Cardiac ScoreCard: A diagnostic multivariate index assay system for predicting a spectrum of cardiovascular disease

Abstract

Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard—a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC = 0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.

Challenges and Opportunities for Translating Medical Microdevices: Insights from The Programmable Bio-Nano-Chip

“…Cardiac disease is the number one killer in the US and on a global basis [43], and staggering direct and indirect costs make cardiac disease a major contributor to the already enormous healthcare burden in the US economy [44]. Toward the goals of saving lives and driving down healthcare costs, we are developing the Cardiac ScoreCard -a series of machine learning algorithms that quantify risk for a spectrum of cardiovascular diseases using multiplexed biomarker measurements, symptoms, medical history, and demographics [45]. Through frequent monitoring, prevention, and early detection of cardiac disease, these new tools have the potential to promote wellness and prevention in populations globally.…”

“…Technologies for advanced screening, diagnosing, and monitoring that are simple to use, rapid, noninvasive, accurate, sensitive, and accessible to the patients in a variety of settings, such as physicians and dentists offices, pharmacies, and at home, are now being developed and promise to overcome (Ai) Reproduced with permission from [45] [53]. However, there is a strong tendency for commercial devices based on microfluidics that originate from academic research to suffer from the gap that separates academia and industry.…”

“…Our group has established a large number of collaborative projects, launched three centers in medical microdevices, founded a number of diagnostic companies in this space, and now manages six major clinical trials involving over 5000 patients at ten clinical sites (Table 2). In the area of cardiac heart disease, we are aiming to validate 15 cardiac biomarkers in serum and saliva for the screening of acute myocardial infarction in chest pain patients presenting to the emergency room [45]. For oral cancer, two major clinical studies have been initiated, resulting in one of the largest cytology databases related to oral cancer ever created.…”

Educational Intervention Through a Group Discussion is Effective on the Self-Esteem of Patients with Heart Failure

“…HF is a common chronic progressive costly disease characterized by an impairment in function or pumping of the heart, recognized as one of the most common and complex health issues worldwide (7)(8)(9). HF disrupts the blood oxygenation process and limits the patient's ability to expel waste materials, especially water, which can lead to serious injury to the patient (10).…”

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

“…From these works, it can be observed that feature selection methods can effectively increase the performance of single classifier algorithms in diagnosing heart disease [19]. Noisy features and dependency relationships in the heart disease dataset can influence the diagnosis process.…”