Estimation of cardiovascular risk: a comparison between the Framingham and the SCORE model in people under 60 years of age

2008 ◽  
Vol 15 (5) ◽  
pp. 562-566 ◽  
Author(s):  
Tjarda Scheltens ◽  
W.M. Monique Verschuren ◽  
Hendriek C. Boshuizen ◽  
Arno W. Hoes ◽  
Nicolaas P. Zuithoff ◽  
...  
Keyword(s):  
Cohort Study ◽  
Cardiovascular Risk ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Treatment Guidelines ◽  
Risk Function ◽  
Area Under The Curve ◽  
Risk Models ◽  
Discriminative Ability ◽  
Score Model

Background The Framingham Heart Study risk model has been used in the majority of cardiovascular risk management guidelines. Recently, a new model based on the SCORE system has been proposed. We compared both risk models with regard to their ability to predict cardiovascular mortality in the Netherlands. Design Cohort study. Methods In a Dutch cohort study of 39 719 persons, three properties of the risk models were investigated: discriminating ability (ranking persons in order of risks, expressed in area under the curve); calibrating ability (prediction of events compared with actual events expressed in goodness of fit); and the number of persons assigned to treatment according to the guideline. Results The discriminative ability of both models was similar: the area under the curve of Framingham was 0.86 and of SCORE 0.85. Calibration of both functions was inadequate. The goodness of fit of the SCORE model was 35 and of the Framingham model 64, whereas a goodness of fit less than 20 is considered acceptable. Using the Dutch guideline treatment threshold of 10% mortality risk, the SCORE risk function assigned 0.4% of the population to drug treatment where the Framingham function assigned 0.7%. Conclusion The findings of this study show that both the SCORE and the Framingham model function have a good discriminative ability but are insufficient in predicting absolute risks. SCORE assigned fewer participants to treatment than Framingham. If a new risk model is implemented in treatment guidelines, comparison with the model in use and evaluation of calibrating features is needed.

scholarly journals External validation and clinical usefulness of three commonly used cardiovascular risk prediction scores in an Emirati population: a retrospective longitudinal cohort study

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e040680
Author(s):  
Saif Al-Shamsi ◽  
Romona Devi Govender ◽  
Jeffrey King
Keyword(s):  
Cohort Study ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Risk Model ◽  
Clinical Usefulness ◽  
Cvd Risk ◽  
Risk Models ◽  
C Statistic ◽  
Framingham Risk ◽  
Predicted Risk

ObjectivesCardiovascular disease (CVD) risk prediction models are useful tools for identifying those at high risk of cardiovascular events in a population. No studies have evaluated the performance of such risk models in an Arab population. Therefore, in this study, the accuracy and clinical usefulness of two commonly used Framingham-based risk models and the 2013 Pooled Cohort Risk Equation (PCE) were assessed in a United Arab Emirates (UAE) national population.DesignA 10-year retrospective cohort study.SettingOutpatient clinics at a tertiary care hospital, Al-Ain, UAE.ParticipantsThe study cohort included 1041 UAE nationals aged 30–79 who had no history of CVD at baseline. Patients were followed until 31 December 2019. Eligible patients were grouped into the PCE and the Framingham validation cohorts.ExposureThe 10-year predicted risk for CVD for each patient was calculated using the 2008 Framingham risk model, the 2008 office-based Framingham risk model, and the 2013 PCE model.Primary outcome measureThe discrimination, calibration and clinical usefulness of the three models for predicting 10-year cardiovascular risk were assessed.ResultsIn women, the 2013 PCE model showed marginally better discrimination (C-statistic: 0.77) than the 2008 Framingham models (C-statistic: 0.74–0.75), whereas all three models showed moderate discrimination in men (C-statistic: 0.69‒0.70). All three models overestimated CVD risk in both men and women, with higher levels of predicted risk. The 2008 Framingham risk model (high-risk threshold of 20%) classified only 46% of women who subsequently developed incident CVD within 10 years as high risk. The 2013 PCE risk model (high-risk threshold of 7.5%) classified 74% of men who did not develop a cardiovascular event as high risk.ConclusionsNone of the three models is accurate for predicting cardiovascular risk in UAE nationals. The performance of the models could potentially be improved by recalibration.

scholarly journals Evaluation of the Association between Obesity Markers and Type 2 Diabetes: A Cohort Study Based on a Physical Examination Population

