Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings

Background No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. Design The construction and validation of statistical models using available data sets. Methods and results Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power ( x2 = 43.8, P < 0.00001). Conclusions Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.

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Prediction of coronary heart disease in rural Chinese adults: a cross sectional study

PeerJ ◽

10.7717/peerj.12259 ◽

2021 ◽

Vol 9 ◽

pp. e12259

Author(s):

Qian Wang ◽

Wenxing Li ◽

Yongbin Wang ◽

Huijun Li ◽

Desheng Zhai ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Prediction Model ◽

Risk Prediction ◽

Prediction Models ◽

Risk Prediction Model ◽

Support Vector ◽

High Morbidity ◽

Rural Residents ◽

Chd Risk

Background Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis. Methods In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors. Results Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models. Conclusion The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.

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Parent and Grandparent Relationships in Emerging Adulthood

10.1093/oso/9780199934263.003.0007 ◽

2018 ◽

Author(s):

Michael W. Pratt ◽

M. Kyle Matsuba

Keyword(s):

Emerging Adulthood ◽

Attachment Theory ◽

Emerging Adults ◽

Family Relationships ◽

Longitudinal Research ◽

Study Data ◽

Family Development ◽

Data Set ◽

Theory Framework

Chapter 7 begins with an overview of Erikson’s ideas about intimacy and its place in the life cycle, followed by a summary of Bowlby and Ainsworth’s attachment theory framework and its relation to family development. The authors review existing longitudinal research on the development of family relationships in adolescence and emerging adulthood, focusing on evidence with regard to links to McAdams and Pals’ personality model. They discuss the evidence, both questionnaire and narrative, from the Futures Study data set on family relationships, including emerging adults’ relations with parents and, separately, with grandparents, as well as their anticipations of their own parenthood. As a way of illustrating the key personality concepts from this family chapter, the authors end with a case study of Jane Fonda in youth and her father, Henry Fonda, to illustrate these issues through the lives of a 20th-century Hollywood dynasty of actors.

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Is Childhood Socioeconomic Status Related to Coronary Heart Disease? Evidence From the Health and Retirement Study (1992-2012)

Gerontology and Geriatric Medicine ◽

10.1177/2333721417696673 ◽

2017 ◽

Vol 3 ◽

pp. 233372141769667 ◽

Cited By ~ 3

Author(s):

Minjee Lee ◽

M. Mahmud Khan ◽

Brad Wright

Keyword(s):

Socioeconomic Status ◽

Coronary Heart Disease ◽

Heart Disease ◽

Later Life ◽

Study Data ◽

Health And Retirement Study ◽

Increased Risk ◽

Childhood Socioeconomic Status ◽

Nationally Representative ◽

Retirement Study

Objective: We investigated the association between childhood socioeconomic status (SES) and coronary heart disease (CHD) in older Americans. Method: We used Health and Retirement Study data from 1992 to 2012 to examine a nationally representative sample of Americans aged ≥50 years ( N = 30,623). We modeled CHD as a function of childhood and adult SES using maternal and paternal educational level as a proxy for childhood SES. Results: Respondents reporting low childhood SES were significantly more likely to have CHD than respondents reporting high childhood SES. Respondents reporting both low childhood and adult SES were 2.34 times more likely to have CHD than respondents reporting both high childhood and adult SES. People with low childhood SES and high adult SES were 1.60 times more likely than people with high childhood SES and high adult SES to report CHD in the fully adjusted model. High childhood SES and low adult SES increased the likelihood of CHD by 13%, compared with high SES both as a child and adult. Conclusion: Childhood SES is significantly associated with increased risk of CHD in later life among older adult Americans.

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Framingham study data and “established wisdom” about cigarette smoking and coronary heart disease

Journal of Clinical Epidemiology ◽

10.1016/0895-4356(89)90070-x ◽

1989 ◽

Vol 42 (8) ◽

pp. 743-750 ◽

Cited By ~ 34

Author(s):

Carl C. Seltzer

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Cigarette Smoking ◽

Study Data ◽

Framingham Study

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Abstract P192: Metabolomic Profiles Associated with Coronary Heart Disease in Women

Circulation ◽

10.1161/circ.133.suppl_1.p192 ◽

2016 ◽

Vol 133 (suppl_1) ◽

Author(s):

Nina P Paynter ◽

Raji Balasubramanian ◽

Shuba Gopal ◽

Franco Giulianini ◽

Leslie Tinker ◽

...

Keyword(s):

Risk Factors ◽

Coronary Heart Disease ◽

Logistic Regression ◽

Heart Disease ◽

Tier 2 ◽

Validation Data ◽

Data Set ◽

Chd Risk ◽

Tier 1 ◽

Chd Risk Factors

Background: Prior studies of metabolomic profiles and coronary heart disease (CHD) have been limited by relatively small case numbers and scant data in women. Methods: The discovery set examined 371 metabolites in 400 confirmed, incident CHD cases and 400 controls (frequency matched on age, race/ethnicity, hysterectomy status and time of enrollment) in the Women’s Health Initiative Observational Study (WHI-OS). All selected metabolites were validated in a separate set of 394 cases and 397 matched controls drawn from the placebo arms of the WHI Hormone Therapy trials and the WHI-OS. Discovery used 4 methods: false-discovery rate (FDR) adjusted logistic regression for individual metabolites, permutation corrected least absolute shrinkage and selection operator (LASSO) algorithms, sparse partial least squares discriminant analysis (PLS-DA) algorithms, and random forest algorithms. Each method was performed with matching factors only and with matching plus both medication use (aspirin, statins, anti-diabetics and anti-hypertensives) and traditional CHD risk factors (smoking, systolic blood pressure, diabetes, total and HDL cholesterol). Replication in the validation set was defined as a logistic regression coefficient of p<0.05 for the metabolites selected by 3 or 4 methods (tier 1), or a FDR adjusted p<0.05 for metabolites selected by only 1 or 2 methods (tier 2). Results: Sixty-seven metabolites were selected in the discovery data set (30 tier 1 and 37 tier 2). Twenty-six successfully replicated in the validation data set (21 tier 1 and 5 tier 2), with 25 significant with adjusting for matching factors only and 11 significant after additionally adjusting for medications and CHD risk factors. Validated metabolites included amino acids, sugars, nucleosides, eicosanoids, plasmologens, polyunsaturated phospholipids and highly saturated triglycerides. These include novel metabolites as well as metabolites such as glutamate/glutamine, which have been shown in other populations. Conclusions: Multiple metabolites in important physiological pathways with robust associations for risk of CHD in women were identified and replicated. These results may offer insights into biological mechanisms of CHD as well as identify potential markers of risk.

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Determination of Significant Features for Building an Efficient Heart Disease Prediction System

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3393.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 4499-4504

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Prediction Model ◽

Prediction Models ◽

Heart Diseases ◽

Medical Diagnostics ◽

Medical Data ◽

Machine Learning Algorithms ◽

Prediction System ◽

Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

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