867 resultados para Framinghan risk score


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Background.  The impact of human genetic background on low-trauma fracture (LTF) risk has not been evaluated in the context of human immunodeficiency virus (HIV) and clinical LTF risk factors. Methods.  In the general population, 6 common single-nucleotide polymorphisms (SNPs) associate with LTF through genome-wide association study. Using genome-wide SNP arrays and imputation, we genotyped these SNPs in HIV-positive, white Swiss HIV Cohort Study participants. We included 103 individuals with a first, physician-validated LTF and 206 controls matched on gender, whose duration of observation and whose antiretroviral therapy start dates were similar using incidence density sampling. Analyses of nongenetic LTF risk factors were based on 158 cases and 788 controls. Results.  A genetic risk score built from the 6 LTF-associated SNPs did not associate with LTF risk, in both models including and not including parental hip fracture history. The contribution of clinical LTF risk factors was limited in our dataset. Conclusions.  Genetic LTF markers with a modest effect size in the general population do not improve fracture prediction in persons with HIV, in whom clinical LTF risk factors are prevalent in both cases and controls.

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Background Cardiovascular disease (CVD) is partially attributed to traditional cardiovascular risk factors, which can be identified and managed based on risk stratification algorithms (Framingham Risk Score, National Cholesterol Education Program, Systematic Cardiovascular Risk Evaluation and Reynolds Risk Score). We aimed to (a) identify the proportion of at risk patients with rheumatoid arthritis (RA) requiring statin therapy identified by conventional risk calculators, and (b) assess whether patients at risk were receiving statins. Methods Patients at high CVD risk (excluding patients with established CVD or diabetes) were identified from a cohort of 400 well characterised patients with RA, by applying risk calculators with or without a ×1.5 multiplier in specific patient subgroups. Actual statin use versus numbers eligible for statins was also calculated. Results The percentage of patients identified as being at risk ranged significantly depending on the method, from 1.6% (for 20% threshold global CVD risk) to 15.5% (for CVD and cerebrovascular morbidity and mortality) to 21.8% (for 10% global CVD risk) and 25.9% (for 5% CVD mortality), with the majority of them (58.1% to 94.8%) not receiving statins. The application of a 1.5 multiplier identified 17% to 78% more at risk patients. Conclusions Depending on the risk stratification method, 2% to 26% of patients with RA without CVD have sufficiently high risk to require statin therapy, yet most of them remain untreated. To address this issue, we would recommend annual systematic screening using the nationally applicable risk calculator, combined with regular audit of whether treatment targets have been achieved.

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BACKGROUND: It is unclear whether diagnostic protocols based on cardiac markers to identify low-risk chest pain patients suitable for early release from the emergency department can be applied to patients older than 65 years or with traditional cardiac risk factors. METHODS AND RESULTS: In a single-center retrospective study of 231 consecutive patients with high-risk factor burden in which a first cardiac troponin (cTn) level was measured in the emergency department and a second cTn sample was drawn 4 to 14 hours later, we compared the performance of a modified 2-Hour Accelerated Diagnostic Protocol to Assess Patients with Chest Pain Using Contemporary Troponins as the Only Biomarker (ADAPT) rule to a new risk classification scheme that identifies patients as low risk if they have no known coronary artery disease, a nonischemic electrocardiogram, and 2 cTn levels below the assay's limit of detection. Demographic and outcome data were abstracted through chart review. The median age of our population was 64 years, and 75% had Thrombosis In Myocardial Infarction risk score ≥2. Using our risk classification rule, 53 (23%) patients were low risk with a negative predictive value for 30-day cardiac events of 98%. Applying a modified ADAPT rule to our cohort, 18 (8%) patients were identified as low risk with a negative predictive value of 100%. In a sensitivity analysis, the negative predictive value of our risk algorithm did not change when we relied only on undetectable baseline cTn and eliminated the second cTn assessment. CONCLUSIONS: If confirmed in prospective studies, this less-restrictive risk classification strategy could be used to safely identify chest pain patients with more traditional cardiac risk factors for early emergency department release.

