Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study
Objectives: Developmental 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 interve...
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MDPI AG
2025-04-01
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| Series: | Children |
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| Online Access: | https://www.mdpi.com/2227-9067/12/5/583 |
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| author | Kun Dai Rong Yu Yushi Meng Xiaomeng Yang Yixin Jiang Jing Luo Kui Fang Suqing Wang Zhihui Rong |
| author_facet | Kun Dai Rong Yu Yushi Meng Xiaomeng Yang Yixin Jiang Jing Luo Kui Fang Suqing Wang Zhihui Rong |
| author_sort | Kun Dai |
| collection | DOAJ |
| description | Objectives: Developmental 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. Methods: This 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. Results: The 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. Conclusions: The 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. |
| format | Article |
| id | doaj-art-e67cff654e0a40228c8e4a1e54f27082 |
| institution | OA Journals |
| issn | 2227-9067 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj-art-e67cff654e0a40228c8e4a1e54f270822025-08-20T01:56:25ZengMDPI AGChildren2227-90672025-04-0112558310.3390/children12050583Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective StudyKun Dai0Rong Yu1Yushi Meng2Xiaomeng Yang3Yixin Jiang4Jing Luo5Kui Fang6Suqing Wang7Zhihui Rong8School of Nursing, Wuhan University, Wuhan 430071, ChinaTongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, ChinaTongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, ChinaSchool of Public Health, Wuhan University, Wuhan 430071, ChinaSchool of Nursing, Wuhan University, Wuhan 430071, ChinaSchool of Nursing, Wuhan University, Wuhan 430071, ChinaThe First Affiliated Hospital of China Medical University, Shenyang 110001, ChinaSchool of Nursing, Wuhan University, Wuhan 430071, ChinaTongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, ChinaObjectives: Developmental 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. Methods: This 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. Results: The 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. Conclusions: The 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.https://www.mdpi.com/2227-9067/12/5/583stuntingrisk factorsprediction modelprincipal component analysispreterm infants |
| spellingShingle | Kun Dai Rong Yu Yushi Meng Xiaomeng Yang Yixin Jiang Jing Luo Kui Fang Suqing Wang Zhihui Rong Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study Children stunting risk factors prediction model principal component analysis preterm infants |
| title | Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study |
| title_full | Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study |
| title_fullStr | Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study |
| title_full_unstemmed | Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study |
| title_short | Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study |
| title_sort | construction and validation of a pca based prediction model for preterm infant stunting risk a retrospective study |
| topic | stunting risk factors prediction model principal component analysis preterm infants |
| url | https://www.mdpi.com/2227-9067/12/5/583 |
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