Utility of established prognostic scores in COVID-19 hospital admissions: multicentre prospective evaluation of CURB-65, NEWS2 and qSOFA

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Abstract

The COVID-19 pandemic is ongoing, yet, due to the lack of a COVID-19-specific tool, clinicians must use pre-existing illness severity scores for initial prognostication. However, the validity of such scores in COVID-19 is unknown.

Methods

The North West Collaborative Organisation for Respiratory Research performed a multicentre prospective evaluation of adult patients admitted to the hospital with confirmed COVID-19 during a 2-week period in April 2020. Clinical variables measured as part of usual care at presentation to the hospital were recorded, including the Confusion, Urea, Respiratory Rate, Blood Pressure and Age Above or Below 65 Years (CURB-65), National Early Warning Score 2 (NEWS2) and Quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) scores. The primary outcome of interest was 30-day mortality.

Results

Data were collected for 830 people with COVID-19 admitted across seven hospitals. By 30 days, a total of 300 (36.1%) had died and 142 (17.1%) had been in the intensive care unit. All scores underestimated mortality compared with pre-COVID-19 cohorts, and overall prognostic performance was generally poor. Among the ‘low-risk’ categories (CURB-65 score<2, NEWS2<5 and qSOFA score<2), 30-day mortality was 16.7%, 32.9% and 21.4%, respectively. NEWS2≥5 had a negative predictive value of 98% for early mortality. Multivariable logistic regression identified features of respiratory compromise rather than circulatory collapse as most relevant prognostic variables.

Conclusion

In the setting of COVID-19, existing prognostic scores underestimated risk. The design of new prognostic tools should focus on features of respiratory compromise rather than circulatory collapse. We provide a baseline set of variables which are relevant to COVID-19 outcomes and may be used as a basis for developing a bespoke COVID-19 prognostication tool.

Article activity feed

  1. Kapil Gururangan

    Review 2: "The utility of established prognostic scores in COVID-19 hospital admissions: a multi-centre prospective evaluation of CURB-65, NEWS2, and qSOFA"

    This robust analysis is novel and of high interest for the medical community. This study informs how new prognostic scores should be created to more accurately guide clinical decision-making in patients with COVID-19.

  2. Michael Meisner

    Review 1: "The utility of established prognostic scores in COVID-19 hospital admissions: a multicentre prospective evaluation of CURB-65, NEWS2, and qSOFA"

    This robust analysis is novel and of high interest for the medical community. This study informs how new prognostic scores should be created to more accurately guide clinical decision-making in patients with COVID-19.

  3. SciScore for 10.1101/2020.07.15.20154815: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: No approach to the patient was made and only fully anonymised routinely available clinical information was collated; on this basis consent was not required under guidance from the NHS Human Research Authority [
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    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:
    An explanation for the sub-optimal outcome of this approach may lie in limitations of the data collected, as although we present a comprehensive, prospectively collected dataset from multiple centres, collection of data was pragmatic and streamlined such that only variables included in common risk scores were collected. It did not include assessment of detailed patient demographics or comorbidities, instead focusing on clinical measurements normally taken at presentation to hospital. Characteristics such as obesity, ethnicity and comorbidities are reported to be relevant to COVID-19 outcomes but are not included here [23,24]. The ISARIC4C consortium has derived a risk calculator from their large inpatient dataset using baseline patient characteristics; it may be that a combination of clinical parameters and patient characteristics is more informative than either in isolation [25]. Some limitations must be addressed. Firstly, we only included a two-week period, and it is possible demographics and outcomes may change across the course of the COVID-19 pandemic [26]. Reassuringly, the characteristics and outcomes in the study population seen here are in keeping with those reported by the ISARIC study, one of the largest studies in this setting to date. For example the median age here was 70 years compared with 72 years in the ISARIC study, 61% were male here compared with 59.9% in ISARIC, 17.1% here were admitted to critical care compared to 17% in ISARIC, and we observed 34% 30-...

    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.

    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.