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tengfei Yang ◽  
Bo Zhao ◽  
Dongmei Pei
Keyword(s):  
Type 2 Diabetes ◽  
Cohort Study ◽  
Physical Examination ◽  
Roc Curve ◽  
Risk Model ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Visceral Adiposity Index ◽  
Digital Repository

Purpose. To evaluate the predictive effect of different obesity markers on the risk of developing type 2 diabetes in a population of healthy individuals who underwent physical examination and to provide a reference for the early detection of individuals at risk of diabetes. Methods. This retrospective cohort study included 15206 healthy subjects who underwent a physical examination (8307 men and 6899 women). Information on the study population was obtained from the Dryad Digital Repository. Cox proportional risk models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) of different obesity markers, including the lipid accumulation index (LAP), body mass index (BMI), waist-to-height ratio (WHtR), visceral adiposity index (VAI), and body roundness index (BRI) on the development of type 2 diabetes. The effectiveness of each obesity marker in predicting the risk of developing type 2 diabetes was analyzed using the receiver operating characteristic curve (ROC curve) and the area under the curve (AUC). Results. After a mean follow-up of 5.4 years, there were 372 new cases of type 2 diabetes. After correcting for confounding factors such as age, sex, smoking, alcohol consumption, exercise, and blood pressure, Cox proportional risk model analysis showed that elevations in BMI, LAP, WHtR, VAI, and BRI increased the risk of developing type 2 diabetes. The ROC curve results showed that LAP was the best predictor of the risk of developing diabetes, with an AUC (95% CI) of 0.759 (0.752–0.766), an optimal cutoff value of 16.04, a sensitivity of 0.72, and a specificity of 0.69. Conclusion. An increase in the BMI, LAP, WHtR, VAI, and BRI can increase the risk of developing type 2 diabetes, with LAP being the best predictor of this risk.

Validity of the Framingham Risk Model Applied to Japanese Men

2002 ◽  
Vol 41 (03) ◽  
pp. 213-215 ◽  
Author(s):  
H. Sugimori ◽  
K. Yoshida ◽  
M. Suka
Keyword(s):  
Goodness Of Fit ◽  
Risk Model ◽  
Area Under The Curve ◽  
Health Examination ◽  
Characteristic Analysis ◽  
Goodness Of Fit Test ◽  
Japanese Men ◽  
Discriminatory Accuracy ◽  
Framingham Risk ◽  
Chd Risk

Summary Objectives: To examine whether the Framingham Risk Model can appropriately predict coronary heart disease (CHD) events detected by electrocardiography (ECG) in Japanese men. Methods: Using the annual health examination database of a Japanese company 5611 male workers, between the ages of 30 to 59, who were free of cardiovascular disease, were followed up to observe the occurrence of CHD events detected by ECG over a period of five to seven years. The probability of CHD was calculated for each individual from the equations of the Framingham risk model (with total cholesterol). Results: The incidence of CHD increased with the estimated CHD risk. The Hosmer-Lemeshow goodness of fit test showed an adequate fit of the risk model to the data of the study subjects. In the receiver operating characteristic analysis, the area under the curve reached 0.67 which indicated an acceptable discriminatory accuracy of the risk model. Conclusions: The Framingham risk model provides useful information on future CHD events in Japanese men.

scholarly journals Proteomics-based prognostic signature and nomogram construction of hypoxia microenvironment on deteriorating glioblastoma (GBM) pathogenesis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ya-Dan Wen ◽  
Xiao-San Zhu ◽  
Dong-Jie Li ◽  
Qing Zhao ◽  
Quan Cheng ◽  
...  
Keyword(s):  
Risk Score ◽  
Risk Model ◽  
Survival Curve ◽  
Statistical Significance ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Curve Analysis ◽  
Glioblastoma Cells ◽  
Molecular Features ◽  
Score Model

