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Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and intera...
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Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.
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The present cross-sectional data demonstrate cardiovascular (CV) risk in patients with rheumatoid arthritis (RA). The cross-sectional data were part of an interventional trial that included 100 patients with defined RA. Traditiona...
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The present cross-sectional data demonstrate cardiovascular (CV) risk in patients with rheumatoid arthritis (RA). The cross-sectional data were part of an interventional trial that included 100 patients with defined RA. Traditional CV risk parameters and risk score calculation for the German population were used to assess the CV risk profile in the collective given. Proatherogenic lipid profile characterised by increased total cholesterol (≥5.2 mmol/l) and LDL cholesterol (≥3.5 mmol/l) levels was measured in 85 and 66%, respectively, of the study population. Elevated concentrations of homocysteine (≥10 lmol/l) were reached by 67%. The prevalence of patients at high CV risk was 12% and increased up to 42% after using a multiplication factor of 1.5. No association was seen between the CV risk SCORE and DAS 28 or disease duration. RA patients in this study showed a proatherogenic risk profile with regard to the CV risk factors evaluated. The calculation of a 10-year risk using German risk charts might have led to an overall underestimation of the mean CV risk. Cardiovascular co-morbidity in RA patients must be seen as a major prevention and treatment target and should be monitored adequately.
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The present cross-sectional data demonstrate cardiovascular (CV) risk in patients with rheumatoid arthritis (RA). The cross-sectional data were part of an interventional trial that included 100 patients with defined RA. Traditiona...
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The present cross-sectional data demonstrate cardiovascular (CV) risk in patients with rheumatoid arthritis (RA). The cross-sectional data were part of an interventional trial that included 100 patients with defined RA. Traditional CV risk parameters and risk score calculation for the German population were used to assess the CV risk profile in the collective given. Proatherogenic lipid profile characterised by increased total cholesterol (≥5.2 mmol/l) and LDL cholesterol (≥3.5 mmol/l) levels was measured in 85 and 66%, respectively, of the study population. Elevated concentrations of homocysteine (≥10 μmol/l) were reached by 67%. The prevalence of patients at high CV risk was 12% and increased up to 42% after using a multiplication factor of 1.5. No association was seen between the CV risk SCORE and DAS 28 or disease duration. RA patients in this study showed a proatherogenic risk profile with regard to the CV risk factors evaluated. The calculation of a 10-year risk using German risk charts might have led to an overall underestimation of the mean CV risk. Cardiovascular co-morbidity in RA patients must be seen as a major prevention and treatment target and should be monitored adequately.
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Introduction: Global cardiovascular (CV) risk stratification is recommended in all outpatients. Risk score charts, however, do not include markers of organ damage (OD).
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BACKGROUND:Prediabetes risk assessment models derived from large sample sizes are scarce.AIM:To establish a robust assessment model for prediabetes and to validate the model in different populations.METHODS:The China National Diab...
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BACKGROUND:Prediabetes risk assessment models derived from large sample sizes are scarce.AIM:To establish a robust assessment model for prediabetes and to validate the model in different populations.METHODS:The China National Diabetes and Metabolic Disorders Study (CNDMDS) collected information from 47325 participants aged at least 20 years across China from 2007 to 2008. The Thyroid Disorders, Iodine Status and Diabetes Epidemiological Survey (TIDE) study collected data from 66108 participants aged at least 18 years across China from 2015 to 2017. A logistic model with stepwise selection was performed to identify significant risk factors for prediabetes and was internally validated by bootstrapping in the CNDMDS. External validations were performed in diverse populations, including populations of Hispanic (Mexican American, other Hispanic) and non-Hispanic (White, Black and Asian) participants in the National Health and Nutrition Examination Survey (NHANES) in the United States and 66108 participants in the TIDE study in China. C statistics and calibration plots were adopted to evaluate the model's discrimination and calibration performance.RESULTS:A set of easily measured indicators (age, education, family history of diabetes, waist circumference, body mass index, and systolic blood pressure) were selected as significant risk factors. A risk assessment model was established for prediabetes with a C statistic of 0.6998 (95%CI: 0.6933 to 0.7063) and a calibration slope of 1.0002. When externally validated in the NHANES and TIDE studies, the model showed increased C statistics in Mexican American, other Hispanic, Non-Hispanic Black, Asian and Chinese populations but a slightly decreased C statistic in non-Hispanic White individuals. Applying the risk assessment model to the TIDE population, we obtained a C statistic of 0.7308 (95%CI: 0.7260 to 0.7357) and a calibration slope of 1.1137. A risk score was derived to assess prediabetes. Individuals with scores ≥ 7 points were at high risk of prediabetes, with a sensitivity of 60.19% and specificity of 67.59%.CONCLUSION:An easy-to-use assessment model for prediabetes was established and was internally and externally validated in different populations. The model had a satisfactory performance and could screen individuals with a high risk of prediabetes.?The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
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OBJECTIVES: Aortic valve replacement (AVR) is accepted as the standard treatment for severe symptomatic aortic valve stenosis and regurgitation. As novel treatments are introduced for patients at high risk for conventional surgery...
