A New Coronavirus Estimation Global Score for Predicting Mortality During Hospitalization in Patients with COVID-19.

Objective: Coronavirus disease 2019 (COVID-19) exists as a pandemic. Mortality during hospitalization is multifactorial, and there is urgent need for a risk stratification model to predict in-hospital death among COVID-19 patients. Here we aimed to construct a risk score system for early identification of COVID-19 patients at high probability of dying during in-hospital treatment. Methods: In this retrospective analysis, a total of 821 confirmed COVID-19 patients from 3 centers were assigned to developmental (n = 411, between January 14, 2020 and February 11, 2020) and validation (n = 410, between February 14, 2020 and March 13, 2020) groups. Based on demographic, symptomatic, and laboratory variables, a new Coronavirus estimation global (CORE-G) score for prediction of in-hospital death was established from the developmental group, and its performance was then evaluated in the validation group. Results: The CORE-G score consisted of 18 variables (5 demographics, 2 symptoms, and 11 laboratory measurements) with a sum of 69.5 points. Goodness-of-fit tests indicated that the model performed well in the developmental group (H = 3.210, P = 0.880), and it was well validated in the validation group (H = 6.948, P = 0.542). The areas under the receiver operating characteristic curves were 0.955 in the developmental group (sensitivity, 94.1%; specificity, 83.4%) and 0.937 in the validation group (sensitivity, 87.2%; specificity, 84.2%). The mortality rate was not significantly different between the developmental (n = 85,20.7%) and validation (n = 94, 22.9%, P = 0.608) groups. Conclusions: The CORE-G score provides an estimate of the risk of in-hospital death. This is the first step toward the clinical use of the CORE-G score for predicting outcome in COVID-19 patients.

Antiviral Abidol is Associated with the Reduction of In-Hospital Mortality in COVID-19 Patients.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a global public health crisis. There are no specific antiviral agents for the treatment of SARS-CoV-2. Information regarding the effect of Abidol on in-hospital mortality is scarce. The present study aimed to evaluate the treatment effect of Abidol for patients with COVID-19 before and after propensity score matching (PSM). METHODS: This retrospective cohort study analyzed 1019 patients with confirmed COVID-19 in China from December 22, 2019 to March 13, 2020. Patients were divided to Abidol (200 mg, tid, 5-7 days, n = 788, 77.3%) and No-Abidol (n = 231, 22.7%) groups. The primary outcome was the mortality during hospitalization. RESULTS: Among 1019 COVID-19 patients, the age was (60.4 ±â€Š14.5) years. Abidol-treated patients, compared with No-Abidol-treated patients, had a shorter duration from onset of symptoms to admission, less frequent renal dysfunction, lower white blood cell counts (lymphocytes <0.8) and erythrocyte sending rate, lower interleukin-6, higher platelet counts and plasma IgG and oxygen saturation, and less frequent myocardial injury. The mortality during hospitalization before PSM was 17.9% in Abidol group and 34.6% in No-Abidol (hazard ratio (HR) = 2.610, 95% confident interval (CI): 1.980-3.440), all seen in severe and critical patients. After PSM, the in-hospital death was 13.6% in Abidol and 28.6% in No-Abidol group (HR = 2.728, 95% CI: 1.598-4.659). CONCLUSIONS: Abidol-treatment results in less in-hospital death for severe and critical patients with COVID-19. Further randomized study is warranted to confirm the findings from this study.

Automatic pulmonary auscultation grading diagnosis of Coronavirus Disease 2019 in China with artificial intelligence algorithms: A cohort study.

BACKGROUND AND OBJECTIVE: Research on automatic auscultation diagnosis of COVID-19 has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis. METHODS: 172 confirmed cases of COVID-19 in Tongji Hospital were divided into moderate, severe and critical group. Pulmonary auscultation were recorded in 6-10 sites per patient through 3M littmann stethoscope and the data were transferred to computer to construct the dataset. Convolutional neural network (CNN) were designed to generate classifications of the auscultation. F1 score, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity and specificity were quantified. Another 45 normal patients were served as control group. RESULTS: There are about 56.52%, 59.46% and 78.85% abnormal auscultation in the moderate, severe and critical groups respectively. The model showed promising performance with an averaged F1 scores (0.9938 95% CI 0.9923-0.9952), AUC ROC score (0.9999 95% CI 0.9998-1.0000), sensitivity (0.9938 95% CI 0.9910-0.9965) and specificity (0.9979 95% CI 0.9970-0.9988) in identifying the COVID-19 patients among normal, moderate, severe and critical group. It is capable in identifying crackles, wheezes, phlegm sounds with an averaged F1 scores (0.9475 95% CI 0.9440-0.9508), AUC ROC score (0.9762 95% CI 0.9848-0.9865), sensitivity (0.9482 95% CI 0.9393-0.9578) and specificity (0.9835 95% CI 0.9806-0.9863). CONCLUSIONS: Our model is accurate and efficient in automatically diagnosing COVID-19 according to different categories, laying a promising foundation for AI-enabled auscultation diagnosing systems for lung diseases in clinical applications.

