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- Title
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.
- Authors
薛玉恒; 茆宁; 刘文强; 杨倩倩; 徐艳; 王军
- 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 nonBPD 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.
- Publication
Tianjin Medical Journal, 2024, Vol 52, Issue 7, p748
- ISSN
0253-9896
- Publication type
Article
- DOI
10.11958/20231602