Assessing the impact of multiple comorbidities on fatal outcome in young COVID-19

  1. Paulino Monroy-Castillero
  2. Eitan Friedman
  3. Arturo Revuelta-Herrera
  4. Arik Yochelis

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

A Bayesian analysis with the use of a rank-biserial correlation algorithm was applied to identify the impact of multiple comorbid conditions on fatal COVID-19 outcome in young adult cases (40-50 years). The demonstration was conducted for a publicly available database provided by the Mexican authority, in the absence of other alternative free-access repositories with information per patient. The methodology here proposed showed that even in the face of small sample sizes, it is possible to highlight deleterious synergistic comorbid conditions.

Young adult cases with COVID-19 and co-existing diabetes, obesity, hypertension, CRF, or COPD were found more likely to have a fatal outcome compared with having no co-morbidities (X2-6 times). With the methodology proposed, we show that having diabetes or hypertension in addition to CRF increased risk for mortality more than what is expected from independent effect (adverse synergistic effect), whereas in patients with obesity, the additional presence of diabetes or hypertension do not increase markedly the death risk due to COVID-19. Quantitative analysis of having two comorbidities highlights the combinations of morbid conditions that are more likely to be associated with fatal outcomes in younger adults COVID-19 cases in a clinically applicable manner.

The clinical implication of this method needs to be prospectively assessed.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.03.29.21254599: (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

    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:
    A major limitation of this study that can be easily improved by worldwide information sharing is the incompleteness of data collection. Another limitation may be inherent to the fact that the data were collected retrospectively and pertain to a single country with distinct genetic makeup. Lastly, there are a limited number of cases in some categories (e.g., COPD females n=172, d=7, and males n=153, d=23 or CRF females n=263, d=36, and males n=280, d=56). Unlike other available predictive risk methods that rely on estimations of parametric models from the whole dataset to predict risk profiles,12–14 the method applied and described herein used solely data of subgroups that are of interest, without further assumptions, and provides an order relation based on statistical comparison of risks. In the meta-analysis of observational studies by Parohan and coworkers3 risk factors (e.g., hypertension, diabetes mellitus, obesity, cerebrovascular stroke (CVS), COPD) individually were associated with an odds ratio (OR) of 1.5-4.59 for fatal COVID-19 outcome. Additional analyses refined these risks by individual risk factors (some of which are shown in Supplementary Table 1). Specifically in Mexico, a study encompassing 51,633 subjects with SARS-CoV-2 and 5,332 deaths, fatal outcome risk factors include early-onset diabetes mellitus, obesity, COPD, advanced age, HTN, immunosuppression, and CRF.14 Yet, it had been difficult to clinically apply the known risk factors in order to prioritize ...

    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.

  2. Version published to 10.1101/2021.03.29.21254599v1 on medRxiv