A heightened awareness of cardiac amyloidosis among the medical community has led to an exponential rise in the number of patients being diagnosed with this condition over the last decade. With the emergence of new pharmacotherapies, there is now a global need to reduce delays in making the diagnosis, improve access to genotyping, and establish treatment pathways that will ensure continued equity of access to treatments for all patients with amyloidosis.
We welcome case reports/series, original science, and review articles on any research areas related to the broad topic of cardiac amyloidosis (including all subtypes).
Contributions might address, but should not be limited to, the following areas:
1) Artificial intelligence/machine learning methods; identification of at-risk patients at risk and who might benefit from early treatment.
2) Improvements in imaging techniques and/or novel findings among patients in any of nuclear, cardiac magnetic resonance, and echocardiography.
3) Risk stratification, outcomes related research.
4) Real-world data on the experience of using targeted novel therapies (TTR stabilisers, RNA silencing therapies, or fibril disruptors).
5) Supportive management - palliation, community heart failure teams, conventional heart failure medications.
6) Anticoagulation.
7) Pacing and/or defibrillator therapies.
8) Management of concomitant aortic stenosis in the setting of cardiac amyloidosis.
9) Management of concomitant coronary disease in the setting of cardiac amyloidosis.
10) Screening - clinical and genetic.
11) Genetics - presymptomatic genetic testing, genetic counselling, penetrance studies specific to variants in TTR or other amyloidosis sub-types.
12) Psychosocial/quality of life assessments.
13) Service development/treatment pathways.
14) Advanced heart failure management - transplantation, LVAD, outcomes depending on amyloidosis sub-type.
15) Differentiating amyloidosis from other hypertrophic phenotypes (imaging, clinical, demographics).
16) Epidemiological studies - estimating true prevalence.
A heightened awareness of cardiac amyloidosis among the medical community has led to an exponential rise in the number of patients being diagnosed with this condition over the last decade. With the emergence of new pharmacotherapies, there is now a global need to reduce delays in making the diagnosis, improve access to genotyping, and establish treatment pathways that will ensure continued equity of access to treatments for all patients with amyloidosis.
We welcome case reports/series, original science, and review articles on any research areas related to the broad topic of cardiac amyloidosis (including all subtypes).
Contributions might address, but should not be limited to, the following areas:
1) Artificial intelligence/machine learning methods; identification of at-risk patients at risk and who might benefit from early treatment.
2) Improvements in imaging techniques and/or novel findings among patients in any of nuclear, cardiac magnetic resonance, and echocardiography.
3) Risk stratification, outcomes related research.
4) Real-world data on the experience of using targeted novel therapies (TTR stabilisers, RNA silencing therapies, or fibril disruptors).
5) Supportive management - palliation, community heart failure teams, conventional heart failure medications.
6) Anticoagulation.
7) Pacing and/or defibrillator therapies.
8) Management of concomitant aortic stenosis in the setting of cardiac amyloidosis.
9) Management of concomitant coronary disease in the setting of cardiac amyloidosis.
10) Screening - clinical and genetic.
11) Genetics - presymptomatic genetic testing, genetic counselling, penetrance studies specific to variants in TTR or other amyloidosis sub-types.
12) Psychosocial/quality of life assessments.
13) Service development/treatment pathways.
14) Advanced heart failure management - transplantation, LVAD, outcomes depending on amyloidosis sub-type.
15) Differentiating amyloidosis from other hypertrophic phenotypes (imaging, clinical, demographics).
16) Epidemiological studies - estimating true prevalence.