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.

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.

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.

Risk factors for adverse clinical outcomes with COVID-19 in China: a multicenter, retrospective, observational study.

Background: The risk factors for adverse events of Coronavirus Disease-19 (COVID-19) have not been well described. We aimed to explore the predictive value of clinical, laboratory and CT imaging characteristics on admission for short-term outcomes of COVID-19 patients. Methods: This multicenter, retrospective, observation study enrolled 703 laboratory-confirmed COVID-19 patients admitted to 16 tertiary hospitals from 8 provinces in China between January 10, 2020 and March 13, 2020. Demographic, clinical, laboratory data, CT imaging findings on admission and clinical outcomes were collected and compared. The primary endpoint was in-hospital death, the secondary endpoints were composite clinical adverse outcomes including in-hospital death, admission to intensive care unit (ICU) and requiring invasive mechanical ventilation support (IMV). Multivariable Cox regression, Kaplan-Meier plots and log-rank test were used to explore risk factors related to in-hospital death and in-hospital adverse outcomes. Results: Of 703 patients, 55 (8%) developed adverse outcomes (including 33 deceased), 648 (92%) discharged without any adverse outcome. Multivariable regression analysis showed risk factors associated with in-hospital death included ≥ 2 comorbidities (hazard ratio [HR], 6.734; 95% CI; 3.239-14.003, p < 0.001), leukocytosis (HR, 9.639; 95% CI, 4.572-20.321, p < 0.001), lymphopenia (HR, 4.579; 95% CI, 1.334-15.715, p = 0.016) and CT severity score > 14 (HR, 2.915; 95% CI, 1.376-6.177, p = 0.005) on admission, while older age (HR, 2.231; 95% CI, 1.124-4.427, p = 0.022), ≥ 2 comorbidities (HR, 4.778; 95% CI; 2.451-9.315, p < 0.001), leukocytosis (HR, 6.349; 95% CI; 3.330-12.108, p < 0.001), lymphopenia (HR, 3.014; 95% CI; 1.356-6.697, p = 0.007) and CT severity score > 14 (HR, 1.946; 95% CI; 1.095-3.459, p = 0.023) were associated with increased odds of composite adverse outcomes. Conclusion: The risk factors of older age, multiple comorbidities, leukocytosis, lymphopenia and higher CT severity score could help clinicians identify patients with potential adverse events.