Accepted and approved in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science. CHD risk calculators are also accessed separately, with their implementation of the calculation code, from calculators predicting death from the disease. Using the same scoring system for each model but a dataset from the CAD registry of the Philippine Heart Association, new estimates for the equations are obtained and used to calculate the probability of death or myocardial infarction from the disease.
The results from the models shown by PHA are shown with their interpretations, as well as the result from the model derived from the Philippine data set.
Background of the Study
Peripheral artery disease is a disorder of the blood vessels that supply the arms and legs. CHD is caused by a buildup of plaque in the coronary arteries, which serve as the heart's main supply of oxygen-rich blood. Interruption of supply results in a heart attack, which would cause the heart muscle to die if not restored.
Examples of calculators that predict mortality are Thrombolysis in Myocardial Infarction (TIMI) [7], Global Registry of Acute Coronary Events (GRACE) calculators [8] and HEART scores.
Statement of the Problem
Objectives of the Study
The system would generate statistical reports based on the data of CHD patients from the information in the database. Determine if the patient has heart problems and calculate the likelihood that this will lead to coronary heart disease using the Framingham risk score. Calculate a person's survival rate for a time frame depending on the model based on the currently entered data using:.
See background on coronary heart disease, which includes causes, signs and symptoms, treatment, and guidelines for prevention.
Significance of the Project
Scope and Limitations
The online calculator is intended solely as a reference for users and not as a completely alternative form of initial diagnosis.
Assumptions
Review of Related Literature 7
The variables that were included in the calculator to predict the risk of coronary heart disease were studied by Wilson et. The same variables are still used today to predict risk using the Framingham risk score model. This system calculates the patient's risk, but is still able to make adjustments in adjusting parameters from the global population to the specific one.
Risk is calculated using a combination of three ability metrics that are analyzed based on three tasks in the game.
Prediction and survival
The point system for the GRACE risk score model is no longer fixed for all probability queries in a given time period. The methods and formulas [28] used to calculate the corresponding probability of death for the GRACE risk score model are as follows. Fox Model for death between hospitalization and 6 months later Figure 6 shows the corresponding probability for different score intervals for the patient in the time period between hospitalization and six months later.
For the Heart Score point system, this is shown in Figures 7 and 8 of the PHA guidelines [9] and the Heart Score website [31].
Difference of coronary heart disease to other types of cardiovascular
If the clot has developed in blood vessels other than the heart and brain, usually the blood vessels in the legs, it is classified as peripheral vascular disease. Hypertension is the increase in blood pressure that pushes too much blood into the heart and the blood vessels that eventually causes disease [33] [34]. From the name itself, Rheumatic heart disease is caused by rheumatic fever attacks that damage the heart valves and muscles.
Cardiomyopathy and cardiac arrhythmias are disorders of the heart muscles and electrical conduction system, respectively, which are less common than heart attacks and strokes [33].
Philippine Heart Association
Congenital heart disease is a malformation of heart structures that is present at birth and includes holes in the heart septum, abnormalities in the valves and heart chambers.
CodeIgniter
Design and Implementation 25
However, patients could still use the website if they know the necessary information they need on the forms. Clinicians would enter patient information into a form on the website, and calculators would then estimate the risk of coronary heart disease in an undiagnosed patient or the risk of death or myocardial infarction in a diagnosed CHD patient. The user can generate reports based on the database filled with information from the patient data in the calculator.
The reports and results from the calculator as well as data can be saved in PDF format.
Use Case Diagram
The information entered into the calculator is stored in the database which can be used later to generate a report.
Entity Relationship Diagram
Data Dictionary
Technical Architecture
Algorithm for processing the calculator inputs
Results 34
Participants included in the analysis were ACS patients with a diagnosis of UA/NSTEMI with at least ST-segment deviation on the qualifying ECG, a history of CHD, or elevated serum cardiac risk markers for TIMI [37] and GRACE [30] . model results. Exclusion criteria were planned revascularization in less than 24 hours, a correctable cause of angina and contraindications to anticoagulation, and patients with ST elevation on admission on ECG or new left bundle branch block [37] [30] . Barbara Backus [38], the HEART score was designed completely a priori; therefore, the coefficients used for the 5 predictors of the HEART score were "all prespecified quite uniquely".
The study they did was simply to calculate how well the HEART score is at predicting an event for patients with chest pain [39]. The following images show the logistic regression analysis performed with the dataset provided by PHA in RSStudio version 3.2.1. After following the same analysis steps [37] only to initially compare the significance of each predictor, regardless of whether they should be included in the final model or not, univariate logistic regression analysis was used.
The variables chosen in the Philippine dataset are based on the predictors already selected in each risk scoring model. The purpose of this analysis is to compare whether the same variables are significant in predicting risk. Even if the variables in the first step are not significant enough, it will still be included in the final model to compare how much change in probability there will be when integrated into the system.
It can be seen that in the analysis that follows on TIMI predictors, only the age (whether greater than or equal to 65 years old or not) and the cardiac biomarkers are the significant variables. After checking the significance of each predictor in the univariate analysis, all predictors were still used for the final model.
Online CHD Calculator
Discussions 53
Coronary heart disease was chosen as the focus of this study because it was found to be one of the leading causes of death in the Philippines [1]. For information purposes, the website also has a functionality where it generates summary reports of the information entered into the calculator within a specified interval. Compared to the implementation used by the programmers to code the GRACE risk calculator [40], the final models generated from their analysis were the ont used in the system's code rather than a one-for-one multiplication of each value of the field to corresponding coefficient code in Javascript.
Part of the calculation for each model is done in the controller part of the framework. As for the original risk points implementation, the calculation part is done in the controller part of the framework and not in Javascript. Models derived from the Philippine dataset using the same predictors were incorporated into the system and also displayed in the results.
As stated in the previous chapters, it can be seen in the analysis done that there are some of the hey predictors that are not significant in the Philippine data set. The results page of the system shows both the probabilities of the original model and the derived model. PHA proposed to have a functionality where a legal record of ACS patients could be entered into the system and it would calculate the corresponding probability for each patient in the data set depending on the model selected.
However, the variables included in the initial analysis must first be validated by a physician until the final stage of the analysis. Doevendans, “Chest pain in the emergency room: a multicenter validation of the cardiac score,” Critical Pathways in Cardiology, vol.
Source Codes
I would like to express my deepest gratitude to the people who made this important request possible. First of all, I would like to thank the people at the Philippine Heart Association, especially the President of the CAD Registry, Dr. Imeldi Caole-Ang, who allowed me to go to their office to do the analysis and who met with me despite her busy schedule.
Gene Banawa, the head of ITC in PHA, who provided the data set necessary for me to complete this requirement and who guided me during the analysis phase of my SP. I would also like to thank Ms. Myrna dela Cruz for receiving me in their office and answering my emails during the initial stages of my SP. To Madam Sheila Magboo, I am grateful to have you as my advisor and mentor for the past few semesters in UP.
Thank you for the guidance and words of support especially when the proposal and defense were close. Thanks for the constructive comments on my PS that helped me figure out what to do. Dad, Mom and Renz, thank you for the support you have given not only financially, but also in other aspects of my well-being.
Although your encouragement initially put me under pressure, I am beginning to understand that you believe in my abilities and that is why you continue to push me forward. To Ms. Liza Billones, thank you for providing slides and references for the regression analysis in my project. To Sir Marvin Ignacio, thank you for the guidance and advise that once before the proposal when I asked about my topic.
It allowed me to see more of the things I was missing in my SP and do something about them.
