Sex-Related Disparities In Atrial Functional Mitral Regurgitation.
HFSA ePoster Library. Kataria R. 09/10/21; 343528; 290
Rachna Kataria
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Abstract
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Background: Tafamidis is an FDA-approved therapy for Transthyretin Amyloid Cardiomyopathy (ATTR-CM) regardless of genotype. Data show treatment decreases hazard of all-cause mortality and cardiovascular-related hospitalizations. Patients also experience a slower decline in exertional capacity and quality of life shortly after receiving the first dose. Current FDA indications and insurance coverage decisions require testing for predisposing genes such as the V122I variant commonly seen in patients of West African descent. Our clinical experience indicates that patients’ likelihood of genetic positivity is impacted by their age, sex, and race. We sought to evaluate the likelihood of patients with ATTR-CM having a predisposing gene mutation based on these demographic factors.
Methods: This is a single-center retrospective cohort study of 634 consecutive patients diagnosed with ATTR-CM who underwent genetic testing. Classification and regression trees (CART), a supervised machine learning method, uses pre-specified independent variables in stepwise fashion to maximize accuracy of outcome predictions in a decision tree. Age, sex, and race were used as independent variables to predict gene positivity.
Results: In our study cohort (median age 76 [69, 82], 86% male, 27% African American [AA]), 181 (29%) patients with ATTR-CM had predisposing gene mutations. V122I and Thr60Ala mutations were present in 20% and 2% of the cohort, respectively. CART analysis showed that race served most efficiently as the root node (Figure 1). 73% of AA had a predisposing mutation, compared to 14.5% in non-AA. Patients with ATTR-CM who were not AA and over age 69 had less than a 4% risk of having a predisposing mutation. Our model accurately predicted 89 (sensitivity 80%; specificity 92%) of the entire cohort and 93% (sensitivity 56%; specificity 98%) in patients who were not AA.
Conclusions: In our single-center experience, non-AA patients with ATTR-CM are less likely to have a detectable predisposing genetic mutation than AA patients. Demographic features appear to be predictive of gene positivity, and should inform pre-test probability. In many cases genetic testing requirements may cause unnecessary delays in tafamidis therapy.
Methods: This is a single-center retrospective cohort study of 634 consecutive patients diagnosed with ATTR-CM who underwent genetic testing. Classification and regression trees (CART), a supervised machine learning method, uses pre-specified independent variables in stepwise fashion to maximize accuracy of outcome predictions in a decision tree. Age, sex, and race were used as independent variables to predict gene positivity.
Results: In our study cohort (median age 76 [69, 82], 86% male, 27% African American [AA]), 181 (29%) patients with ATTR-CM had predisposing gene mutations. V122I and Thr60Ala mutations were present in 20% and 2% of the cohort, respectively. CART analysis showed that race served most efficiently as the root node (Figure 1). 73% of AA had a predisposing mutation, compared to 14.5% in non-AA. Patients with ATTR-CM who were not AA and over age 69 had less than a 4% risk of having a predisposing mutation. Our model accurately predicted 89 (sensitivity 80%; specificity 92%) of the entire cohort and 93% (sensitivity 56%; specificity 98%) in patients who were not AA.
Conclusions: In our single-center experience, non-AA patients with ATTR-CM are less likely to have a detectable predisposing genetic mutation than AA patients. Demographic features appear to be predictive of gene positivity, and should inform pre-test probability. In many cases genetic testing requirements may cause unnecessary delays in tafamidis therapy.
Background: Tafamidis is an FDA-approved therapy for Transthyretin Amyloid Cardiomyopathy (ATTR-CM) regardless of genotype. Data show treatment decreases hazard of all-cause mortality and cardiovascular-related hospitalizations. Patients also experience a slower decline in exertional capacity and quality of life shortly after receiving the first dose. Current FDA indications and insurance coverage decisions require testing for predisposing genes such as the V122I variant commonly seen in patients of West African descent. Our clinical experience indicates that patients’ likelihood of genetic positivity is impacted by their age, sex, and race. We sought to evaluate the likelihood of patients with ATTR-CM having a predisposing gene mutation based on these demographic factors.
Methods: This is a single-center retrospective cohort study of 634 consecutive patients diagnosed with ATTR-CM who underwent genetic testing. Classification and regression trees (CART), a supervised machine learning method, uses pre-specified independent variables in stepwise fashion to maximize accuracy of outcome predictions in a decision tree. Age, sex, and race were used as independent variables to predict gene positivity.
Results: In our study cohort (median age 76 [69, 82], 86% male, 27% African American [AA]), 181 (29%) patients with ATTR-CM had predisposing gene mutations. V122I and Thr60Ala mutations were present in 20% and 2% of the cohort, respectively. CART analysis showed that race served most efficiently as the root node (Figure 1). 73% of AA had a predisposing mutation, compared to 14.5% in non-AA. Patients with ATTR-CM who were not AA and over age 69 had less than a 4% risk of having a predisposing mutation. Our model accurately predicted 89 (sensitivity 80%; specificity 92%) of the entire cohort and 93% (sensitivity 56%; specificity 98%) in patients who were not AA.
Conclusions: In our single-center experience, non-AA patients with ATTR-CM are less likely to have a detectable predisposing genetic mutation than AA patients. Demographic features appear to be predictive of gene positivity, and should inform pre-test probability. In many cases genetic testing requirements may cause unnecessary delays in tafamidis therapy.
Methods: This is a single-center retrospective cohort study of 634 consecutive patients diagnosed with ATTR-CM who underwent genetic testing. Classification and regression trees (CART), a supervised machine learning method, uses pre-specified independent variables in stepwise fashion to maximize accuracy of outcome predictions in a decision tree. Age, sex, and race were used as independent variables to predict gene positivity.
Results: In our study cohort (median age 76 [69, 82], 86% male, 27% African American [AA]), 181 (29%) patients with ATTR-CM had predisposing gene mutations. V122I and Thr60Ala mutations were present in 20% and 2% of the cohort, respectively. CART analysis showed that race served most efficiently as the root node (Figure 1). 73% of AA had a predisposing mutation, compared to 14.5% in non-AA. Patients with ATTR-CM who were not AA and over age 69 had less than a 4% risk of having a predisposing mutation. Our model accurately predicted 89 (sensitivity 80%; specificity 92%) of the entire cohort and 93% (sensitivity 56%; specificity 98%) in patients who were not AA.
Conclusions: In our single-center experience, non-AA patients with ATTR-CM are less likely to have a detectable predisposing genetic mutation than AA patients. Demographic features appear to be predictive of gene positivity, and should inform pre-test probability. In many cases genetic testing requirements may cause unnecessary delays in tafamidis therapy.
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