Construction of risk prediction model for preterm infant respiratory distress syndrome in Dali Prefecture
10.3969/j.issn.1006-5725.2025.15.008
- VernacularTitle:大理高海拔地区早产儿呼吸窘迫综合征发生风险的列线图预测模型构建
- Author:
Hong ZHANG
1
;
Rong ZHANG
;
Pengcheng YANG
;
Liyan LUO
;
Wenlong ZHANG
;
Yurong CHENG
;
Wenlin LIU
;
Wenbin DONG
Author Information
1. 西南医科大学附属医院儿童医学中心新生儿科(四川 泸州 646000);大理州妇幼保健院新生儿科(云南 大理 671000)
- Publication Type:Journal Article
- Keywords:
preterm infants;
respiratory distress syndrome;
nomogram;
high-altitude areas;
multi-variate logistic regression;
prediction model
- From:
The Journal of Practical Medicine
2025;41(15):2342-2348
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a nomogram-based predictive model for assessing the risk of respiratory distress syndrome(RDS)in premature infants in the high-altitude region of Dali.The predictive performance and clinical applicability of the model will be systematically evaluated to provide evidence-based guidance for the early diagnosis and clinical management of respiratory distress in premature infants.Methods A total of 680 preterm infants admitted to the Dali Maternal and Child Health Hospital between January 2020 and December 2024 were enrolled in the study and randomly divided into a training set(n=476)and a validation set(n=204)at a ratio of 7∶3.Independent predictors were identified through univariate logistic regression and multivariate stepwise regression analyses,and a nomogram model was subsequently developed using R software.The performance of the model,including its discrimination,calibration,stability,and clinical applicability,was evaluated using the receiver operating characteristic curve(ROC),Hosmer-Lemeshow goodness-of-fit test,bootstrap resampling method,and decision curve analysis(DCA).Results The final model incorporated seven independent variables:gestational age,birth weight,Apgar score,blood oxygen saturation,gestational hyperglycemia,prenatal glucocor-ticoid therapy,and maternal history of infection.The areas under the curve(AUCs)for the training and validation sets were 0.88(95%CI:0.84～0.92)and 0.83(95%CI:0.76～0.89),respectively,with all Hosmer-Lemeshow test p-values exceeding 0.05.The bootstrap-corrected AUC was 0.85(95%CI:0.81～0.89).DCA indicated that the model achieved the highest net benefit at a risk threshold range of 10%to 35%.Conclusions This model integrates multiple risk factors associated with the occurrence of RDS in plateau environments,demonstrating robust predictive performance for RDS in preterm infants residing in high-altitude areas such as Dali.It can serve as a valuable tool for risk stratification and clinical decision-making,and may also provide a reference for future multicenter prospective studies.
