Construction and validation of a risk prediction model for bronchopulmonary dysplasia based on early platelet-related parameters
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.
NETL
NSTL
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