BackgroundsSince December 2019, a novel coronavirus epidemic has emerged in Wuhan city, China and then rapidly spread to other areas. As of 20 Feb 2020, a total of 2,055 medical staff confirmed with coronavirus disease 2019 (COVID-19) caused by SARS-Cov-2 in China had been reported. We sought to explore the epidemiological, clinical characteristics and prognosis of novel coronavirus-infected medical staff.
MethodsIn this retrospective study, 64 confirmed cases of novel coronavirus-infected medical staff admitted to Union Hospital, Wuhan between 16 Jan, 2020 to 15 Feb, 2020 were included. Two groups concerned were extracted from the subjects based on duration of symptoms: group 1 ([≤]10 days) and group 2 (>10 days). Epidemiological and clinical data were analyzed and compared across groups. The Kaplan-Meier plot was used to inspect the change in hospital discharge rate. The Cox regression model was utilized to identify factors associated with hospital discharge.
FindingsThe median age of medical staff included was 35 years old. 64% were female and 67% were nurses. None had an exposure to Huanan seafood wholesale market or wildlife. A small proportion of the cohort had contact with specimens (5%) as well as patients in fever clinics (8%) and isolation wards (5%). Fever (67%) was the most common symptom, followed by cough (47%) and fatigue (34%). The median time interval between symptoms onset and admission was 8.5 days. On admission, 80% of medical staff showed abnormal IL-6 levels and 34% had lymphocytopenia. Chest CT mainly manifested as bilateral (61%), septal/subpleural (80%) and ground-glass (52%) opacities. During the study period, no patients was transferred to intensive care unit or died, and 34 (53%) had been discharged. Higher body mass index (BMI) ([≥] 24 kg/m2) (HR 0.14; 95% CI 0.03-0.73), fever (HR 0.24; 95% CI 0.09-0.60) and higher levels of IL-6 on admission (HR 0.31; 95% CI 0.11-0.87) were unfavorable factors for discharge.
InterpretationIn this study, medical staff infected with COVID-19 have relatively milder symptoms and favorable clinical course, which may be partly due to their medical expertise, younger age and less underlying diseases. Smaller BMI, absence of fever symptoms and normal IL-6 levels on admission are favorable for discharge for medical staff. Further studies should be devoted to identifying the exact patterns of SARS-CoV-2 infection among medical staff.
BACKGROUND: Since early December 2019, the 2019 novel coronavirus disease (COVID-19) has caused pneumonia epidemic in Wuhan, Hubei province of China. This study aims to investigate the factors affecting the progression of pneumonia in COVID-19 patients. Associated results will be used to evaluate the prognosis and to find the optimal treatment regimens for COVID-19 pneumonia.
METHODS: Patients tested positive for the COVID-19 based on nucleic acid detection were included in this study. Patients were admitted to 3 tertiary hospitals in Wuhan between December 30, 2019, and January 15, 2020. Individual data, laboratory indices, imaging characteristics, and clinical data were collected, and statistical analysis was performed. Based on clinical typing results, the patients were divided into a progression group or an improvement/stabilization group. Continuous variables were analyzed using independent samples t-test or Mann-Whitney U test. Categorical variables were analyzed using Chi-squared test or Fisher exact test. Logistic regression analysis was performed to explore the risk factors for disease progression.
