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Blood glucose may be an alternative to cholesterol in CVD risk prediction charts.

ABSTRACT: BACKGROUND: Established risk models for the prediction of cardiovascular disease (CVD) include blood pressure, smoking and cholesterol parameters. The use of total cholesterol for CVD risk prediction has been questioned, particularly for primary prevention. We evaluated whether glucose could be used instead of total cholesterol for prediction of fatal CVD using data with long follow-up. METHODS: We followed-up 6,095 men and women aged ?16 years who participated 1977-79 in a community based health study and were anonymously linked with the Swiss National Cohort until the end of 2008. During follow-up, 727 participants died of CVD. Based on the ESC SCORE methodology (Weibull regression), we used age, sex, blood pressure, smoking, and fasting glucose or total cholesterol. The mean Brier score (BS), area under the receiver-operating characteristic curve (AUC) and integrated discrimination improvement (IDI) were used for model comparison. We validated our models internally using cross-validation and externally using another data set. RESULTS: In our models, the p-value of total cholesterol was 0.046, that of glucose was p?

SUBMITTER: Braun J

PROVIDER: S-EPMC3599220 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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Blood glucose may be an alternative to cholesterol in CVD risk prediction charts.

Braun Julia J Bopp Matthias M Faeh David D

Cardiovascular diabetology 20130125

<h4>Background</h4>Established risk models for the prediction of cardiovascular disease (CVD) include blood pressure, smoking and cholesterol parameters. The use of total cholesterol for CVD risk prediction has been questioned, particularly for primary prevention. We evaluated whether glucose could be used instead of total cholesterol for prediction of fatal CVD using data with long follow-up.<h4>Methods</h4>We followed-up 6,095 men and women aged ≥16 years who participated 1977-79 in a communit ...[more]

PMID: 23351551

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Project description:OBJECTIVE:The objective of this study was to estimate the population distribution of 10-year cardiovascular disease (CVD) risk among Bangladeshi adults aged 40 years and above, using the 2019 WHO CVD risk prediction charts. Additionally, we compared the cost of CVD pharmacological treatment based on the total CVD risk (thresholds ?30%/?20%) and the single risk factor (hypertension) cut-off levels in the Bangladeshi context. STUDY DESIGN:Cross-sectional, population-based study. SETTING AND PARTICIPANTS:From 2013 to 2014, we collected data from a nationally representative cross-sectional survey of adults aged ?40 years from urban and rural areas of Bangladesh (n=6189). We estimated CVD risk using the 2019 WHO CVD risk prediction charts and categorised as very low (<5%), low (5% to <10%), moderate (10% to <20%), high (20% to <30%) and very high risk (?30%). We estimated drug therapy costs using the lowest price of each drug class available (aspirin, thiazide diuretics, statins and ACE inhibitors). We compared the total cost of drug therapy using the total CVD risk versus single risk factor approach. PRIMARY OUTCOME MEASURES:Our primary outcome was 10-year CVD risk categorised as very low (<5%), low (5% to <10%), moderate (10% to <20%), high (20% to <30%) and very high risk (?30%). RESULTS:The majority of adults (85.2%, 95%?CI 84.3 to 86.1) have a 10-year CVD risk of less than 10%. The proportion of adults with a 10-year CVD risk of ?20% was 0.51%. Only one adult was categorised with a 10-year CVD risk of ?30%. Among adults with CVD risk groups of very low, low and moderate, 17.4%, 27.9% and 41.4% had hypertension (blood pressure (BP) ?140/90) and 0.1%, 1.7% and 2.9% had severe hypertension (BP ?160/100), respectively. Using the total CVD risk approach would reduce drug costs per million populations to US$144?540 (risk of ?20%). CONCLUSION:To reduce healthcare expenditure for the prevention and treatment of CVD, a total risk approach using the 2019 WHO CVD risk prediction charts may lead to cost savings.

