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A Comparison of Echocardiogram and Cardioproteomic Predictors of Post- Aortic Valve Replacement Outcomes

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A Comparison of Echocardiogram and Cardioproteomic Predictors of Post- Aortic Valve Replacement Outcomes Improving Predictions in the Era of Precision Medicine: A Comparison of Echocardiogram and Cardioproteomic Predictors of Post- Aortic Valve Replacement Outcomes The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Camacho, Alexander. 2021. Improving Predictions in the Era of Precision Medicine: A Comparison of Echocardiogram and Cardioproteomic Predictors of Post-Aortic Valve Replacement Outcomes. Master's thesis, Harvard University Division of Continuing Education. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37367621 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Improving Predictions in the Era of Precision Medicine: A Comparison of Echocardiogram and Cardioproteomic Predictors of Post-Aortic Valve Replacement Outcomes Alexander Camacho, Ph.D. A Thesis in the Field of Bioengineering & Nanotechnology for the Degree of Master of Liberal Arts in Extension Studies Harvard University May 2021 Abstract Aortic stenosis (AS) is a serious form valvular heart disease characterized by stiffening and calcification of aortic valve leaflets. Aortic valve replacement (AVR) remains the only FDA-approved treatment for AS; however, up to 40% of patients can experience worsening of symptoms or death after intervention. Imaging measurements obtained by transthoracic echocardiogram (TTE) have been widely used to predict post- AVR outcomes; however, significant interobserver variability and lack of data standards limit their prognostic utility. The advent of Precision Medicine and cardioproteomics has enabled novel research into disease pathophysiology, given that proteins are proximal markers of disease state and severity. Thus, we hypothesized that modeling post-AVR outcomes with protein analytes would yield better accuracy than with TTE measurements. In this cohort study of patients who underwent AVR (N=75), we constructed two types of classification models—Logistic Regression and Convolutional Neural Networks—to compare areas under the curve (AUC) between protein analyte and TTE measurement models. Results showed that protein analyte models achieved better global accuracy; however, further works are needed to determine whether combining predictor types offers the best approach for predicting post-AVR adverse outcomes. Acknowledgements I'd like to thank my thesis director, Dr. Sammy Elmariah, for his invaluable guidance and support throughout this process. iv Table of Contents Abstract .............................................................................................................................. iii Acknowledgements ............................................................................................................ iv List of Tables .................................................................................................................... vii List of Figures .................................................................................................................. viii Chapter I: Introduction ........................................................................................................ 1 Diagnosing & Monitoring AS: The Role of Transthoracic Echocardiograms ................ 2 Prognostic Utility of TTE Measurements .................................................................... 4 Strengths & Challenges of TTE Measurements .......................................................... 5 Precision Medicine & Cardioproteomics ........................................................................ 8 Cardioproteomic Studies of Aortic Stenosis ............................................................... 8 Prognostic Utility of Protein Concentrations............................................................... 9 Limitations of Cardioproteomics ............................................................................... 10 Gaps & Summary .......................................................................................................... 13 Chapter II: Materials and Methods ................................................................................... 14 Research Question & Hypothesis .................................................................................. 14 Data Sources & Data Collection Methods .................................................................... 15 Patient Population ...................................................................................................... 15 Imaging Measurements .............................................................................................. 16 v Cardioproteomics....................................................................................................... 16 Statistical Methods ........................................................................................................ 17 Statistical Modeling ................................................................................................... 17 Model Training Strategy & Hypothesis Testing........................................................ 21 Chapter III: Results ........................................................................................................... 22 Model Set 1: Logistic Regressions ................................................................................ 25 Model 1A: Logistic Regression with TTE Measurements ........................................ 25 Model 1B: Logistic Regression with Protein Analytes ............................................. 27 Model Set 2: Convolutional Neural Networks .............................................................. 30 Model 2A: CNN with TTE Measurements ................................................................ 30 Model 2B: CNN with Protein Analytes ..................................................................... 32 Comparison of Predictive Accuracy ............................................................................. 35 Chapter IV: Discussion ..................................................................................................... 37 Implications ................................................................................................................... 39 Limitations .................................................................................................................... 40 Conclusions ................................................................................................................... 41 Appendix A ....................................................................................................................... 42 Appendix B ....................................................................................................................... 44 References ......................................................................................................................... 51 vi List of Tables Table 1. Imaging Measurements Used to Characterize Aortic Stenosis Severity. ............. 4 Table 2. Prior Works Examining Prognostic Utility of TTE Measurements. ..................... 7 Table 3. Prior Works Examining Prognostic Utility of Protein Analytes......................... 12 Table 4. Transthoracic Echocardiogram Measurements Predictors. ................................ 16 Table 5. Specification for Logistic Regression Models. ................................................... 18 vii List of Figures Figure 1. Disease States in Aortic Stenosis ........................................................................ 2 Figure 2. Parasternal and Apical Four-Chamber Views on TTE ........................................ 3 Figure 3. Use of Aortic Valve Mean Gradients for Comparing Improvement Post-AVR . 6 Figure 4. Adjusted Hazard Ratios for 6-Protein Core Signature Prognostic of AS .......... 10 Figure 5. Top-Down and Bottom-Up Quantitation Approaches in Proteomics ............... 11 Figure 6. Conceptual Visualization of Convolutional Neural Networks .......................... 19 Figure 7. Convolutional Neural Network Architectures. .................................................. 20 Figure 8. Boxplots of Baseline Demographics by Group ................................................. 24 Figure 9. Confusion Matrix (A) and Accuracy Statistics (B) for Model 1A. ................... 26 Figure 10. Feature Importance for Predicting Post-AVR Adverse Events (Model 1A). .. 26 Figure 11. Confusion Matrix (A) and Accuracy Statistics (B) for Model 1B. ................. 27 Figure 12. Feature Importance for Predicting Post-AVR Adverse Events (Model 1B). .. 28 Figure 13. Boxplots for Proteins with High Relative Importance (Model 1B). .............. 29 Figure 14. Accuracy, Confusion Matrix, and Performance Metrics for Model 2A. ......... 31 Figure 15. Feature Importance for Predicting Post-AVR Adverse Events (Model 2A). .. 31 Figure 16. Accuracy, Confusion Matrix, and Performance Metrics for Model 2B. ......... 32 Figure 17. Feature Importance for Predicting Post-AVR Adverse Events (Model 2B). .. 33 Figure 18. Boxplots for Proteins with High Relative Importance (Model 2B). ............... 34 Figure 19. AUC and ROC Curves for Models 1A and Model 1B. ................................... 35 Figure 20. F1 Scores by Model and Predictor Type. ........................................................ 36 viii Chapter
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