Automatic pulmonary auscultation grading diagnosis of Coronavirus Disease 2019 in China with artificial intelligence algorithms: a cohort study

Background and Objective Research on automatic auscultation diagnosis of COVID-19 has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis. Methods 172 confirmed cases of COVID-19 in Tongji Hospital were divided into moderate, severe and critical group. Pulmonary auscultation were recorded in 6-10 sites per patient through 3M littmann stethoscope and the data were transferred to computer to construct the dataset. Convolutional neural network (CNN) were designed to generate classifications of the auscultation. F1 score, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity and specificity were quantified. Another 45 normal patients were served as control group. Results There are about 56.52%, 59.46% and 78.85% abnormal auscultation in the moderate, severe and critical groups respectively. The model showed promising performance with an averaged F1 scores (0.9938 95% CI 0.9923–0.9952), AUC ROC score (0.9999 95% CI 0.9998–1.0000), sensitivity (0.9938 95% CI 0.9910–0.9965) and specificity (0.9979 95% CI 0.9970–0.9988) in identifying the COVID-19 patients among normal, moderate, severe and critical group. It is capable in identifying crackles, wheezes, phlegm sounds with an averaged F1 scores (0.9475 95% CI 0.9440–0.9508), AUC ROC score (0.9762 95% CI 0.9848–0.9865), sensitivity (0.9482 95% CI 0.9393–0.9578) and specificity (0.9835 95% CI 0.9806–0.9863). Conclusions Our model is accurate and efficient in automatically diagnosing COVID-19 according to different categories, laying a promising foundation for AI-enabled auscultation diagnosing systems for lung diseases in clinical applications.

Widespread myocardial dysfunction in COVID-19 patients detected by myocardial strain imaging using 2-D speckle-tracking echocardiography.

COVID-19 is a multiorgan systemic inflammatory disease caused by SARS-CoV-2 virus. Patients with COVID-19 often exhibit cardiac dysfunction and myocardial injury, but imaging evidence is lacking. In the study we detected and evaluated the severity of myocardial dysfunction in COVID-19 patient population using two-dimensional speckle-tracking echocardiography (2-D STE). A total of 218 consecutive patients with confirmed diagnosis of COVID-19 who had no underlying cardiovascular diseases were enrolled and underwent transthoracic echocardiography. This study cohort included 52 (23.8%) critically ill and 166 noncritically ill patients. Global longitudinal strains (GLSs) and layer-specific longitudinal strains (LSLSs) were obtained using 2-D STE. Changes in GLS were correlated with the clinical parameters. We showed that GLS was reduced (<-21.0%) in about 83% of the patients. GLS reduction was more common in critically sick patients (98% vs. 78.3%, P < 0.001), and the mean GLS was significantly lower in the critically sick patients than those noncritical (-13.7% ± 3.4% vs. -17.4% ± 3.2%, P < 0.001). The alteration of GLS was more prominent in the subepicardium than in the subendocardium (P < 0.001). GLS was correlated to mean serum pulse oxygen saturation (SpO2, RR = 0.42, P < 0.0001), high-sensitive C-reactive protein (hsCRP, R = -0.20, P = 0.006) and inflammatory cytokines, particularly IL-6 (R = -0.21, P = 0.003). In conclusions, our results demonstrate that myocardial dysfunction is common in COVID-19 patients, particularly those who are critically sick. Changes in indices of myocardial strain were associated with indices of inflammatory markers and hypoxia, suggesting partly secondary nature of myocardial dysfunction.

Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (platelet lymphocyte age neutrophil sex) model.

BACKGROUND: Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. METHODS: Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). RESULTS: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. CONCLUSIONS: The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.

Factors associated with acute cardiac injury and their effects on mortality in patients with COVID-19.

To determine the incidence of acute cardiac injury (ACI), the factors associated with ACI and the in-hospital mortality in patients with COVID-19, especially in severe patients. All consecutive in-patients with laboratory-confirmed COVID-19 from Tongji Hospital in Wuhan during February 1 and March 29, 2020 were included. The demographic, clinical characteristics, laboratory, radiological and treatment data were collected. Univariate and Firth logistic regression analyses were used to identify factors associated with ACI and in-hospital mortality, and Kaplan-Meier method was used to estimate cumulative in-hospital mortality. Among 1031 patients included, 215 (20.7%) had ACI and 501 (48.6%) were severe cases. Overall, 165 patients died; all were from the severe group, and 131 (79.39%) had ACI. ACI (OR = 2.34, P = 0.009), male gender (OR = 2.58, P = 0.001), oximeter oxygen saturation (OR = 0.90, P < 0.001), lactate dehydrogenase (OR = 3.26, P < 0.001), interleukin-6 (IL-6) (OR = 8.59, P < 0.001), high sensitivity C-reactive protein (hs-CRP) (OR = 3.29, P = 0.016), N-terminal pro brain natriuretic peptide (NT-proBNP) (OR = 2.94, P = 0.001) were independent risk factors for the in-hospital mortality in severe patients. The mortality was significantly increased among severe patients with elevated hs-CRP, IL-6, hs-cTnI, and/or NT-proBNP. Moreover, the mortality was significantly higher in patients with elevation of both hs-cTnI and NT proBNP than in those with elevation of either of them. ACI develops in a substantial proportion of patients with COVID-19, and is associated with the disease severity and in-hospital mortality. A combination of hs-cTnI and NT-proBNP is valuable in predicting the mortality.

