Cardiac ScoreCard: A diagnostic multivariate index assay system for predicting a spectrum of cardiovascular disease |
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| Affiliation: | 1. Department of Bioengineering, Rice University, Houston, TX, USA\n;2. Michael E. DeBakey VA Medical Center, Houston, TX, USA;3. Section of Cardiology, Baylor College of Medicine, Houston, TX, USA;4. Department of Chemistry, Rice University, Houston, TX, USA;5. Ben Taub General Hospital, Houston, TX, USA;6. Department of Oral Health Practice, Center for Oral Health Research, College of Dentistry University of Kentucky, Lexington, KY, USA;7. Department of Cardiology, Erlanger Health System, Chattanooga, TN, USA;8. Department of Biomaterials, New York University, New York, NY, USA;1. Graduate Program in Applied Computing (PPGCA);2. Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology - Parana (UTFPR). Av. Sete de Setembro, 3165. CEP 80230-901, Curitiba, Brazil;3. Institut National de Recherche en Informatique et en Automatique (INRIA) Saclay - Ile de France. 4, rue Jacques Monod, 91893 Orsay Cedex, France;1. Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;2. Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;1. Research Center of Intelligent Signal Processing (RCISP), Tehran, Iran;2. Department of Biomedical Engineering, Amirkabir University of Technology, 424 Hafez Ave, Tehran 15875-4413, Iran;1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong\n;2. Information Technology Services Centre, The Chinese University of Hong Kong, Shatin, Hong Kong\n;3. American Express, 18850 N. 56th Street, Phoenix, AZ 85054, USA |
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| 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. |
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