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中华临床医师杂志(电子版) ›› 2022, Vol. 16 ›› Issue (11) : 1120 -1125. doi: 10.3877/cma.j.issn.1674-0785.2022.11.015

临床研究

基于贝叶斯网络模型在脓毒症患者血小板减少症发生因素中的分析
童译庆1, 张建明1, 贺星星1, 傅一牧1, 赵刚1, 封启明1,()   
  1. 1. 200030 上海,上海市第六人民医院急诊科
  • 收稿日期:2021-12-24 出版日期:2022-11-15
  • 通信作者: 封启明
  • 基金资助:
    上海市第六人民医院2021年度院级回顾性临床研究课题(ynhg202107); 上海促进市级医院临床技能与临床创新能力三年行动计划项目(SHDC2020CR6030)

Identification of factors related to thrombocytopenia in patients with sepsis based on Bayesian network model

Yiqing Tong1, Jianming Zhang1, Xingxing He1, Yimu Fu1, Gang Zhao1, Qiming Feng1,()   

  1. 1. Department of Emergency, Shanghai Sixth People's Hospital, Shanghai 200030, China
  • Received:2021-12-24 Published:2022-11-15
  • Corresponding author: Qiming Feng
引用本文:

童译庆, 张建明, 贺星星, 傅一牧, 赵刚, 封启明. 基于贝叶斯网络模型在脓毒症患者血小板减少症发生因素中的分析[J]. 中华临床医师杂志(电子版), 2022, 16(11): 1120-1125.

Yiqing Tong, Jianming Zhang, Xingxing He, Yimu Fu, Gang Zhao, Qiming Feng. Identification of factors related to thrombocytopenia in patients with sepsis based on Bayesian network model[J]. Chinese Journal of Clinicians(Electronic Edition), 2022, 16(11): 1120-1125.

目的

分析脓毒症患者血小板减少症发生因素,并构建脓毒症患者发生血小板减少症的贝叶斯网络模型,探讨脓毒症患者发生血小板减少症及其相关因素间的网络关系,通过网络模型推理反映各影响因素对脓毒症患者发生血小板减少症的影响程度。

方法

选取上海市第六人民医院急诊重症监护病房(EICU)2019年1月至2020年12月收治的98例脓毒症患者。其中男53例,女45例,年龄(59.37±4.28)岁。统计所有患者ICU住院期间血小板减少症发生情况并依此将患者分为发生组与未发生组,设计基线资料调查表收集两组基线资料,应用Logistics回归分析对脓毒症患者发生血小板减少症的影响因素进行初筛,各影响因素间及其与脓毒症患者发生血小板减少症的关系运用贝叶斯网络模型分析。

结果

98例脓毒症患者中发生血小板减少症33例(33.67%)。两组患者的真菌性感染、感染性休克占比及白细胞介素6(IL-6)、血栓弹力图最大振幅(MA)值比较差异有统计学意义(均P<0.05),组间其他资料比较差异无统计学意义(P>0.05)。经Logistic回归分析结果显示,真菌性感染(OR=7.185,95%CI为1.168-44.184)、感染性休克(OR=4.024,95%CI为1.081-14.983)、血清IL-6过表达(OR=9.360,95%CI为2.283-38.379)均是脓毒症患者血小板减少症发生的危险因素(P<0.05),MA值过表达(OR=0.814,95%CI为0.734-0.902)是脓毒症患者血小板减少症发生的保护因素(P<0.05);有向无环的贝叶斯网络结构图形显示,真菌性感染、感染性休克、IL-6、MA值与脓毒症患者血小板减少症发生有关。

结论

脓毒症患者血小板减少症发生可能与真菌性感染、感染性休克、高IL-6水平、低MA值有关。

Objective

To identify the factors related to thrombocytopenia in patients with sepsis, and to construct a Bayesian network model of thrombocytopenia in those patients to explore the network relationship between thrombocytopenia and its related factors and to reflect the extent of influence of various factors on thrombocytopenia in patients with sepsis by network model reasoning.

Methods

Ninety-eight patients with sepsis admitted to the intensive care unit (ICU) of Shanghai Sixth People's Hospital from January 2019 to December 2020 were selected. Among them, there were 53 males and 45 females, with an age of (59.37±4.28) years. The occurrence of thrombocytopenia in all patients during ICU stay was statistically analyzed and the patients were divided into either an occurrence group or a non-occurrence group according to the occurrence of thrombocytopenia or not. Baseline data questionnaire was designed to collect baseline data of the two groups. Logistic regression analysis was used to screen the influencing factors of thrombocytopenia in patients with sepsis, and the relationship between each influencing factor and thrombocytopenia in patients with sepsis was analyzed using the Bayesian network model.

Results

Among the 98 patients with sepsis, 33 had thrombocytopenia (33.67%). Fungal infection, septic shock, interleukin-6 (IL-6) level, and maximum amplitude of thromboelastogram (MA) differed significantly between the two groups (P<0.05 for all), but there was no statistical significant difference in other data between the two groups (P>0.05 for all). Logistic regression analysis demonstrated that fungal infection (odds ratio [OR]=7.185, 95% confidence interval [CI]:1.168-44.184), septic shock (OR=4.024, 95%CI:1.081-14.983), and overexpression of serum IL-6 (OR=9.360, 95%CI:2.283-38.379) were risk factors for thrombocytopenia in patients with sepsis (P<0.05 for all), while elevation of MA value (OR=0.814, 95%CI:0.734-0.902) was a protective factor for thrombocytopenia (P<0.05). Directed acyclic Bayesian network structure graph showed that fungal infection, septic shock, IL-6, and MA value were associated with thrombocytopenia in patients with sepsis.

Conclusion

The occurrence of thrombocytopenia in patients with sepsis may be related to fungal infection, septic shock, high IL-6 level, and low MA value

表1 两组脓毒症患者一般资料比较
表2 2组脓毒症患者的实验室检查资料比较(
xˉ
±s
表3 脓毒症患者血小板减少症发生因素的logistics回归分析
表4 受试者工作特征(ROC)曲线检验各指标预测脓毒症患者发生血小板减少症最佳截断值
表5 指标赋值说明
表6 脓毒症患者血小板减少症发生的条件概率分布表
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