Age-adjusted Charlson comorbidity index score is the best predictor for severe clinical outcome in the hospitalized patients with COVID-19 infection

  1. Do Hyoung Kim
  2. Hayne Cho Park
  3. Ajin Cho
  4. Juhee Kim
  5. Kyu-sang Yun
  6. Jinseog Kim
  7. Young-Ki Lee

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Abstract

Aged population with comorbidities demonstrated high mortality rate and severe clinical outcome in the patients with coronavirus disease 2019 (COVID-19). However, whether age-adjusted Charlson comorbidity index score (CCIS) predict fatal outcomes remains uncertain.

This retrospective, nationwide cohort study was performed to evaluate patient mortality and clinical outcome according to CCIS among the hospitalized patients with COVID-19 infection. We included 5621 patients who had been discharged from isolation or had died from COVID-19 by April 30, 2020. The primary outcome was composites of death, admission to intensive care unit, use of mechanical ventilator or extracorporeal membrane oxygenation. The secondary outcome was mortality. Multivariate Cox proportional hazard model was used to evaluate CCIS as the independent risk factor for death.

Among 5621 patients, the high CCIS (≥ 3) group showed higher proportion of elderly population and lower plasma hemoglobin and lower lymphocyte and platelet counts. The high CCIS group was an independent risk factor for composite outcome (HR 3.63, 95% CI 2.45–5.37, P  < .001) and patient mortality (HR 22.96, 95% CI 7.20–73.24, P  < .001). The nomogram showed that CCIS was the most important factor contributing to the prognosis followed by the presence of dyspnea (hazard ratio [HR] 2.88, 95% confidence interval [CI] 2.16–3.83), low body mass index < 18.5 kg/m 2 (HR 2.36, CI 1.49–3.75), lymphopenia (<0.8 x10 9 /L) (HR 2.15, CI 1.59–2.91), thrombocytopenia (<150.0 x10 9 /L) (HR 1.29, CI 0.94–1.78), anemia (<12.0 g/dL) (HR 1.80, CI 1.33–2.43), and male sex (HR 1.76, CI 1.32–2.34). The nomogram demonstrated that the CCIS was the most potent predictive factor for patient mortality.

The predictive nomogram using CCIS for the hospitalized patients with COVID-19 may help clinicians to triage the high-risk population and to concentrate limited resources to manage them.

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  1. Version published to 10.1097/md.0000000000025900
  2. ScreenIT

    SciScore for 10.1101/2020.10.26.20220244: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study protocol was reviewed and approved by the Institutional Review Board of the Kangnam Sacred Heart Hospital, Seoul, Korea (HKS 2020-06-025).
    Consent: The informed consent was waived due to retrospective nature of the study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableWe used categorical variables to assess independent risk factors for clinical outcomes as follows: age group of <50 (reference), 50-69, and ≥70 years, female (reference) vs. male, BMI ≥18.5 (reference) vs. <18.5 kg/m2, systolic BP ≥120 (reference) vs. <120 mmHg, diastolic BP ≥80 (reference) vs. <80 mmHg, heart rate <100 (reference) vs. ≥100 beats per minute, body temperature <37.5 (reference) vs. ≥37.5□, CCIS <3 (Reference) vs. ≥3 points, lymphocyte count≥0.8 (reference) vs. <0.8 x 109/L, Hb≥12.0 (reference) vs. <12.0 g/dL, and platelet count ≥150.0 (reference) vs. <150.0 x 109/L.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analysis was performed by using R version 4.0.2 (R Foundation for Statistical Computing; http://www.r-project.org/).
    http://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    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: We detected the following sentences addressing limitations in the study:
    There are some limitations to our study. First, the nationwide database from KCDC only offered limited data. For example, there are no information about chest radiograph findings or laboratory findings such as C-reactive protein or serum creatinine. Therefore, we could not adjust potential risk factors in our study. Second, due to the nature of survey without collecting actual previous medical history from ICD-10 diagnosis code, some information about comorbidities are missing. Therefore, we modified some categories of chronic illness included in the original CCIS. For example, we scored all forms of coronary artery disease including myocardial infarction and chronic heart disease with a score of 1. Previous report also demonstrated the usefulness of modified form of CCIS in prediction of clinical outcome in renal patients24. In addition, the information about some chronic conditions such as peripheral vascular disease, cerebrovascular disease or peptic ulcer disease were missing. We calculated CCIS based on the available data excluding those categories of missing data. Therefore, CCIS in our cohort may be underestimated than actual CCIS. Lastly, there can be ethnic or racial difference in clinical outcomes of COVID-19. Therefore, our result may not be applicated to the different ethnicity or population. However, recent paper suggested that racial difference did not contribute to different clinical outcome22. Nevertheless, there should be a validation test in each population ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.10.26.20220244 on medRxiv