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OBJECTIVES: Aortic valve replacement (AVR) is accepted as the standard treatment for severe symptomatic aortic valve stenosis and regurgitation. As novel treatments are introduced for patients at high risk for conventional surgery, it is important to have models that accurately predict procedural risk. The aim of this study was to develop and validate a risk-stratification model to predict in-hospital risk of death for patients undergoing AVR and to compare the model with existing algorithms. METHODS: We reviewed data from the Central Cardiac Adult Database, which holds prospectively collected clinical information on all adult patients undergoing cardiac surgery in National Health Service (NHS) hospitals and some private providers in the UK and Ireland. We included all the patients undergoing AVR with or without coronary artery bypass grafting. The study population consists of 55 157 patients undergoing surgery between 1 April 2001 and 31 March 2009. The model was built using data from April 2001 to March 2008 and validated using data from patients undergoing surgery from April 2008 to March 2009. The model was compared against the additive and logistic EuroSCORE models and a valve-specific risk-prediction model. RESULTS: The final multivariable model includes items describing cardiovascular risk status and procedural factors. Applying the model to the independent validation dataset provided a c-statistic (index of rank correlation) of 0.791, which was substantially better than that achieved by previously developed risk models in Europe, and significantly improved risk prediction in higher-risk patients. CONCLUSIONS: We have produced an accurate risk model to predict outcome following AVR surgery. It will be of use for patient selection and informed consent, and of particular interest in defining those patients at high risk who may benefit from novel approaches to AVR. ? The Author 2012. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
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Frailty is an important prognostic factor in hospitalised patients but typically requires face-to-face assessment by trained observers to detect. Thus, frail patients are not readily apparent from a systems perspective for those i...
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Frailty is an important prognostic factor in hospitalised patients but typically requires face-to-face assessment by trained observers to detect. Thus, frail patients are not readily apparent from a systems perspective for those interested in implementing quality improvement measures to optimise their outcomes. This study was designed to externally validate and compare two recently described tools using administrative data as potential markers for frailty: the Hospital Frailty Risk Score (HFRS) and the Hospital-patient One-year Mortality Risk (HOMR) Score.Retrospective cohort study.Ontario, Canada.All patients over 75 with at least one urgent non-psychiatric hospitalisation between 2004 and 2010.Prolonged hospital length of stay (>10 days), 30-day mortality after admission and 30-day postdischarge rates of urgent readmission or emergency department (ED) visits.In 452?785 patients (25.9% with intermediate or high-risk HFRS), increased HFRS was associated with higher Charlson scores, older age and decreased likelihood of baseline independence. Patients with high or intermediate HFRS had significantly increased risks of prolonged hospitalisation (70.0% (OR 8.64, 95% ?CI 8.30 to 8.99) or 49.7% (OR 3.66, 95% ?CI 3.60 to 3.71) vs 21.3% in low-risk HFRS group) and 30-day mortality (15.5% (OR 1.27, 95% CI 1.20 to 1.33) or 16.8% (OR 1.39, 95% ?CI 1.36 to 1.41) vs 12.7% in low-risk), but The HFRS best identified hospitalised older patients at higher risk of prolonged length of stay and the HOMR score better predicted 30-day mortality. However, neither score was suitable for predicting risk of readmission or ED visit in the 30 days after discharge. Thus, a single score is inadequate to prognosticate for all outcomes associated with frailty.
