Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Formin Homology 2 Domain Containing 3 (FHOD3) Is a Genetic Basis for Hypertrophic Cardiomyopathy

BACKGROUND: The genetic cause of hypertrophic cardiomyopathy remains unexplained in a substantial proportion of cases. Formin homology 2 domain containing 3 (FHOD3) may have a role in the pathogenesis of cardiac hypertrophy but has not been implicated in hypertrophic cardiomyopathy. OBJECTIVES: This study sought to investigate the relation between FHOD3 mutations and the development of hypertrophic cardiomyopathy. METHODS: FHOD3 was sequenced by massive parallel sequencing in 3,189 hypertrophic cardiomyopathy unrelated probands and 2,777 patients with no evidence of cardiomyopathy (disease control subjects). The authors evaluated protein-altering candidate variants in FHOD3 for cosegregation, clinical characteristics, and outcomes. RESULTS: The authors identified 94 candidate variants in 132 probands. The variants' frequencies were significantly higher in patients with hypertrophic cardiomyopathy (74 of 3,189 [2.32%]) than in disease control subjects (18 of 2,777 [0.65%]; p < 0.001) or in the gnomAD database (1,049 of 138,606 [0.76%]; p < 0.001). FHOD3 mutations cosegregated with hypertrophic cardiomyopathy in 17 families, with a combined logarithm of the odds score of 7.92, indicative of very strong segregation. One-half of the disease-causing variants were clustered in a small conserved coiled-coil domain (amino acids 622 to 655); odds ratio for hypertrophic cardiomyopathy was 21.8 versus disease control subjects (95% confidence interval: 1.3 to 37.9; p < 0.001) and 14.1 against gnomAD (95% confidence interval: 6.9 to 28.7; p < 0.001). Hypertrophic cardiomyopathy patients carrying (likely) pathogenic mutations in FHOD3 (n = 70) were diagnosed after age 30 years (mean 46.1 ± 18.7 years), and two-thirds (66%) were males. Of the patients, 82% had asymmetric septal hypertrophy (mean 18.8 ± 5 mm); left ventricular ejection fraction <50% was present in 14% and hypertrabeculation in 16%. Events were rare before age 30 years, with an annual cardiovascular death incidence of 1% during follow-up. CONCLUSIONS: FHOD3 is a novel disease gene in hypertrophic cardiomyopathy, accounting for approximately 1% to 2% of cases. The phenotype and the rate of cardiovascular events are similar to those reported in unselected cohorts. The FHOD3 gene should be routinely included in hypertrophic cardiomyopathy genetic testing panels

Differential clinical characteristics and prognosis of intraventricular conduction defects in patients with chronic heart failure

Intraventricular conduction defects (IVCDs) can impair prognosis of heart failure (HF), but their specific impact is not well established. This study aimed to analyse the clinical profile and outcomes of HF patients with LBBB, right bundle branch block (RBBB), left anterior fascicular block (LAFB), and no IVCDs. Clinical variables and outcomes after a median follow-up of 21 months were analysed in 1762 patients with chronic HF and LBBB (n = 532), RBBB (n = 134), LAFB (n = 154), and no IVCDs (n = 942). LBBB was associated with more marked LV dilation, depressed LVEF, and mitral valve regurgitation. Patients with RBBB presented overt signs of congestive HF and depressed right ventricular motion. The LAFB group presented intermediate clinical characteristics, and patients with no IVCDs were more often women with less enlarged left ventricles and less depressed LVEF. Death occurred in 332 patients (interannual mortality = 10.8%): cardiovascular in 257, extravascular in 61, and of unknown origin in 14 patients. Cardiac death occurred in 230 (pump failure in 171 and sudden death in 59). An adjusted Cox model showed higher risk of cardiac death and pump failure death in the LBBB and RBBB than in the LAFB and the no IVCD groups. LBBB and RBBB are associated with different clinical profiles and both are independent predictors of increased risk of cardiac death in patients with HF. A more favourable prognosis was observed in patients with LAFB and in those free of IVCDs. Further research in HF patients with RBBB is warranted

Therapy-related myelodysplastic syndromes deserve specific diagnostic sub-classification and risk-stratification-an approach to classification of patients with t-MDS.

