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Heart Disease Biosurveillance Algorithm

There are various types of heart disease diagnosed among the citizens of the U.S. Coronary Heart Disease (CHD) is considered the most common of them, killing approximately 370,000 people every year (Coronary Heart Disease, 2016). Various algorithms have been created for the purpose of surveillance and prediction of the development of CHD. The algorithm uses parameters, such as blood pressure and cholesterol levels to predict the onset of heart disease. Covariates used are usually risk factors, including smoking, alcohol consumption, body weight, diabetes and exercise levels (Allan et al., 2013). The paper will describe the algorithm and covariates used in heart disease surveillance, in particular CHD, its limitations and implications in public health.

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Algorithm Variants

The development of CHD is related to cholesterol levels and blood pressure. Cholesterol measurements include tests for Low-Density Lipoprotein (LDL-C), Total Cholesterol (TC), and High-Density Lipoprotein (HDL-C) Cholesterol (Holmes, 2014). Blood pressure is considered either normal or high. The measurements of the variants are recorded in the Electronic Health Records from patients’ hospital visits.

High blood pressure levels common in hypertension are measured in regard to systolic and diastolic pressures. The categorization of blood pressure is done using existing categories that are used to classify them, and the algorithm is used to detect hypertension levels that are systolic ≥ 140 mm Hg and diastolic ≥ 90 mm Hg (Kottke & Baechler, 2013). The normal blood pressure will not be considered in the algorithm because only high blood pressure creates risks of developing heart disease. The hypertension levels will be divided into four categories of lowest to highest risks levels.

The TC levels ≥ 200mg per dL, LDL-C ≥100 mg per dL and HDL-C <40 for men and < 50 for women are also the detection point for the algorithm. In the TC levels and LDL-C, values higher than the normal indicate a high risk for the development of CHD (Holmes, 2014). HDL-C levels below ideal levels are also common in CHD cases as HDL offers some protection from any heart disease. The algorithm is designed to allow different categorizations of each parameter, preferably four levels from lowest risk to highest risk. The TC and LDL-C will be categorized from lowest risk to highest risk in four levels. HDL-C, on the other hand, will be categorized from high to low-risk levels because higher levels of HDL mean more protection against heart disease.


Troponin levels are used as a covariate. The levels of troponin above normal are associated with high risks of developing heart diseases and will offer control variables that will be used together with the dependent variables in the algorithm. Men and women have sex-specific troponin levels. In men, the levels reach ≥ 7.4pg/mL and in women ≥ 6.4pg/mL. They are associated with high-risk levels of developing heart disease that will require hospitalization (Allan et al., 2013).

The algorithm will use these levels that will also be divided into two risk level categories, namely from low-risk level to high-risk level. The troponin I and T levels have been associated for many years with the risks of developing heart disease (Allan et al., 2013). Patients with stable CHD have been noted to have measurable levels of troponin T, while higher levels have been associated with heart failure. The measurement will, therefore, offer an excellent covariate.

Other covariates may include diabetes, exercise levels, smoking, and alcohol abuse levels. The covariates are associated with lifestyle choices that affect the probability of developing heart disease (Kottke & Baechler, 2013). People with diabetes are at a higher risk of developing heart disease than people without diabetes. People with smoking and alcohol drinking problems are also more probe to development of CHD (Kottke & Baechler, 2013). People who exercise less are also at high risks of having high TC and LDL-C levels that are associated with CHD.

Algorithm Limitations

The algorithm has various limitations. Most of the variable are dependent on age and sex of the patients, therefore, requiring complex programming that takes age and sex into consideration to ensure accuracy (Kottke & Baechler, 2013). Such covariates as exercise levels and smoking habits are also hard to measure. In ensuring that the algorithm is accurate and sensitive, all the limitations must be taken into consideration.


The algorithm is effective in predicting heart disease cases, especially CHD. The algorithm has taken into consideration two of the most common indicators of heart disease, which are blood pressure and cholesterol levels. Despite the limitation, the algorithm can be used to warn people before they develop heart disease as most of the illnesses can be prevented if detected early enough. Prevention of such heart disease as CHD will lead to the preservation of life, reduced cost of treatment, as well as reduced loss of productivity.

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