逻辑回归
医学
范畴变量
人口
风险评估
有序逻辑
人口学
序数回归
统计
多元统计
优势比
回归分析
可能性
环境卫生
数学
内科学
计算机科学
社会学
计算机安全
作者
Paulin Paul,Noel George,Bo Shan
出处
期刊:Current Medical Imaging Reviews
[Bentham Science]
日期:2021-01-12
卷期号:16 (10): 1300-1322
被引量:5
标识
DOI:10.2174/1573405616666200103144559
摘要
Background: 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. Objectives: To evaluate the traditional risk factors (TRFs) based CV risk estimation done in Kerala based population. Methods: 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. Results: 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. Conclusion: 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.
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