Project description:Cardiovascular disease (CVD) risk in India is currently assessed using the World Health Organization/International Society for Hypertension (WHO/ISH) risk prediction charts since no population-specific models exist. The WHO/ISH risk prediction charts have two versions-one with total cholesterol as a predictor (the high information (HI) model) and the other without (the low information (LI) model). However, information on the WHO/ISH risk prediction charts including guidance on which version to use and when, as well as relative performance of the LI and HI models, is limited. This article aims to, firstly, quantify the relative performance of the LI and HI WHO/ISH risk prediction (for WHO-South East Asian Region D) using data from rural India. Secondly, we propose a pre-screening (simplified) point-of-care (POC) test to identify patients who are likely to benefit from a total cholesterol (TC) test, and subsequently when the LI model is preferential to HI model. Analysis was performed using cross-sectional data from rural Andhra Pradesh collected in 2005 with recorded blood cholesterol measurements (N = 1066). CVD risk was computed using both LI and HI models, and high risk individuals who needed treatment(THR) were subsequently identified based on clinical guidelines. Model development for the POC assessment of a TC test was performed through three machine learning techniques: Support Vector Machine (SVM), Regularised Logistic Regression (RLR), and Random Forests (RF) along with a feature selection process. Disagreement in CVD risk predicted by LI and HI WHO/ISH models was 14.5% (n = 155; p<0.01) overall and comprised 36 clinically relevant THR patients (31% of patients identified as THR by using either model). Using two patient-specific parameters (age, systolic blood pressure), our POC assessment can pre-determine the benefit of TC testing and choose the appropriate risk model (out-of-sample AUCs:RF-0.85,SVM-0.84,RLR:0.82 and maximum sensitivity-98%). The identification of patients benefitting from a TC test for CVD risk stratification can aid planning for resource-allocation and save costs for large-scale screening programmes.

Project description:BackgroundAlthough risk prediction has become an integral part of clinical practice guidelines for cardiovascular disease (CVD) prevention, multiple studies have shown that patients' risk still plays almost no role in clinical decision-making. Because little is known about why this is so, we sought to understand providers' views on the opportunities, barriers, and facilitators of incorporating risk prediction to guide their use of cardiovascular preventive medicines.MethodsWe conducted semi-structured interviews with primary care providers (n = 33) at VA facilities in the Midwest. Facilities were chosen using a maximum variation approach according to their geography, size, proportion of MD to non-MD providers, and percentage of full-time providers. Providers included MD/DO physicians, physician assistants, nurse practitioners, and clinical pharmacists. Providers were asked about their reaction to a hypothetical situation in which the VA would introduce a risk prediction-based approach to CVD treatment. We conducted matrix and content analysis to identify providers' reactions to risk prediction, reasons for their reaction, and exemplar quotes.ResultsMost providers were classified as Enthusiastic (n = 14) or Cautious Adopters (n = 15), with only a few Non-Adopters (n = 4). Providers described four key concerns toward adopting risk prediction. Their primary concern was that risk prediction is not always compatible with a "whole patient" approach to patient care. Other concerns included questions about the validity of the proposed risk prediction model, potential workflow burdens, and whether risk prediction adds value to existing clinical practice. Enthusiastic, Cautious, and Non-Adopters all expressed both doubts about and support for risk prediction categorizable in the above four key areas of concern.ConclusionsProviders were generally supportive of adopting risk prediction into CVD prevention, but many had misgivings, which included concerns about impact on workflow, validity of predictive models, the value of making this change, and possible negative effects on providers' ability to address the whole patient. These concerns have likely contributed to the slow introduction of risk prediction into clinical practice. These concerns will need to be addressed for risk prediction, and other approaches relying on "big data" including machine learning and artificial intelligence, to have a meaningful role in clinical practice.

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Blood glucose may be an alternative to cholesterol in CVD risk prediction charts.

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Blood glucose may be an alternative to cholesterol in CVD risk prediction charts.

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