RESULTS: Seventy-eight patients with COVID-19-induced pneumonia met the inclusion criteria and were included in this study. Efficacy evaluation at 2 weeks after hospitalization indicated that 11 patients (14.1%) had deteriorated, and 67 patients (85.9%) had improved/stabilized. The patients in the progression group were significantly older than those in the disease improvement/stabilization group (66 [51, 70] vs. 37 [32, 41] years, U = 4.932, P = 0.001). The progression group had a significantly higher proportion of patients with a history of smoking than the improvement/stabilization group (27.3% vs. 3.0%, χ = 9.291, P = 0.018). For all the 78 patients, fever was the most common initial symptom, and the maximum body temperature at admission was significantly higher in the progression group than in the improvement/stabilization group (38.2 [37.8, 38.6] vs. 37.5 [37.0, 38.4]°C, U = 2.057, P = 0.027). Moreover, the proportion of patients with respiratory failure (54.5% vs. 20.9%, χ = 5.611, P = 0.028) and respiratory rate (34 [18, 48] vs. 24 [16, 60] breaths/min, U = 4.030, P = 0.004) were significantly higher in the progression group than in the improvement/stabilization group. C-reactive protein was significantly elevated in the progression group compared to the improvement/stabilization group (38.9 [14.3, 64.8] vs. 10.6 [1.9, 33.1] mg/L, U = 1.315, P = 0.024). Albumin was significantly lower in the progression group than in the improvement/stabilization group (36.62 ± 6.60 vs. 41.27 ± 4.55 g/L, U = 2.843, P = 0.006). Patients in the progression group were more likely to receive high-level respiratory support than in the improvement/stabilization group (χ = 16.01, P = 0.001). Multivariate logistic analysis indicated that age (odds ratio [OR], 8.546; 95% confidence interval [CI]: 1.628-44.864; P = 0.011), history of smoking (OR, 14.285; 95% CI: 1.577-25.000; P = 0.018), maximum body temperature at admission (OR, 8.999; 95% CI: 1.036-78.147, P = 0.046), respiratory failure (OR, 8.772, 95% CI: 1.942-40.000; P = 0.016), albumin (OR, 7.353, 95% CI: 1.098-50.000; P = 0.003), and C-reactive protein (OR, 10.530; 95% CI: 1.224-34.701, P = 0.028) were risk factors for disease progression.
CONCLUSIONS: Several factors that led to the progression of COVID-19 pneumonia were identified, including age, history of smoking, maximum body temperature on admission, respiratory failure, albumin, C-reactive protein. These results can be used to further enhance the ability of management of COVID-19 pneumonia.
BACKGROUND: The clinical characteristics of novel coronavirus disease (COVID-2019) patients outside the epicenter of Hubei province are less understood.
METHODS: We analyzed the epidemiological and clinical features of all COVID-2019 cases in the only referral hospital in Shenzhen city, China from January 11, 2020 to February 6, 2020 and followed until March 6, 2020.
RESULTS: Among the 298 confirmed cases, 233 (81.5%) had been to Hubei while 42 (14%) did not have a clear travel history. Only 192(64.4%) cases had a fever as the initial symptom. Compared to the non-severe cases, severe cases were associated with older age, those with underlying diseases, as well as higher levels of C-reactive protein, interleukin-6, and erythrocyte sedimentation rate. Slower clearance of the virus was associated with a higher risk of progression to critical condition. As of March 6, 2020, 268 (89.9%) patients were discharged and the overall case fatality ratio was 1.0%.
CONCLUSIONS: In a designated hospital outside Hubei Province, COVID-2019 patients could be effectively managed by properly using the existing hospital system. Mortality may be lowered when cases are relatively mild and there are sufficient medical resources to care and treat the disease.
The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China has been declared a public health emergency of international concern. The cardiac injury was dominate in the process. However, whether N terminal pro B type natriuretic peptide (NT-proBNP) predicted outcome of COVID-19 patients was unknown. The study initially enrolled 102 patients with severe COVID-19 pneumonia from a continuous sample. After screening out the ineligible cases, 54 patients were analyzed in this study. Results found that patients with higher NT-proBNP (above 88.64 pg/mL) level had more risks of in-hospital death. After adjusting for potential cofounders in separate modes, NT-proBNP presented as an independent risk factor of in-hospital death in patients with severe COVID-19.
BackgroundSARS-CoV-2-caused coronavirus disease (COVID-19) is posing a large casualty. The features of COVID-19 patients with and without pneumonia, SARS-CoV-2 transmissibility in asymptomatic carriers, and factors predicting disease progression remain unknown.
