Evidence map›Paper›PMID 41562993›Full record

ArticlePediatric reports2025

A Clinical Prediction Model for Genetic Risk in Children with GDD/ID: A Retrospective Study.

Yunshu Jiang, Ran Chen, Mengyin Chen, Luting Peng, Yuchen Zhao, Rong Li, Xiaonan Li

Abstract read
In one paragraph

Article in Pediatric reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yunshu JiangDepartment of Child Healthcare, Children's Hospital of Nanjing Medical University, Nanjing 210008, China.
Ran ChenDepartment of Child Healthcare, Children's Hospital of Nanjing Medical University, Nanjing 210008, China.
Mengyin ChenDepartment of Child Healthcare, Children's Hospital of Nanjing Medical University, Nanjing 210008, China.
Luting PengDepartment of Child Healthcare, Children's Hospital of Nanjing Medical University, Nanjing 210008, China.
Yuchen ZhaoDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Rong LiDepartment of Child Healthcare, Children's Hospital of Nanjing Medical University, Nanjing 210008, China.
Xiaonan LiDepartment of Child Healthcare, Children's Hospital of Nanjing Medical University, Nanjing 210008, China.

Funding

Jiangsu Medical Association Scientific Research Special Fund SYH-32034-0097Pediatric Medical Research Special Fund project of Jiangsu Medical Association SYH-32034-0070
6 · The paper itself

Abstract

objectivesGlobal Developmental Delay (GDD) and Intellectual Disability (ID) are prevalent neurodevelopmental disorders with significant disability burden, and genetic factors play a crucial role in their etiology. This study aimed to develop and validate a clinical prediction model for identifying children with GDD/ID at high genetic risk, facilitating targeted genetic testing.

methodsWe retrospectively analyzed clinical data of children with GDD/ID treated at Nanjing Children's Hospital from January 2019 to December 2023. Children with comorbid Autism Spectrum Disorder (ASD) were excluded. The dataset was randomly split into training and validation sets (7:3 ratio). Lasso regression was used to identify potential predictive factors for positive genetic test results, followed by multivariable logistic regression to select independent predictors, which were incorporated into a nomogram. Model performance was evaluated by discrimination, calibration, and clinical utility using decision curve analysis in both sets.

resultsFour independent predictors-craniofacial abnormalities, visceral abnormalities, physical growth abnormalities, and family history of ID-were identified. The resulting nomogram demonstrated an area under the curve (AUC) of 0.734., with good calibration and positive net benefit on decision curve analysis. Validation confirmed the reliability of the model.

conclusionsWe developed a clinically applicable prediction model to identify high genetic risk among children with GDD/ID without ASD. This model may serve as a preliminary screening tool to assist clinicians in prioritizing genetic testing and improving diagnostic efficiency in clinical practice.

Indexed as

geneglobal developmental delayintellectual disabilityprediction model

Identifiers

PMID41562993
PMCPMC12821521

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