基于大数据挖掘下多重耐药菌风险评估的研究价值

doi:10.19871/j.cnki.xfcrbzz.2024.01.007

摘要/Abstract

摘要： 目的基于大数据构建多重耐药菌感染的风险预测模型,并对其应用价值进行评估。方法收集2018年1月至2022年12月于新疆医科大学第五附属医院诊治的405例患者,根据是否发生多重耐药菌（multidrug-resistant organisms,MDRO）感染分为非MDRO组（n=324）和MDRO组（n=81）,比较并分析各指标与MDRO发生风险的相关性。构建大数据风险预测模型,分析各指标重要性,验证其准确性。结果 MDRO组合并糖尿病、原发肺部感染的患者比例,机械通气、广谱抗菌药物使用时间及降钙素原水平显著高于非MDRO组,而血红蛋白、白蛋白水平显著低于非MDRO组（均P<0.05）;相关性分析显示,合并糖尿病、原发肺部感染等因素与MDRO风险的相关性较高,且合并糖尿病与原发肺部感染及联合使用抗生素等指标间相关性较高;大数据模型示抗生素使用时间、吞咽功能障碍等因素重要性较高,而血红蛋白及白蛋白重要性较低;大数据模型预测MDRO发生风险的AUC显著高于Logistic回归模型（Z=2.415,P=0.016）,两种预测模型的训练集预测准确率差异无统计学意义（P>0.05）;但测试集大数据模型预测准确率、敏感度及特异度均显著高于Logistic回归模型（χ²=9.062,5.385,4.267;均P<0.05）。结论合并糖尿病、原发肺部感染及联合使用抗生素等因素与MDRO发生风险具有一定相关性,基于MDRO危险因素指标的大数据模型对MDRO发生风险具有较高预测价值。

关键词: 多重耐药菌, 危险因素, 机器学习, 筛查, 预测模型

Abstract: Objective Based on big data, the risk prediction model of multidrug resistant organisms infection was constructed and its application value was evaluated. Method From January 2018 to December 2022, 405 patients in the Fifth Affiliated Hospital of Xinjiang Medical University were divided into non-MDRO group（n=324） and MDRO group（n=324） according to whether multidrug-resistant organisms (MDRO) were infected, and the correlation between each index and the risk of MDRO was compared and analyzed. Build a big data risk prediction model, analyzing the importance of each index and verify its accuracy. Result The proportion of patients with diabete and primary lung infection in MDRO group, the time of mechanical ventilation, the use of broad-spectrum antibiotics and the level of procalcitonin were significantly higher than those in non-MDRO group, while the levels of hemoglobin and albumin were significantly lower than those in non-MDRO group (all P<0.05). Correlation analysis showed that the risk of MDRO was highly correlated with factors such as diabete mellitus and primary lung infection, and there was a high correlation between diabete mellitus and primary lung infection and combined use of antibiotics. The big data model shows that factors such as antibiotic using time and swallowing dysfunction are more important, while hemoglobin and albumin are less important. The AUC of the big data model in predicting the risk of MDRO is significantly higher than that of the Logistic regression model (Z=2.415, P=0.016), and there is no statistical difference in the prediction accuracy of the training set between the two prediction models (P>0.05). However, the prediction accuracy, sensitivity and specificity of test set are significantly higher than those of Logistic regression model (χ²=9.062, 5.385, 4.267;All P<0.05). Conclusion Supplementary factors such as diabetes mellitus, primary lung infection and combined use of antibiotics have certain correlation with the risk of MDRO, and the big data model based on MDRO risk factor indicators has high predictive value for the risk of MDRO.

Key words: Multi-drug resistant organisms, Risk factors, Machine learning, Screening, Prediction model

中图分类号:

R-331

王晓兢, 姚艳玲, 李文玉, 田萍. 基于大数据挖掘下多重耐药菌风险评估的研究价值[J]. 新发传染病电子杂志, 2024, 9(1): 31-36.

Wang Xiaojing, Yao Yanling, Li Wenyu, Tian Ping. Research value of risk assessment of multidrug resistant organisms based on big data mining[J]. Electronic Journal of Emerging Infectious Diseases, 2024, 9(1): 31-36.

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