天津医药

天津医药 ›› 2025, Vol. 53 ›› Issue (9): 976-980.doi: 10.11958/20251021

• 临床研究 • 上一篇    下一篇

儿童肺炎住院时间延长预测模型的构建及临床应用价值

蔡淼1(), 梁爽1, 刘洋2,△()   

  1. 1 西安市儿童医院中西医结合科（邮编710000）
    2 西安市儿童医院新生儿重症医学科（邮编710000）
  • 收稿日期:2025-03-12 修回日期:2025-06-18 出版日期:2025-09-15 发布日期:2025-09-16
  • 通讯作者: △E-mail：790362828@qq.com
  • 作者简介:蔡淼（1984），女，主管护师，主要从事儿科呼吸系统疾病方面研究。E-mail：caimiao1453@163.com
  • 基金资助:
    陕西省重点研发计划项目(2022SF-039)

Construction of a prediction model for prolonged hospital stay in children with pneumonia and its clinical application value

CAI Miao1(), LIANG Shuang1, LIU Yang2,△()   

  1. 1 Department of Integrated Traditional and Western Medicine, Xi 'an Children's Hospital, Xi 'an 710000, China
    2 Department of Neonatal Intensive Care Medicine, Xi 'an Children's Hospital, Xi 'an 710000, China
  • Received:2025-03-12 Revised:2025-06-18 Published:2025-09-15 Online:2025-09-16
  • Contact: △E-mail: 790362828@qq.com

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摘要：

目的 构建基于临床特征的儿童肺炎住院时间预测模型。方法 回顾性分析1 255例肺炎患儿的临床资料，以住院时间中位数将患儿分为≤7 d组（628例）和>7 d组（627例）。比较2组患儿人口学特征、既往病史、临床表现、实验室检查结果、影像学检查、治疗方案等临床资料差异。多因素逐步Logistic回归分析患儿住院时间>7 d的影响因素并构建预测模型，利用受试者工作特征（ROC）曲线、临床决策曲线对模型进行评价。结果 与≤7 d组比较，>7 d组患儿月龄更小，身高、早产儿比例、既往肺炎史比例和入院体温更高。此外>7 d组患儿白细胞计数、中性粒细胞计数、血小板计数、C-反应蛋白、降钙素原水平升高，影像学检查双侧病变比例、使用氧疗和呼吸支持比例及胸腔积液比例更高，而淋巴细胞计数、血红蛋白水平下降（P<0.05）。多因素Logistic回归分析结果显示，月龄（OR=0.979，95%CI：0.972~0.987）、早产史（OR=1.751，95%CI：1.216~2.521）、既往肺炎史（OR=1.520，95%CI：1.037~2.228）、入院体温（OR=1.290，95%CI：1.097~1.518）、血清C反应蛋白（OR=1.019，95%CI：1.013~1.025）、胸腔积液（OR=1.980，95%CI：1.309~2.994）和氧疗（OR=2.849，95%CI：1.851~4.385）是儿童肺炎住院时间超过7 d的独立影响因素。模型预测准确率为79.2%，ROC曲线下面积为0.919（95%CI：0.854~0.961）。结论 基于临床特征构建的回归模型可有效预测儿童肺炎住院时间，为临床早期识别高危患儿、优化治疗方案、缩短住院时间提供科学依据。

关键词: 儿童, 肺炎, 住院时间, Logistic模型, 预测模型

Abstract:

Objective To construct a prediction model for the length of hospitalization in children with pneumonia based on clinical characteristics. Methods A retrospective analysis of the clinical data of 1 255 children with pneumonia was conducted. The patients were divided into two groups based on the median length of hospitalization: the ≤7 days group (628 cases) and the >7 days group (627 cases). The differences between the two groups in demographic characteristics, past medical history, clinical manifestations, laboratory test results, imaging findings, treatment plans and other clinical data were compared. A multivariate stepwise Logistic regression analysis was performed to identify the factors influencing hospitalization for >7 days and to construct a prediction model. The model was evaluated using the receiver operating characteristic (ROC) curve and the clinical decision curve. Results Compared to the ≤7 days group, children in the >7 days group were younger in gae, had higher height, a higher proportion of preterm infants, a higher proportion of previous pneumonia history, and a higher body temperatures at admission. Furthermore, in the >7 days group, white blood cell count, neutrophil count, platelet count, C-reactive protein (CRP) and procalcitonin levels were elevated. The proportion of bilateral lesions, oxygen therapy, respiratory support and pleural effusion were higher, while lymphocyte count and hemoglobin levels were lower (P < 0.05). The results of the multivariate Logistic regression analysis showed that age (OR=0.979, 95% CI: 0.972-0.987), history of prematurity (OR=1.751, 95% CI: 1.216-2.521), previous history of pneumonia (OR=1.520, 95% CI: 1.037-2.228), admission temperature (OR=1.290, 95% CI: 1.097-1.518), serum CRP (OR=1.019, 95% CI: 1.013-1.025), pleural effusion (OR=1.980, 95% CI: 1.309-2.994) and oxygen therapy (OR=2.849, 95% CI: 1.851-4.385) were independent risk factors for a hospital stay >7 days in children with pneumonia. The model had an accuracy of 79.2%, and the area under the curve (AUC) was 0.919 (95% CI: 0.854-0.961). Conclusion The regression model constructed based on clinical characteristics can effectively predict the length of hospitalization in children with pneumonia. It provides scientific evidence for the early identification of high-risk children, optimization of treatment plans and shortening of hospital stays.

Key words: child, pneumonia, length of stay, Logistic models, prediction model

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