Suppr超能文献

评估急性阑尾炎患儿术后发热概率的预测列线图

Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis.

作者信息

Chen Yang, Ren Feng, Xiao Dong, Guan Ai-Hui, Zhu Le-Dao, Ma Xiao-Peng, Wang Zhi-Yong

机构信息

Shenzhen Children's Hospital, Shenzhen, China.

College of Medicine, Shantou University, Shantou, China.

出版信息

Front Pediatr. 2022 Aug 23;10:982614. doi: 10.3389/fped.2022.982614. eCollection 2022.

Abstract

OBJECTIVE

The purpose of this study was to establish a predictive model of postoperative fever in children with acute appendicitis through retrospective analysis, and the prediction ability of the model is demonstrated by model evaluation and external validation.

METHODS

Medical records information on children undergoing surgery for acute appendicitis within 2 years were retrospectively collected, prospective collection was performed for external validation in the next 3 months. The patients were divided into two groups according to whether the postoperative body temperature exceeded 38.5°C. Multivariate logistic regression analysis was used to determine independent risk factors and develop regression equations and nomogram. ROC curve, calibration curve and decision curve were made for model evaluation. Finally, the clinical implication of the prediction model was clarified by associating postoperative fever with prognosis.

RESULTS

High risk factors of postoperative fever included in the prediction model were onset time (X1), preoperative temperature (X2), leukocyte count (X3), C-reactive protein (X4) and operation time (X5). The regression equation is logit (P) = 0.005X1+0.166X2+0.056X3+0.004X4+0.005X5-9.042. ROC curve showed that the area under the curve (AUC) of the training set was 0.660 (0.621, 0.699), and the AUC of the verification set was 0.712 (0.639, 0.784). The calibration curve suggested that the prediction probability was close to the actual probability. Decision curve analysis (DCA) showed that patients could benefit from clinician's judgment. Furthermore, prognostic analysis showed children presenting with postoperative fever had the more duration of postoperative fever, hospitalization stays and cost, except for rehospitalization.

CONCLUSION

All the results revealed that the model had good predictive ability. Pediatricians can calculate the probability of postoperative fever and make timely interventions to reduce pain for children and parents.

摘要

目的

本研究旨在通过回顾性分析建立急性阑尾炎患儿术后发热的预测模型,并通过模型评估和外部验证来证明该模型的预测能力。

方法

回顾性收集2年内接受急性阑尾炎手术患儿的病历信息,在接下来的3个月进行前瞻性收集以进行外部验证。根据术后体温是否超过38.5°C将患者分为两组。采用多因素logistic回归分析确定独立危险因素,建立回归方程和列线图。绘制ROC曲线、校准曲线和决策曲线进行模型评估。最后,通过将术后发热与预后相关联来阐明预测模型的临床意义。

结果

预测模型中术后发热的高危因素包括发病时间(X1)、术前体温(X2)、白细胞计数(X3)、C反应蛋白(X4)和手术时间(X5)。回归方程为logit(P)=0.005X1+0.166X2+0.056X3+0.004X4+0.005X5-9.042。ROC曲线显示,训练集的曲线下面积(AUC)为0.660(0.621,0.699),验证集的AUC为0.712(0.639,0.784)。校准曲线表明预测概率接近实际概率。决策曲线分析(DCA)表明患者可从临床医生的判断中获益。此外,预后分析表明,除再次住院外,术后发热患儿的术后发热持续时间、住院时间和费用更长。

结论

所有结果表明该模型具有良好的预测能力。儿科医生可以计算术后发热的概率,并及时进行干预,以减轻患儿和家长的痛苦。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/083e/9445266/d308f12af603/fped-10-982614-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验