Cardiac amyloidosis: a significant blind spot of the H2FPEF score

BACKGROUND

Cardiac amyloidosis (CA) often mimics heart failure with preserved ejection fraction (HFpEF). Due to very different treatment strategies, an exact diagnosis and differentiation between pure HFpEF and CA-related heart failure (HF) is important. In the present study, we assessed the recently published H2FPEF score in patients with pure HFpEF, transthyretin (ATTR), as well as light chain (AL) amyloidosis-related HFpEF and tested whether it differentiates between these entities.

METHODS

The H2FPEF scores consists of easy-to-assess clinical (Body Mass Index, number of hypertensive drugs, presence of atrial fibrillation, age) and echocardiographic (systolic pulmonary arterial pressure, E/E´) parameters. It can be computed in a categorical way resulting in scores between 0 and 9 points (0-1: HFpEF rule out, 2-5: further testing required, 6-9: HFpEF rule in), or in a continual way providing an exact percentage of a patient's HFpEF probability. Continuous and categorical variables were compared using the Kruskal-Wallis, Mann-Whitney-U, and χ2-tests. Diagnostic accuracy was computed from 2x2 tables. Survival analysis was performed with Kaplan-Meier curves. A P value of <0.05 was set as the level of significance.

RESULTS

A total of 100 patients with pure HFpEF, 53 patients with ATTR, and 34 patients with AL CA were included in the present study. Median age (HFpEF: 71.5 years; ATTR CA: 77.0 years; AL CA: 60.0 years; P<0.001), gender distribution (HFpEF [female]: 73.0%, ATTR (female): 18.9%, AL [female]: 38.2%; P<0.001), and N-terminal prohormone of brain natriuretic peptide (HFpEF: 1045pg/mL; ATTR CA: 1927pg/mL; AL CA: 4308pg/mL; P<0.001) differed significantly between study cohorts. Median H2FPEF scores were highest among HFpEF (categorical: 5.0 points; continual: 95.1%), followed by ATTR (categorical: 4.0 points; continual: 89.0%), and AL CA (categorical: 3.0 points; continual: 31.2%). Respective P values were <0.001. Low H2FPEF scores (0-1 points) were found among patients in the AL CA cohort (29.4%), but not among HFpEF or ATTR CA patients (P<0.001). The majority of patients, irrespective of disease entity were in the intermediate score range (2-5 points, HFpEF: 80.0% ATTR CA: 94.3%, AL CA: 67.9%; P=0.006). High scores (6-9 points) were most often found among HFpEF patients (20.0%), followed by ATTR CA (5.7%) and AL CA (2.9%), (P=0.007).

CONCLUSIONS

The H2FPEF score should be used with caution, as there is a significant overlap between HFpEF and CA-related HF.

Validation of an electrocardiographic algorithm for the detection of cardiac amyloidosis

Background

Despite new therapies, diagnosis of cardiac amyloidosis (CA) is often delayed. We recently developed a simple electrocardiographic (ECG) algorithm to suspect CA without the aid of advanced imaging modalities (Figure).

Methods

The aim of this study was to validate the algorithms' usefulness in clinical practice. ECG readings from patients with CA, heart failure with preserved ejection fraction (HFpEF), and hypertrophic cardiomyopathy (HCMP) were analyzed in a blinded fashion.

Results

884 patients were included. Patients with pacemakers were excluded, leaving 827 ECGs (237 CA, 407 HFpEF, 183 HCMP) for final analysis. A characteristic pattern defined by the algorithm was visually perceptible in 165 ECGs (69.6%) of the amyloidosis patients vs. 114 (28%) of HFpEF vs. 22 (12.0%) of HCMP patients (p&lt;0.001). The area under the curve (AUC) for the detection CA was 0.75 with a sensitivity of 69.6% and a specificity of 76.9% (Figure). Binary logistic regression analysis revealed that the presence of a distinctive pattern increased the probability of CA with an odds ratio of 7.66 (CI: 5.47–10.72; p&lt;0.001).

Conclusion

This easy-to-use ECG algorithm has proven helpful to suspect CA. Our tool may significantly improve the treatment of heart failure patients by identifying those with amyloidosis-related disease.

Funding Acknowledgement

Type of funding sources: None.

A machine learning-derived electrocardiographic algorithm for the detection of cardiac amyloidosis

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.