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AimsThe aim of this study was to examine the short-term and long-term cumulative risk of coronary heart disease (CHD) and stroke separately based on age, sex, smoking status, systolic blood pressure, and total serum cholesterol.Me...
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AimsThe aim of this study was to examine the short-term and long-term cumulative risk of coronary heart disease (CHD) and stroke separately based on age, sex, smoking status, systolic blood pressure, and total serum cholesterol.Methods and resultsThe Primary Prevention Study comprising 7174 men aged between 47 and 55 free from a previous history of CHD, stroke, and diabetes at baseline examination (1970-73) was followed up for 35 years. To estimate the cumulative effect of CHD and stroke, all participants were stratified into one of five risk groups, defined by their number of risk factors. The estimated 10-year risk for high-risk individuals when adjusted for age and competing risk was 18.1% for CHD and 3.2% for stroke which increased to 47.8 and 19.6%, respectively, after 35 years. The estimates based on risk factors performed well throughout the period for CHD but less well for stroke.ConclusionThe prediction of traditional risk factors (systolic blood pressure, total serum cholesterol, and smoking status) on short-term risk (0-10 years) and long-term risk (0-35 years) of CHD of stroke differs substantially. This indicates that the cumulative risk in middle-aged men based on these traditional risk factors can effectively be used to predict CHD but not stroke to the same extent.
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Risk can be defined as the effect of uncertainty on the achievement of objectives. All organizations are exposed to risk and uncertainty, and organized risk management is highly regarded in the construction industry. The Generic R...
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Risk can be defined as the effect of uncertainty on the achievement of objectives. All organizations are exposed to risk and uncertainty, and organized risk management is highly regarded in the construction industry. The Generic Risk Maturity Model (GRMM) is a tool for evaluating and identifying the current level, weaknesses, strengths, and areas that need improvement in construction companies. The purpose of this study is to diagnose and analyze domestic construction companies' risk management maturity and status using the GRMM and to suggest areas for improvement. In this research, domestic construction companies were analyzed using GRMM by targeting 25 companies that ranked in the top 100 in terms of the 2019 South Korean construction evaluation. An online survey was conducted through e-mail and a total of 131 responses from 18 construction companies were collected and analyzed. As a result, the average maturity score (MS) of domestic construction companies was 5.6 but ambition score (AS) became 7.9 points. As a result of evaluating the risk system and execution level, the risk system (RS) score was 6.0, which was higher than the risk execution (RE) score of 5.3, indicating that the risk execution power was lower than that of the risk system. Therefore, in order to improve the level of risk management of Korean construction companies, it is necessary to improve the ability to execute risk management.
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Purpose The purpose of this study was to develop a risk model for the prediction of 30-day unplanned readmission rate after surgery for colon cancer. Method This study was a cross-sectional analysis of data from Nationwide Readmis...
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Purpose The purpose of this study was to develop a risk model for the prediction of 30-day unplanned readmission rate after surgery for colon cancer. Method This study was a cross-sectional analysis of data from Nationwide Readmissions Database, collected during 2010-2014. Patients >= 18 years of age who underwent surgery for colon cancer were included in the study. The primary outcome of the study was 30-day unplanned readmission rate. Results There were 141,231 index hospitalizations for surgical treatment of colon cancers and 16,551 had unplanned readmissions. Age, sex, primary payer, Elixhauser comorbidity index, node positive or metastatic disease, length of stay, hospital bedsize, teaching status, hospital ownership, presence of stoma, surgery types, surgery procedures, infectious complications, surgical complications, mechanical wounds, pulmonary complications, and gastrointestinal complications were selected for the risk analysis during backward regression model. Based on the estimated coefficients of selected variables, risk scores were developed and stratified as low risk ( 1.08 to 1.5) for unplanned readmission. Validation analysis (n = 42,269) showed that 7.1% of low-risk individuals, 11.1% of moderate-risk individuals, and 17.1% of high-risk individuals experienced unplanned readmissions (P < 0.001). Pairwise comparisons also showed statistically significant differences between low-risk and moderate-risk participants (P < 0.001), between moderate-risk and high-risk participants (P < 0.001), and between low-risk and high-risk participants (P < 0.001). The area under the ROC curve was 0.622. Conclusions Our risk model could be helpful for risk-stratifying patients for readmission after surgical treatment for colon cancer. This model needs further validation by incorporating all possible clinical variables.
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