Evidence map›Paper›PMID 34276789›Full record

ArticleFrontiers in genetics2021

Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information.

Dan Dai, Huiyao Chen, Xinran Dong, Jinglong Chen, Mei Mei, Yulan Lu, Lin Yang, Bingbing Wu, Yun Cao, Jin Wang and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
5.6field-weighted citation impact, top 3% 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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

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  11. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024
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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

12 authors at 1 institution in 1 country.

Dan DaiDivision of Pulmonary Medicine, Children's Hospital of Fudan University, Shanghai, China.
Huiyao ChenMolecular Medical Center, Children's Hospital of Fudan University, Shanghai, China.
Xinran DongMolecular Medical Center, Children's Hospital of Fudan University, Shanghai, China.
Jinglong ChenDivision of Pulmonary Medicine, Children's Hospital of Fudan University, Shanghai, China.
Mei MeiDivision of Pulmonary Medicine, Children's Hospital of Fudan University, Shanghai, China.
Yulan LuMolecular Medical Center, Children's Hospital of Fudan University, Shanghai, China.
Lin YangMolecular Medical Center, Children's Hospital of Fudan University, Shanghai, China.
Bingbing WuMolecular Medical Center, Children's Hospital of Fudan University, Shanghai, China.
Yun CaoDepartment of Neonatology, Children's Hospital of Fudan University, Shanghai, China.
Jin WangDepartment of Neonatology, Children's Hospital of Fudan University, Shanghai, China.
Wenhao ZhouMolecular Medical Center, Children's Hospital of Fudan University, Shanghai, China.
Liling QianDivision of Pulmonary Medicine, Children's Hospital of Fudan University, Shanghai, China.
Children's Hospital of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn early and accurate evaluation of the risk of bronchopulmonary dysplasia (BPD) in premature infants is pivotal in implementing preventive strategies. The risk prediction models nowadays for BPD risk that included only clinical factors but without genetic factors are either too complex without practicability or provide poor-to-moderate discrimination. We aim to identify the role of genetic factors in BPD risk prediction early and accurately.

methodsExome sequencing was performed in a cohort of 245 premature infants (gestational age <32 weeks), with 131 BPD infants and 114 infants without BPD as controls. A gene burden test was performed to find risk genes with loss-of-function mutations or missense mutations over-represented in BPD and severe BPD (sBPD) patients, with risk gene sets (RGS) defined as BPD-RGS and sBPD-RGS, respectively. We then developed two predictive models for the risk of BPD and sBPD by integrating patient clinical and genetic features. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC).

resultsThirty and 21 genes were included in BPD-RGS and sBPD-RGS, respectively. The predictive model for BPD, which combined the BPD-RGS and basic clinical risk factors, showed better discrimination than the model that was only based on basic clinical features (AUROC, 0.915

conclusionThis study suggests that genetic information contributes to susceptibility to BPD. The predictive model in this study, which combined BPD-RGS with basic clinical risk factors, can thus accurately stratify BPD risk in premature infants.

Indexed as

bronchopulmonary dysplasiaexome sequencingmachine learningprediction modelpremature infants

Identifiers

PMID34276789
PMCPMC8283015
OpenAlexW3183486067

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.