Prognostic Factors of Initial Chest CT Findings for ICU Admission and Mortality in Patients with COVID-19 Pneumonia

  1. Mohammad Ali Kazemi
  2. Hossein Ghanaati
  3. Behnaz Moradi
  4. Mohammadreza Chavoshi
  5. Hassan Hashemi
  6. Samira Hemmati
  7. Pouria Rouzrokh
  8. Masoumeh Gity
  9. Zahra Ahmadinejad
  10. Hamed Abdollahi

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Abstract

Background: Studies have shown that computed tomography (CT) could be valuable for prognostic issues in COVID-19. Objectives: To investigate the prognostic factors of early chest CT findings in COVID-19 patients. Patients and Methods: This retrospective study included 91 patients (34 women, and 57 men) of real-time reverse transcription polymerase chain reaction (RT-PCR) positive COVID-19 from three hospitals in Iran between February 25, 2020, to March 15, 2020. Patients were divided into two groups as good prognosis, discharged from the hospital and alive without symptoms (48 patients), and poor prognosis, died or needed ICU care (43 patients). The first CT images of both groups that were obtained during the first 8 days of the disease presentation were evaluated considering the pattern, distribution, and underlying disease. The total CT-score was calculated for each patient. Univariate and multivariate analysis with IBM SPSS Statistics v.26 was used to find the prognostic factors. Results: There was a significant correlation between poor prognosis and older ages, dyspnea, presence of comorbidities, especially cardiovascular and comorbidities. Considering CT features, peripheral and diffuse distribution, anterior and paracardiac involvement, crazy paving pattern, and pleural effusion were correlated with poor prognosis. There was a correlation between total CT-score and prognosis and an 11.5 score was suggested as a cut-off with 67.4% sensitivity and 68.7% specificity in differentiation of poor prognosis patients (patients who needed ICU admission or died). Multivariate analysis revealed that a model consisting of age, male gender, underlying comorbidity, diffused lesions, total CT-score, and dyspnea would predict the prognosis better. Conclusion: Total chest CT-score and chest CT features can be used as prognostic factors in COVID-19 patients. A multidisciplinary approach would be more accurate in predicting the prognosis.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.30.20223024: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Ethics committee of Tehran University of Medical Sciences with the ethical code of IR.TUMS.MEDICINE.REC.1399.095 and written informed consent was waived.
    Consent: This study was approved by the Ethics committee of Tehran University of Medical Sciences with the ethical code of IR.TUMS.MEDICINE.REC.1399.095 and written informed consent was waived.
    RandomizationFour main distribution patterns were evaluated: peripheral (pleural based or/and pleural sparing), central (Patchy opacities that extended to the lung hila and showed lobar bronchial contact without obvious peri-broncho-vascular appearance), peri-broncho-vascular (involvement with the disease and not edema) and diffuse (randomly central and peripheral lesions).
    BlindingDuring the review, both radiologists were blinded about the patent’s information and outcome.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis: IBM SPSS Statistics v.25 (Armonk,
    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 finding that the underlying comorbidity did not have a significant contribution to our model might also be the result of the same limitation. This article faced some limitations. The number of cases was not sufficient for further analytic studies and enrollment of more factors in predicting the prognosis. We couldn’t get any follow up CT scan of the patients. All 3 hospitals were referral centers for COVID-19 patients, so it is possible that the overall CT-score of the patients in this study would not be representative of the general population. Finally, the clinical and laboratory data of the patients were not complete to be entered into the study and we couldn’t include them in multivariate analysis.

    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 10.5812/iranjradiol.106879
  3. Version published to 10.1101/2020.10.30.20223024 on medRxiv