AbstractThe present study aimed to construct and evaluate a novel experiment-based hypoxia signature to help evaluations of GBM patient status. First, the 426 proteins, which were previously found to be differentially expressed between normal and hypoxia groups in glioblastoma cells with statistical significance, were converted into the corresponding genes, among which 212 genes were found annotated in TCGA. Second, after evaluated by single-variable Cox analysis, 19 different expressed genes (DEGs) with prognostic value were identified. Based on λ value by LASSO, a gene-based survival risk score model, named RiskScore, was built by 7 genes with LASSO coefficient, which were FKBP2, GLO1, IGFBP5, NSUN5, RBMX, TAGLN2 and UBE2V2. Kaplan–Meier (K–M) survival curve analysis and the area under the curve (AUC) were plotted to further estimate the efficacy of this risk score model. Furthermore, the survival curve analysis was also plotted based on the subtypes of age, IDH, radiotherapy and chemotherapy. Meanwhile, immune infiltration, GSVA, GSEA and chemo drug sensitivity of this risk score model were evaluated. Third, the 7 genes expression were evaluated by AUC, overall survival (OS) and IDH subtype in datasets, importantly, also experimentally verified in GBM cell lines exposed to hypoxic or normal oxygen condition, which showed significant higher expression in hypoxia than in normal group. Last, combing the hypoxia RiskScore with clinical and molecular features, a prognostic composite nomogram was generated, showing the good sensitivity and specificity by AUC and OS. Meanwhile, univariate analysis and multivariate analysis were used for performed to identify variables in nomogram that were significant in independently predicting duration of survival. It is a first time that we successfully established and validated an independent prognostic risk model based on hypoxia microenvironment from glioblastoma cells and public database. The 7 key genes may provide potential directions for future biochemical and pharmaco-therapeutic research.

scholarly journals Development and Validation of a Novel Predictive Nomogram for Disease Progression of COVID-19: A Multicenter Retrospective Cohort Study of 4,086 Cases in China

2020 ◽  
Author(s):  
Li Li ◽  
Shen Li ◽  
Xiaohua Zhang ◽  
Lixia Cheng ◽  
Qing Mao ◽  
...  
Keyword(s):  
Cohort Study ◽  
Disease Progression ◽  
Disease Severity ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Goodness Of Fit ◽  
Calibration Plot ◽  
Net Benefit ◽  
Discriminative Ability ◽  
Healthcare Crisis

Abstract Aim: Coronavirus disease 2019 (COVID-19) has caused an unprecedented healthcare crisis. We aim to develop and validate a nomogram for predicting disease progression based on a large cohort of hospitalized COVID-19 patients. Methods: This is a multicenter retrospective cohort study, with a total of 4,086 hospitalized COVID-19 patients enrolled from two hospitals in Wuhan, China between February 3rd and Apr 10th. Nomogram was developed based on a cohort of 3, 022 patients from one hospital, and externally validated in another cohort of 1,064 patients from the other hospital. The calibration was assessed by a calibration plot and the HL test to evaluate the goodness of fit, and the Area under the ROC Curve (AUROC) as a measure of discriminative ability.Results: Six independent predictors, including age, dyspnea, platelet count, lactate dehydrogenase, D-dimer and cardiovascular disease, were finally identified for construction of the nomogram for predicting disease progression of COVID-19 patients during hospitalization. The AUROC was 0.877 and 0.817 for development cohort and validation cohort, respectively. The calibration plots AND Hosmer-Lemeshow test showed optimal agreement between nomogram prediction and actual observation. The decision curve analysis showed the performance of the nomograms were better than all univariable models, and had greater net benefit. Next, a predictive nomogram for disease severity on admission was formulated and the six independent factors used were similar to that of the nomogram for disease progression, which indicates that those factors play important roles in determining disease severity and the risk of disease progression. Conclusion: In the current study, a nomogram was developed based on generally readily available variables at hospital admission to help predict disease progression of COVID-19.

scholarly journals Accuracy of deep learning based computed tomography diagnostic system of COVID-19: a consecutive sampling external validation cohort study

2020 ◽  
Author(s):  
Tatsuyoshi Ikenoue ◽  
Yuki Kataoka ◽  
Yoshinori Matsuoka ◽  
Junichi Matsumoto ◽  
Junji Kumasawa ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cohort Study ◽  
Sensitivity And Specificity ◽  
Goodness Of Fit ◽  
Tertiary Care ◽  
External Validation ◽  
Area Under The Curve ◽  
Chest Ct ◽  
List Type ◽  
Rt Pcr

