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.

Bangbo Zhao,Bangbo Zhao,Xingtong Zhou,Yujun Wang,Yujun Wang,Yingxin Wei,Yingxin Wei,Tianhao Li,Tianhao Li,Zihao Wang,Zihao Wang,Wenwu Sun,Cheng Qin,Wenwu Sun,Xingtong Zhou,Hongtao Cao,Hongtao Cao,Weibin Wang,Weibin Wang

doi:10.3389/fmed.2021.601941

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.

Highlights

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
The abdominal infection causing acute abdomen resulted in more abnormal laboratory test results, such as C-reactive protein (CRP), PCT, white blood cell (WBC), neutrophils, and fibrinogen, in infectious acute abdomen patients than in COVID-19 patients (p < 0.001)
To make our prediction model more concise and practical in the context of emergency surgery, we simplified the scoring criterion of the COVID-19 and Infectious Acute Abdomen Distinguishment (CIAAD) nomogram and created a new scale, named the CIAAD scale (Figure 5)

Summary

Introduction

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. The diagnosis of COVID-19 mainly depends on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid detection [7]. There is a pressing need for an easier and more feasible method to help surgeons preliminarily distinguish COVID-19 patients from other infectious acute abdomen patients who have symptoms mimicking those of COVID-19 and take proper precautions in emergency operations

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Discussion

Conclusion