scholarly journals Prospective, longitudinal, cohort study for assessment of vascular ageing among adults of urban and rural area of Central India: research protocol

2017 ◽  
Vol 4 (9) ◽  
pp. 3273
Author(s):  
Vijay Bhagat ◽  
Shubhangi Baviskar ◽  
Abhay B. Mudey ◽  
Ramachandra Goyal
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Cvd Risk ◽  
Framingham Risk ◽  
Vascular Ageing ◽  
Vascular Age

Background: Considering the complex interaction of risk factors in causation of CVD; assessment of vascular ageing among the high risk group through non-interventional statistical models was useful in controlling CVD. While, many CVD risk assessment models were especially designed for application in the specific population or region such as SCORE scales for Europeans, ASSIGN scores for people of Scotland. The Framingham Risk Score were modified, validated and used in several countries. Though Indians have significantly higher predilection for CVD, no indigenous scores were developed or validated to assess the CV risk. The objective of the study were to determine vascular age of the study participants using Framingham risk prediction model, to assess its relationship with development of cardiovascular disease and to develop, validate and compare cardiovascular risk prediction model based on the follow up observations of the study participants.Methods: Community based cohort study will be conducted in large urban and rural population aged 31-60 years of age those who have no evidence of CVD. The study population will be followed up for three years and will be assessed for development of CVD. The vascular age will be determined using Framingham Risk Scores. Based on the risk factors associated with occurrence of CVD during the study period, the risk prediction model will be designed and tested for validity and accuracy. Results: The newly developed CVD risk prediction will be more accurate in assessment of CV risk among the study subjects. Conclusions: The newly developed and validated CV risk prediction model specific for Indians may be one of the first prospective CV risk assessment cohort study. 

scholarly journals A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaona Jia ◽  
Mirza Mansoor Baig ◽  
Farhaan Mirza ◽  
Hamid GholamHosseini
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Model ◽  
Clinical Laboratory ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
Cvd Risk

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

scholarly journals Early vascular aging risk assessment in patients with metabolic syndrome

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Salasyuk ◽  
S Nedogoda ◽  
I Barykina ◽  
V Lutova ◽  
E Popova
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Vascular Aging ◽  
Clinical Markers ◽  
Cvd Risk ◽  
Vascular Age

Abstract Background Metabolic syndrome (MetS) and abdominal obesity are one of the most common CVD risk factors among young and mature patients. However, the currently used CVD risk assessment scales may underestimate the CV risk in people with obesity and MS. Early vascular aging rather than chronological aging can conceptually offer better risk prediction. MetS, as accumulation of classical risk factors, leads to acceleration of early vascular aging. Since an important feature of MetS is its reversibility, an adequate risk assessment and early start of therapy is important in relation to the possibilities of preventing related complications. Purpose To derive a new score for calculation vascular age and predicting EVA in patients with MetS. Methods Prospective open cohort study using routinely collected data from general practice. The derivation cohort consisted of 1000 patients, aged 35–80 years with MetS (IDF,2005 criteria). The validation cohort consisted of 484 patients with MetS and carotid-femoral pulse wave velocity (cfPWV) values exceeding expected for average age values by 2 or more SD (EVA syndrome). Results In univariate analysis, EVA was significantly correlated with the presence of type 2 diabetes and clinical markers of insulin resistance (IR), body mass index (BMI), metabolic syndrome severity score (MetS z-score), uric acid (UA) level, hsCRP, HOMA-IR, total cholesterol (TC), triglycerides (TG), heart rate (HR), central aortic blood pressure (CBP), diastolic blood pressure (DBP). Multiple logistic regression shown, that presence of type 2 diabetes and IR were associated with greater risk of EVA; the odds ratios were 2.75 (95% CI: 2.34, 3.35) and 1.57 (95% CI: 1.16, 2.00), respectively. In addition, the risk of having EVA increased by 76% with an increase in HOMA-IR by 1 unit, by 17% with an increase in hsCRP by 1 mg/l, by 4% with an increase in DBP by 1 mm Hg, and by 1% with each 1 μmol / L increase in the level of UA. The area under the curve for predicting EVA in patients with MetS was 0,949 (95% CI 0,936 to 0,963), 0,630 (95% CI 0,589 to 0,671), 0,697 (95% CI 0,659 to 0,736) and 0,686 (95% CI 0,647 to 0,726), for vascular age, calculated from cfPWV, SCORE scale, QRISK-3 scale and Framingham scale, respectively. Diabetes mellitus and clinical markers of IR (yes/no), HOMA-IR and UA level were used to develop a new VAmets score for EVA prediction providing a total accuracy of 0.830 (95% CI 0,799 to 0,860). Conclusion cfPWV at present the most widely studied index of arterial stiffness, fulfills most of the stringent criteria for a clinically useful biomarker of EVA in patients with MetS. Although, parallel efforts for effective integration simple clinical score into clinical practice have been offered. Our score (VAmets) may accurately identify patients with MetS and EVA on the basis of widely available clinical variables and classic cardiovascular risk factors can prioritize using of vascular age in routine care. ROC-curves for predicting EVA in MetS Funding Acknowledgement Type of funding source: None

scholarly journals New Zealand Diabetes Cohort Study cardiovascular risk score for people with Type 2 diabetes: validation in the PREDICT cohort

