Cardiovascular vulnerability predicts hospitalisation in primary care clinically suspected and confirmed COVID-19 patients: A model development and validation study

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Abstract

Cardiovascular conditions were shown to be predictive of clinical deterioration in hospitalised patients with coronavirus disease 2019 (COVID-19). Whether this also holds for outpatients managed in primary care is yet unknown. The aim of this study was to determine the incremental value of cardiovascular vulnerability in predicting the risk of hospital referral in primary care COVID-19 outpatients.

Design

Analysis of anonymised routine care data extracted from electronic medical records from three large Dutch primary care registries.

Setting

Primary care.

Participants

Consecutive adult patients seen in primary care for COVID-19 symptoms in the ‘first wave’ of COVID-19 infections (March 1 2020 to June 1 2020) and in the ‘second wave’ (June 1 2020 to April 15 2021) in the Netherlands.

Outcome measures

A multivariable logistic regression model was fitted to predict hospital referral within 90 days after first COVID-19 consultation in primary care. Data from the ‘first wave’ was used for derivation (n = 5,475 patients). Age, sex, the interaction between age and sex, and the number of cardiovascular conditions and/or diabetes (0, 1, or ≥2) were pre-specified as candidate predictors. This full model was (i) compared to a simple model including only age and sex and its interaction, and (ii) externally validated in COVID-19 patients during the ‘second wave’ (n = 16,693).

Results

The full model performed better than the simple model (likelihood ratio test p <0.001). Older male patients with multiple cardiovascular conditions and/or diabetes had the highest predicted risk of hospital referral, reaching risks above 15–20%, whereas on average this risk was 5.1%. The temporally validated c-statistic was 0.747 (95%CI 0.729–0.764) and the model showed good calibration upon validation.

Conclusions

For patients with COVID-19 symptoms managed in primary care, the risk of hospital referral was on average 5.1%. Older, male and cardiovascular vulnerable COVID-19 patients are more at risk for hospital referral.

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  1. SciScore for 10.1101/2021.05.12.21257075: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The need for formal ethical reviewing was waived by the local medical research ethics committee of the University Medical Center Utrecht, the Netherlands as the research did not require direct patient or physician involvement.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Recombinant DNA
    SentencesResources
    All statistical analyses were performed in R version 4.0.3 with R base, rms, pROC, DescTools, and rmda packages.
    pROC
    suggested: None

    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:
    Strengths and limitations: This research contributes to the evidence-based prognostication of community COVID-19. We were able to use a large and representative database capturing both the ‘first’ and ‘second’ wave of the COVID-19 in the Netherlands. We used state-of-the-art methodology including external temporal validation to predict clinical deterioration in a patient population that currently is understudied. For full appreciation of our findings, however, some limitations also need to be addressed. First, the model was developed in a dataset with a low event fraction of the outcome hospital referral, yet the actual number of hospital referral events did allow us to perform robust multivariable regression techniques. Second, there are limitations to using routine care registry data that could have resulted in misclassification of the study population, predictors and outcome, and most importantly has the risk of missing values. For example, uncertainty concerning COVID-19 infection status may exist as COVID-19 PCR test results are not automatically linked to the primary care electronic medical records. However, the model proved its transportability in primary care patients in a different time period with satisfactory calibration and discrimination, during a time window where PCR testing was widely performed. Furthermore, the outcome hospital referral was based upon a rigorous manual extraction of medical records by pairs of researchers, yet not based upon actual linkage to...

    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.

    Results from scite Reference Check: We found no unreliable references.


    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.