Mora-Jiménez Inmaculada, Tarancón-Rey Jorge, Álvarez-Rodríguez Joaquín, Soguero-Ruiz Cristina
Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, Spain.
University Hospital of Fuenlabrada, 28943 Fuenlabrada, Madrid, Spain.
Antibiotics (Basel). 2021 Feb 27;10(3):239. doi: 10.3390/antibiotics10030239.
Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission. We considered clinical and demographic features, mechanical ventilation and the antibiotics taken by the patients during this time interval. Three feature selection strategies were applied to identify statistically significant differences between MDR and non-MDR patient episodes, ending up in 24 selected features. Among them, SAPS III and Apache II scores, the age and the department of origin were identified. Considering these features, we analyzed the potential of machine learning methods for predicting whether a patient will develop a MDR germ during the first 48 h from the ICU admission. Though the results presented here are just a first incursion into this problem, artificial intelligence approaches have a great impact in this scenario, especially when enriching the set of features from the electronic health records.
多重耐药性（MDR）是当今全球卫生系统面临的最紧迫且最严重的威胁之一。这种情况在重症监护病房（ICU）中尤为突出，因为这些患者的危急健康状况使他们更容易受到影响。由于微生物实验室确认MDR通常需要48小时，我们提出了几种人工智能方法，以便在患者入住ICU后的头48小时内了解MDR风险因素。我们考虑了临床和人口统计学特征、机械通气以及患者在此时间段内使用的抗生素。应用了三种特征选择策略来识别MDR和非MDR患者事件之间的统计学显著差异，最终确定了24个选定特征。其中，识别出了序贯器官衰竭评估（SAPS）III和急性生理与慢性健康状况评分系统（Apache）II评分、年龄以及患者来源科室。考虑到这些特征，我们分析了机器学习方法在预测患者从入住ICU起的头48小时内是否会出现MDR病菌方面的潜力。尽管此处呈现的结果只是对该问题的初步探索，但人工智能方法在这种情况下具有重大影响，尤其是在丰富电子健康记录中的特征集时。
