Sathyabama Institute of Science and Technology, Chennai, India.
Department of Biostatistics, St. Thomas College, Pala, Mahatma Gandhi University, Kottayam, India.
Curr Med Imaging. 2020;16(10):1300-1322. doi: 10.2174/1573405616666200103144559.
Accuracy of Joint British Society calculator3 (JBS3) cardiovascular (CV) risk assessment tool may vary across the Indian states, which is not verified in south Indian, Kerala based population.
To evaluate the traditional risk factors (TRFs) based CV risk estimation done in Kerala based population.
This cross-sectional study uses details of 977 subjects aged between 30 and 80 years, recorded from the medical archives of clinical locations at Ernakulum district, in Kerala. The risk categories used are Low (<7.5%), Intermediate (≥7.5% and <20%), and High (≥20%) 10-year risk classifications. The lifetime classifications are Low lifetime (≤39%) and High lifetime (≥40%) are used. The study evaluated using statistical analysis; the Chi-square test was used for dependent and categorical CV risk variable comparisons. A multivariate ordinal logistic regression analysis for the 10-year risk and odds logistic regression analysis for the lifetime risk model identified the significant risk variables.
The mean age of the study population is 52.56±11.43 years. With 39.1% in low, 25.0% in intermediate, and 35.9% has high 10-year risk. Low lifetime risk with 41.1%, the high lifetime risk has 58.9% subjects. The intermediate 10-year risk category shows the highest reclassifications to High lifetime risk. The Hosmer-Lemeshow goodness-of-fit statistics indicates a good model fit.
Timely interventions using risk predictions can aid in appropriate therapeutic and lifestyle modifications useful for primary prevention. Precaution to avoid short-term incidences and reclassifications to a high lifetime risk can reduce the CVD related mortality rates.
联合英国学会计算器 3（JBS3）心血管（CV）风险评估工具的准确性可能因印度各邦而异，而在印度南部喀拉拉邦的人群中尚未得到验证。
评估基于喀拉拉邦人群的传统风险因素（TRFs）的 CV 风险评估。
这项横断面研究使用了来自喀拉拉邦 Ernakulum 区临床地点的医疗档案中记录的 977 名年龄在 30 至 80 岁之间的受试者的详细信息。使用的风险类别为低（<7.5%）、中（≥7.5%和<20%）和高（≥20%）10 年风险分类。使用低终生（≤39%）和高终生（≥40%）风险分类。该研究使用统计分析进行评估；卡方检验用于依赖和分类 CV 风险变量比较。10 年风险的多变量有序逻辑回归分析和终生风险模型的优势比逻辑回归分析确定了显著的风险变量。
研究人群的平均年龄为 52.56±11.43 岁。低 10 年风险者占 39.1%，中 10 年风险者占 25.0%，高 10 年风险者占 35.9%。低终生风险者占 41.1%，高终生风险者占 58.9%。中间 10 年风险类别显示出向高终生风险的最高重新分类。Hosmer-Lemeshow 拟合优度统计数据表明模型拟合良好。
使用风险预测进行及时干预可以有助于适当的治疗和生活方式改变，有助于初级预防。警惕短期发病和向高终生风险的重新分类，可以降低与 CVD 相关的死亡率。
