| Literature DB >> 34188785 |
Kai Zhou1, Yaoting Sun2,3,4, Lu Li2,3,4, Zelin Zang5, Jing Wang1, Jun Li1, Junbo Liang1, Fangfei Zhang2,3,4, Qiushi Zhang6, Weigang Ge6, Hao Chen6, Xindong Sun2,3,4, Liang Yue2,3,4, Xiaomai Wu1, Bo Shen1, Jiaqin Xu1, Hongguo Zhu1, Shiyong Chen1, Hai Yang1, Shigao Huang7, Minfei Peng1, Dongqing Lv1, Chao Zhang1, Haihong Zhao1, Luxiao Hong1, Zhehan Zhou1, Haixiao Chen1, Xuejun Dong8, Chunyu Tu8, Minghui Li8, Yi Zhu2,3,4, Baofu Chen1, Stan Z Li5, Tiannan Guo2,3,4.
Abstract
Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.Entities:
Keywords: ABG, arterial blood gas; APTT, activated partial thromboplastin time; AST, aspartate aminotransferase; AUC, area under the curve; BASO#, basophil counts; CFDA, China Food and Drug Administration; CK, creatine kinase; COVID-19; CRP, C-reactive protein; CT, computed tomography; ESR, erythrocyte sedimentation rate; GA, genetic algorithm; GGT, gamma glutamyl transpeptidase; HIS, hospital information system; LAC, lactate; LDH, lactate dehydrogenase; LOESS, locally estimated scatterplot smoothing; LOS, length of stay; Longitudinal dynamics; Machine learning; Mg, magnesium; NETs, neutrophil extracellular traps; NPV, negative predictive value; PCT, procalcitonin; PPV, positive predictive value; ROC, receiver operating characteristics; RT-PCR, reverse transcriptase -polymerase chain reaction; Routine clinical test; SARS-CoV-2; SHAP, SHapley Additive exPlanations; SVM, support vector machine; SaO2, oxygen saturation; Severity prediction; TT, thrombin time; eGFR, estimated glomerular filtration rate
Year: 2021 PMID： 34188785 PMCID： PMC8225590 DOI： 10.1016/j.csbj.2021.06.022
Source DB: PubMed Journal: Comput Struct Biotechnol J ISSN： 2001-0370 Impact factor: 7.271