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 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.

Cardiovascular Risk Based on ASCVD and KDIGO Categories in Chinese Adults: A Nationwide, Population-Based, Prospective Cohort Study

2021 ◽  
pp. ASN.2020060856
Author(s):  
Yu Xu ◽  
Mian Li ◽  
Guijun Qin ◽  
Jieli Lu ◽  
Li Yan ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cohort Study ◽  
Risk Prediction ◽  
Cox Regression ◽  
Rank Test ◽  
Atherosclerotic Cardiovascular Disease ◽  
Cvd Risk ◽  
Cox Regression Analysis ◽  
Ascvd Risk ◽  
Risk Categories

BackgroundThe Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline used eGFR and urinary albumin-creatinine ratio (ACR) to categorize risks for CKD prognosis. The utility of KDIGO’s stratification of major CVD risks and predictive ability beyond traditional CVD risk prediction scores are unknown.MethodsTo evaluate CVD risks on the basis of ACR and eGFR (individually, together, and in combination using the KDIGO risk categories) and with the atherosclerotic cardiovascular disease (ASCVD) score, we studied 115,366 participants in the China Cardiometabolic Disease and Cancer Cohort study. Participants (aged ≥40 years and without a history of cardiovascular disease) were examined prospectively for major CVD events, including nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death.ResultsDuring 415,111 person-years of follow-up, 2866 major CVD events occurred. Incidence rates and multivariable-adjusted hazard ratios of CVD events increased significantly across the KDIGO risk categories in ASCVD risk strata (all P values for log-rank test and most P values for trend in Cox regression analysis <0.01). Increases in c statistic for CVD risk prediction were 0.01 (0.01 to 0.02) in the overall study population and 0.03 (0.01 to 0.04) in participants with diabetes, after adding eGFR and log(ACR) to a model including the ASCVD risk score. In addition, adding eGFR and log(ACR) to a model with the ASCVD score resulted in significantly improved reclassification of CVD risks (net reclassification improvements, 4.78%; 95% confidence interval, 3.03% to 6.41%).ConclusionsUrinary ACR and eGFR (individually, together, and in combination using KDIGO risk categories) may be important nontraditional risk factors in stratifying and predicting major CVD events in the Chinese population.

scholarly journals Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Lu Lu ◽  
Le-Ping Liu ◽  
Qiang-Qiang Zhao ◽  
Rong Gui ◽  
Qin-Yu Zhao
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Tumor Cells ◽  
Risk Prediction ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Prediction Model ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.

Abstract 043: Health Insurance and the Risk of Incident Cardiovascular Disease in the Multi-Ethnic Study of Atherosclerosis

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Matthew T Crim ◽  
Joe X Xie ◽  
Yi-An Ko ◽  
Roger S Blumenthal ◽  
Michael J Blaha ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Health Insurance ◽  
Lower Risk ◽  
Cox Regression ◽  
Subclinical Atherosclerosis ◽  
Private Insurance ◽  
Lipid Lowering ◽  
Lipid Lowering Therapy ◽  
Cvd Risk ◽  
Ethnic Study