MethodsWe collected information on clinical characteristics, exposure history, and laboratory examinations of all laboratory-confirmed COVID-19 patients admitted to PLA General Hospital. Cox regression analysis was applied to identify prognostic factors. The last follow-up was February 18, 2020.
ResultsWe characterized 55 consecutive COVID-19 patients. The mean incubation was 8.42 (95% confidence interval [CI], 6.55-10.29) days. The mean SARS-CoV-2-positive duration from first positive test to conversion was 9.71 (95%CI, 8.21-11.22) days. COVID-19 course was approximately 2 weeks. Asymptomatic carriers might transmit SARS-CoV-2. Compared to patients without pneumonia, those with pneumonia were 15 years older and had a higher rate of hypertension, higher frequencies of having a fever and cough, and higher levels of interleukin-6 (14.61 vs. 8.06pg/mL, P=0.040), B lymphocyte proportion (13.0% vs.10.0%, P=0.024), low account (<190/{micro}L) of CD8+ T cells (33.3% vs. 0, P=0.019). Multivariate Cox regression analysis indicated that circulating interleukin-6 and lactate independently predicted COVID-19 progression, with a hazard ratio (95%CI) of 1.052 (1.000-1.107) and 1.082 (1.013-1.155), respectively. During disease course, T lymphocytes were generally lower, neutrophils higher, in pneumonia patients than in pneumonia-free patients. CD8+ lymphocytes did not increase at the 20th days after illness onset.
ConclusionThe epidemiological features are important for COVID-19 prophylaxis. Circulating interleukin-6 and lactate are independent prognostic factors. CD8+ T cell exhaustion might be critical in the development of COVID-19.
Background and purposeThe worldwide pandemic of coronavirus disease 2019 (COVID-19) greatly challenges public medical systems. With limited medical resources, the treatment priority is determined by the severity of patients. However, many mild outpatients quickly deteriorate into severe/critical stage. It is crucial to early identify them and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression.
MethodsA total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at the severe/critical stage of the two groups were compared. Both multivariate logistic regression and deep learning-based methods were used to build the prediction models, with their area under ROC curves (AUC) compared.
ResultsMultivariate logistic regression depicted 6 risk factors for malignant progression: age >55years (OR 5.334, 95%CI 1.8-15.803), comorbid with hypertension (OR 5.093, 95%CI 1.236-20.986), a decrease of albumin (OR 4.01, 95%CI 1.216-13.223), a decrease of lymphocyte (OR 3.459, 95%CI 1.067-11.209), the progressive consolidation from CT1 to CTsevere (OR 1.235, 95%CI 1.018-1.498), and elevated HCRP (OR 1.015, 95%CI 1.002-1.029); and one protective factor: the presence of fibrosis at CT1 (OR 0.656, 95%CI 0.473-0.91). By combining the clinical data and the temporal information of the CT data, our deep learning-based models achieved the best AUC of 0.954, which outperformed logistic regression (AUC: 0.893),
ConclusionsOur deep learning-based methods can identify the mild patients who are easy to deteriorate into severe/critical cases efficiently and accurately, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.
BackgroundCoronavirus disease 2019 (COVID-19) triggered by infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been widely pandemic all over the world. The aim of this study was to analyze the influence factors of death risk among 200 COVID-19 patients.
MethodsTwo hundred patients with confirmed SARS-CoV-2 infection were recruited. Demographic data and clinical characteristics were collected from electronic medical records. Biochemical indexes on admission were measured and patients prognosis was tracked. The association of demographic data, clinical characteristics and biochemical indexes with death risk was analyzed.