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Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income.

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AIMS: Our aims were to evaluate the distribution of troponin I concentrations in population cohorts across Europe, to characterize the association with cardiovascular outcomes, to determine the predictive value beyond the variables used in the ESC SCORE, to test a potentially clinically relevant cut-off value, and to evaluate the improved eligibility for statin therapy based on elevated troponin I concentrations retrospectively.

METHODS AND RESULTS: Based on the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project, we analysed individual level data from 10 prospective population-based studies including 74 738 participants. We investigated the value of adding troponin I levels to conventional risk factors for prediction of cardiovascular disease by calculating measures of discrimination (C-index) and net reclassification improvement (NRI). We further tested the clinical implication of statin therapy based on troponin concentration in 12 956 individuals free of cardiovascular disease in the JUPITER study. Troponin I remained an independent predictor with a hazard ratio of 1.37 for cardiovascular mortality, 1.23 for cardiovascular disease, and 1.24 for total mortality. The addition of troponin I information to a prognostic model for cardiovascular death constructed of ESC SCORE variables increased the C-index discrimination measure by 0.007 and yielded an NRI of 0.048, whereas the addition to prognostic models for cardiovascular disease and total mortality led to lesser C-index discrimination and NRI increment. In individuals above 6 ng/L of troponin I, a concentration near the upper quintile in BiomarCaRE (5.9 ng/L) and JUPITER (5.8 ng/L), rosuvastatin therapy resulted in higher absolute risk reduction compared with individuals <6 ng/L of troponin I, whereas the relative risk reduction was similar.

CONCLUSION: In individuals free of cardiovascular disease, the addition of troponin I to variables of established risk score improves prediction of cardiovascular death and cardiovascular disease.

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Although epidemiological studies suggest that type 2 diabetes mellitus (T2DM) increases the risk of late-onset Alzheimer's disease (LOAD), the biological basis of this relationship is not well understood. The aim of this study was to examine the genetic comorbidity between the 2 disorders and to investigate whether genetic liability to T2DM, estimated by a genotype risk scores based on T2DM associated loci, is associated with increased risk of LOAD. This study was performed in 2 stages. In stage 1, we combined genotypes for the top 15 T2DM-associated polymorphisms drawn from approximately 3000 individuals (1349 cases and 1351 control subjects) with extracted and/or imputed data from 6 genome-wide studies (>10,000 individuals; 4507 cases, 2183 controls, 4989 population controls) to form a genotype risk score and examined if this was associated with increased LOAD risk in a combined meta-analysis. In stage 2, we investigated the association of LOAD with an expanded T2DM score made of 45 well-established variants drawn from the 6 genome-wide studies. Results were combined in a meta-analysis. Both stage 1 and stage 2 T2DM risk scores were not associated with LOAD risk (odds ratio = 0.988; 95% confidence interval, 0.972-1.004; p = 0.144 and odds ratio = 0.993; 95% confidence interval, 0.983-1.003; p = 0.149 per allele, respectively). Contrary to expectation, genotype risk scores based on established T2DM candidates were not associated with increased risk of LOAD. The observed epidemiological associations between T2DM and LOAD could therefore be a consequence of secondary disease processes, pleiotropic mechanisms, and/or common environmental risk factors. Future work should focus on well-characterized longitudinal cohorts with extensive phenotypic and genetic data relevant to both LOAD and T2DM.

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Thesis (Master's)--University of Washington, 2016-08

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Developing a robust method to study characteristics of vascular flow using ultrasound may be useful to assess endothelial function and vasodilatation. There are four stages in this proposal. 1.The first stage is to standardise and validate the methodology to enable computational risk flow data and other flow characteristics to be used clinically. (Current Study). Further development of fluid modelling methods will enable particulate haemodynamics to be investigated, and incorporate detailed endothelial structure together with cellular pathways. 2. This should be followed up by studies in different patient groups investigating the association between the derived values and estimated risk (using other methods such as Framingham risk score). 3. Then, associated with underlying cardiovascular risk, prospective studies would be made to establish whether computational flow dynamic data can predict outcome. If successful it could prove to be a very useful marker of benefit following treatment in a clinical setting.

