Yujun Wang

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.

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. Death was regarded as poor prognosis in this study. Of 567 patients, 200 (35.3%) had AST/ALT ≥ 1.38. Of the 200 patients, older age (median age 60 years), myalgia (64 [32%] cases), fatigue (91 [45.5%] cases), some comorbidities and outcomes were significantly different from patients with AST/ALT < 1.38. They also had worse chest computed tomography (CT) findings, laboratory results and severity scores. Levels of platelet count (OR 0.995, 95% CI [0.992–0.998]) and hemoglobin (OR 0.984, 95% CI [0.972–0.995]) were independently associated with AST/ALT ≥ 1.38 on admission. Furthermore, a high AST/ALT ratio on admission was an independent risk factor for poor prognosis (OR 99.9, 95% CI [2.1–4280.5]). In subsequent monitoring, both survivors and non-survivors showed decreased AST/ALT ratio during hospitalization. In conclusion, high AST/ALT ratio might be the indication of worse status and outcomes in COVID-19 patients.

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.

ABATRACT: 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.

Summary: Background: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in a cluster of coronavirus disease 2019 (COVID-19). We reported the clinical characteristics of COVID-19 patients with acute respiratory distress syndrome (ARDS), and further investigated the treatment and progression of ARDS in COVID-19. Methods: This study enrolled 109 patients with COVID-19 admitted to the Central Hospital of Wuhan, a designated hospital in Wuhan, China, from January 2 to February 1, 2020. Patients were followed up to February 12, 2020. The clinical data were collected from the electronic medical records. The differences in the treatment and progression with the time and the severity of ARDS were determined. Findings: Among 109 patients, mean age was 55 years, and 59 patients were male. With a median 15 days (range, 4 to 30 days) follow-up period, 31 patients (28.4%) died, while 78 (71.6%) survived and discharged. Of all patients, 53 (48.6%) developed ARDS. Compared to non-ARDS patients, ARDS patients were elder (mean age, 61 years vs . 49 years), and more likely to have the coexistent conditions, including diabetes (20.8% vs . 1.8%), cerebrovascular disease (11.3% vs . 0%), and chronic kidney disease (15.1% vs . 3.6%). Compared to mild ARDS patients, those with moderate and severe ARDS had higher mortality rates. No significant effect of antivirus, glucocorticoid, or immunoglobulin treatment on survival was observed in patients with ARDS. Interpretation: The mortality rate increased with the severity of ARDS in COVID-19, and the effects of current therapies on the survival for these patients were not satisfactory, which needs more attention from clinicians. Funding: Health and Family Planning Commission of Wuhan Municipality.

Metal−‐organic frameworks (MOFs), a new type of porous crystalline material, hold great potential in biomedical applications, such as drug delivery. However, the efficacy of drug delivery is limited by low drug loading. In this work, we synthesized hollow mesoporous silica (HMS)@MOF capsules that can be used as a pH‐responsive drug delivery system for the anticancer drug doxorubicin (DOX). DOX is loaded into the inner cavity of HMS. Zeolitic imidazolate framework‐8 (ZIF‐8) nanoparticles are then coated on the outer surface of the DOX‐loaded HMS. The obtained material is a capsule (denoted as DOX/HMS@ZIF), in which DOX is encapsulated. The DOX/HMS@ZIF can be used as an efficient pH‐responsive drug delivery system. DOX is not released under physiological conditions (pH 7.4), but is released at low pH (4–6) from DOX/HMS@ZIF. The DOX/HMS@ZIF capsule shows much higher cytotoxicity than free DOX and alters the delivery pathway for DOX in cancer cells, while the drug‐free HMS@ZIF shows excellent biocompatibility. This opens new opportunities to construct a safe and efficient delivery system for targeted molecules using pH‐responsive release for a wide range of applications.