Increased levels of lactate dehydrogenase and hypertension are associated with severe illness of COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is amid an ongoing pandemic. It has been shown that patients with cardiovascular comorbidities are at higher risk of severe illness of COVID-19. AIM: To find out the relationship between cardiovascular comorbidities and severe illness of COVID-19. METHODS: The clinical data of 140 COVID-19 patients treated from January 22, 2020 to March 3, 2020 at our hospital were retrospectively collected. The clinical characteristics were compared between patients with mild illness and those with severe illness. RESULTS: There were 75 male patients and 65 female patients (53.6% vs 46.4%). The mean age was 45.4 ± 14.6 years (range, 2-85 years). Most of the patients had mild illness (n = 114, 81.4%) and 26 patients had severe illness (18.6%). The most common symptom was fever (n = 110, 78.6%), followed by cough (n = 82, 58.6%) and expectoration (n = 51, 36.4%). Eight patients were asymptomatic but were positive for severe acute respiratory syndrome coronavirus 2 RNA. Patients with severe illness were significantly more likely to be hypertensive than those with mild illness [(10/26, 38.4%) vs (22/114, 19.3%), P = 0.036]. The levels of lactate dehydrogenase were significantly higher in the severe illness group than in the mild illness group (299.35 ± 68.82 vs 202.94 ± 63.87, P < 0.001). No patient died in either the severe illness or the mild illness group. CONCLUSION: Hypertension and elevated levels of lactate dehydrogenase may be associated with severe illness of COVID-19.

Correlation Analysis Between Serum Uric Acid, Prealbumin Level, Lactate Dehydrogenase, and Severity of COVID-19.

Objective: To analyze the correlation between serum uric acid, prealbumin levels, lactate dehydrogenase (LDH), and the severity of COVID-19. Methods: The data from 135 patients with COVID-19 was collected, and the patients were divided into a non-severe group (110 cases) and a severe group (25 cases), according to the severity of illness. Sixty cases with normal physical examinations over the same period and 17 cases diagnosed with other viral pneumonia in the past five years were selected as the control group to analyze the correlation between the detection index and the severity of COVID-19. Results: Serum albumin and prealbumin in the severe group were significantly lower than those in the non-severe group (p < 0.01); serum uric acid in the severe group was lower than that in the non-severe group (p < 0.05). LDH and C-reaction protein (CRP) in the severe group were higher than those in non-severe group (p < 0.01); the levels of albumin, prealbumin, serum uric acid, and LDH in the severe group were significantly different from those in healthy control group (p < 0.01) and the levels of prealbumin, serum uric acid, LDH, and CRP in the severe group were significantly different from those in the other viral pneumonia group (p < 0.01). Serum albumin and prealbumin were positively correlated with the oxygenation index (p < 0.001), while LDH was negatively correlated with oxygenation index (p < 0.001). Conclusion: Serum albumin, prealbumin, the oxygenation index, and LDH are risk factors of COVID-19.

Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

Clinical Characteristics of Patients With Re-admitted of Novel Coronavirus 2019 (nCOVID-19) in Wenzhou, China.

Background: During the COVID-19 pandemic, many patients admitted to hospital for treatment have recovered and been discharged; however, in some instances, these same patients are re-admitted due to a second fever or a positive COVID-19 PCR test result. To ascertain whether it is necessary to treat these patients in hospitals, especially in asymptomatic cases, we summarize and analyze the clinical and treatment characteristics of patients re-admitted to hospital with a second COVID-19 infection. Methods: Of the 141 COVID-19 cases admitted to the Wenzhou Central Hospital between January 17, 2020, to March 5, 2020, which were followed until March 30, 2020, 12 patients were re-admitted with a second COVID-19 infection. Data was collected and analyzed from their clinical records, lab indexes, commuted tomography (CT), and treatment strategies. Results: Most of the 141 patients had positive outcomes from treatment, with only 12 (8.5%) being re-admitted. In this sub-group: one (8.3%) had a fever, a high white blood cell count (WBC), and progressive CT changes; and one (8.3%) had increased transaminase. The PCR tests of these two patients returned negative results. Another 10 patients were admitted due to a positive PCR test result, seven of which were clinically asymptomatic. Compared to the CT imaging following their initial discharge, the CT imaging of all patients was significantly improved, and none required additional oxygen or mechanical ventilation during their second course of treatment. Conclusions: The prognoses of the re-admitted patients were good with no serious cases. We conclude that home treatment with concentrated medical observation is a safe and feasible course of treatment if the patient returns a positive PCR test result but does not display serious clinical symptoms. During medical observation, patients with underlying conditions should remain a primary focus, but most do not need to be re-admitted to the hospital.

