Dataset Information

Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India.

ABSTRACT: 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.

SUBMITTER: Raghu A

PROVIDER: S-EPMC4545825 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India.

Raghu Arvind A Praveen Devarsetty D Peiris David D Tarassenko Lionel L Clifford Gari G

PloS one 20150819 8

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 u ...[more]

PMID: 26287807

Similar Datasets

Project description:BackgroundWorld Health Organization (WHO) released region-specific cardiovascular disease (CVD) risk prediction charts recently, but the extent to which the charts can apply to Chinese population is unknown. We aimed to validate the WHO CVD risk charts for East Asia, and evaluate their practicability combining with China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) equations among Chinese adults.MethodsThe China-PAR cohort with 93,234 participants aged 40-80 years was followed up during 1992-2015, including 29,337 participants from three sub-cohorts with follow-up period of over 10 years. We validated the WHO CVD risk charts using the China-PAR cohort by assessment of the predicted number of events, C index, calibration χ², and calibration plots, further elaborated the concordance between the China-PAR equations and the WHO risk charts.FindingsDuring an average follow-up of 13•64 years, 1849 incident CVD cases were identified from 29,337 participants. Both the laboratory-based and non-laboratory-based charts overestimated CVD events by 59% and 58% in men, and by 72% and 85% in women, respectively. However, 92% of participants identified as high risk by the China-PAR equations could be successfully detected by the laboratory-based charts at the cut-off point of 10%. We also observed that the non-laboratory-based charts demonstrated the poor performance for diabetic population, with high proportion of high-risk individuals (17% for men, 31% for women) would be missed.InterpretationAlthough the WHO CVD risk charts for East Asia apparently overestimated CVD risk among Chinese population, they could be pragmatic pre-selection tools, as potential supplement to the China-PAR equations. The widespread use of the WHO risk charts along with the China-PAR equations might facilitate the implementation of the risk-based CVD prevention in China.FundingFull funding sources are listed at the end of the paper (see Acknowledgments).

Project description:BackgroundEpicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented.ObjectivesThis study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care.MethodsThe deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value.ResultsExternal validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post-cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01).ConclusionsAutomated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.

Project description:This study was designed to develop a risk assessment chart for the clinical management and prevention of the risk of cardiovascular disease (CVD) in Iranian population, which is vital for developing national prevention programs. The Isfahan Cohort Study (ICS) is a population-based prospective study of 6504 Iranian adults ?35 years old, followed-up for ten years, from 2001 to 2010. Behavioral and cardiometabolic risk factors were examined every five years, while biennial follow-ups for the occurrence of the events was performed by phone calls or by verbal autopsy. Among these participants, 5432 (2784 women, 51.3%) were CVD free at baseline examination and had at least one follow-up. Cox proportional hazard regression was used to predict the risk of ischemic CVD events, including sudden cardiac death due to unstable angina, myocardial infarction, and stroke. The model fit statistics such as area under the receiver-operating characteristic (AUROC), calibration chi-square and the overall bias were used to assess the model performance. We also tested the Framingham model for comparison. Seven hundred and five CVD events occurred during 49452.8 person-years of follow-up. The event probabilities were calculated and presented color-coded on each gender-specific PARS chart. The AUROC and Harrell's C indices were 0.74 (95% CI, 0.72-0.76) and 0.73, respectively. In the calibration, the Nam-D'Agostino ?2 was 10.82 (p = 0.29). The overall bias of the proposed model was 95.60%. PARS model was also internally validated using cross-validation. The Android app and the Web-based risk assessment tool were also developed as to have an impact on public health. In comparison, the refitted and recalibrated Framingham models, estimated the CVD incidence with the overall bias of 149.60% and 128.23% for men, and 222.70% and 176.07% for women, respectively. In conclusion, the PARS risk assessment chart is a simple, accurate, and well-calibrated tool for predicting a 10-year risk of CVD occurrence in Iranian population and can be used in an attempt to develop national guidelines for the CVD management.

Dataset Information

Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India.

Publications

Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets