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.

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.

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.

Differential diagnosis for suspected cases of coronavirus disease 2019: a retrospective study.

BACKGROUND: Since December 2019, the coronavirus disease 2019 (COVID-19) has infected more than 12,322,000 people and killed over 556,000 people worldwide. However, Differential diagnosis remains difficult for suspected cases of COVID-19 and need to be improved to reduce misdiagnosis. METHODS: Sixty-eight cases of suspected COVID-19 treated in Wenzhou Central Hospital from January 21 to February 20, 2020 were divided into confirmed and COVID-19-negative groups based on the results of real-time reverse transcriptase polymerase chain reaction (RT-PCR) nucleic acid testing of the novel coronavirus in throat swab specimens to compare the clinical symptoms and laboratory and imaging results between the groups. RESULTS: Among suspected patients, 17 were confirmed to COVID-19-positive group and 51 were distinguished to COVID-19-negative group. Patients with reduced white blood cell (WBC) count were more common in the COVID-19-positive group than in the COVID-19-negative group (29.4% vs 3.9%, P = 0.003). Subsequently, correlation analysis indicated that there was a significant inverse correlation existed between WBC count and temperature in the COVID-19-positive patients (r = - 0.587, P = 0.003), instead of the COVID-19-negative group. But reduced lymphocyte count was no different between the two groups (47.1% vs 25.5%, P = 0.096). More common chest imaging characteristics of the confirmed COVID-19 cases by high-resolution computed tomography (HRCT) included ground-glass opacities (GGOs), multiple patchy shadows, and consolidation with bilateral involvement than COVID-19-negative group (82.4% vs 31.4%, P = 0.0002; 41.2% vs 17.6% vs P = 0.048; 76.5% vs 43.1%, P = 0.017; respectively). The rate of clustered infection was higher in COVID-19-positive group than COVID-19-negative group (64.7% vs 7.8%, P = 0.001). Through multiplex PCR nucleic acid testing, 2 cases of influenza A, 3 cases of influenza B, 2 cases of adenovirus, 2 cases of Chlamydia pneumonia, and 7 cases of Mycoplasma pneumoniae were diagnosed in the COVID-19-negative group. CONCLUSIONS: WBC count inversely correlated with the severity of fever, GGOs, multiple patchy shadows, and consolidation in chest HRCT and clustered infection are common but not specific features in the confirmed COVID-19 group. Multiplex PCR nucleic acid testing helped differential diagnosis for suspected COVID-19 cases.

A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia.

The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.

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.

Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series.

OBJECTIVE: To study the clinical characteristics of patients in Zhejiang province, China, infected with the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) responsible for coronavirus disease 2019 (covid-2019). DESIGN: Retrospective case series. SETTING: Seven hospitals in Zhejiang province, China. PARTICIPANTS: 62 patients admitted to hospital with laboratory confirmed SARS-Cov-2 infection. Data were collected from 10 January 2020 to 26 January 2020. MAIN OUTCOME MEASURES: Clinical data, collected using a standardised case report form, such as temperature, history of exposure, incubation period. If information was not clear, the working group in Hangzhou contacted the doctor responsible for treating the patient for clarification. RESULTS: Of the 62 patients studied (median age 41 years), only one was admitted to an intensive care unit, and no patients died during the study. According to research, none of the infected patients in Zhejiang province were ever exposed to the Huanan seafood market, the original source of the virus; all studied cases were infected by human to human transmission. The most common symptoms at onset of illness were fever in 48 (77%) patients, cough in 50 (81%), expectoration in 35 (56%), headache in 21 (34%), myalgia or fatigue in 32 (52%), diarrhoea in 3 (8%), and haemoptysis in 2 (3%). Only two patients (3%) developed shortness of breath on admission. The median time from exposure to onset of illness was 4 days (interquartile range 3-5 days), and from onset of symptoms to first hospital admission was 2 (1-4) days. CONCLUSION: As of early February 2020, compared with patients initially infected with SARS-Cov-2 in Wuhan, the symptoms of patients in Zhejiang province are relatively mild.