Prediction of COVID-19 mortality among hospitalized patients in Sudan

  1. Ghada Omer Hamad Abd El-Raheem
  2. Maysoun Ahmed Awad Yousif
  3. Doaa Salih Ibrahim Mohamed

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Abstract

Background

COVID-19 was primarily reported in China. The mortality rate across countries had ranged from 1% up to more than 10% and it is underestimated in some countries. Advanced age is the most frequently reported factor associated to mortality. Other factors were the presence of comorbidities such as diabetes mellitus, hypertension and obesity. Several models for mortality prediction had been developed to assist in improving the prognosis. The aim of our study was to assess the factors related to mortality among COVID-19 patients and develop a prediction model based on these factors.

Methods

A retrospective cohort study assessed the factors related to the mortality among COVID-19 patients who attended Imperial Hospital isolation centre on November-December, 2020, Khartoum, Sudan. Statistical tests performed were chi-square test, odds ratio and regression to develop the prediction model. Tests were considered statistically significant when p < 0.05.

Results

105 patients were studied. 29% of the patients were deceased, while, 71% were discharged alive. A statistically significant association was found between the age and severity with regards to mortality rate ( p =0.034, 0.018 respectively). The model equation for mortality prediction: Mortality = −14.724+ (1.387* Age) + (−0.323* Gender) + (1.814* Admission) + (0.193* Ischemic Heart Disease) + (−0.369* Fever) + (1.595* Cough) + (1.953* Complications) + (0.149* Duration of hospitalization) + (0.999* Enoxaparin dose).

Conclusions

Age, admission ward, cough and enoxaparin dose were statistically significant predictors for COVID-19 mortality ( p = 0.014, 0.011, 0.015, 0.006 respectively).

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  1. ScreenIT

    SciScore for 10.1101/2021.03.09.21253179: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationThe sample of 105 files of adult COVID-19 patients was collected randomly from the hospital registry.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical package for social sciences (SPSS version 23) was used to describe and analyse the data.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    The limitations of our study were the size of the study, as it was a single centre study. The data collection sheet was not standardized, only validated through an expert in research methods. Besides the retrospective nature of data collection which might affect the quality of the data collection.

    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 10.1101/2021.03.09.21253179v1 on medRxiv