Cardiovascular risk in the Mediterranean area is low: Impact on clinical decision-making

2005 ◽  
Vol 15 (6) ◽  
pp. 399-401 ◽  
Author(s):  
Paolo Rubba
Keyword(s):  
Decision Making ◽  
Cardiovascular Risk ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Mediterranean Area ◽  
The Mediterranean

scholarly journals Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

2020 ◽  
Vol 17 (21) ◽  
pp. 7923
Author(s):  
Davide Barbieri ◽  
Nitesh Chawla ◽  
Luciana Zaccagni ◽  
Tonći Grgurinović ◽  
Jelena Šarac ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cardiovascular Risk ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Classification Performance ◽  
Biomedical Data ◽  
Sport Practice

Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine sport medical examinations, which included electrocardiography at rest. Subjects were involved in competitive sport practice, for which medical clearance was needed. Outcomes were negative for the largest majority, as expected in an active population. Resampling was applied to balance positive/negative class ratio. A decision tree and logistic regression were used to classify individuals as either at risk or not. The receiver operating characteristic curve was used to assess classification performances. Data mining and resampling improved cardiovascular risk assessment in terms of increased area under the curve. The proposed methodology can be effectively applied to biomedical data in order to optimize clinical decision making, and—at the same time—minimize the amount of unnecessary examinations.

Cardiovascular risk in clinical decision making

2007 ◽  
Vol 9 (5) ◽  
pp. 339-341 ◽  
Author(s):  
Fernando Rodríguez-Artalejo ◽  
José R. Banegas
Keyword(s):  
Decision Making ◽  
Cardiovascular Risk ◽  
Clinical Decision Making ◽  
Clinical Decision

Usefulness of novel martin/hopkins and sampson equations over friedewald equation in cardiology outpatient: CVSCORE-TR substudy

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
L Dinc Asarcikli ◽  
M Kis ◽  
T Guvenc ◽  
V Tosun ◽  
B Acar ◽  
...  
Keyword(s):  
Decision Making ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Clinical Decision Making ◽  
Reference Method ◽  
Clinical Decision ◽  
P Value ◽  
Treatment Intensification ◽  
Cholesterol Lowering ◽  
Friedewald Equation

Abstract Funding Acknowledgements Type of funding sources: None. OnBehalf CVSCORE-TR study group Background Friedewald equation (LDL-Cf) is known to produce inaccurate estimations of low-density lipoprotein cholesterol (LDL-C) when triglycerides are high (>400 mg/dl) or LDL-C is low (<70 mg/dl). Martin/Hopkins (LDL-Cmh) and Sampson (LDL-Cs) equations were developed to overcome these limitations, but few data are available whether these equations offer incremental usefulness over LDL-Cf. Purpose   In this pragmatic study, we aimed to evaluate the agreement between LDL-C calculated using LDL-Cmh, LDL-Cs and LDL-Cf equations and to understand whether using LDL-Cmh or LDL-Cs instead of LDL-Cf leads to significant changes on the clinical decision-making  Methods 4196 cardiology outpatient cases that were included in a multicenter registry database were analyzed. Each case was assigned into a cardiovascular risk class using web-based SCORE (Systematic COronary Risk Evaluation) algorithm calibrated for high-risk European countries, and relevant European guidelines were used to assess LDL-C targets. LDL-Cf, LDL-Cs and LDL-Cmh were calculated as previously described.  Results Compared to LDL-Cmh and LDL-Cs, LDL-Cf was able to correctly identify 96.9%-98.08% of cases as within or out of LDL-C target, respectively, while 1.95%-2.8% of cases were falsely identified as within LDL-C target. Kappa coefficients for agreement between LDL-Cf vs. LDL-Cmh and LDL-Cf vs. LDL-Cs were 0.868 and 0.918 (p < 0.001 for both). For patients not on anticholesterolemic drugs, decision to initiate treatment would be different in 1.2%-1.8% of cases if LDL-Cs or LDL-Cmh were used, respectively. For those already on anticholesterolemic drugs, decisions regarding to treatment intensification would be different in 1.5%-2.4% of cases if LDL-Cs or LDL-Cmh were used. Conclusions Friedewald equation had an excellent degree of agreement with the novel Martin/Hopkins and Sampson formulas in most cardiology outpatients, especially those within the lower end of the cardiovascular risk spectrum. In selected patients, especially those with high or very high risk in whom LDL-Cf < 70 mg/dl or those with a TG > 400 mg/dl, agreement was far worse and thus novel equations might have an incremental usefulness for clinical decision making. Table 1 Reference Comparison Correct estimation Underestimation Overestimation Kappa (p value) All patients that were not on cholesterol-lowering treatment LDL-Cmh LDL-Cf 2785 (98.1%) 51 (1.8%) 3 (0.1%) 0.962 (<0.001) LDL-Cs LDL-Cf 2804 (98.8%) 35 (1.2%) 0 (0.0%) 0.975 (<0.001) Agreement for the indication of cholesterol-lowering treatment for patients not already on cholesterol-lowering drugs. Leftmost column shows the reference method, and the second row shows equation which is compared to the reference method. Abstract Figure

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

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