(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. While novel therapies improve outcomes, many affected individuals remain undiagnosed due to a lack of awareness among clinicians. This study was undertaken to develop an expert-independent machine learning (ML) prediction model for CA relying on routinely determined laboratory parameters. (2) Methods: In a first step, we developed baseline linear models based on logistic regression. In a second step, we used an ML algorithm based on gradient tree boosting to improve our linear prediction model, and to perform non-linear prediction. Then, we compared the performance of all diagnostic algorithms. All prediction models were developed on a training cohort, consisting of patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF) patients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate prognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best model, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver operating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2% and 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and specificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it possible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared with CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the diagnostic workup of CA and may assist physicians in clinical reasoning.

Aims. Two thirds of patients with heart failure and preserved ejection fraction (HFpEF) have an indication for oral anticoagulation (OAC) to prevent thromboembolic events. However, evidence regarding the safety of OAC in HFpEF is limited. Therefore, our aim was to describe bleeding events and to find predictors of bleeding in a large HFpEF cohort. Methods and Results. We recorded bleeding events in a prospective HFpEF cohort. Out of 328 patients (median age 71 years (interquartile range (IQR) 67–77)), 64.6% (n = 212) were treated with OAC. Of those, 65.1% (n = 138) received vitamin-K-antagonists (VKA) and 34.9% (n = 72) non-vitamin K oral anticoagulants (NOACs). During a median follow-up time of 42 (IQR 17–63) months, a total of 54 bleeding events occurred. Patients on OAC experienced more bleeding events (n = 49 (23.1%) versus n = 5 (4.3%), p < 0.001). Major drivers of events were gastrointestinal (GI) bleeding (n = 18 (36.7%)]. HAS-BLED (Hypertension, Abnormal Renal/Liver Function, Stroke, Bleeding History or Predisposition, Labile INR, Elderly, Drugs/Alcohol Concomitantly) score (hazard ratios (HR) of 2.15 (95% confidence interval (CI) 1.65–2.79, p < 0.001)) was the strongest independent predictor for overall bleeding. In the subgroup of GI bleeding, mean right atrial pressure (mRAP: HR of 1.13 (95% CI 1.03–1.25, p = 0.013)) and HAS-BLED score (HR of 1.74 (95% CI 1.15–2.64, p = 0.009)] remained significantly associatiated with bleeding events after adjustment. mRAP provided additional prognostic value beyond the HAS-BLED score with an improvement from 0.63 to 0.71 (95% CI 0.58–0.84, p for comparison 0.032), by C-statistic. This additional prognostic value was confirmed by significant improvements in net reclassification index (61.3%, p = 0.019) and integrated discrimination improvement (3.4%, p = 0.015). Conclusion. OAC-treated HFpEF patients are at high risk of GI bleeding. High mRAP as an indicator of advanced stage of disease was predictive for GI bleeding events and provided additional risk stratification information beyond that obtained by HAS-BLED score.

This study sought to characterize cardiac amyloidosis (CA) patients with respect to hemodynamic parameters and asses their prognostic impact in different CA cohorts. Intracardiac and pulmonary arterial pressures (PAPs) are among the strongest predictors of outcomes in patients with heart failure (HF). Despite that, the hemodynamic profiles of patients with CA and their relation to prognosis have rarely been investigated. Invasive hemodynamic, clinical, and laboratory assessment, as well as cardiac magnetic resonance imaging were performed in our CA cohort. A total of 61 patients, 35 (57.4%) with wild-type transthyretin amyloidosis (ATTRwt) and 26 (42.6%) with light-chain amyloidosis (AL) were enrolled. ATTRwt patients had lower N-terminal prohormone of brain natriuretic peptide values and were less frequently in New York Heart Association class ≥ III. Intracardiac and PAPs were elevated, but hemodynamic parameters did not differ between CA groups. Whereas in ATTRwt, the median mean PAP (hazard ratio (HR): 1.130, p = 0.040) and pulmonary vascular resistance (HR: 1.010, p = 0.046) were independent predictors of outcome, no hemodynamic parameter was associated with outcome in the AL group. Cardiac ATTRwt and AL patients feature elevated intracardiac and PAPs and show similar hemodynamic profiles. However, hemodynamic parameters are of greater prognostic relevance in ATTRwt, potentially providing a new therapeutic target.