ResultsOf 200 COVID-19 patients, 163 (81.5%) had at least one of comorbidities, including diabetes, hypertension, hepatic disease, cardiac disease, chronic pulmonary disease and others. Among all patients, critical cases, defined as oxygenation index lower than 200, accounted for 26.2%. Severe cases, oxygenation index from 200 to 300, were 29.7%. Besides, common cases, oxygenation index higher than 300, accounted for 44.1%. At the end of follow-up, 34 (17%) were died on mean 10.9 day after hospitalization. Stratified analysis revealed that older ages, lower oxygenation index and comorbidities elevated death risk of COVID-19 patients. On admission, 85.5% COVID-19 patients were with at least one of extrapulmonary organ injuries. Univariable logistic regression showed that ALT and TBIL, two indexes of hepatic injury, AST, myoglobin and LDH, AST/ALT ratio, several markers of myocardial injury, creatinine, urea nitrogen and uric acid, three indexes of renal injury, were positively associated with death risk of COVID-19 patients. Multivariable logistic regression revealed that AST/ALT ratio, urea nitrogen, TBIL and LDH on admission were positively correlated with death risk of COVID-19 patients.
ConclusionOlder age, lower oxygenation index and comorbidities on admission elevate death risk of COVID-19 patients. AST/ALT ratio, urea nitrogen, TBIL and LDH on admission may be potential prognostic indicators. Early hospitalization is of great significance to prevent multiple organ damage and improve the survival of COVID-19 patients.
SummaryIn this hospital-based case-cohort study, we found that serum urea nitrogen, TBIL, LDH and AST/ALT ratio, several markers of extrapulmonary organ injuries, were positively correlated with death risk of COVID-19 patients. We provide evidence for the first time that multiple organ damage on admission influences the prognosis of COVID-19 patients. Early hospitalization is beneficial for elevating the survival rate of COVID-19 patients especially critical ill patients.
BackgroundThe outbreaks of coronavirus disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain a huge threat to the public health worldwide. Clinical data is limited up to now regarding nosocomial infection of COVID-19 and the risk factors in favor of severe conversion of non-severe case with COVID-19.
AimsThis study analyzed a hospital staff data to figure out general clinical features of COVID-19 in terms of nosocomial infection and explain the association of cardiovascular manifestations (CVMs) with in-hospital outcomes of COVID-19 cases.
MethodsRetrospective, single-center case series of 41 consecutive hospitalized health staff with confirmed COVID-19 were collected at the Central Hospital of Wuhan in Wuhan, China, from January 15 to January 24, 2020. Epidemiological, demographic, clinical, laboratory, radiological, treatment data and in-hospital adverse events were collected and analyzed. Final date of follow-up was March 3, 2020. A comparative study was applied between cases with CVMs and those without CVMs.
ResultsOf all, clinicians and clinical nurses accounted for 80.5%, while nosocomial infection by patient contact accounted for 87.8%. The population was presented with a mean age of 39.1 {+/-} 9.2 and less comorbidities than community population. The three most frequent symptoms of COVID-19 cases analyzed were fever (82.9%), myalgia or fatigue (80.5%) and cough (63.4%). While, the three most frequent initial symptoms were myalgia or fatigue (80.5%), fever (73.2%) and cough (41.5%). There were 95.1% cases featured as non-severe course of disease according to the official standard in China. Patients with CVMs and those without CVMs accounted for 58.5% and 41.5%, respectively. Compared with cases without CVMs, patients with CVMs were presented with lower baseline lymphocyte count (0.99 {+/-} 0.43 and 1.55 {+/-} 0.61, P<0.001), more who had at least once positive nucleic acid detection of throat swab during admission (50.0% and 11.8%, P=0.011), and more received oxygen support (79.2% and 23.5%, P<0.001). The rate of in-hospital adverse events was significantly higher in patients with CVMs group (75.0% and 23.5%, P=0.001). Multivariable logistic regression model indicated that, coexisting with CVMs in COVID-19 patients was not independently associated with in-hospital adverse events.
ConclusionsMost of hospital staff with COVID-19 were nosocomial infection, featured non-severe course of disease. Cases with CVMs suffered from more in-hospital adverse events than those without CVMs. But concomitant CVMs were not independently associated with in-hospital adverse events in COVID-19 patients.
