检验医学 ›› 2024, Vol. 39 ›› Issue (10): 999-1004.DOI: 10.3969/j.issn.1673-8640.2024.10.012

• 论著 • 上一篇    下一篇

基于MIMIC-Ⅳ数据库数据构建重症监护病房金黄色葡萄球菌血流感染预后决策树模型

秦泽辉1, 陈秋宇2, 梁峰瑞1, 宋煜琦1, 叶莉萍3, 刘享田3, 田行瀚3()   

  1. 1.潍坊医学院附属医院(临床医学院),山东 潍坊 261000
    2.滨州医学院第二临床医学院,山东 烟台 264000
    3.烟台毓璜顶医院重症监护病房,山东 烟台 264000
  • 收稿日期:2023-10-26 修回日期:2024-04-15 出版日期:2024-10-30 发布日期:2024-11-08
  • 通讯作者: 田行瀚,E-mail:tianfenger987@sina.com
  • 作者简介:秦泽辉,男,1996年生,学士,主要从事重症医学研究。
  • 基金资助:
    山东省医药卫生科技发展计划项目(2019WS281)

Construction of prognostic decision tree model of Staphylococcus aureus bloodstream infection in intensive care unit based on MIMIC-Ⅳ database data

QIN Zehui1, CHEN Qiuyu2, LIANG Fengrui1, SONG Yuqi1, YE Liping3, LIU Xiangtian3, TIAN Xinghan3()   

  1. 1. The Affiliated Hospital of Weifang Medical College(Clinical College),Weifang 261000,Shandong,China
    2. The Second School of Clinical Medicine of Binzhou Medical University,Yantai 264000,Shandong,China
    3. Intensive Care Unit,Yuhuangding Hospital,Yantai 264000,Shandong,China
  • Received:2023-10-26 Revised:2024-04-15 Online:2024-10-30 Published:2024-11-08

摘要:

目的 基于MIMIC-Ⅳ数据库构建用于评估重症监护病房(ICU)金黄色葡萄球菌血流感染(SABI)患者病情和预后的决策树模型。方法 从MIMIC-Ⅳ数据库中提取1 030例ICU中SABI患者人口学信息和相关实验室指标检测数据。以患者是否发生住院死亡为主要结局。按3:1的比例将纳入的患者分为训练集(773例)和验证集(257例)。基于训练集数据构建随机森林模型,并根据重要性评分筛选出变量,构建决策树模型。将模型纳入验证集进行验证。 结果 血钾离子(K+)、血钠离子(Na+)、肌酐(Cr)、阴离子间隙(AG)、血清钙离子(Ca2+)、血细胞比容(HCT)、血红蛋白(Hb)、国际标准化比值(INR)重要性评分得分较高,基于这8项指标构建决策树模型。该模型判断ICU中SABI患者预后(死亡率)的受试者工作特征(ROC)曲线下面积为0.769 4。结论 构建了ICU中SABI患者病情和预后评估决策树模型,有助于快速识别该类患者中的高危死亡者,以提示临床及时调整治疗方案,改善患者预后。

关键词: 金黄色葡萄球菌, 血流感染, 重症监护病房, 预后, 决策树模型, MIMIC-Ⅳ数据库

Abstract:

Objective To construct a decision tree model based on MIMIC-Ⅳ database data to evaluate the condition and prognosis of patients with Staphylococcus aureus bloodstream infection(SABI) in intensive care unit(ICU). Methods The demographic information of 1 030 patients with SABI in ICU and related laboratory indicators were extracted from MIMIC-Ⅳ database. Whether the patient died in hospital was the main outcome. The included patients were classified into training set(773 cases) and validation set(257 cases) in a ratio of 3:1. The random forest model was constructed based on the training set data,and the variables are screened according to the importance score to construct the decision tree model. The model was included in the validation set for validation. Results The importance scores of serum potassium ion(K+),serum sodium ion(Na+),creatinine(Cr),anion gap(AG),serum calcium ion(Ca2+),hematocrit(HCT),hemoglobin(Hb) and international normalized ratio(INR) were high. The decision tree model was constructed based on these 8 indexes. The area under curve(AUC) of receiver operating characteristic(ROC) curve of this model for predicting prognosis of SABI patients (mortality)in ICU was 0.769 4. Conclusions A decision tree model has been constructed to evaluate the condition and prognosis of SABI patients in ICU,which is helpful to rapidly identify the high-risk mortality in this type of patients,which suggests the clinical adjustment of treatment plan in time to improve the prognosis of patients.

Key words: Staphylococcus aureus, Bloodstream infection, Intensive care unit, Prognosis, Decision tree model, MIMIC-Ⅳ database

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