scholarly journals Can the Risk of Cardiovascular Disease in HIV-Infected Patients Be Estimated from Conventional Risk Prediction Tools?

2007 ◽  
Vol 45 (8) ◽  
pp. 1082-1084 ◽  
Author(s):  
N. Friis-Moller ◽  
S. W. Worm
Keyword(s):  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Prediction Tools

scholarly journals Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

2019 ◽  
Vol 9 (5) ◽  
pp. 522-532 ◽  
Author(s):  
Xavier Rossello ◽  
Jannick AN Dorresteijn ◽  
Arne Janssen ◽  
Ekaterini Lambrinou ◽  
Martijn Scherrenberg ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Clinical Practice ◽  
Risk Prediction ◽  
European Association ◽  
Risk Measures ◽  
Preventive Cardiology ◽  
Prediction Tools ◽  
Risk Algorithm ◽  
Preventive Medication ◽  
The Right

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

scholarly journals Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

2019 ◽  
Vol 18 (7) ◽  
pp. 534-544 ◽  
Author(s):  
Xavier Rossello ◽  
Jannick AN Dorresteijn ◽  
Arne Janssen ◽  
Ekaterini Lambrinou ◽  
Martijn Scherrenberg ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Clinical Practice ◽  
Risk Prediction ◽  
European Association ◽  
Risk Measures ◽  
Preventive Cardiology ◽  
Prediction Tools ◽  
Risk Algorithm ◽  
Preventive Medication ◽  
The Right

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

Prediction of cardiovascular risk in cancer patients of South India using WHO/ISH risk prediction charts and Framingham score – A prospective study

2017 ◽  
Vol 24 (5) ◽  
pp. 354-358 ◽  
Author(s):  
MG Rajanandh ◽  
S Suresh ◽  
K Manobala ◽  
R Nandhakumar ◽  
G Jaswanthi ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Prediction ◽  
Smoking Status ◽  
Risk Groups ◽  
Framingham Score ◽  
Prediction Tools ◽  
Significant Difference

Objective Despite the fact that cancer and heart diseases are interconnected, there is lack of information about the prevalence of cardiovascular risk in cancer patients in the South Indian population. With this background, the present study sought to predict the cardiovascular disease in cancer patients. Methods A prospective, cross-sectional study was conducted in the Department of Medical Oncology, Sri Ramachandra University and Hospital, India. Patients’ demographic details, medical information, height, weight, body mass index, blood pressure, total cholesterol and HDL-cholesterol were measured. Two risk prediction tools, namely World Health Organization/International Society of hypertension (WHO/ISH) risk prediction charts and Framingham score were used to assess the prevalence of cardiovascular risk over 10 years. Results A total of 70 patients were included for the study. Breast and stomach cancer were found to be most among the study patients. Cardiovascular disease was assessed using WHO/ISH and Framingham risk assessment tool. With respect to WHO/ISH risk, there is a significant difference in gender, type of cancer, smoking status and age between the risk groups. Males have a high risk compared to females, and smokers have a high risk compared to non-smokers. With respect to Framingham score, there is a significant difference in gender, smoking status and systolic blood pressure between the risk groups. Males have a high risk compared to females, and smokers have a high risk compared to non-smokers. A moderate degree of agreement exists between the two risk prediction tools. Conclusion The findings of the study revealed that there is a low risk of cardiovascular disease in cancer patients.

scholarly journals Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

2019 ◽  
Vol 26 (14) ◽  
pp. 1534-1544 ◽  
Author(s):  
Xavier Rossello ◽  
Jannick AN Dorresteijn ◽  
Arne Janssen ◽  
Ekaterini Lambrinou ◽  
Martijn Scherrenberg ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Clinical Practice ◽  
Risk Prediction ◽  
European Association ◽  
Risk Measures ◽  
Preventive Cardiology ◽  
Prediction Tools ◽  
Risk Algorithm ◽  
Preventive Medication ◽  
The Right

Risk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

scholarly journals Test characteristics of history, examination and investigations in the evaluation for septic arthritis in the child presenting with acute non-traumatic limp. A systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e038088
Author(s):  
Jacky Tu ◽  
Peter Gowdie ◽  
Julian Cassar ◽  
Simon Craig
Keyword(s):  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Septic Arthritis ◽  
Quality Assessment ◽  
Likelihood Ratio ◽  
Risk Prediction ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Prediction Tools ◽  
Clinical Risk

BackgroundSeptic arthritis is an uncommon but potentially significant diagnosis to be considered when a child presents to the emergency department (ED) with non-traumatic limp. Our objective was to determine the diagnostic accuracy of clinical findings (history and examination) and investigation results (pathology tests and imaging) for the diagnosis of septic arthritis among children presenting with acute non-traumatic limp to the ED.MethodsSystematic review of the literature published between 1966 and June 2019 on MEDLINE and EMBASE databases. Studies were included if they evaluated children presenting with lower limb complaints and evaluated diagnostic performance of items from history, physical examination, laboratory testing or radiological examination. Data were independently extracted by two authors, and quality assessment was performed using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 tool.Results18 studies were identified, and included 2672 children (560 with a final diagnosis of septic arthritis). There was substantial heterogeneity in inclusion criteria, study setting, definitions of specific variables and the gold standard used to confirm septic arthritis. Clinical and investigation findings were reported using varying definitions and cut-offs, and applied to differing study populations. Spectrum bias and poor-to-moderate study design quality limit their applicability to the ED setting.Single studies suggest that the presence of joint tenderness (n=189; positive likelihood ratio 11.4 (95% CI 5.9 to 22.0); negative likelihood ratio 0.2 (95% CI 0.0 to 1.2)) and joint effusion on ultrasound (n=127; positive likelihood ratio 8.4 (95% CI 4.1 to 17.1); negative likelihood ratio 0.2 (95% CI 0.1 to 0.3)) appear to be useful. Two promising clinical risk prediction tools were identified, however, their performance was notably lower when tested in external validation studies.DiscussionDifferentiating children with septic arthritis from non-emergent disorders of non-traumatic limp remains a key diagnostic challenge for emergency physicians. There is a need for prospectively derived and validated ED-based clinical risk prediction tools.

scholarly journals The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Eun Pyo Hong ◽  
Seong Gu Heo ◽  
Ji Wan Park
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Linear Mixed Model ◽  
Threshold Model ◽  
Snp Markers ◽  
Polygenic Risk Score ◽  
Phenotypic Variance ◽  
Prediction Approach

Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT231) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.

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