Insulin may promote SARS-CoV-2 cell entry and replication in diabetes patients.

Patients with diabetes often have severe hyperglycemia triggered by novel coronavirus disease 2019 (COVID-19). Insulin treatment should be the main approach to the control of acute hyperglycemia in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, clinical investigation found that insulin treatment is associated with a significant increase in mortality risk in patients with diabetes and SARS-CoV-2 infection. The reason for this high mortality rate remains obscure. Previous studies have demonstrated that insulin is an activator of Na+/H+ exchanger (NHE) which could decrease extracellular pH and increase intracellular pH and glycolysis. Here, the author emphasizes insulin may contribute to SARS-CoV-2 cell entry and multiplication in host cells through activation of Na+/H+ exchange. Additionally, the inhibition of Na+ /H+ exchange activity or glycolytic flux can result in reduced mortality in patients with COVID-19 and diabetes mellitus during insulin treatment.

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.

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.

Association of Early-Phase In-Hospital Glycemic Fluctuation With Mortality in Adult Patients With Coronavirus Disease 2019.

OBJECTIVE: To investigate the association of in-hospital early-phase glycemic control with adverse outcomes among inpatients with coronavirus disease 2019 (COVID-19) in Wuhan, China. RESEARCH DESIGN AND METHODS: The study is a large case series, and data were obtained regarding consecutive patients hospitalized with COVID-19 in the Central Hospital of Wuhan between 2 January and 15 February 2020. All patients with definite outcomes (death or discharge) were included. Demographic, clinical, treatment, and laboratory information were extracted from electronic medical records. We collected daily fasting glucose data from standard morning fasting blood biochemistry to determine glycemic status and fluctuation (calculated as the square root of the variance of daily fasting glucose levels) during the 1st week of hospitalization. RESULTS: A total of 548 patients were included in the study (median age 57 years; 298 [54%] were women, and n = 99 had diabetes [18%]), 215 suffered acute respiratory distress syndrome (ARDS), 489 survived, and 59 died. Patients who had higher mean levels of glucose during their 1st week of hospitalization were older and more likely to have a comorbidity and abnormal laboratory markers, prolonged hospital stays, increased expenses, and greater risks of severe pneumonia, ARDS, and death. Compared with patients with the lowest quartile of glycemic fluctuation, those who had the highest quartile of fluctuation magnitude had an increased risk of ARDS (risk ratio 1.97 [95% CI 1.01, 4.04]) and mortality (hazard ratio 2.73 [95% CI 1.06, 7.73]). CONCLUSIONS: These results may have implications for optimizing glycemic control strategies in COVID-19 patients during the early phase of hospitalization.

Clinical characteristics of "re-positive" discharged COVID-19 pneumonia patients in Wuhan, China.

To analyze the clinical characteristics of re-positive discharged COVID-19 patients and find distinguishing markers. The demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, illness severities and chest CT scan results of 267 patients were collected from 1st January to 15th February 2020. COVID-19 was diagnosed by RT-PCR. Clinical symptoms and nucleic acid test results were collected during the 14 days post-hospitalization quarantine. 30 out of 267 COVID-19 patients were detected re-positive during the post-hospitalization quarantine. Re-positive patients could not be distinguished by demographic features, clinical symptoms, laboratory results, comorbidities, co-infections, treatments, chest CT scan results or subsequent clinical symptoms. However, re-positive rate was found to be correlated to illness severity, according the Acute Physiology and Chronic Health Evaluation II (APACHE II) severity-of-disease classification system, and the confusion, urea, respiratory rate and blood pressure (CURB-65) score. Common clinical characteristics were not able to distinguish re-positive patients. However, severe and critical cases classified high according APACHE II and CURB-65 scores, were more likely to become re-positive after discharge.

Serum Cystatin C and Coronavirus Disease 2019: A Potential Inflammatory Biomarker in Predicting Critical Illness and Mortality for Adult Patients.

