Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning.

BACKGROUND: Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk and phenotyping score for HFrEF, using Machine Learning (ML). METHODS: Prior to modeling, patients in training and validation HF cohorts were assigned to 1 of 5 risk categories based on the composite endpoint of death, left ventricular assist device (LVAD) implantation or transplantation (DeLvTx), and rehospitalization in 6 months of follow-up using unsupervised clustering. The goal of our novel interpretable ML modeling approach, which is robust to missing data, was to predict this risk category (1, 2, 3, 4, or 5) using either invasive hemodynamics alone or a rich and inclusive feature set that included noninvasive hemodynamics (all features). The models were trained using the ESCAPE trial and validated using 4 advanced HF patient cohorts collected from previous trials, then compared with traditional ML models. Prediction accuracy for each of these 5 categories was determined separately for each risk category to generate 5 areas under the curve (AUCs, or C-statistics) for belonging to risk category 1, 2, 3, 4, or 5, respectively. RESULTS: Across all outcomes, our models performed well for predicting the risk category for each patient. Accuracies of 5 separate models predicting a patient's risk category ranged from 0.896 +/- 0.074 to 0.969 +/- 0.081 for the invasive hemodynamics feature set and 0.858 +/- 0.067 to 0.997 +/- 0.070 for the all features feature set. CONCLUSION: Novel interpretable ML models predicted risk categories with a high degree of accuracy. This approach offers a new paradigm for risk stratification that differs from prediction of a binary outcome. Prospective clinical evaluation of this approach is indicated to determine utility for selecting the best treatment approach for patients based on risk and prognosis.

Right atrial structural remodeling predict worse outcomes in transcatheter mitral valve repair.

BACKGROUND: In the current study, we assess the predictive role of right and left atrial volume indices (RAVI and LAVI) as well as the ratio of RAVI/LAVI (RLR) on mortality following transcatheter mitral valve repair (TMVr). METHODS: Transthoracic echocardiograms of 158 patients who underwent TMVr at a single academic medical center from 2011 to 2018 were reviewed retrospectively. RAVI and LAVI were calculated using Simpson's method. Patients were stratified based on etiology of mitral regurgitation (MR). Cox proportional-hazard regression was created utilizing MR type, STS-score, and RLR to assess the independent association of RLR with survival. Kaplan-Meier analysis was used to analyze the association between RAVI and LAVI with all-cause mortality. Hemodynamic values from preprocedural right heart catheterization were also compared between RLR groups. RESULTS: Among 123 patients included (median age 81.3 years; 52.5% female) there were 50 deaths during median follow-up of 3.0 years. Patients with a high RAVI and low LAVI had significantly higher all-cause mortality while patients with high LAVI and low RAVI had significantly improved all-cause mortality compared to other groups (p = 0.0032). RLR was significantly associated with mortality in patients with both functional and degenerative MR (p = 0.0038). Finally, Cox proportion-hazard modeling demonstrated that an elevated RLR above the median value was an independent predictor of all-cause mortality [HR = 2.304; 95% CI = 1.26-4.21, p = 0.006] when MR type and STS score were accounted for. CONCLUSION: Patients with a high RAVI and low LAVI had significantly increased mortality than other groups following TMVr suggesting RA remodeling may predict worse outcomes following the procedure. Concordantly, RLR was predictive of mortality independent of MR type and preprocedural STS-score. These indices may provide additional risk stratification in patients undergoing evaluation for TMVr.

A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups.

Often the biological and/or clinical experiments result in longitudinal data from multiple related groups. The analysis of such data is quite challenging due to the fact that groups might have shared information on the mean and/or covariance functions. In this article, we consider a Bayesian semiparametric approach of modeling the mean trajectories for longitudinal response coming from multiple related groups. We consider matrix stick-breaking process priors on the group mean parameters which allows information sharing on the mean trajectories across the groups. Simulation studies are performed to demonstrate the effectiveness of the proposed approach compared to the more traditional approaches. We analyze data from a one-year follow-up of nutrition education for hypercholesterolemic children with three different treatments where the children are from different age-groups. Our analysis provides more clinically useful information than the previous analysis of the same dataset. The proposed approach will be a very powerful tool for analyzing data from clinical trials and other medical experiments.