Literature DB >> 33956870

Covichem: A biochemical severity risk score of COVID-19 upon hospital admission.

Marie-Lise Bats1,2, Benoit Rucheton3, Tara Fleur1, Arthur Orieux4, Clément Chemin1, Sébastien Rubin2,5, Brigitte Colombies1, Arnaud Desclaux6, Claire Rivoisy7, Etienne Mériglier7, Etienne Rivière8, Alexandre Boyer4, Didier Gruson4, Isabelle Pellegrin9,10, Pascale Trimoulet11,12, Isabelle Garrigue11,12, Rana Alkouri3, Charles Dupin13,14, François Moreau-Gaudry1,13, Aurélie Bedel1,13, Sandrine Dabernat1,13.   

Abstract

Clinical and laboratory predictors of COVID-19 severity are now well described and combined to propose mortality or severity scores. However, they all necessitate saturable equipment such as scanners, or procedures difficult to implement such as blood gas measures. To provide an easy and fast COVID-19 severity risk score upon hospital admission, and keeping in mind the above limits, we sought for a scoring system needing limited invasive data such as a simple blood test and co-morbidity assessment by anamnesis. A retrospective study of 303 patients (203 from Bordeaux University hospital and an external independent cohort of 100 patients from Paris Pitié-Salpêtrière hospital) collected clinical and biochemical parameters at admission. Using stepwise model selection by Akaike Information Criterion (AIC), we built the severity score Covichem. Among 26 tested variables, 7: obesity, cardiovascular conditions, plasma sodium, albumin, ferritin, LDH and CK were the independent predictors of severity used in Covichem (accuracy 0.87, AUROC 0.91). Accuracy was 0.92 in the external validation cohort (89% sensitivity and 95% specificity). Covichem score could be useful as a rapid, costless and easy to implement severity assessment tool during acute COVID-19 pandemic waves.

Entities:  

Year:  2021        PMID: 33956870     DOI: 10.1371/journal.pone.0250956

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  5 in total

1.  CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.

Authors:  R Karthik; R Menaka; M Hariharan; Daehan Won
Journal:  Appl Soft Comput       Date:  2022-03-29       Impact factor: 8.263

2.  A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile.

Authors:  Wandong Hong; Xiaoying Zhou; Shengchun Jin; Yajing Lu; Jingyi Pan; Qingyi Lin; Shaopeng Yang; Tingting Xu; Zarrin Basharat; Maddalena Zippi; Sirio Fiorino; Vladislav Tsukanov; Simon Stock; Alfonso Grottesi; Qin Chen; Jingye Pan
Journal:  Front Cell Infect Microbiol       Date:  2022-04-12       Impact factor: 6.073

3.  Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study.

Authors:  Relu Cocoş; Beatrice Mahler; Adina Turcu-Stiolica; Alexandru Stoichiță; Andreea Ghinet; Elena-Silvia Shelby; Laurențiu Camil Bohîlțea
Journal:  Viruses       Date:  2022-05-31       Impact factor: 5.818

Review 4.  The Associations of Iron Related Biomarkers with Risk, Clinical Severity and Mortality in SARS-CoV-2 Patients: A Meta-Analysis.

Authors:  Shuya Zhou; Huihui Li; Shiru Li
Journal:  Nutrients       Date:  2022-08-19       Impact factor: 6.706

5.  Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis.

Authors:  Arnab Sarkar; Surojit Sanyal; Agniva Majumdar; Devendra Nath Tewari; Uttaran Bhattacharjee; Juhi Pal; Alok Kumar Chakrabarti; Shanta Dutta
Journal:  PLoS One       Date:  2022-09-09       Impact factor: 3.752

  5 in total

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