2012 ◽  
Vol 4 (3) ◽  
pp. 181 ◽  
Author(s):  
Tom Robinson ◽  
C Raina Elley ◽  
Sue Wells ◽  
Elizabeth Robinson ◽  
Tim Kenealy ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Risk Assessment ◽  
Cohort Study ◽  
New Zealand ◽  
Cardiovascular Event ◽  
Cvd Risk ◽  
Risk Equation ◽  
Framingham Risk

INTRODUCTION: New Zealand (NZ) guidelines recommend treating people for cardiovascular disease (CVD) risk on the basis of five-year absolute risk using a NZ adaptation of the Framingham risk equation. A diabetes-specific Diabetes Cohort Study (DCS) CVD predictive risk model has been developed and validated using NZ Get Checked data. AIM: To revalidate the DCS model with an independent cohort of people routinely assessed using PREDICT, a web-based CVD risk assessment and management programme. METHODS: People with Type 2 diabetes without pre-existing CVD were identified amongst people who had a PREDICT risk assessment between 2002 and 2005. From this group we identified those with sufficient data to allow estimation of CVD risk with the DCS models. We compared the DCS models with the NZ Framingham risk equation in terms of discrimination, calibration, and reclassification implications. RESULTS: Of 3044 people in our study cohort, 1829 people had complete data and therefore had CVD risks calculated. Of this group, 12.8% (235) had a cardiovascular event during the five-year follow-up. The DCS models had better discrimination than the currently used equation, with C-statistics being 0.68 for the two DCS models and 0.65 for the NZ Framingham model. DISCUSSION: The DCS models were superior to the NZ Framingham equation at discriminating people with diabetes who will have a cardiovascular event. The adoption of a DCS model would lead to a small increase in the number of people with diabetes who are treated with medication, but potentially more CVD events would be avoided. KEYWORDS: Cardiovascular disease; diabetes; prevention; risk assessment; reliability and validity

A risk prediction model for xerostomia: a retrospective cohort study

Gerodontology ◽  
2015 ◽  
Vol 33 (4) ◽  
pp. 562-568 ◽  
Author(s):  
Alessandro Villa ◽  
Francesco Nordio ◽  
Anita Gohel
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Risk Prediction Model

scholarly journals Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study (Preprint)

2018 ◽  
Author(s):  
Anabela Correia Martins ◽  
Juliana Moreira ◽  
Catarina Silva ◽  
Joana Silva ◽  
Cláudia Tonelo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Prospective Study ◽  
Risk Prediction ◽  
Fall Risk ◽  
Screening Tool ◽  
Community Dwelling ◽  
Risk Prediction Model ◽  
Risk Of Falling ◽  
Home Hazards

BACKGROUND Falls are a major health problem among older adults. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling, and changes in the function of musculoskeletal and sensory systems that are associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless, falls can be prevented by screening for known risk factors. OBJECTIVE The objective of our study was to develop a multifactorial, instrumented, screening tool for fall risk, according to the key risk factors for falls, among Portuguese community-dwelling adults aged 50 years or over and to prospectively validate a risk prediction model for the risk of falling. METHODS This prospective study, following a convenience sample method, will recruit community-dwelling adults aged 50 years or over, who stand and walk independently with or without walking aids in parish councils, physical therapy clinics, senior’s universities, and other facilities in different regions of continental Portugal. The FallSensing screening tool is a technological solution for fall risk screening that includes software, a pressure platform, and 2 inertial sensors. The screening includes questions about demographic and anthropometric data, health and lifestyle behaviors, a detailed explanation about procedures to accomplish 6 functional tests (grip strength, Timed Up and Go, 30 seconds sit to stand, step test, 4-Stage Balance test “modified,” and 10-meter walking speed), 3 questionnaires concerning environmental home hazards, and an activity and participation profile related to mobility and self-efficacy for exercise. RESULTS The enrollment began in June 2016 and we anticipate study completion by the end of 2018. CONCLUSIONS The FallSensing screening tool is a multifactorial and evidence-based assessment which identifies factors that contribute to fall risk. Establishing a risk prediction model will allow preventive strategies to be implemented, potentially decreasing fall rate. REGISTERED REPORT IDENTIFIER RR1-10.2196/10304

scholarly journals Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

PLoS Medicine ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. e1003498
Author(s):  
Luanluan Sun ◽  
Lisa Pennells ◽  
Stephen Kaptoge ◽  
Christopher P. Nelson ◽  
Scott C. Ritchie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Scores ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
History Of ◽  
Predicted Risk ◽  
Conventional Risk Factors

Background Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findings Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703–0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009–0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40–75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to <10%) 10-year CVD risk could help prevent 1 additional CVD event for approximately every 340 individuals screened. Such a targeted strategy could help prevent 7% more CVD events than conventional risk prediction alone. Potential gains afforded by assessment of PRSs on top of conventional risk factors would be about 1.5-fold greater than those provided by assessment of C-reactive protein, a plasma biomarker included in some risk prediction guidelines. Potential limitations of this study include its restriction to European ancestry participants and a lack of health economic evaluation. Conclusions Our results suggest that addition of PRSs to conventional risk factors can modestly enhance prediction of first-onset CVD and could translate into population health benefits if used at scale.

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