BACKGROUND: The coronavirus disease 2019 (Covid-19) outbreak is evolving rapidly worldwide.
OBJECTIVE: To evaluate the risk of serious adverse outcomes in patients with coronavirus disease 2019 (Covid-19) by stratifying the comorbidity status.
METHODS: We analysed the data from 1590 laboratory-confirmed hospitalised patients 575 hospitals in 31 province/autonomous regions/provincial municipalities across mainland China between December 11th, 2019 and January 31st, 2020. We analyse the composite endpoints, which consisted of admission to intensive care unit, or invasive ventilation, or death. The risk of reaching to the composite endpoints was compared according to the presence and number of comorbidities.
RESULTS: The mean age was 48.9 years. 686 patients (42.7%) were females. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD [hazards ratio (HR) 2.681, 95% confidence interval (95%CI) 1.424-5.048], diabetes (HR 1.59, 95%CI 1.03-2.45), hypertension (HR 1.58, 95%CI 1.07-2.32) and malignancy (HR 3.50, 95%CI 1.60-7.64) were risk factors of reaching to the composite endpoints. The HR was 1.79 (95%CI 1.16-2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61-4.17) among patients with two or more comorbidities.
CONCLUSION: Among laboratory-confirmed cases of Covid-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.
BACKGROUND: Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described.
METHODS: In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death.
FINDINGS: 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03-1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61-12·23; p<0·0001), and d-dimer greater than 1 μg/L (18·42, 2·64-128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0-24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days.
INTERPRETATION: The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/L could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
FUNDING: Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
BackgroundsSince December 2019, a novel coronavirus epidemic has emerged in Wuhan city, China and then rapidly spread to other areas. As of 20 Feb 2020, a total of 2,055 medical staff confirmed with coronavirus disease 2019 (COVID-19) caused by SARS-Cov-2 in China had been reported. We sought to explore the epidemiological, clinical characteristics and prognosis of novel coronavirus-infected medical staff.
MethodsIn this retrospective study, 64 confirmed cases of novel coronavirus-infected medical staff admitted to Union Hospital, Wuhan between 16 Jan, 2020 to 15 Feb, 2020 were included. Two groups concerned were extracted from the subjects based on duration of symptoms: group 1 ([≤]10 days) and group 2 (>10 days). Epidemiological and clinical data were analyzed and compared across groups. The Kaplan-Meier plot was used to inspect the change in hospital discharge rate. The Cox regression model was utilized to identify factors associated with hospital discharge.
FindingsThe median age of medical staff included was 35 years old. 64% were female and 67% were nurses. None had an exposure to Huanan seafood wholesale market or wildlife. A small proportion of the cohort had contact with specimens (5%) as well as patients in fever clinics (8%) and isolation wards (5%). Fever (67%) was the most common symptom, followed by cough (47%) and fatigue (34%). The median time interval between symptoms onset and admission was 8.5 days. On admission, 80% of medical staff showed abnormal IL-6 levels and 34% had lymphocytopenia. Chest CT mainly manifested as bilateral (61%), septal/subpleural (80%) and ground-glass (52%) opacities. During the study period, no patients was transferred to intensive care unit or died, and 34 (53%) had been discharged. Higher body mass index (BMI) ([≥] 24 kg/m2) (HR 0.14; 95% CI 0.03-0.73), fever (HR 0.24; 95% CI 0.09-0.60) and higher levels of IL-6 on admission (HR 0.31; 95% CI 0.11-0.87) were unfavorable factors for discharge.
InterpretationIn this study, medical staff infected with COVID-19 have relatively milder symptoms and favorable clinical course, which may be partly due to their medical expertise, younger age and less underlying diseases. Smaller BMI, absence of fever symptoms and normal IL-6 levels on admission are favorable for discharge for medical staff. Further studies should be devoted to identifying the exact patterns of SARS-CoV-2 infection among medical staff.
