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.

Abstract 14508: Development of a Risk Prediction Model for 1-year Readmission Among Younger Adults Hospitalized for Acute Myocardial Infarction

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Rachel P Dreyer ◽  
Terrence E Murphy ◽  
Valeria Raparelli ◽  
Sui Tsang ◽  
Gail Onofrio ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Model ◽  
Risk Prediction ◽  
Bayesian Model Averaging ◽  
Risk Model ◽  
Young Patients ◽  
Risk Prediction Model ◽  
Younger Adults

Introduction: Although readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (18-55 yrs), there is no available risk prediction model for this age group. Existing risk models have been developed in older populations, have modest predictive ability, and exhibit methodological drawbacks. We developed a risk prediction model that considered a broad range of demographic, clinical, and psychosocial factors for readmission within 1-year of hospitalization for AMI among young adults. Methods: Young AMI adults (18-55 yrs) were enrolled from the prospective observational VIRGO study (2008-2012) of 3,572 patients. Data were obtained from medical record abstraction, interviews, and adjudicated hospitalization records. The outcome was all-cause readmission within 1-year. We used a two-stage selection process (LASSO followed by Bayesian Model Averaging) to develop a risk model. Results: The median age was 48 years (IQR: 44,52), 67.1% were women, and 20.1% were Non-white or Hispanic. Within 1-year, 906 patients (25.3%) were readmitted. Patients who were readmitted were more likely to be female, black, and had a clustering of adverse risk factors and co-morbidities. From 61 original variables considered, the final multivariable model of readmission within 1-year of discharge consisted of 14 predictors (Figure) . The model was well calibrated (Hosmer-Lemeshow P >0.05) with moderate discrimination (C statistic over 33 imputations: 0.69 development cohort). Conclusion: Adverse clinical risk factors such as diabetes, hypertension and prior AMI, but also female sex, access to specialist care, and major depression were associated with a higher risk of readmission at 1-year post AMI. This information is important to inform the development of interventions to reduce readmissions in young patients with AMI.

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 Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study

2021 ◽  
Author(s):  
Daniel Mølager Christensen ◽  
Matthew Phelps ◽  
Thomas Gerds ◽  
Morten Malmborg ◽  
Anne-Marie Schjerning ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Sex Education ◽  
Cox Regression ◽  
Population Level ◽  
Risk Prediction Model ◽  
Lipid Lowering ◽  
Chronic Obstructive ◽  
Cvd Risk

Abstract Aims To derive and validate a risk prediction model with nationwide coverage to predict individual and population-level risk of cardiovascular disease (CVD). Methods and Results All 2.98 million Danish residents aged 30-85 years free of CVD were included on January 1, 2014 and followed through December 31, 2018 using nationwide administrative healthcare registries. Model predictors and outcome were pre-specified. Predictors were: Age, sex, education, use of antithrombotic, blood pressure-lowering, glucose-lowering, or lipid-lowering drugs, and a smoking proxy of smoking-cessation drug use or chronic obstructive pulmonary disease. Outcome was 5-year risk of first CVD event, a combination of ischemic heart disease, heart failure, peripheral artery disease, stroke, or cardiovascular death. Predictions were computed using cause-specific Cox regression models. The final model fitted in the full data was internally-externally validated in each Danish Region. The model was well-calibrated in all Regions. Areas under the curve (AUC) and Brier scores ranged from 76.3% to 79.6% and 3.3 to 4.4. The model was superior to an age-sex benchmark model with differences in AUC and Brier scores ranging from 1.2% to 1.5% and -0.02 to -0.03. Average predicted risks in each Danish municipality ranged from 2.8% to 5.9%. Predicted risks for a 66-year-old ranged from 2.6% to 25.3%. Personalized predicted risks across ages 30-85 were presented in an online calculator (https://hjerteforeningen.shinyapps.io/cvd-risk-manuscript/). Conclusion A CVD risk prediction model based solely on nationwide administrative registry data provided accurate prediction of personal and population-level 5-year first CVD event risk in the Danish population. This may inform clinical and public health primary prevention efforts.

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.

scholarly journals Identification of risk factors for daptomycin-associated creatine phosphokinase elevation and development of a risk prediction model for incidence probability

2021 ◽  
Author(s):  
Masaru Samura ◽  
Naoki Hirose ◽  
Takenori Kurata ◽  
Keisuke Takada ◽  
Fumio Nagumo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Logistic Regression Analysis ◽  
Creatine Phosphokinase ◽  
Risk Prediction Model ◽  
Risk Scores

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and &lt;30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were &lt;10% (low risk), 10%–&lt;25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

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