天津医药 ›› 2024, Vol. 52 ›› Issue (7): 748-754.doi: 10.11958/20231602

• 临床研究 • 上一篇    下一篇

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

薛玉恒(), 茆宁, 刘文强, 杨倩倩, 徐艳, 王军()   

  1. 徐州医科大学附属医院儿科(邮编221000)
  • 收稿日期:2023-10-25 修回日期:2023-12-26 出版日期:2024-07-15 发布日期:2024-07-11
  • 通讯作者: E-mail:664586331@qq.com
  • 作者简介:薛玉恒(1995),女,硕士在读,主要从事新生儿支气管肺发育不良方面研究。E-mail:719741950@qq.com
  • 基金资助:
    江苏省妇幼健康科研项目(F201850)

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

XUE Yuheng(), MAO Ning, LIU Wenqiang, YANG Qianqian, XU Yan, WANG Jun()   

  1. Department of Neonatology, the Affliated Hospital of Xuzhou Medical University, Xuzhou 221000, China
  • Received:2023-10-25 Revised:2023-12-26 Published:2024-07-15 Online:2024-07-11
  • Contact: E-mail:664586331@qq.com

摘要:

目的 构建并验证基于新生儿重症监护病房(NICU)的早产儿早期血小板相关参数对支气管肺发育不良(BPD)风险预测模型,从而早期识别高风险人群并进行干预。方法 收集NICU收治的291例胎龄(GA)≤32周或出生体质量(BW)<1 500 g的早产儿的临床资料。最终纳入214例作为建模组,根据生后28 d是否吸氧分为BPD组(76例)和非BPD组(138例)。比较2组围生期资料、血小板相关参数等指标,采用Logistic回归筛选BPD的影响因素,应用受试者工作特征(ROC)曲线评价模型的预测价值,并构建列线图。收集同一中心收治的GA≤32周或BW<1 500 g的105例早产儿作为验证组,分为BPD组(43例)和非BPD组(62例),利用ROC曲线、校准曲线内部验证预测模型的效能。结果 建模组Logistic回归分析显示,GA、BW、Apgar 5 min≤7分、有创通气、血小板计数(PLT)、平均血小板体积(MPV)在模型中有统计学意义(P<0.05)。根据多因素分析结果以R语言分别绘制预测模型列线图。3个模型的ROC曲线下面积(AUC)分别为0.908、0.931和0.918(P<0.05)。采用Bootstrap法对验证组进行验证,校准曲线显示拟合度良好,3个模型内部验证的AUC分别为0.877、0.890和0.886。结论 GA、BW、有创通气、Apgar 5 min≤7分、MPV、PLT可作为BPD发生的预测因素,有助于临床医生早期发现并干预BPD的发生发展。

关键词: 支气管肺发育不良, 婴儿, 早产, 血小板计数, 危险因素, 预测

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.

Key words: bronchopulmonary dysplasia, infant, premature, platelet count, risk factors, forecasting

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