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10.1016/j.jcv.2020.104431

http://scihub22266oqcxt.onion/10.1016/j.jcv.2020.104431
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32442756!7219384!32442756
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  • Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19 #MMPMID32442756
  • Sun L; Song F; Shi N; Liu F; Li S; Li P; Zhang W; Jiang X; Zhang Y; Sun L; Chen X; Shi Y
  • J Clin Virol 2020[Jul]; 128 (ä): 104431 PMID32442756show ga
  • BACKGROUND: Despite the death rate of COVID-19 is less than 3%, the fatality rate of severe/critical cases is high, according to World Health Organization (WHO). Thus, screening the severe/critical cases before symptom occurs effectively saves medical resources. METHODS AND MATERIALS: In this study, all 336 cases of patients infected COVID-19 in Shanghai to March 12th, were retrospectively enrolled, and divided in to training and test datasets. In addition, 220 clinical and laboratory observations/records were also collected. Clinical indicators were associated with severe/critical symptoms were identified and a model for severe/critical symptom prediction was developed. RESULTS: Totally, 36 clinical indicators significantly associated with severe/critical symptom were identified. The clinical indicators are mainly thyroxine, immune related cells and products. Support Vector Machine (SVM) and optimized combination of age, GSH, CD3 ratio and total protein has a good performance in discriminating the mild and severe/critical cases. The area under receiving operating curve (AUROC) reached 0.9996 and 0.9757 in the training and testing dataset, respectively. When the using cut-off value as 0.0667, the recall rate was 93.33 % and 100 % in the training and testing datasets, separately. Cox multivariate regression and survival analyses revealed that the model significantly discriminated the severe/critical cases and used the information of the selected clinical indicators. CONCLUSION: The model was robust and effective in predicting the severe/critical COVID cases.
  • |*Disease Outbreaks[MESH]
  • |Adult[MESH]
  • |Age Factors[MESH]
  • |Aged[MESH]
  • |Area Under Curve[MESH]
  • |Betacoronavirus[MESH]
  • |Biomarkers/blood[MESH]
  • |CD3 Complex/blood[MESH]
  • |Cohort Studies[MESH]
  • |Coronary Disease/blood/complications/*diagnosis/mortality[MESH]
  • |Coronavirus Infections/blood/complications/*diagnosis/mortality[MESH]
  • |Diabetes Complications/blood/*diagnosis/mortality[MESH]
  • |Diabetes Mellitus/blood/*diagnosis/mortality[MESH]
  • |Female[MESH]
  • |Glutathione/blood[MESH]
  • |Humans[MESH]
  • |Hypertension/blood/complications/*diagnosis/mortality[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/blood/complications/*diagnosis/mortality[MESH]
  • |Prognosis[MESH]
  • |ROC Curve[MESH]
  • |Severity of Illness Index[MESH]
  • |Support Vector Machine[MESH]
  • |Survival Analysis[MESH]
  • |Thyroxine/blood[MESH]


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  • suck abstract from ncbi

    104431 ä.128 2020