Drugs which interact with the renin angiotensin aldosterone system (RAAS) aim to reduce the negative effects of angiotensin (Ang) II. Treatment with these drugs anticipate a compensatory up-regulation of renin; however, it has been shown that there is a large variability in circulating plasma renin (PRA), even in patients with optimal medical therapy in patients with heart failure (HF) with reduced ejection fraction (HFrEF). Our aim was to measure plasma renin activity (PRA-S), its response to RAAS inhibitor (RAASi) therapies and its effects on outcome in patients with HF with preserved ejection fraction (HFpEF). For this purpose, 150 HFpEF patients were included into a prospective single-center registry. Equilibrium (eq) angiotensin metabolites were measured from serum samples using mass spectroscopy. PRA-S (eqAng I + eqAng II) was calculated and compared in respect to the primary endpoint defined as all-cause death. PRA-S in patients with RAASi therapy was not significantly higher than in patients without RAASi (p = 0.262). Even after adjusting for confounding factors, PRA-S remained predictive for all-cause death in the multivariable model with a hazard ratio of 2.14 (95%CI 1.20–3.82, p = 0.010). We conclude that high PRA-S is associated with poor prognosis in patients with HFpEF, regardless of RAASi treatment, which could ultimately result in hyperactivated RAAS and consecutive negative effects on the cardiovascular and renal system, leading to poor outcome in patients with HFpEF.

The PARAGON-HF clinical trial suggested that sacubitril/valsartan may become a treatment option for particular subgroups of patients with heart failure and preserved ejection fraction (HFpEF). However, the proportion of real-world HFpEF patients who are theoretically superimposable with the PARAGON-HF population is yet unknown. The present study was performed to define the proportion of real-world PARAGON-HF-like patients and to describe their clinical characteristics and long-term prognosis in comparison with those who would not meet PARAGON-HF criteria. We systematically applied PARAGON-HF inclusion and exclusion criteria to a total of 427 HFpEF patients who have been participating in a prospective national registry between December 2010 and December 2019. In total, only 170 (39.8%) registry patients were theoretically eligible for PARAGON-HF. Patients not meeting inclusion criteria (41.0%) were less impaired with respect to exercise capacity (median 6-min walk distance: 385 m (IQR: 300–450) versus 323 m (IQR: 240–383); p < 0.001) had lower pulmonary pressures (mean pulmonary artery pressure (mPAP): 31.2 mmHg, standard deviation (SD): ±10.2 versus 32.8 mmHg, SD: ±9.7; p < 0.001) and better outcomes (log-rank: p < 0.001) as compared to the PARAGON-like cohort. However, patients theoretically excluded from the trial (19.2%) were those with most advanced heart failure symptoms (median 6-min walk test: 252 m (IQR: 165–387); p < 0.001), highest pulmonary pressures (mPAP: 38.2 mmHg, SD: ±12.4; p < 0.001) and worst outcome (log-rank: p = 0.037). We demonstrate here that < 40% of real-world HFpEF patients meet eligibility criteria for PARAGON-HF. We conclude that despite reasons for optimism after PARAGON-HF, a large proportion of HFpEF patients will remain without meaningful treatment options.

Background: The diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic value is limited. Purpose: The aim of this study was to perform a comprehensive electrophysiological characterization in CA patients and to develop a robust, easy-to-use diagnostic tool. Methods: First, we applied electrocardiographic imaging (ECGI) to generate detailed electroanatomical maps in CA patients and controls. Then, a machine learning approach was used to generate a surface ECG-based diagnostic algorithm from the complex dataset. Results: Areas of low voltage were localized in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualized in the right ventricle. Potential maps showed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1 to V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in inferior leads II, III, aVF. Ten blinded cardiologists were then asked to identify CA patients by analyzing 12-lead ECGs before and after training for the defined ECG patterns. Training resulted in significant improvements in the detection rate of CA with an AUC of 0.69 before and 0.97 after training (Figure). Conclusion: Using a machine learning approach, a robust ECG-based tool was developed to detect CA from detailed electroanatomical mapping of CA patients. The developed tool proved to be a simple and reliable diagnostic tool to suspect CA without the aid of advanced imaging modalities. Funding Acknowledgement: Type of funding sources: None.