Background: The coronavirus disease 2019 outbreak is evolving rapidly worldwide. Objective: To evaluate the risk of serious adverse outcomes in patients with COVID-19 by stratifying the comorbidity status. Methods: We analysed data from 1590 laboratory confirmed hospitalised patients from 575 hospitals in 31 provinces/autonomous regions/provincial municipalities across mainland China between 11 December 2019 and 31 January 2020. We analysed the composite end-points, which consisted of admission to an intensive care unit, invasive ventilation or death. The risk of reaching the composite end-points was compared according to the presence and number of comorbidities. Results: The mean age was 48.9 years and 686 (42.7%) patients were female. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached the composite end-points. 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 (HR (95% CI) 2.681 (1.424-5.048)), diabetes (1.59 (1.03-2.45)), hypertension (1.58 (1.07-2.32)) and malignancy (3.50 (1.60-7.64)) were risk factors of reaching the composite end-points. The hazard ratio (95% CI) was 1.79 (1.16-2.77) among patients with at least one comorbidity and 2.59 (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. This article has supplementary material available from

Please cite this article as: Liang W-hua, Guan W-jie, Li C-chen, et al. Clinical characteristics and outcomes of hospitalised patients with COVID-19 treated in Hubei (epicenter) and outside Hubei (non-epicenter): A Nationwide Analysis of China. Eur Respir J 2020; in press (https://doi.Abstract BACKGROUND: During the outbreak of coronavirus disease 2019 (COVID-19), consistent and considerable differences in disease severity and mortality rate of patients treated in Hubei province compared to those in other parts of China has been observed. We sought to compare the clinical characteristics and outcomes of patients being treated inside and outside Hubei province, and explore the factors underlying these differences. METHODS:Collaborating with the National Health Commission, we established a retrospective cohort to study hospitalized COVID-19 cases in China. Clinical characteristics, the rate of severe events and deaths, and the time to critical illness (invasive ventilation or intensive care unit admission or death) were compared between patients in and outside of Hubei. The impact of Wuhan-related exposure (a presumed key factor that drove the severe situation in Hubei, as Wuhan is the epicenter as well the administrative center of Hubei province) and the duration between symptom onset and admission on prognosis were also determined. RESULTS:Upon data cut-off (Jan 31st, 2020), 1,590 cases from 575 hospitals in 31 provincial administrative regions were collected (core cohort). The overall rate of severe cases and mortality was 16.0% and 3.2%, respectively. Patients in Hubei (predominantly with Wuhan-related exposure, 597/647, 92.3%) were older (mean: 49.7 vs. 44.9 years), had more cases with comorbidity (32.9% vs. 19.7%), higher symptomatic burden, abnormal radiologic manifestations, and, especially, a longer waiting time between symptom onset and admission (5.7 vs. 4.5 days) compared with patients outside Hubei. Patients in Hubei [severe event rate 23.0% vs. 11.1%, death rate 7.3% vs. 0.3%, hazards ratio (HR) for critical illness 1.59, 95%CI 1.05-2.41] have a poorer prognosis compared with patients outside of Hubei after adjusting for age and comorbidity. However, among patients outside of Hubei, the duration from symptom onset to hospitalization (mean: 4.4 vs. 4.7 days) and prognosis (HR 0.84, were similar between patients with or without Wuhan-related exposure. In the overall population, the waiting time, but neither treated in Hubei nor Wuhan-related exposure, remained an independent prognostic factor (HR 1.05, 1.01-1.08).CONCLUSION: There were more severe cases and poorer outcomes for COVID-19 patients treated in Hubei, which might be attributed to the prolonged duration of symptom onset to hospitalization in the epicenter. Future studies to determine the reason for delaying hospitalization are warranted.