Cardiac Involvement in Patients Recovered From COVID-2019 Identified Using Magnetic Resonance Imaging.

Objectives: This study evaluated cardiac involvement in patients recovered from coronavirus disease-2019 (COVID-19) using cardiac magnetic resonance (CMR). Background: Myocardial injury caused by COVID-19 was previously reported in hospitalized patients. It is unknown if there is sustained cardiac involvement after patients' recovery from COVID-19. Methods: Twenty-six patients recovered from COVID-19 who reported cardiac symptoms and underwent CMR examinations were retrospectively included. CMR protocols consisted of conventional sequences (cine, T2-weighted imaging, and late gadolinium enhancement [LGE]) and quantitative mapping sequences (T1, T2, and extracellular volume [ECV] mapping). Edema ratio and LGE were assessed in post-COVID-19 patients. Cardiac function, native T1/T2, and ECV were quantitatively evaluated and compared with controls. Results: Fifteen patients (58%) had abnormal CMR findings on conventional CMR sequences: myocardial edema was found in 14 (54%) patients and LGE was found in 8 (31%) patients. Decreased right ventricle functional parameters including ejection fraction, cardiac index, and stroke volume/body surface area were found in patients with positive conventional CMR findings. Using quantitative mapping, global native T1, T2, and ECV were all found to be significantly elevated in patients with positive conventional CMR findings, compared with patients without positive findings and controls (median [interquartile range]: native T1 1,271 ms [1,243 to 1,298 ms] vs. 1,237 ms [1,216 to 1,262 ms] vs. 1,224 ms [1,217 to 1,245 ms]; mean ± SD: T2 42.7 ± 3.1 ms vs. 38.1 ms ± 2.4 vs. 39.1 ms ± 3.1; median [interquartile range]: 28.2% [24.8% to 36.2%] vs. 24.8% [23.1% to 25.4%] vs. 23.7% [22.2% to 25.2%]; p = 0.002; p < 0.001, and p = 0.002, respectively). Conclusions: Cardiac involvement was found in a proportion of patients recovered from COVID-19. CMR manifestation included myocardial edema, fibrosis, and impaired right ventricle function. Attention should be paid to the possible myocardial involvement in patients recovered from COVID-19 with cardiac symptoms.

[Consensus of Chinese experts on diagnosis and treatment processes of acute myocardial infarction in the context of prevention and control of COVID-19 (first edition)].

The SARS-CoV-2 epidemic starting in Wuhan in December, 2019 has spread rapidly throughout the nation. The control measures to contain the epidemic also produced influences on the transport and treatment process of patients with acute myocardial infarction (AMI), and adjustments in the management of the patients need to be made at this particular time. AMI is characterized by an acute onset with potentially fatal consequence, a short optimal treatment window, and frequent complications including respiratory infections and respiratory and circulatory failure, for which active on-site treatment is essential. To standardize the management and facilitate the diagnosis and treatment, we formulated the guidelines for the procedures and strategies for the diagnosis and treatment of AMI, which highlight 5 Key Principles, namely Nearby treatment, Safety protection, Priority of thrombolysis, Transport to designated hospitals, and Remote consultation. For AMI patients, different treatment strategies are selected based on the screening results of SARS-CoV-2, the time window of STEMI onset, and the vital signs of the patients. During this special period, the cardiologists, including the interventional physicians, should be fully aware of the indications and contraindications of thrombolysis. In the transport and treatment of AMI patients, the physicians should strictly observe the indications for patient transport with appropriate protective measurements of the medical staff.

The experience of treating patients with acute myocardial infarction under the COVID-19 epidemic.

Worldwide Coronavirus Disease 2019 (COVID-19) epidemic makes the management of acute myocardial infarction (AMI) more complicated, effective treatment without further dissemination is thus quite challenging. Recently, we successfully treated three representative AMI cases, by sharing these detailed procedures, we summarized some important issues including patient screening, reperfusion strategy selecting, personnel/catheter lab protection principle, as well as operation tactics, which may lend precious experience on AMI treating during the ongoing COVID-19 pandemic situation.

CSC Expert Consensus on Principles of Clinical Management of Patients With Severe Emergent Cardiovascular Diseases During the COVID-19 Epidemic.

In response to the outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, the Chinese Society of Cardiology (CSC) issued this consensus statement after consulting with 125 medical experts in the fields of cardiovascular disease and infectious disease. The over-arching principles laid out here are the following: 1) Consider the prevention and control of COVID-19 transmission as the highest priority, including self-protection of medical staff; 2) Patient risk assessment of both infection and cardiovascular issues. Where appropriate, preferential use of conservative medical therapeutic approaches to minimize disease spread; 3) At all times, medical practices and interventional procedures should be conducted in accordance with the directives of the infection control department of local hospitals and local health commissions.