ANXA2、ACE2动态变化对儿童急性胰腺炎预后的增益价值

Incremental prognostic value of dynamic changes in ANXA2 and ACE2 for children with acute pancreatitis

摘要

目的 分析血清膜联蛋白A2(ANXA2)、血管紧张素转化酶2(ACE2)及自噬相关基因7(ATG7)的动态变化对儿童急性胰腺炎(AP)预后预测模型的增量价值。方法 选取2020年1月至2025年3月海南省妇女儿童医学中心收治的AP患儿212例,以入院后30天的预后情况为依据,将患儿分为两组,即预后不良组(51例)和预后良好组(161例)。收集两组患儿一般资料及入院时、入院7天后血清ANXA2、ACE2、ATG7参数,并计算变化值(Δ)进行组间比较。采用多因素Logistic回归分析预后不良的影响因素,分别构建常规模型(模型1)和联合ΔANXA2、ΔACE2的增量模型(模型2),通过受试者工作特征(ROC)曲线及DeLong检验比较两模型预测效能。结果 预后不良组入院时急性生理功能和慢性健康状况评分系统Ⅱ(APACHEⅡ)评分、白介素-6(IL-6)、肿瘤坏死因子-α(TNF-α)水平高于预后良好组(t=6.564、5.303、6.653,P均<0.05);入院时及入院7天后,预后不良组血清ANXA2、ATG7水平均高于预后良好组,ACE2水平均低于预后良好组(t=2.119~8.582,均P<0.05);ΔANXA2、ΔACE2、ΔATG7均低于预后良好组(t=6.297~10.393,均P<0.05)。多因素Logistic回归显示,APACHEⅡ评分(OR=1.283,95%CI:1.123~1.465)、IL-6(OR=1.237,95%CI:1.107~1.382)、TNF-α(OR=1.312,95%CI:1.135~1.516)为独立危险因素,ΔANXA2(OR=0.878,95%CI:0.832~0.927)、ΔACE2(OR=0.858,95%CI:0.815~0.903)为独立保护因素(P均<0.05),ΔATG7未纳入最终模型。ΔANXA2、ΔACE2预测AP患儿预后不良的AUC分别为0.767和0.772(均P<0.05)。模型2的曲线下面积(AUC)(0.931)显著优于模型1(0.857)(Z=2.014,P=0.036)。结论 急性胰腺炎患儿入院时与入院7天后血清ANXA2、ACE2水平变化值与预后密切相关,ΔANXA2、ΔACE2对儿童急性胰腺炎预后具有独立预测价值,联合二者可显著提升传统模型的预测效能。

Abstract

Objective To investigate the incremental value of dynamic changes in serum annexin A2 (ANXA2),angiotensin-converting enzyme 2 (ACE2),and autophagy related gene 7 (ATG7) in prognostic prediction models for children with acute pancreatitis (AP). Methods A total of 212 children with AP admitted to Hainan Women And Children's Medical Center between January 2020 and March 2025 were enrolled.According to the 30-day prognosis after admission,the patients were divided into a poor prognosis group (n=51) and a good prognosis group (n=161).General clinical data and serum levels of ANXA2,ACE2,and ATG7 at admission and 7 days after admission were collected.The changes in these biomarkers (Δ) were calculated and compared between the two groups.Multivariate Logistic regression analysis was performed to identify independent factors associated with poor prognosis.A conventional prediction model (Model 1) and an incremental model incorporating ΔANXA2 and ΔACE2 (Model 2) were established.The predictive performance of the two models was evaluated using receiver operating characteristic (ROC) curves and compared by the DeLong test. Results At admission,the poor prognosis group had significantly higher acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) scores and higher levels of Interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) than the good prognosis group (t=6.564,5.303,and 6.653,respectively,P<0.05).Both at admission and 7 days after admission,serum ANXA2 and ATG7 levels were significantly higher,whereas ACE2 levels were significantly lower in the poor prognosis group than in the good prognosis group (t=2.119-8.582,all P<0.05).The changes in ANXA2 (ΔANXA2),ACE2 (ΔACE2),and ATG7 (ΔATG7) were all significantly smaller in the poor prognosis group than in the good prognosis group (t=6.297-10.393,all P<0.05).Multivariate Logistic regression analysis showed that APACHEⅡ score (OR=1.283,95%CI:1.123-1.465),IL-6 (OR=1.237,95%CI:1.107-1.382),and TNF-α (OR=1.312,95%CI:1.135-1.516) were independent risk factors for poor prognosis,whereas ΔANXA2 (OR=0.878,95%CI:0.832-0.927) and ΔACE2 (OR=0.858,95%CI:0.815-0.903) were independent protective factors (all P<0.05).ΔATG7 was not retained in the final model.The areas under the ROC curve (AUC) of ΔANXA2 and ΔACE2 for predicting poor prognosis were 0.767 and 0.772,respectively (both P<0.05).The AUC of Model 2 (0.931) was significantly higher than that of Model 1 (0.857) (Z=2.014,P=0.036). Conclusion Dynamic changes in serum ANXA2 and ACE2 levels between admission and 7 days after admission are closely associated with the prognosis of children with acute pancreatitis.Both ΔANXA2 and ΔACE2 independently predict poor prognosis,and their incorporation into a conventional prediction model significantly improves its predictive performance.

关键词

儿童 / 急性胰腺炎 / 膜联蛋白A2 / 血管紧张素转化酶2 / 预后 / 预测模型

Key words

child / acute pancreatitis / annexin A2 / angiotensin-converting enzyme 2 / prognosis / prediction model

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基金资助

海南省自然科学基金高层次人才项目(821RC1135)