Gamma-Glutamyltransferase Elevation Is Frequent in Patients With COVID-19: A Clinical Epidemiologic Study.

A newly identified coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the infectious coronavirus disease 2019 (COVID-19), emerged in December 2019 in Wuhan, Hubei Province, China, and now poses a major threat to global public health. Previous studies have observed highly variable alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in patients with COVID-19. However, circulating levels of the cholangiocyte injury biomarker gamma-glutamyltransferase (GGT) have yet to be reported in the existing COVID-19 case studies. Herein, we describe the relationship between GGT levels and clinical and biochemical characteristics of patients with COVID-19. Our study is a retrospective case series of 98 consecutive hospitalized patients with confirmed COVID-19 at Wenzhou Central Hospital in Wenzhou, China, from January 17 to February 5, 2020. Clinical data were collected using a standardized case report form. Diagnosis of COVID-19 was assessed by symptomatology, reverse-transcription polymerase chain reaction (RT-PCR), and computed tomography scan. The medical records of patients were analyzed by the research team. Of the 98 patients evaluated, elevated GGT levels were observed in 32.7%; increased C-reactive protein (CRP) and elevated ALT and AST levels were observed in 22.5%, 13.3%, and 20.4%, respectively; and elevated alkaline phosphatase (ALP) and triglycerides (TGs) were found in 2% and 21.4%, respectively. Initially, in the 82 patients without chronic liver disease and alcohol history, age older than 40 years (P = 0.027); male sex (P = 0.0145); elevated CRP (P = 0.0366), ALT (P < 0.0001), and ALP (P = 0.0003); and increased TGs (P = 0.0002) were found to be associated with elevated GGT levels. Elevated GGT (P = 0.0086) and CRP (P = 0.0162) levels had a longer length of hospital stay. Conclusion: A sizable number of patients with COVID-19 infection have elevated serum GGT levels. This elevation supports involvement of the liver in persons with COVID-19.

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Epidemiological Features of 105 Patients Infected with the COVID-19.

OBJECTIVE: To investigate and evaluate the clinical features of patients infected with the 2019 novel coronavirus (COVID-19) outside of Wuhan. METHODS: 105 patients admitted to our hospital with clinical- and laboratory-confirmed COVID-19 infection were studied. Data were collected from January 17, 2020 to March 5, 2020. RESULTS: 105 patients (57 male and 48 female) were confirmed to have COVID-19 infection. Among the 105 patients, 55 (52%) had made short trips to Wuhan during the two weeks before the onset of illness, and these were the first-generation confirmed cases. An exact date of close contact with someone in Wenzhou with confirmed or suspected COVID-19 infection from Wuhan (the second-generation confirmed cases) could be provided by 38 (36%) patients. Of the remaining patients, six (6%; the third-generation confirmed cases) were familial clusters of the second-generation confirmed cases, three (3%) had no definite epidemiological features, and 16 (15%) were from the same location as for the case report. CONCLUSION: Due to the infectiousness of COVID-19, patients with infections should be diagnosed and treated as early as possible after developing fever symptoms or showing other clinical characteristics or imaging features. With respect to high-risk cases, we must focus on any complications that arise and take effective measures to treat them immediately. This will significantly improve the prognosis of patients with severe infections.

Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.

Lactate dehydrogenase and susceptibility to deterioration of mild COVID-19 patients: a multicenter nested case-control study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has infected more than 4 million people within 4 months. There is an urgent need to properly identify high-risk cases that are more likely to deteriorate even if they present mild diseases on admission. METHODS: A multicenter nested case-control study was conducted in four designated hospitals in China enrolling confirmed COVID-19 patients who were mild on admission. Baseline clinical characteristics were compared between patients with stable mild illness (stable mild group) and those who deteriorated from mild to severe illness (progression group). RESULTS: From Jan 17, 2020, to Feb 1, 2020, 85 confirmed COVID-19 patients were enrolled, including 16 in the progression group and 69 in the stable mild group. Compared to stable mild group (n = 69), patients in the progression group (n = 16) were more likely to be older, male, presented with dyspnea, with hypertension, and with higher levels of lactase dehydrogenase and c-reactive protein. In multivariate logistic regression analysis, advanced age (odds ratio [OR], 1.012; 95% confidence interval [CI], 1.020-1.166; P = 0.011) and the higher level of lactase dehydrogenase (OR, 1.012; 95% CI, 1.001-1.024; P = 0.038) were independently associated with exacerbation in mild COVID-19 patients. CONCLUSION: Advanced age and high LDH level are independent risk factors for exacerbation in mild COVID-19 patients. Among the mild patients, clinicians should pay more attention to the elderly patients or those with high LDH levels.