ArticleAdvances in preventive medicine2019
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.
Article in Advances in preventive medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of Health and Social Factors on the Cardiometabolic Risk in People with Food Insecurity: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
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- Development of AI based behavioral feature patterns on influencing asymptomatic cardiovascular disease attributes: a dataset standardization approach.Scientific reports · 2026Article
- Predicting Metabolic Dysfunction-Associated Fatty Liver Disease Phenotypes Among Adults: 2-Stage Contrastive Learning Method.JMIR medical informatics · 2025Article
- Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder.Brain informatics · 2025Article
- Prediction of survival after neoadjuvant therapy in locally advanced rectal cancer - a retrospective analysis.Frontiers in oncology · 2024Article
- A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection.Bioengineering (Basel, Switzerland) · 2023Article
- Machine learning versus regression for prediction of sporadic pancreatic cancer.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2023Article
- Optimizing exercise testing-based risk stratification to predict poor prognosis after acute heart failure.ESC heart failure · 2023Article
- A Unified Model of Age-Related Cardiovascular Disease.Biology · 2022Review
- Novel Application of Survival Models for Predicting Microbial Community Transitions with Variable Selection for Environmental DNA.Applied and environmental microbiology · 2022Article
- Probabilistic ratiocination of hepatocellular carcinoma after resection: evaluation of expected to be promising approaches.Annals of translational medicine · 2021Article
- Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk.Frontiers in public health · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveCurrent 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.
methodsA 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.
resultsA 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.
conclusionThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.