Evidence map›Paper›PMID 40426762›Full record

ArticleChildren (Basel, Switzerland)2025

Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study.

Kun Dai, Rong Yu, Yushi Meng, Xiaomeng Yang, Yixin Jiang, Jing Luo, Kui Fang, Suqing Wang, Zhihui Rong

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Article in Children (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Kun DaiSchool of Nursing, Wuhan University, Wuhan 430071, China.
Rong YuTongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Yushi MengTongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Xiaomeng YangSchool of Public Health, Wuhan University, Wuhan 430071, China.
Yixin JiangSchool of Nursing, Wuhan University, Wuhan 430071, China.
Jing LuoSchool of Nursing, Wuhan University, Wuhan 430071, China.
Kui FangThe First Affiliated Hospital of China Medical University, Shenyang 110001, China.
Suqing WangSchool of Nursing, Wuhan University, Wuhan 430071, China.
Zhihui RongTongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesDevelopmental delay in preterm infants is a critical clinical issue, and early risk identification and prediction are essential. This study aims to develop and validate a predictive model for developmental delay, providing a scientific basis for clinical risk assessment and early intervention.

methodsThis study included preterm infants and their primary caregivers who were followed up at our center from May 2023 to September 2024. The samples were randomly divided into a training cohort, an internal validation cohort, and an external validation cohort. Independent risk factors for stunting were identified through univariate and multivariate logistic regression analyses, and predictive models and calibration were constructed accordingly.

resultsThe five standardized indicators at 3, 6, 9, and 12 months for 507 preterm infants were analyzed using principal component analysis, and their developmental outcomes were grouped accordingly. Logistic regression analyses showed that gestational age, high-risk factors, knowledge of caregiving, caregiving experience, and the presence of other caregivers in the home were independent risk factors for the risk of preterm infants with stunted growth at 3, 6, 9, and 12 months. The nomogram showed the area under the receiver operating characteristic curve values of 0.743, 0.735, 0.752, and 0.774 in the training cohort; 0.855, 0.771, 0.870, and 0.786 in the internal validation cohort; 0.822, 0.804, 0.717, and 0.678 in the external validation cohort, respectively. The calibration curves, consistency index, and decision curve analysis all showed that the model was significantly better than a single indicator in predicting the risk of stunting in preterm infants.

conclusionsThe stunting risk prediction model constructed in this study shows good predictive ability, which can help clinicians assess the risk of stunting in preterm infants and support the development of early intervention strategies.

Indexed as

prediction modelpreterm infantsprincipal component analysisrisk factorsstunting

Identifiers

PMID40426762
PMCPMC12110705

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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.