Clinical Performance of The call Score for the Prediction of Admission to ICU and Death in Hospitalized Patients with Covid-19 Pneumonia in a Reference Hospital in Peru

  1. Rafael Pichardo- Rodriguez
  2. Marcos Saavedra- Velasco
  3. Willy Peña- Oscuvilca
  4. Cesar Sanchez- Alvarez
  5. Gino Patron- Ordoñez
  6. Oscar Ruiz- Franco
  7. Herney Andres Garcia- Perdomo

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Abstract

Objective: Determine the CALL SCORE's diagnostic accuracy for the prediction of ICU admission and death in patients hospitalized for COVID-19 pneumonia in a reference hospital in Peru. Methods: We performed an analytical cross-sectional observational study. We included patients with COVID-19 pneumonia treated at the "Dos de Mayo" National Hospital. Patients over 18 years old with a diagnosis confirmed by rapid or molecular testing were included. Those with an incomplete, illegible, or missing medical history and/or bacterial or fungal pneumonia were excluded. Data were extracted from medical records. The primary outcomes were mortality and admission to the ICU. The Call Score was calculated for each patient (4 to 13 points) and classified into three risk groups. Summary measures were presented for qualitative and quantitative variables. The area under the model curve and the operational characteristics (sensitivity, specificity) were calculated for the best cut-off point. Results: The Call Score reported an area under the curve of 0.59 (IC95%: 0.3 to 0.07), p = 0.43 for predicting death. However, for a cut-off point of 5.5, a sensitivity of 87% and a specificity of 65% were obtained. The area under the curve for ICU admission was 0.67 (95%CI: 0.3 to 0.07), p = 0.43; the 5.5 cut-off point showed a sensitivity of 82% and a specificity of 51%. Conclusions: The Call Score shows a low performance for predicting mortality and admission to the ICU in Peruvian patients. Keyword: (MESH) Mortality; Intensive Care Units; Clinical Decision Rules

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  1. Version published to 10.53350/pjmhs22161474
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    SciScore for 10.1101/2021.02.09.21250884: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data were processed in the SPSS statistical software version 20.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2021.02.09.21250884v1 on medRxiv