High Aspartate Aminotransferase to Alanine Aminotransferase Ratio on Admission as Risk Factor for Poor Prognosis in COVID-19 Patients (preprint)

This study aimed to analyze aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio in COVID-19 patients. After exclusion, 567 inpatients were included in this study and separated into two groups according to their AST/ALT ratio on admission. Poor prognosis included death and transfer to other hospitals due to deterioration. Of 567 patients, 56 (9.9%) had AST/ALT ≥ 2. Of the 56 patients, older age (median age 65.5 years), fatigue (29 [51.8%] cases), comorbidities (33 [58.9%] cases) and outcomes were significantly different from patients with AST/ALT < 2. They also had worse chest computed tomography (CT) findings, laboratory results and severity scores. Levels of platelet count (OR = 0.989, 95% CI [0.983-0.996]) were independently associated with AST/ALT ≥ 2 on admission. Furthermore, a high AST/ALT ratio on admission was an independent risk factor for poor prognosis (OR = 22.02, 95% CI [1.84-263.2]), especially in patients with AST levels > 40 U/L. In subsequent monitoring, the AST/ALT ratio was decreased in both patients with AST/ALT < 2 or ≥ 2 on admission. COVID-19 patients who are older, or have fatigue, comorbidities are more likely to have AST/ALT ≥ 2 on admission, which might be the indication of worse status and outcomes.

Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China (preprint)

IMPORTANCE In the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively. OBJECTIVE To develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease 2019 (COVID-19). DESIGN Diagnostic model based on retrospective case series. SETTING Two hospitals in Wuhan and Beijing, China. PTRTICIPANTS 584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020. METHODS LASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram. RESULTS Six potential COVID-19 prediction variables were selected and the variable abdominal pain was excluded for overmuch weight. The five potential predictors, including fever, chest computed tomography (CT), leukocytes (white blood cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p[≤]0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale. CONCLUSIONS We established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.