Traditional risk factors do not adequately explain coronary heart disease (CHD) risk after kidney transplantation. We used a large, multicenter database to compare traditional and nontraditional CHD risk factors, and to develop risk-prediction equations for kidney transplant patients in standard clinical practice. We retrospectively assessed risk factors for CHD (acute myocardial infarction, coronary artery revascularization or sudden death) in 23 575 adult kidney transplant patients from 14 transplant centers worldwide. The CHD cumulative incidence was 3.1%, 5.2% and 7.6%, at 1, 3 and 5 years posttransplant, respectively. In separate Cox proportional hazards analyses of CHD in the first posttransplant year (predicted at time of transplant), and predicted within 3 years after a clinic visit occurring in posttransplant years 1-5, important risk factors included pretransplant diabetes, new onset posttransplant diabetes, prior pre-and posttransplant cardiovascular disease events, estimated glomerular filtration rate, delayed graft function, acute rejection, age, sex, race and duration of pretransplant end-stage kidney disease. The risk-prediction equations performed well, with the time-dependent c-statistic greater than 0.75. Traditional risk factors (e.g. hypertension, dyslipidemia and cigarette smoking) added little additional predictive value. Thus, transplant-related risk factors, particularly those linked to graft function, explain much of the variation in CHD after kidney transplantation. (4,5), and several nontraditional risk factors are reported to be associated with CHD after kidney transplantation (2,4,(6)(7)(8)(9)(10)(11)(12) Materials and Methods the discriminatory ability of the prediction model was not significantly improved by adding these Framingham risk factors and the prediction model performed much better than the Framingham Heart Study equation (Figure 2). We also examined CHD risk in the subset of patients from the 7 (of 14) 340 American Journal of Transplantation 2010; 10: 338-353Predicting Posttransplant CHD Figure 1: Predicted probability of coronary heart disease (dark line) with 95% confidence interval (lighter lines), and distribution of risk scores in the Patient Outcomes after Renal Transplant population (bars). (Panel A) Prediction of coronary heart disease in the first year posttransplant from variables available at the time of transplant (Table 1). (Panel B) Prediction of coronary heart disease in the first year posttransplant from variables available at the time of transplant and during the first week after transplant (Table 2). (Panel C) Prediction of coronary heart disease within 3 years after a visit occurring 1-5 years posttransplant (Table 3). study centers that routinely collected information on smoking status at the time of transplant (n = 9785 with 361 CHD events; Table A.4). Smoking status did not independently predict CHD, significantly affect which variables predicted CHD events, or affect the discriminatory ability of the prediction model (data not shown). Smokers were...

We studied the incidence, trends and clinical correlates of infections following kidney transplantation in the United States Renal Data System over the years 1995-2003 in 46,471 adults with Medicare primary coverage at the time of their first kidney transplant. The incidence of most infections has declined only slightly since 1995 but infection with cytomegalovirus significantly declined while that with hepatitis C significantly increased. Relative frequencies of different types of infections (bacterial, viral, fungal and parasitic) were relatively constant, both during early and late periods following transplant. Using the Cox proportional hazards analysis we found that the clinical correlates for post-transplant bacterial and viral infections included older age, female gender, diabetes as the cause of end-stage renal disease, deceased (vs. living) donor source, time on dialysis before transplant, hepatitis B and C viral pre-transplant serologic status and pre-transplant donor-recipient cytomegalovirus serology. Our study shows that despite identifiable risk factors, the incidence of most post-transplant infections has changed little since 1995.

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BackgroundThe prevalence of metastatic bone disease in the US population is not well understood. We sought to estimate the current number of US adults with metastatic bone disease using two large administrative data sets.MethodsPrevalence was estimated from a commercially insured cohort (ages 18–64 years, MarketScan database) and from a fee-for-service Medicare cohort (ages ≥65 years, Medicare 5% database) with coverage on December 31, 2008, representing approximately two-thirds of the US population in each age group. We searched for claims-based evidence of metastatic bone disease from January 1, 2004, using a combination of relevant diagnosis and treatment codes. The number of cases in the US adult population was extrapolated from age- and sex-specific prevalence estimated in these cohorts. Results are presented for all cancers combined and separately for primary breast, prostate, and lung cancer.ResultsIn the commercially insured cohort (mean age = 42.3 years [SD = 13.1]), we identified 9505 patients (0.052%) with metastatic bone disease. Breast cancer was the most common primary tumor type (n = 4041). In the Medicare cohort (mean age = 75.6 years [SD = 7.8]), we identified 6427 (0.495%) patients with metastatic bone disease. Breast (n = 1798) and prostate (n = 1862) cancers were the most common primary tumor types. We estimate that 279,679 (95% confidence interval: 274,579–284,780) US adults alive on December 31, 2008, had evidence of metastatic bone disease in the previous 5 years. Breast, prostate, and lung cancers accounted for 68% of these cases.ConclusionOur findings suggest that approximately 280,000 US adults were living with metastatic bone disease on December 31, 2008. This likely underestimates the true frequency; not all cases of metastatic bone disease are diagnosed, and some diagnosed cases might lack documentation in claims data.

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In order to simplify the management of the traditional network, software-defined networking (SDN) has been proposed as a promising paradigm shift that decouples control plane and data plane, providing programmability to configure the network. With the deployment and the applications of SDN, researchers have found that the controller placement directly affects network performance in SDN. In this paper, the state of the art of controller placement problem is surveyed from the perspective of optimization objective. First, we introduce the overview of SDN and controller placement problem. Then, we classify this paper of controller placement problem into four aspects (latency, reliability, and cost and multi-objective) depending on their objective and analyze specific algorithms in different application scenarios. Finally, we identify some relevant open issues and research challenge to deal with in the future and conclude the controller placement problem.