Development and Validation of the Patient History COVID-19 (PH-Covid19) Scoring System: A Multivariable Prediction Model of Death in Mexican Patients with COVID-19

  1. J. Mancilla-Galindo
  2. J. M. Vera-Zertuche
  3. A. R. Navarro-Cruz
  4. O. Segura-Badilla
  5. G. Reyes-Velázquez
  6. F. J. Tepepa-López
  7. P. Aguilar-Alonso
  8. J. de J. Vidal-Mayo
  9. A. Kammar-García

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Abstract

We sought to develop and validate a multivariable prediction model of death in Mexican patients with COVID-19, by using demographic and patient history predictors. We conducted a national retrospective cohort in two different sets of patients from the Mexican COVID-19 Epidemiologic Surveillance Study. To develop the model, we included 264,026 patients tested for SARS-CoV-2 between February 28 and May 30, 2020. To validate the model, 592,160 patients studied between June 1 and July 23, 2020 were included. Patients with a positive RT-PCR for SARS-CoV-2 and complete unduplicated data were eligible. Demographic and patient history variables were analyzed through Multivariable Cox regression models to evaluate predictors to be included in the prognostic scoring system called PH-Covid19. 83,779 patients were included to develop the model; 100,000, to validate the model. Eight predictors (age, sex, diabetes, COPD, immunosuppression, hypertension, obesity, and CKD) were included in the PH-Covid19 scoring system (range of values: −2 to 25 points). The predictive model has a discrimination of death of 0.8 (95%CI:0.796-0.804). The PH-Covid19 scoring system was developed and validated in Mexican patients to aid clinicians to stratify patients with COVID-19 at risk of fatal outcomes, allowing for better and efficient use of resources.

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    SciScore for 10.1101/2020.09.05.20189142: (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 AnalysisWe further performed simple random sampling of positive cases to increase statistical power in approximately 15% with respect to the sample used for developing the model.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analyses were performed using the SPSS software v.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    21 and the R statistical software v.3.6.2; figures were created in GraphPad Prism v.6.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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:
    Observed mortality in COVID-19 patients under 60 years is lower when access to healthcare is not a limitation [18]; non-survivors in our cohorts were younger (mean age 60-61.4 years) than those in other studies (67-80 years) [5,6,15,19]. Patients diagnosed with pneumonia in both cohorts were 12.8-21% among survivors and 77.4-87.5% of non-survivors. These numbers are low compared to the prevalence of chest CT-scan abnormalities which occur in 67.3-70.8% of asymptomatic/pre-symptomatic patients [20,21], 95.5% of patients with mild COVID-19 [22], and 98% of all COVID-19 patients included in a meta-analysis [23]. Chest X-ray, on the other hand, may be normal in up to 63% of patients with early COVID-19 pneumonia [24]. Nonetheless, the low proportion of pneumonia in non-survivors suggests non-optimal diagnosis of pneumonia may be occurring in Mexico. The lack of an operational definition may have contributed since clinicians could have defined pneumonia differently based on clinical and/or radiographical findings. Other possibilities should be explored, including knowledge of Mexican clinicians on how to diagnose pneumonia and access to radiological studies during the pandemic in low-resource settings. Three prognostic COVID-19 models have been developed in Mexican patients. The LOW-HARM model is a 100-point scoring system calculated by inputting patient history and laboratory values, setting 65 points as the cut-off value of greater discrimination of risk of death [25]. Predictio...

    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

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  2. Version published to 10.1101/2020.09.05.20189142v1 on medRxiv
  3. Version published to 10.1017/s0950268820002903