AbstractObjectivesAli-M3, an artificial intelligence, analyses chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) in the range of 0 to 1. It demonstrates excellent performance for the detection of COVID-19 patients with a sensitivity and specificity of 98.5 and 99.2%, respectively. However, Ali-M3 has not been externally validated. Our purpose is to evaluate the external validity of Ali-M3 using Japanese sequential sampling data.MethodsIn this retrospective cohort study, COVID-19 infection probabilities were calculated using Ali-M3 in 617 symptomatic patients who underwent reverse transcription-polymerase chain reaction (RT-PCR) tests and chest CT for COVID-19 diagnosis at 11 Japanese tertiary care facilities, between January 1 and April 15, 2020.ResultsOf 617 patients, 289 patients (46.8%) were RT-PCR-positive. The area under the curve (AUC) of Ali-M3 for predicting a COVID-19 diagnosis was 0.797 (95% confidence intervals [CI]: 0.762-0.833) and goodness-of-fit was P = 0.156. With a cut-off of probability of COVID-19 by Ali-M3 diagnosis set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively, while a cut-off of 0.2 yielded a sensitivity and specificity of 89.2% and 43.2%, respectively. Among 223 patients who required oxygen support, the AUC was 0.825 and sensitivity at a cut-off of 0.5 and 0.2 were 88.7% and 97.9%, respectively. Although the sensitivity was lower when the days from symptom onset were few, sensitivity increased for both cut-off values after 5 days.ConclusionsAli-M3 was evaluated by external validation and shown to be useful to exclude a diagnosis of COVID-19.Key PointsThe area under the curve (AUC) of Ali-M3, which is an AI system for diagnosis of COVID-19 based on chest CT images, was 0.797 and goodness-of-fit was P = 0.156.With a cut-off of probability of COVID-19 by Ali-M3 diagnosis set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively, while a cut-off of 0.2 yielded 89.2% and 43.2%.Although low sensitivity was observed in less number of days from symptoms onset, after 5 days high increasing sensitivity was observed. In patients requiring oxygen support, the AUC was higher that is 0.825.

scholarly journals Could clinical decision rules relying on cardiovascular risk models increase psychosocial inequalities in health? Results from the PRIME cohort study

2011 ◽  
Vol 52 (6) ◽  
pp. 439-444 ◽  
Author(s):  
Benoît Lepage ◽  
Philippe Amouyel ◽  
Dominique Arveiler ◽  
Jean Ferrières ◽  
Pierre Ducimetière ◽  
...  
Keyword(s):  
Cohort Study ◽  
Cardiovascular Risk ◽  
Decision Rules ◽  
Clinical Decision ◽  
Risk Models ◽  
Clinical Decision Rules ◽  
Inequalities In Health

scholarly journals Adjusting preoperative risk models of post heart transplant survival to a European cohort in the age of a new cardiac allocation score in Europe

2018 ◽  
Vol 21 (6) ◽  
pp. E527-E533
Author(s):  
Tarik Alp Sargut ◽  
Panagiotis Pergantis ◽  
Christoph Knosalla ◽  
Jan Knierim ◽  
Manfred Hummel ◽  
...  
Keyword(s):  
Heart Transplant ◽  
Scoring System ◽  
Graft Failure ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Risk Scores ◽  
Risk Models ◽  
Adjusted Risk ◽  
The Impact

Background Several risk models target the issue of posttransplant survival, but none of them have been validated in a large European cohort. This aspect is important, in a time of the planned change of the Eurotransplant allocation system to a scoring system. Material and Methods Data of 761 heart transplant recipients from the Eurotransplant region with a total follow up of 5027 patient-years were analyzed. We assessed 30-day to 10-year freedom from graft failure. Existing post-transplant mortality risk models, IMPACT, Meld-XI and Columbia Risk Stratification Score were (RSS) were evaluated. A new risk model was created and the predictive accuracy was compared with the existing risk scores, with a focus on LVAD patients. Results Thirty-day, 1-year, 5-year and 10-year rates of freedom from graft failure were 78.3±1.5%, 68.8±1.71%, 59.1±1.8% and 44.1±1.9. The 1-year incidence of graft failure varied from 14.1% to 50% (RSS), from 22.9% to 57.1 (IMPACT) and from 24.9% to 42.6% using MELD-XI. Our newly adjusted risk score showed an improved area under the curve (AUC) of 0.69 (95% CI 0.64-0.72) with better discrimination in the intermediate to moderate risk cohort (CABDES Score). Conclusion IMPACT, Meld-XI and RSS were suitable to predict posttransplant graft failure only in a high and low risk cohort. CABDES Score, might be an alternative scoring system, with donor age and eGFR beeing the strongest predictors. Implementation of the IMPACT score within the new Eurotransplant Cardiac Allocation Score for patient prioritization for heart transplantation, should be reevaluated.

