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.

Change in Systemic Arterial Pulsatility Index (SAPi) during Heart Failure Hospitalization is Associated with Improved Outcomes.

Study Objective: To identify Change in Systemic Arterial Pulsatitlity index (ΔSAPi) as a novel hemodynamic marker associated with outcomes in heart failure (HF). Design: The ESCAPE trial was a randomized controlled trial. Setting: The ESCAPE trial was conducted at 26 sites. Participants: 134 patients were analyzed (mean age 56.8 ± 13.4 years, 29% female). Interventions: We evaluated the change in SAPi, ([systemic pulse pressure/pulmonary artery wedge pressure) obtained at baseline and at the final hemodynamic measurement in the ESCAPE trial. Main Outcome Measures: Change in SAPi, (ΔSAPi), was analyzed for the primary outcomes of death, heart transplant, left ventricular assist device (DTxLVAD) or hospitalization, (DTxLVADHF) and secondary outcome of DTxLVAD using Cox proportional hazards regression. Results: Median change in SAPi was 0.81 (IQR 0.20-1.68). ΔSAPi in uppermost quartile was associated with reductions in DTxLVADHF (HR 0.55 [95% CI 0.32, 0.93]). ΔSAPi in the uppermost and lowermost quartiles combined was similarly associated with significant reductions in DTxLVADHF (HR 0.62 [95% CI 0.41, 0.94]). ΔSAPi higher than 1.17 was associated with improved DTxLVADHF. ΔSAPi was also associated with troponin levels at discharge (regression coefficient p = 0.001) and trended with 6-minute walk at discharge (Spearman correlation r = 0.179, p = 0.058). Conclusion: ΔSAPi was strongly associated with improved HF clinical profile and adverse outcomes. These findings support further exploration of Δ SAPi in the risk stratification of HF.

Systemic arterial pulsatility index (SAPi) predicts adverse outcomes in advanced heart failure patients.

Ventriculo-arterial (VA) coupling has been shown to have physiologic importance in heart failure (HF). We hypothesized that the systemic arterial pulsatility index (SAPi), a measure that integrates pulse pressure and a proxy for left ventricular end-diastolic pressure, would be associated with adverse outcomes in advanced HF. We evaluated the SAPi ([systemic systolic blood pressure-systemic diastolic blood pressure]/pulmonary artery wedge pressure) obtained from the final hemodynamic measurement in patients randomized to therapy guided by a pulmonary arterial catheter (PAC) and with complete data in the Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness (ESCAPE) trial. Cox proportional hazards regression was performed for the outcomes of (a) death, transplant, left ventricular assist device (DTxLVAD) or hospitalization, (DTxLVADHF) and (b) DTxLVAD. Among 142 patients (mean age 56.8 ± 13.3 years, 30.3% female), the median SAPi was 2.57 (IQR 1.63-3.45). Increasing SAPi was associated with significant reductions in DTxLVAD (HR 0.60 per unit increase in SAPi, 95% CI 0.44-0.84) and DTxLVADHF (HR 0.81 per unit increase, 95% CI 0.70-0.95). Patients with a SAPi ≤ 2.57 had a marked increase in both outcomes, including more than twice the risk of DTxLVAD (HR 2.19, 95% CI 1.11-4.30) over 6 months. Among advanced heart failure patients with invasive hemodynamic monitoring in the ESCAPE trial, SAPi was strongly associated with adverse clinical outcomes. These findings support further investigation of the SAPi to guide treatment and prognosis in HF undergoing invasive hemodynamic monitoring.