Distinguishing Coronavirus Disease 2019 Patients From General Surgery Emergency Patients With the CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

  1. Bangbo Zhao
  2. Yingxin Wei
  3. Wenwu Sun
  4. Cheng Qin
  5. Xingtong Zhou
  6. Zihao Wang
  7. Tianhao Li
  8. Hongtao Cao
  9. Yujun Wang
  10. Weibin Wang

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Abstract

Background: During the epidemic, surgeons cannot identify infectious acute abdomen patients with suspected coronavirus disease 2019 (COVID-19) immediately using the current widely applied methods, such as double nucleic acid detection. We aimed to develop and validate a prediction model, presented as a nomogram and scale, to identify infectious acute abdomen patients with suspected COVID-19 more effectively and efficiently.

Methods: A total of 584 COVID-19 patients and 238 infectious acute abdomen patients were enrolled. The least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses were conducted to develop the prediction model. The performance of the nomogram was evaluated through calibration curves, Receiver Operating Characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves in the training and validation cohorts. A simplified screening scale and a management algorithm were generated based on the nomogram.

Results: Five potential COVID-19 prediction variables, fever, chest CT, WBC, CRP, and PCT, were selected, all independent predictors of multivariable logistic regression analysis, and the nomogram, named the COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration, and it was validated in the validation cohort. Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified as the CIAAD scale.

Conclusion: We established an easy and effective screening model and scale for surgeons in the emergency department to use to distinguish COVID-19 patients. The algorithm based on the CIAAD scale will help surgeons more efficiently manage infectious acute abdomen patients suspected of having COVID-19.

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  1. Version published to 10.3389/fmed.2021.601941
  2. ScreenIT

    SciScore for 10.1101/2020.04.18.20071019: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Patients: Ethical approval was obtained from the Ethics Committees of Peking Union Medical College Hospital and the Central Hospital of Wuhan for this retrospective stud.
    RandomizationPotential predictors selection: The primary cohort of the whole 822 patients was divided into a training cohort and a validation cohort randomly by ratio of 2:1.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis was conducted with R software (version 3.6.1; http://www.Rproject.org) and SPSS statistical software package (version 25.0).
    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:
    Strengths and limitations of this study: The set of our model has the superiority of strong pertinence. The recommended user of CIAAD scale is surgeon in the emergency department and the recommended assessed population is infectious acute abdomen patients suspected COVID-19. To this end, we collected firsthand and high-quality data of COVID-19 patients and enrolled infectious acute abdomen patients strictly. In addition, by virtue of LASSO regression analysis, five quantifiable indicators were successfully selected. Though many variables like diabetes, cough and D-dimer varied considerably between COVID-19 and infectious acute abdomen patients, they were ruled out by LASSO regression analysis as overmuch weight or causing the prediction model cumbersome. The selected indictors were all included in previous prediction models, which verified the prediction capacity of these variables from the side17. Whereas, there is some inadequacy in our study as well. Firstly, the disturbance for routine medical work by the epidemic resulted in the appropriate lack of patients with both COVID-19 and acute abdomen. As the number of emergency operations of acute abdomen decreased sharply in Wuhan, the data of acute abdomen patients was from Peking Union Medical College Hospital, a renowned and leader hospital in China. Implications for practice and future: An algorithm helpful for allowing both a focused workup and expeditious therapy was given, including necessary prevention advice for medic...

    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.
    • Thank you for including a protocol registration statement.

    About SciScore

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  3. Version published to 10.1101/2020.04.18.20071019v1 on medRxiv