively diagnosed DWMA is an independent predictor of cognitive and language development in very preterm infants. We also demonstrated again that visually-diagnosed DWMA is not predictive of neurodevelopmental outcomes.

Indexed as

Infant, Extremely PrematureMagnetic Resonance ImagingAlgorithmsChild, PreschoolCognition DisordersCohort StudiesFemaleFollow-Up StudiesHumansInfant, NewbornLanguage Development DisordersMaleMultivariate AnalysisSensitivity and SpecificityWhite Matter

Identifiers

PMID32147220
PMCPMC7583652
OpenAlexW3010700177

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.