Dai Kun, Yu Rong, Meng Yushi, Yang Xiaomeng, Jiang Yixin, Luo Jing, Fang Kui, Wang Suqing, Rong Zhihui
School of Nursing, Wuhan University, Wuhan 430071, China.
Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Children (Basel). 2025 Apr 30;12(5):583. doi: 10.3390/children12050583.
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.
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.
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.
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.
早产儿发育迟缓是一个关键的临床问题，早期风险识别和预测至关重要。本研究旨在建立并验证一种发育迟缓预测模型，为临床风险评估和早期干预提供科学依据。
本研究纳入了2023年5月至2024年9月在本中心进行随访的早产儿及其主要照顾者。样本被随机分为训练队列、内部验证队列和外部验证队列。通过单因素和多因素逻辑回归分析确定发育迟缓的独立危险因素，并据此构建预测模型和进行校准。
对507例早产儿在3、6、9和12个月时的五项标准化指标进行主成分分析，并据此对其发育结局进行分组。逻辑回归分析显示，胎龄、高危因素、育儿知识、育儿经验以及家中其他照顾者的存在是早产儿在3、6、9和12个月时生长发育迟缓风险的独立危险因素。列线图在训练队列中的受试者操作特征曲线下面积值分别为0.743、0.735、0.752和0.774；在内部验证队列中分别为0.855、0.771、0.870和0.786；在外部验证队列中分别为0.822、0.804、0.717和0.678。校准曲线、一致性指数和决策曲线分析均表明，该模型在预测早产儿发育迟缓风险方面明显优于单一指标。
本研究构建的发育迟缓风险预测模型具有良好的预测能力，可帮助临床医生评估早产儿发育迟缓的风险，并支持早期干预策略的制定。
