The Applicability Of The Ib Risk Assessment Tool In Iran And EMR

These measurements include the assessment of risk factors[61], quality of care[62], diagnostic criteria[63], etc. Most of these studies used rule-based method[62, 63] to detect clearly defined and less complex (fewer expression variations) measurements, such as glucose level and body mass index. For some ambiguous and complex measurements, such as coronary artery disease and obesity status, machine learning plus external terminologies[61] are often

Furthermore, despite the UK Prospective Diabetes Study (UKPDS) did not show a significant trend in the reduction of myocardial infarction (MI) rates, the 10-year follow up of this trial

Subjects with undiagnosed Type 2 diabetes are at superior risk of developing coronary artery disease and stroke. Therefore it is significant to screen for diabetes in a cost efficient manner in subjects who demonstrate major risk factors for diabetes.

The Centers for Disease Control and Prevention (CDC) reports, the cause of death to be roughly 720,000 citizens in the United States die from cardiovascular disease each year (Centers for Disease Control and Prevention, 2014.) Deaths’ relating to cardiovascular disease have slightly dropped annually since the 1980’s. Coronary artery disease is the most prevalent type of cardiovascular disease in the U.S. and it kills around 400,000 Americans annually (Murphy, Xu, Kochanek, 2013.) Statistics like these are astounding and suggest the seriousness of this epidemic we are dealing with. Determinants of one’s health are vast but vary based on each individual.

CHD is the most common type of cardiovascular disease [2] and was the leading cause of death globally between 2000 and 2012 [3] – accounting for 14.6% of all deaths in Australia in 2011 (21,513 persons) and 7.8% of all disability-adjusted life years (DALYs) in Australasia in 2010 (471,550 DALYs) [4]. In 2011-12, CHD was estimated to affect 585,900 Australians, with the condition being more common in men (3.3%) than women (2.0%) and among those aged 70 and over (15% compared with 2.2% for those aged 25-69) [4]. In 2008-09 more than $2 billion was spent on CHD treatment and expenses – more than any other disease in Australia [5].

In the 2015 JACC: Heart Failure paper, Finocchiaro et al. investigated the effects of stress-induced cardiomyopathy (SCM) on right ventricular function. SCM is a reversible type of cardiomyopathy observed in patients without significant coronary disease. It is most observed in postmenopausal woman and is triggered by acute emotional or physical stress. They conducted a

Coronary heart disease (CHD) and chronic heart failure (CHF) are a major public health concern in the world; each year both of them causes huge number of direct and indirect economic losses for many developed and developing countries, which is the ultimate outcome of disease progression in all patients with cardiovascular disease, is even greater for medical and public investments in patients with heart failure. In the United States every 1000 people over the age of 65 in 10 patients with chronic heart failure, Projections show that by 2030, the total cost of HF will increase almost 127% to $69.7 billion for every US adult. The costs associated with treating HF comorbidities and HF exacerbations in youths are significant, totaling nearly $1 billion in inpatient costs, and may be rising [8]. Even today's progress in medical technology and the development of medicine, the prognosis of patients with heart failure variability is more serious. Therefore, it is extremely important to classify the risk stratification accurately and to distinguish the high-risk population and start

The Framingham Heart Study was the first longitudinal cohort study to try to elucidate the etiology and determinants of CVDs, and it’s currently the longest running medical study in the world, celebrating it’s 68 years in 2016. It is in it’s third generation now (grandchildren of the original cohort), and although it’s population is mainly of white European descent, the results were fundamental in understanding the risk factors of CVDs. We recognize the need to know if the results of this study, and its risk estimation module, could be applied to the Lebanese population, since no major comparison study was done before in our country.

Being anxious or experiencing symptoms of anxiety is usually harmless in moderation. Individuals go through times of high stress many times throughout their lives. However, not many individuals stop to think of the possible consequences that can occur due to how they choose to cope with their anxiety. One potential consequence that has the potential to occur is heart disease or major cardiovascular events, unfortunately this still remains a very recent study concept. Stroke, heart failure and heart disease can all fall under examples of major cardiovascular events (Edmin, Odutayo, Wong, Tran, Hsiao, & Hunn, 2016). Even though the research that examines the correlation between heart disease and anxiety remains fairly new, studies regarding

Cardiovascular Disease (CVD) is a diagnosis, however, consider the functional aspects of the heart. The heart is vital for our body to function appropriately. When the heart is not operating at 100% other systems can not function properly. The heart is like the public health system, whereas if the system does not operate correctly we see other systems decline. As Turnock stated,” Landmark epidemiological investigations, such as the Framingham Heart Study, established the major risk factors of high blood cholesterol, high blood pressure, and smoking and dietary factors (particularly dietary cholesterol, fat, and sodium” (Turnock,2012). This portion would fall within the assessment portion. Through assessment we gain the knowledge that is necessary to see how the population is functioning. With many other health inequalities,

Statistical and qualitative analyses can determine extensive medical data and expose concealed patterns and trends (Koh & Tan, 2011; Soni, Ansari, Sharma, & Soni, 2011). Sharek et al. (2011) and Kurosaki et al. (2012) also proposed that using statistical techniques to analyze EHR data can help to categorize patients at high-risk, and hence promote evidence-based healthcare. Data mining is a predictive and descriptive analytic tool that improves quality patient care through preventions and early diagnosis.

CDSS are often incorporated within EHR systems and integrated with other computer-based functions that offer patient-care summary reports, feedback on quality indicators, and benchmarking2-3. Knowledge management systems providing access to scientific literature and strategies for CVD prevention may also be linked with CDSS1-4. Many studies conducted in different health care settings have documented the effectiveness of CDSS in improving quality of care and clinical outcomes for a variety of conditions such as CVD prevention, cancer screening, immunization, antenatal care, just to mention a few1,5-11. The mission of this paper is to review the roles and the applications of CDSS in the prevention of IHD.

The International classification of diseases and related health problems, version 10 (ICD-10) in the United States, was the idea of the National center for health statistics (NCHS). The reason for this change was intended for organizing morbidity status. The new coding include has more addition into it that provides specificity and space for expansion of the code (CDC, 2017). The increased intricacy in the coding will cause challenges in documentation and coding. Proper training is necessary to overcome this kind of situation. Even though there are some downfalls in the initial stage of transformation the benefits are promising. The health care world will be more precise and in identifying diseases, and documenting procedures accurate prediction

Having considered the variety of predictors and their interactions in a multivariate Cox regression, significant predictors of CVD events were age, sex, high WHR, SBP level, TC level, diabetes mellitus, smoking status and family history of CVD. The optimal PARS model is presented in Table

In the Kaiser Permanente case, to understand the causes of and contributors to mortality, the system Kaiser Permanente Northern California hospitals leveraged the data in their system to find mortality rates by clinical condition. Specifically, they used the International Classification of Diseases, ninth Revision ICD-9 coding system as a way to organize the rate of mortality by disease. The ICD-9 codes were used as a key-value identifier of different the different types of diseases that a patient may have a diagnosis for. From there, the ICD-9 codes were then reorganized individual insurance codes into broader clinical bins, which supported the analysis of patients by clinical conditions. This information likely was extracted from the databases of Kaiser Permanente’s Electronic Health Record systems, where they pulled out data on either the “primary visit reason” or “primary diagnosis” from each visit. As noted in the report by Basel Kayyali, the ability to capture the large amounts of data from the Electronic Health Record systems is enabled by an integrated technology system facilitates the collaboration between the many hospitals via the exchange of information.