This study aimed at determining the relationship between baseline cystatin C levels and coronavirus disease 2019 (COVID-19) and investigating the potential prognostic value of serum cystatin C in adult patients with COVID-19. 481 patients with COVID-19 were consecutively included in this study from January 2, 2020, and followed up to April 15, 2020. All clinical and laboratory data of COVID-19 patients with definite outcomes were reviewed. For every measure, COVID-19 patients were grouped into quartiles according to the baseline levels of serum cystatin C. The highest cystatin C level was significantly related to more severe inflammatory conditions, worse organ dysfunction, and worse outcomes among patients with COVID-19 (P values < 0.05). In the adjusted logistic regression analyses, the highest cystatin C level and ln-transformed cystatin C levels were independently associated with the risks of developing critically ill COVID-19 and all-cause death either in overall patients or in patients without chronic kidney disease (P values < 0.05). As a potential inflammatory marker, increasing baseline levels of serum cystatin C might independently predict adverse outcomes for COVID-19 patients. Serum cystatin C could be routinely monitored during hospitalization, which showed clinical importance in prognosticating for adult patients with COVID-19.

Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. RESULTS: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

Clinical Course and Mortality of Stroke Patients With Coronavirus Disease 2019 in Wuhan, China.

BACKGROUND AND PURPOSE: No studies have reported the effect of the coronavirus disease 2019 (COVID-19) epidemic on patients with preexisting stroke. We aim to study the clinical course of COVID-19 patients with preexisting stroke and to investigate death-related risk factors. METHODS: We consecutively included 651 adult inpatients with COVID-19 from the Central Hospital of Wuhan between January 2 and February 15, 2020. Data on the demography, comorbidities, clinical manifestations, laboratory findings, treatments, complications, and outcomes (ie, discharged or death) of the participants were extracted from electronic medical records and compared between patients with and without preexisting stroke. The association between risk factors and mortality was estimated using a Cox proportional hazards regression model for stroke patients infected with severe acute respiratory syndrome coronavirus 2. RESULTS: Of the 651 patients with COVID-19, 49 with preexisting stroke tended to be elderly, male, had more underlying comorbidities and greater severity of illness, prolonged length of hospital stay, and greater hospitalization expenses than those without preexisting stroke. Cox regression analysis indicated that the patients with stroke had a higher risk of developing critical pneumonia (adjusted hazard ratio, 2.01 [95% CI, 1.27-3.16]) and subsequent mortality (adjusted hazard ratio, 1.73 [95% CI, 1.00-2.98]) than the patients without stroke. Among the 49 stroke patients, older age and higher score of Glasgow Coma Scale or Sequential Organ Failure Assessment were independent risk factors associated with in-hospital mortality. CONCLUSIONS: Preexisting stroke patients infected with severe acute respiratory syndrome coronavirus 2 were readily predisposed to death, providing an important message to individuals and health care workers that preventive measures must be implemented to protect and reduce transmission in stroke patients in this COVID-19 crisis.

Association between platelet parameters and mortality in coronavirus disease 2019: Retrospective cohort study.

BACKGROUND: Thrombocytopenia has been implicated in patients infected with severe acute respiratory syndrome coronavirus 2, while the association of platelet count and changes with subsequent mortality remains unclear. METHODS: The clinical and laboratory data of 383 patients with the definite outcome by March 1, 2020 in the Central Hospital of Wuhan were reviewed. The association between platelet parameters and mortality risk was estimated by utilizing Cox proportional hazard regression models. RESULTS: Among the 383 patients, 334 (87.2%) were discharged and survived, and 49 (12.8%) died. Thrombocytopenia at admission was associated with mortality of almost three times as high as that for those without thrombocytopenia (P < 0.05). Cox regression analyses revealed that platelet count was an independent risk factor associated with in-hospital mortality in a dose-dependent manner. An increment of per 50 × 109/L in platelets was associated with a 40% decrease in mortality (hazard ratio: 0.60, 95%CI: 0.43, 0.84). Dynamic changes of platelets were also closely related to death during hospitalization. CONCLUSIONS: Baseline platelet levels and changes were associated with subsequent mortality. Monitoring platelets during hospitalization may be important in the prognosis of patients with coronavirus disease in 2019.