Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece.
Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, GR45100, Greece.
Comput Biol Med. 2022 Feb;141:105176. doi: 10.1016/j.compbiomed.2021.105176. Epub 2021 Dec 27.
The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.
由严重急性呼吸综合征冠状病毒 2 型（SARS-CoV-2）引起的 2019 年冠状病毒病（COVID-19）由于其传染性增加，持续对全球医疗保健系统造成严重创伤。目前，迫切需要确定 COVID-19 患者之间的潜在动态关联，并区分具有共同临床特征的患者亚组，以便为重症监护病房（ICU）入院和死亡率开发强大的分类器。为了满足这一需求，我们提出了一个四步流程，该流程：（i）通过自动化数据管理工作流程增强多次时间序列临床数据的质量，（ii）部署动态贝叶斯网络（DBN）以基于多个点的动态关联分析检测连接性增加的特征，（iii）利用自组织映射（SOM）和轨迹分析来早期识别具有共同临床特征的 COVID-19 患者，（iv）基于提取的同质聚类训练强大的多个加性回归树（MART）进行 ICU 入院和死亡率分类，以识别疾病进展的危险因素和生物标志物。提取的聚类和动态关联的临床数据提高了 ICU 入院的分类性能，灵敏度为 0.83，特异性为 0.83，死亡率为 0.74，特异性为 0.76。包含了更多信息来增强分类器的性能，死亡率的灵敏度和特异性分别提高了 4%。根据危险因素分析，淋巴细胞数、SatO2、PO2/FiO2 和 O2 供应类型被突出为 ICU 入院的危险因素，中性粒细胞和淋巴细胞百分比、PO2/FiO2、LDH 和 ALP 等是死亡率的危险因素。据我们所知，这是第一项结合动态建模和聚类分析来识别 COVID-19 同质患者组以开发强大的 ICU 入院和死亡率分类器的研究。