scholarly journals Estimation of the 10-Year Risk of Cardiovascular Diseases: Using the SCORE, WHO/ISH, and Framingham Models in the Shahrekord Cohort Study in Southwestern Iran

2020 ◽  
Author(s):  
Pooneh Samaniyan Bavarsad ◽  
Solieman Kheiri ◽  
Ali Ahmadi
Keyword(s):  
Cohort Study ◽  
Cardiovascular Diseases ◽  
High Risk ◽  
Laboratory Data ◽  
Risk Function ◽  
Low Risk ◽  
World Health ◽  
Moderate Risk ◽  
Risk Class ◽  
Score Model

Background: Predicting the risk of cardiovascular diseases (CVDs) helps the management of high-risk individuals by the health system. We sought to determine the 10-year risk of CVDs in the Shahrekord Cohort Study (SCS). Methods: In this cross-sectional study based on the SCS in the southwest of Iran, the demographic, anthropometric, clinical, and laboratory data of 5152 persons recruited in the SCS by census method from 2016 to 2017 were used. R software was utilized to calculate the 10-year risk of CVDs according to the World Health Organization/International Society of Hypertension (WHO/ISH) chart, the Framingham Risk Score (FRS) model, and the Systematic Coronary Risk Evaluation (SCORE) model. Results: The mean age of the participants was 49.49±9.40 years, and 50.3% of them were female. According to the WHO/ ISH chart, 94.1% of the participants were in the low-risk class, 4.1% in the moderate-risk class, and 0.4% in the high-risk class. Based on the FRS model, 72.2% of the participants were in the low-risk class, 18% in the middle-risk class, and 9.8% in the high-risk class. On the basis of the SCORE model for low-risk areas, 55.3% of the participants were in the low-risk class, 39.6% in the moderate-risk class, and 5.1% in the high-risk class. The agreement concerning risk estimation between the models was approximately 70%. Conclusion: The risk estimated in this study was higher than that in other similar studies. For monitoring risk trends over time, it is essential to nativize a valid risk function, including ethnicity and geographical characteristics, for the Iranian population.

The ability of the SCORE high-risk model to predict 10-year cardiovascular disease mortality in Norway

2007 ◽  
Vol 14 (4) ◽  
pp. 501-507 ◽  
Author(s):  
Anja S. Lindman ◽  
Marit B. Veierød ◽  
Jan I. Pedersen ◽  
Aage Tverdal ◽  
Inger Njølstad ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Risk Function ◽  
Age Groups ◽  
Population Based ◽  
Low Risk ◽  
Risk Algorithm ◽  
Score Model

Aims To evaluate the predictive accuracy of the Systematic Coronary Risk Evaluation (SCORE) project high-risk function in Norway. Methods and results We included 57229 individuals screened in 1985-1992 from two population-based surveys in Norway (age groups 40-49, 50-59, and 60-69 years). The data have been linked to the Norwegian Cause of Death Registry. The SCORE high-risk algorithm for the prediction of 10-year cardiovascular disease (CVD) mortality was applied, and the risk factors entered into the model were age, sex, total cholesterol, systolic blood pressure, and smoking (yes/no). The number of expected events estimated by the SCORE model (E) was compared with the observed numbers (O). The SCORE low-risk algorithm was studied for comparison. In men, the observed number of CVD deaths was 718, compared with 1464 estimated by the SCORE high-risk function (O/E ratios 0.53, 0.53 and 0.45, for age groups 40-49, 50-59 and 60-69, respectively). In women, the observed and expected numbers were 226 and 547. The O/E ratios decreased with age (ratios 0.60, 0.45 and 0.37, respectively), i.e. the overestimation increased with age. The low-risk function predicted reasonably well for men (ratios 0.85, 0.92 and 0.79, respectively), whereas an overestimation was found for women aged 50-59 and 60-69 years (ratios 0.69 and 0.56, respectively). Conclusion The SCORE high-risk model overestimated the number of CVD deaths in Norway. Before implementation in clinical practice, proper adjustments to national levels are required.

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