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BACKGROUND The recent occurrence and spread of African swine fever (ASF) in Eastern Europe is perceived as a serious risk for the pig industry in the European Union (EU). In order to estimate the potential risk of ASF virus (ASFV) entering the EU, several pathways of introduction were previously assessed separately. The present work aimed to integrate five of these assessments (legal imports of pigs, legal imports of products, illegal imports of products, fomites associated with transport and wild boar movements) into a modular tool that facilitates the visualization and comprehension of the relative risk of ASFV introduction into the EU by each analyzed pathway. RESULTS The framework's results indicate that 48% of EU countries are at relatively high risk (risk score 4 or 5 out of 5) for ASFV entry for at least one analyzed pathway. Four of these countries obtained the maximum risk score for one pathway: Bulgaria for legally imported products during the high risk period (HRP); Finland for wild boar; Slovenia and Sweden for legally imported pigs during the HRP. Distribution of risk considerably differed from one pathway to another; for some pathways, the risk was concentrated in a few countries (e.g., transport fomites), whereas other pathways incurred a high risk for 4 or 5 countries (legal pigs, illegal imports and wild boar). CONCLUSIONS The modular framework, developed to estimate the risk of ASFV entry into the EU, is available in a public domain, and is a transparent, easy-to-interpret tool that can be updated and adapted if required. The model's results determine the EU countries at higher risk for each ASFV introduction route, and provide a useful basis to develop a global coordinated program to improve ASFV prevention in the EU.

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Predicting risk of adverse healthcare outcomes is important to enable targeted delivery of interventions. The Risk Instrument for Screening in the Community (RISC), designed for use by public health nurses (PHNs), measures the one-year risk of hospitalisation, institutionalisation and death in community-dwelling older adults according to a five-point global risk score: from low (score 1,2), medium (3) and high (4,5). We examined the inter-rater reliability (IRR) of the RISC between student PHNs (n=32) and expert raters using six cases (two low, medium and high-risk), scored before and after RISC training. Correlations increased for each adverse outcome, statistically significantly for institutionalisation (r=0.72 to 0.80,p=0.04) and hospitalisation, (r=0.51 to 0.71,p<0.01) but not death. Training improved accuracy for low-risk but not all high-risk cases. Overall, the RISC showed good IRR, which increased after RISC training. That reliability reduced for some high-risk cases suggests that the training programme requires adjustment to further improve IRR.

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The objectives of this study are to investigate the association between cardiorespiratory fitness and cardiovascular risk factors in schoolchildren and to evaluate the degree of association between overall and abdominal adiposity and cardiorespiratory fitness. A total of 1,875 children and adolescents attending public schools in Bogota, Colombia (56.2% girls; age range of 9–17.9 years). A cardiovascular risk score (Z-score) was calculated and participants were divided into tertiles according to low and high levels of overall (sum of the skinfold thicknesses) and abdominal adiposity. Schoolchildren with a high level of overall adiposity demonstrated significant differences in seven of the 10 variables analyzed (i.e. systolic and diastolic blood pressure, triglycerides, triglycerides/HDL-c ratio, total cholesterol, glucose and cardiovascular risk score). Schoolchildren with high levels of both overall and abdominal adiposity and low cardiorespiratory fitness had the least favorable cardiovascular risk factors score. These findings may be relevant to health promotion in Colombian youth.