In the current World Health Organization (WHO)-classification, therapy-related myelodysplastic syndromes (t-MDS) are categorized together with therapy-related acute myeloid leukemia (AML) and t-myelodysplastic/myeloproliferative neoplasms into one subgroup independent of morphologic or prognostic features. Analyzing data of 2087 t-MDS patients from different international MDS groups to evaluate classification and prognostication tools we found that applying the WHO classification for p-MDS successfully predicts time to transformation and survival (both p &lt; 0.001). The results regarding carefully reviewed cytogenetic data, classifications, and prognostic scores confirmed that t-MDS are similarly heterogeneous as p-MDS and therefore deserve the same careful differentiation regarding risk. As reference, these results were compared with 4593 primary MDS (p-MDS) patients represented in the International Working Group for Prognosis in MDS database (IWG-PM). Although a less favorable clinical outcome occurred in each t-MDS subset compared with p-MDS subgroups, FAB and WHO-classification, IPSS-R, and WPSS-R separated t-MDS patients into differing risk groups effectively, indicating that all established risk factors for p-MDS maintained relevance in t-MDS, with cytogenetic features having enhanced predictive power. These data strongly argue to classify t-MDS as a separate entity distinct from other WHO-classified t-myeloid neoplasms, which would enhance treatment decisions and facilitate the inclusion of t-MDS patients into clinical studies

Therapy-related myelodysplastic syndromes deserve specific diagnostic sub-classification and risk-stratification-an approach to classification of patients with t-MDS

Data de publicació electrónica: 29-06-2020In the current World Health Organization (WHO)-classification, therapy-related myelodysplastic syndromes (t-MDS) are categorized together with therapy-related acute myeloid leukemia (AML) and t-myelodysplastic/myeloproliferative neoplasms into one subgroup independent of morphologic or prognostic features. Analyzing data of 2087 t-MDS patients from different international MDS groups to evaluate classification and prognostication tools we found that applying the WHO classification for p-MDS successfully predicts time to transformation and survival (both p < 0.001). The results regarding carefully reviewed cytogenetic data, classifications, and prognostic scores confirmed that t-MDS are similarly heterogeneous as p-MDS and therefore deserve the same careful differentiation regarding risk. As reference, these results were compared with 4593 primary MDS (p-MDS) patients represented in the International Working Group for Prognosis in MDS database (IWG-PM). Although a less favorable clinical outcome occurred in each t-MDS subset compared with p-MDS subgroups, FAB and WHO-classification, IPSS-R, and WPSS-R separated t-MDS patients into differing risk groups effectively, indicating that all established risk factors for p-MDS maintained relevance in t-MDS, with cytogenetic features having enhanced predictive power. These data strongly argue to classify t-MDS as a separate entity distinct from other WHO-classified t-myeloid neoplasms, which would enhance treatment decisions and facilitate the inclusion of t-MDS patients into clinical studies

Therapy-Related MDS Can be Separated into Different Risk-Groups According to Tools for Classification and Prognostication of Primary MDS

