Construction and validation of a risk prediction model for bronchopulmonary dysplasia based on early platelet-related parameters
- VernacularTitle:基于早期血小板相关参数的支气管肺发育不良风险预测模型的构建与验证
- Author:
Yuheng XUE
1
;
Ning MAO
;
Wenqiang LIU
;
Qianqian YANG
;
Yan XU
;
Jun WANG
Author Information
- Keywords: bronchopulmonary dysplasia; infant,premature; platelet count; risk factors; forecasting
- From: Tianjin Medical Journal 2024;52(7):748-754
- CountryChina
- Language:Chinese
- Abstract: Objective To develop and validate a risk prediction model based on early platelet-related parameters for bronchopulmonary dysplasia(BPD)in neonates admitted to the neonatal intensive care unit(NICU),and to facilitate early identification and intervention in high-risk populations.Methods Clinical data of 291 preterm infants with a gestational age(GA)≤32 weeks or a birth weight(BW)＜1 500 g,admitted to the NICU,were retrospectively analyzed.Out of these,214 cases were selected as the modeling group.This group was further categorized into the BPD group(n=76)and the non-BPD group(n=138),based on whether they required oxygen therapy at 28 days post-birth.Perinatal data,platelet-related parameters and other indicators between the two groups.Univariate and multivariate Logistic regression analyses were conducted to identify BPD risk factors,followed by the construction of a nomogram.An additional cohort of 105 preterm infants with GA≤32 weeks or BW＜1 500 g,were used to validate the model.This cohort was divided into the BPD group(n=43)and the non-BPD(n=62)group.Receiver operating characteristic(ROC)curve and calibration curve were used to internally verify the efficiency of the prediction model.Results The Logistic regression analysis identified GA,BW,Apgar score at 5 minutes≤7,invasive ventilation,platelet count(PLT)and mean platelet volume(MPV)as significant factors in the model(P＜0.05).The constructed nomogram was formulated using R language,and the areas under the ROC curve(AUC)for the three models were 0.908,0.931 and 0.918,respectively(P＜0.05).The verification group was verified by Bootstrap.The calibration curve showed a good fit.The internal validation AUC values of the three models were 0.877,0.890 and 0.886,respectively.Conclusion GA,BW,invasive ventilation,Apgar score at 5 minutes≤7,MPV and PLT are key risk factors for BPD onset.The risk prediction model based on these indicators can effectively predict BPD,providing clinicians with a valuable tool for early detection and intervention in the development of BPD.