Background: Health insurance plays an important role in access to medical care and is the focus of extensive policy efforts. We examined the association of health insurance with cardiovascular disease (CVD) incidence. Methods and Results: The Multi-Ethnic Study of Atherosclerosis, sponsored by the National Heart, Lung and Blood Institute of the NIH, followed a US cohort, aged 45-84 without clinical CVD at baseline, for a median of 12.2 years; 788 events occurred among 6,674 individuals. Data were stratified by baseline health insurance status. Kaplan-Meier survival and Cox regression analyses were used to assess the association between health insurance and incident CVD (myocardial infarction, resuscitated cardiac arrest, stroke, CVD death, and angina), adjusting for biomedical CVD risk (traditional risk factors, including age and race/ethnicity, and markers of subclinical atherosclerosis) and socioeconomic status (SES). The majority of individuals had private insurance (51%). Uninsured individuals (9%) were more likely to have untreated hypertension and diabetes, less likely to be on lipid-lowering therapy, and more likely to receive care in an Emergency Department (p < 0.0001). Income, 10-year CVD risk, and 10-year event-free survival varied across insurance groups ( Table ). After adjustment for biomedical CVD risk, individuals with health insurance had a lower risk of incident CVD compared to the uninsured (HR 0.72, p=0.03). However, with additional adjustment for SES (income, education, and employment), insurance was no longer associated with incident CVD (HR 0.78, p=0.12). Among the insurance groups, those with private insurance had a lower risk of incident CVD after adjustment for both biomedical CVD risk and SES (HR 0.70, p=0.03). Medicare and Medicaid coverage were not associated with incident CVD. The military/VA group had a lower risk of incident CVD with adjustment for biomedical CVD risk (HR 0.57, p=0.02) that was no longer significant after adjustment for SES (HR 0.66, p=0.09). Conclusions: The association of health insurance with CVD incidence varied by insurance group, and private insurance was associated with a lower risk of incident CVD. Further exploration of the features of health insurance coverage that impact CVD incidence may facilitate improvements in the primary prevention of CVD.

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. 

Development and Validation of a Risk Prediction Model for Diabetic Retinopathy in Type 2 Diabetic Patients (Preprint)

2021 ◽  
Author(s):  
Chengjun Zhu ◽  
Jiaxi Zhu ◽  
Lei Wang ◽  
Shizheng Xiong ◽  
Yijian Zou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Prediction Model ◽  
Risk Prediction ◽  
Characteristic Curve ◽  
Risk Prediction Model ◽  
Lipid Lowering ◽  
Diabetic Patients ◽  
Type 2 Diabetic Patients

BACKGROUND Diabetes mellitus (DM) has become one of the most serious public health problems in the 21st century. chronic complications associated with type 2 DM (T2DM) increase the rate of disability, leading to untimely death and reduce the quality of life. In these complications, diabetic retinopathy (DR) is the most common one and could lead to secondary blindness. Despite retinal screening is first-of-choice for DR diagnosis, the limits of such screening equipments and experienced image readers restricted its applications, especially in those rural areas where DR risks even higher. Therefore, it’s essential to construct an easy-to-implement predictive model of the risk of DR in order to help predict individual morbidity and identify the risk factors of DR. OBJECTIVE Diabetic retinopathy (DR) has a high incidence rate in diabetic patients, the quality of life of whom will be seriously affected if not treated in time. This study aims to develop a risk prediction model for DR in type 2 diabetic patients. METHODS According to the retrieval strategy, inclusion and exclusion criteria, the relevant Meta analyses on DR risk factors were searched and evaluated. The pooled odds ratio (OR) or relative risk (RR) of each risk factor was obtained and calculated for β coefficients using logistic regression (LR) model. Besides, an electronic patient-reported outcome questionnaire was developed and 60 cases of DR and non-DR T2DM patients were investigated to validate the developed model. Receiver operating characteristic curve (ROC) was drawn to verify the prediction accuracy of the model. RESULTS After retrieving, eight Meta analysis with a total of 15654 cases and 12 risk factors associated with the onset of DR in T2DM, including weight loss surgery, myopia, lipid-lowing drugs, blood glucose control, course of T2DM, glycosylated hemo-globin, fasting blood glucose, hypertension, gender, insulin treatment, residence, and smoking were included for LR modeling. These factors, followed by the respective β coefficient was bariatric surgery(-0.942), myopia(-0.357), lipid-lowering drug follow-up <3y(-0.994), lipid-lowering drug follow-up >3y(-0.223), course of T2DM(0.174), glycated hemoglobin (0.372), fasting blood sugar(0.223), insulin therapy(0.688), rural residence(0.199), smoking(-0.083), hypertension(0.405), male(0.548), blood sugar control(-0.400) with constant term α = -0.949 in the constructed model. The area under receiver operating characteristic curve (AUC) of ROC curve of the model in the external validation was 0.912. An application was presented as an example of use. CONCLUSIONS In this study, the risk prediction model of DR was developed, which make individualized assessment for the susceptible DR population feasible and need to be further verified with large sample size application.

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