Abstract The current classification system for Myelodysplastic Syndromes lumps all therapy-related (tMDS) into one subgroup assuming all tMDS had the same poor prognosis. We have put together a database including 2032 patients with a diagnosis of tMDS from several different IWG centers and the MDS clinical research consortium. With the idea of developing an individual scoring system for tMDS, we decided to start by optimizing the cytogenetic part of the IPSSR. First, we did an extensive review of karyotypes. Finally, 1245 patients had complete data and correct ISCN formula to be used for score development. We could show regarding karyotypes there are very limited differences between primary and tMDS. Mainly the distribution of risk groups differs with complex occurring more (37%) and normal karyotypes occurring less frequent, although still accounting for 30%. There are few exceptions that are relatively special for tMDS, like translocations including 11q23. A few karyotypes are less frequent; therefore, we could not evaluate the value of IPSS-R cytogenetics for all karyotypes. However, if we apply IPSS-R cytogenetics to our patient cohort, we can separate 5 different risk groups as in pMDS. We tested the performance of the score by using the Dxy. As main endpoint we chose transformation-free survival giving better information about the severity of the disease compared to the single endpoints survival and AML transformation that where calculated for completeness as well. The Dxy for the IPSS-R cytogenetic part is 0.31 for transformation-free survival. This indicates an effective prognostic performance although not as good as in pMDS. Several attempts were done to develop a tMDS specific cytogenetic score. The best draft scoring component achieves a Dxy of 0.33. Counting the number of aberrations achieves a score of 0.30. If normal clone present or not is added, the performance of this very simple model is improved with a Dxy of 0.32. As we could show, all these different approaches lead to a comparable performance. One can argue that still regarding a few karyotypes the prognostic impact is slightly different between p and tMDS (e.g. +8). On the other hand, the most practical approach seems to be to adopt the original cytogenetic part of the IPSS-R for further score development since clinicians do not need to use different scoring systems for different MDS subtypes. While the final analyses for the development of a tMDS specific risk score are currently under way, extensive calculations regarding the performance of different scores like WHO- (Dxy 0.24), FAB-classification (Dxy 0.19), WPSS-R (Dxy 0.35), IPSS-R (Dxy 0.37), and IPSS-R+age (Dxy 0.36), show all these systems can separate different risk groups within our cohort. However, these results also show an inferior performance of the scoring systems in t compared to pMDS. There are multiple possible reasons for this. The most important seem to be tMDS patients are often not cured from the primary disease and its disease specific risk of death should ideally be considered. Unfortunately, we don't have that data. And second, we included treated as well as untreated patients. It seems not to be feasible otherwise since the selection bias for old unfit patients would be unacceptable. We could show already in pMDS that the score performances are considerably worse if we analyze treated patients and the score performance in our cohort is better if limited to untreated patients. To conclude, we can say existing classification and scoring systems work in tMDS and can separate groups with clearly different risk for death and transformation. Although we could not develop a tMDS specific cytogenetic score this could be seen positively since it underlines tMDS do not seem to be much different regarding disease specific risk. This should initiate a discussion of a revision of the WHO-classification and encourage clinicians to use the existing tools for risk assessment and treatment decisions. A simple solution could be to use the WHO classification for pMDS and precede each subgroup with a t, like tMDS-SLD, and so on. Such an approach would be of importance for patients falsely classified as tMDS. After all this classification is done according to anamnestic information only and sporadic cases cannot be excluded. Until now, in the first analyzes performed with the final tMDS-database, we did not find any indication that risk factors established in pMDS would lose or change their meaning in tMDS. Figure. Figure. Disclosures Komrokji: Celgene: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees. List:Celgene: Research Funding. Roboz:Orsenix: Consultancy; Eisai: Consultancy; Novartis: Consultancy; Celltrion: Consultancy; Astex Pharmaceuticals: Consultancy; Argenx: Consultancy; Janssen Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Argenx: Consultancy; Janssen Pharmaceuticals: Consultancy; Pfizer: Consultancy; Cellectis: Research Funding; Daiichi Sankyo: Consultancy; Sandoz: Consultancy; Otsuka: Consultancy; Daiichi Sankyo: Consultancy; Eisai: Consultancy; Pfizer: Consultancy; Roche/Genentech: Consultancy; Novartis: Consultancy; Celltrion: Consultancy; Celgene Corporation: Consultancy; Cellectis: Research Funding; Orsenix: Consultancy; Aphivena Therapeutics: Consultancy; Otsuka: Consultancy; Jazz Pharmaceuticals: Consultancy; Sandoz: Consultancy; Roche/Genentech: Consultancy; Aphivena Therapeutics: Consultancy; AbbVie: Consultancy; Bayer: Consultancy; Bayer: Consultancy; Astex Pharmaceuticals: Consultancy; Celgene Corporation: Consultancy; AbbVie: Consultancy. Döhner:Jazz: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Pfizer: Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; Celator: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Astellas: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Amgen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Pfizer: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Valent:Pfizer: Honoraria; Novartis: Honoraria; Incyte: Honoraria. Platzbecker:Celgene: Research Funding. Lübbert:TEVA: Other: Study drug; Celgene: Other: Travel Support; Cheplapharm: Other: Study drug; Janssen: Honoraria, Research Funding. Díez-Campelo:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Stauder:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Teva: Research Funding. Germing:Janssen: Honoraria; Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding

Therapy-related myelodysplastic syndromes deserve specific diagnostic sub-classification and risk-stratification-an approach to classification of patients with t-MDS

In the current World Health Organization (WHO)-classification, therapy-related myelodysplastic syndromes (t-MDS) are categorized together with therapy-related acute myeloid leukemia (AML) and t-myelodysplastic/myeloproliferative neoplasms into one subgroup independent of morphologic or prognostic features. Analyzing data of 2087 t-MDS patients from different international MDS groups to evaluate classification and prognostication tools we found that applying the WHO classification for p-MDS successfully predicts time to transformation and survival (both p < 0.001). The results regarding carefully reviewed cytogenetic data, classifications, and prognostic scores confirmed that t-MDS are similarly heterogeneous as p-MDS and therefore deserve the same careful differentiation regarding risk. As reference, these results were compared with 4593 primary MDS (p-MDS) patients represented in the International Working Group for Prognosis in MDS database (IWG-PM). Although a less favorable clinical outcome occurred in each t-MDS subset compared with p-MDS subgroups, FAB and WHO-classification, IPSS-R, and WPSS-R separated t-MDS patients into differing risk groups effectively, indicating that all established risk factors for p-MDS maintained relevance in t-MDS, with cytogenetic features having enhanced predictive power. These data strongly argue to classify t-MDS as a separate entity distinct from other WHO-classified t-myeloid neoplasms, which would enhance treatment decisions and facilitate the inclusion of t-MDS patients into clinical studies