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.
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.
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DOI：
10.1155/2019/8392348
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发表时间：
2019-01-01
影响因子：
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通讯作者：
GholamHosseini, Hamid
中科院分区：
文献类型：
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作者：
Jia, Xiaona;Baig, Mirza Mansoor;GholamHosseini, Hamid
背景和目的：当前的心血管疾病（CVD）风险模型通常基于传统的实验室预测因子。本研究的目的是确定影响CVD风险预测的关键风险因素，并利用已确定的风险因素开发10年CVD风险预测模型。方法：应用Cox比例风险回归方法生成所提出的风险模型。我们使用了 Framingham 原始队列的数据集，该队列包含 5079 名年龄在 30-62 岁之间的男性和女性，他们在基线时没有明显的 CVD 症状；结果：建立了基于多种危险因素（如年龄、性别、体重指数（BMI）、高血压、收缩压（SBP）、每天吸烟支数、脉率和糖尿病）的 10 年 CVD 风险模型，其中心率被确定为新的危险因素之一。所提出的模型具有良好的区分和校准能力，验证数据集中的 C 指数（接收器操作特性（ROC））为 0.71。我们通过统计和实证验证对该模型进行了验证。结论：所提出的 CVD 风险预测模型基于标准风险因素，这有助于减少进行临床/实验室测试所需的成本和时间。医疗保健提供者、临床医生和患者可以使用此工具查看个人 10 年 CVD 风险。心率被纳入作为一种新颖的预测因子，扩展了过去现有风险方程的预测能力。
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.