Selected article for: "clinical severity and rapid assessment"
Author: Bats, Marie-Lise; Rucheton, Benoit; Fleur, Tara; Orieux, Arthur; Chemin, Clément; Rubin, Sébastien; Colombies, Brigitte; Desclaux, Arnaud; Rivoisy, Claire; Mériglier, Etienne; Rivière, Etienne; Boyer, Alexandre; Gruson, Didier; Pellegrin, Isabelle; Trimoulet, Pascale; Garrigue, Isabelle; Alkouri, Rana; Dupin, Charles; Moreau-Gaudry, François; Bedel, Aurélie; Dabernat, Sandrine
Title: Covichem: A biochemical severity risk score of COVID-19 upon hospital admission
  • Cord-id: zxibbbcr
  • Document date: 2021_5_6
    • ID: zxibbbcr
    Snippet: 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 assessme
    Document: 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.

    Search related documents:
    Co phrase search for related documents
    • acute admission respiratory distress syndrome and admission respiratory distress syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • acute admission respiratory distress syndrome and admission time: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • acute admission respiratory distress syndrome and liver disease: 1, 2, 3, 4
    • acute admission respiratory distress syndrome and liver failure: 1
    • acute admission respiratory distress syndrome and logistic regression: 1, 2, 3, 4, 5, 6
    • acute admission respiratory distress syndrome and logistic regression model: 1
    • acute admission respiratory distress syndrome patient and admission respiratory distress syndrome: 1, 2, 3
    • admission present and liver disease: 1, 2
    • admission present and liver failure: 1, 2, 3
    • admission present and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
    • admission present and logistic regression model: 1, 2, 3
    • admission respiratory distress syndrome and liver disease: 1, 2, 3, 4
    • admission respiratory distress syndrome and liver failure: 1
    • admission respiratory distress syndrome and logistic regression: 1, 2, 3, 4, 5, 6
    • admission respiratory distress syndrome and logistic regression model: 1
    • admission time and liver disease: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
    • admission time and liver failure: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
    • admission time and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • admission time and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23