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.
ABSTRACT
BACKGROUND: Since December 2019, there had been an outbreak of COVID-19 in Wuhan, China. At present, diagnosis COVID-19 were based on real-time RT-PCR, which have to be performed in biosafe laboratory and is unsatisfactory for suspect case screening. Therefore, there is an urgent need for rapid diagnostic test for COVID-19. OBJECTIVE: To evaluate the diagnostic performance and clinical utility of the colloidal gold immunochromatography assay for SARS-Cov-2 specific IgM/IgG anti-body detection in suspected COVID-19 cases. METHODS: In the prospective cohort, 150 patients with fever or respiratory symptoms were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang province, China, between January 20 to February 2, 2020. All patients were tested by the colloidal gold immunochromatography assay for COVID-19. At least two samples of each patient were collected for RT-PCR assay analysis, and the PCR results were performed as the reference standard of diagnosis. Meanwhile 26 heathy blood donor were recruited. The sensitivity and specificity of the immunochromatography assay test were evaluated. Subgroup analysis were performed with respect to age, sex, period from symptom onset and clinical severity. RESULTS: The immunochromatography assay test had 69 positive result in the 97 PCR-positive cases, achieving sensitivity 71.1% [95% CI 0.609-0.797], and had 2 positive result in the 53 PCR-negative cases, achieving specificity 96.2% [95% CI 0.859-0.993]. In 26 healthy donor blood samples, the immunochromatography assay had 0 positive result. In subgroup analysis, the sensitivity was significantly higher in patients with symptoms more than 14 days 95.2% [95% CI 0.741-0.998] and patients with severe clinical condition 86.0% [95% CI 0.640-0.970]. CONCLUSIONS: The colloidal gold immunochromatography assay for SARS-Cov-2 specific IgM/IgG anti-body had 71.1% sensitivity and 96.2% specificity in this population, showing the potential for a useful rapid diagnosis test for COVID-19. Further investigations should be done to evaluate this assay in variety of clinical settings and populations.
ABSTRACT
Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.