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Abstract Objective: Evidence shows an association between muscular strength (MS) and health among youth, however low muscular strength cut-points for the detection of high metabolic risk in Latin-American populations are scarce. The aim of this study was two-fold: to explore potential age- and sex-specific thresholds of MS, for optimal cardiometabolic risk categorization among Colombian children and adolescents; and to investigate if cardiometabolic risk differed by MS group by applying the receiver operating characteristic curve (ROC) cut point. Methods: This is a secondary analysis of a cross-sectional study (the FUPRECOL study), published elsewhere. The FUPRECOL study assessments were conducted during the 2014– 2015 school year. MS was estimated by a handle dynamometer on 1,950 children and adolescents from Colombia, using the MS relative to weight (handgrip strength/body mass). A metabolic risk score was computed from the following components: waist circumference, triglycerides, HDL-c, glucose, systolic and diastolic blood pressure. ROC analysis showed a significant discriminatory accuracy of MS in identifying the low/high metabolic risk in children and adolescents and both gender. Results: In children, handgrip strength/body mass level for a low metabolic risk were 0.359 and 0.376 in girls and boys, respectively. In adolescents, these points were 0.440 and 0.447 in girls and boys, respectively. Conclusion: In conclusion, the results suggest a hypothetical MS level relative to weight for having a low metabolic risk, which could be used to identify youths at risk.

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This project aimed to identify novel genetic risk variants associated with migraine in the Norfolk Island population. Statistical analysis and bioinformatics approaches such as polygenic modeling and gene clustering methods were carried out to explore genotypic and expression data from high-throughput techniques. This project had a particular focus on hormonal genes and other genetic variants and identified a modest effect size on the migraine phenotype.

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Introduction: Patients with rheumatoid arthritis (RA) have increased risk of cardiovascular (CV) events. We sought to test the hypothesis that due to increased inflammation, CV disease and risk factors are associated with increased risk of future RA development. Methods: The population-based Nord-Trøndelag health surveys (HUNT) were conducted among the entire adult population of Nord-Trøndelag, Norway. All inhabitants 20 years or older were invited, and information was collected through comprehensive questionnaires, a clinical examination, and blood samples. In a cohort design, data from HUNT2 (1995-1997, baseline) and HUNT3 (2006-2008, follow-up) were obtained to study participants with RA (n = 786) or osteoarthritis (n = 3,586) at HUNT3 alone, in comparison with individuals without RA or osteoarthritis at both times (n = 33,567). Results: Female gender, age, smoking, body mass index, and history of previous CV disease were associated with self-reported incident RA (previous CV disease: odds ratio 1.52 (95% confidence interval 1.11-2.07). The findings regarding previous CV disease were confirmed in sensitivity analyses excluding participants with psoriasis (odds ratio (OR) 1.70 (1.23-2.36)) or restricting the analysis to cases with a hospital diagnosis of RA (OR 1.90 (1.10-3.27)) or carriers of the shared epitope (OR 1.76 (1.13-2.74)). History of previous CV disease was not associated with increased risk of osteoarthritis (OR 1.04 (0.86-1.27)). Conclusion: A history of previous CV disease was associated with increased risk of incident RA but not osteoarthritis.

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Background Schizophrenia is associated with lower pre-morbid intelligence (IQ) in addition to (pre-morbid) cognitive decline. Both schizophrenia and IQ are highly heritable traits. Therefore, we hypothesized that genetic variants associated with schizophrenia, including copy number variants (CNVs) and a polygenic schizophrenia (risk) score (PSS), may influence intelligence. Method IQ was estimated with the Wechsler Adult Intelligence Scale (WAIS). CNVs were determined from single nucleotide polymorphism (SNP) data using the QuantiSNP and PennCNV algorithms. For the PSS, odds ratios for genome-wide SNP data were calculated in a sample collected by the Psychiatric Genome-Wide Association Study (GWAS) Consortium (8690 schizophrenia patients and 11 831 controls). These were used to calculate individual PSSs in our independent sample of 350 schizophrenia patients and 322 healthy controls. Results Although significantly more genes were disrupted by deletions in schizophrenia patients compared to controls (p = 0.009), there was no effect of CNV measures on IQ. The PSS was associated with disease status (R 2 = 0.055, p = 2.1 × 10 -7) and with IQ in the entire sample (R 2 = 0.018, p = 0.0008) but the effect on IQ disappeared after correction for disease status. Conclusions Our data suggest that rare and common schizophrenia-associated variants do not explain the variation in IQ in healthy subjects or in schizophrenia patients. Thus, reductions in IQ in schizophrenia patients may be secondary to other processes related to schizophrenia risk. © Cambridge University Press 2013.