Browsing by Author "Olgin, Jeffrey E."

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    The effects of remodeling with heart failure on mode of initiation of ventricular fibrillation and its spatiotemporal organization
    (Springer, 2015-09) Everett, Thomas H.; Hulley, George S.; Lee, Ken W.; Chang, Roger; Wilson, Emily E.; Olgin, Jeffrey E.; Department of Medicine, IU School of Medicine
    Purpose The effect of the heart failure substrate on the initiation of ventricular fibrillation (VF) and its resulting mechanism is not known. The objective of this study was to determine the effects of substrate on VF initiation and its spatiotemporal organization in the heart failure model. Methods Optical action potentials were recorded from LV wedge preparations either from structurally normal hearts (control, n = 11) or from congestive heart failure (CHF; n = 7), at the epicardial surface, endocardial surface which included a papillary muscle, and a transmural cross section. Action potential duration (APD80) was determined, and VF was initiated. A fast Fourier transform was calculated, and the dominant frequency (DF) was determined. Results The CHF group showed increased VF vulnerability (69 vs 26 %, p < 0.03), and every mapped surface showed an APD80 gradient which included islands of higher APDs on the transmural surface (M cells) which was not observed in controls. VF in the CHF group was characterized by stable, discrete, high-DF areas that correlated to either foci or spiral waves located on the transmural surface at the site of the papillary muscle. Overall, the top 10 % of DFs correlated to an APD of 101 ms while the bottom 10 % of DFs correlated to an APD of 126 ms (p < 0.01). Conclusions In the CHF model, APD gradients correlated with an increased vulnerability to VF, and the highest stable DFs were located on the transmural surface which was not seen in controls. This indicates that the CHF substrate creates unique APD and DF characteristics.
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    Predicting Incident Heart Failure in Women With Machine Learning: The Women's Health Initiative Cohort
    (Elsevier, 2021) Tison, Geoffrey H.; Avram, Robert; Nah, Gregory; Klein, Liviu; Howard, Barbara V.; Allison, Matthew A.; Casanova, Ramon; Blair, Rachael H.; Breathett, Khadijah; Foraker, Randi E.; Olgin, Jeffrey E.; Parikh, Nisha I.; Medicine, School of Medicine
    Background: Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women's Health Initiative (WHI). Methods: We used 2 machine-learning methods-Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)-to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atherosclerosis Risk in Communities (ARIC) HF prediction model. We analyzed 43,709 women who had 2222 incident HF events; median follow-up was 14.3 years. Results: LASSO selected 10 predictors, and CART selected 11 predictors. The highest correlation between selected variables was 0.46. In addition to selecting well-established predictors such as age, myocardial infarction, and smoking, novel predictors included physical function, number of pregnancies, number of previous live births and age at menopause. In ROC analysis, the CART-derived model had the highest C-statistic of 0.83 (95% confidence interval [CI], 0.81-0.85), followed by LASSO 0.82 (95% CI, 0.81-0.84) and ARIC 0.73 (95% CI, 0.70-0.76). Conclusions: Machine-learning approaches can be used to develop HF risk-prediction models that can have better discrimination compared with an established HF risk model and may provide a basis for investigating novel HF predictors.