Comparison Of LVAD Outcomes In Patients With Non-ischemic Cardiomyopathy And Ischemic Cardiomyopathy: Insight From National Readmission Database
HFSA ePoster Library. Abumoawad A. 09/10/21; 343368; 144
Abdelrhman Abumoawad
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Abstract
Discussion Forum (0)
Introduction: Temporal conversions among ejection fraction (EF) classes can occur across the heart failure (HF) spectrum reflecting amended structural and functional outcomes unaccounted for by current taxonomy. This retrospective study aims to investigate the differences in laboratory biomarkers, guideline-directed medical therapy (GDMT), and comorbidity burden across EF conversion groups.
Methods: HF patients at least 18-year-old who obtained at least two echocardiograms between January 2018 and January 2020 were identified using ICD-10 codes. Analysis of variance, chi-square test, and analysis of means for proportions were used as appropriate to identify associations with class conversion groups.
Results: A total of 874 patients who underwent 1,748 echocardiograms on unique visits were categorized according to initial EF as HF with preserved EF (HFpEF) (n=531, 61%), HF with midrange EF (HFmrEF) (n=132, 15%), or HF with reduced EF (HFrEF) (n=211, 24%). In accordance with follow-up EF, class conversions were categorized into HF with improved EF (HFiEF) (n=143, 16%), HF with worsened EF (HFwEF) (n=171, 20%), or HF with stable EF (HFsEF) (n=560, 64%). The average age was 75±13 years old; 54% were male, 85% were Caucasian, 11% were African American, and 4% other. Mean time between EF assessments was 208.6±170.2 days. Biomarker analysis demonstrated significantly higher average baseline Pro-BNP levels (12,150 pg/mL) in HFiEF versus HFsEF (6,671 pg/mL) (p=0.01). Angiotensin receptor-neprilysin inhibitor (ARNI) orders on index visit were more likely to be seen in the HFiEF group, but no other significant differences in GDMT were identified. Despite similar Elixhauser Comorbidity Measure (ECM) scores, ECM categorical analysis revealed that HFwEF was more likely to have an established diagnosis of depression (p=0.04) and a spectrum of psychiatric illnesses inclusive of diagnosis codes such as schizoaffective disorder and bipolar disorder (p=0.04) on preliminary visit compared to HFsEF or HFiEF. HFsEF was less likely to have an established diagnosis of blood loss anemia versus HFiEF or HFwEF (n=874, p=0.04).
Conclusions: Salient differences in baseline Pro-BNP levels as well as psychiatric and hematologic comorbidity patterns were observed across the EF class conversion spectrum despite similar baseline comorbidity burden, GDMT, and laboratory data. Findings demonstrate biomarker patterns associated with future EF improvement and targetable comorbid conditions which likely play a role in EF class conversion. Dedicated study evaluating measurements related to quality of life, functionality, hospital readmissions, and mortality are next steps in this field of HF.
Methods: HF patients at least 18-year-old who obtained at least two echocardiograms between January 2018 and January 2020 were identified using ICD-10 codes. Analysis of variance, chi-square test, and analysis of means for proportions were used as appropriate to identify associations with class conversion groups.
Results: A total of 874 patients who underwent 1,748 echocardiograms on unique visits were categorized according to initial EF as HF with preserved EF (HFpEF) (n=531, 61%), HF with midrange EF (HFmrEF) (n=132, 15%), or HF with reduced EF (HFrEF) (n=211, 24%). In accordance with follow-up EF, class conversions were categorized into HF with improved EF (HFiEF) (n=143, 16%), HF with worsened EF (HFwEF) (n=171, 20%), or HF with stable EF (HFsEF) (n=560, 64%). The average age was 75±13 years old; 54% were male, 85% were Caucasian, 11% were African American, and 4% other. Mean time between EF assessments was 208.6±170.2 days. Biomarker analysis demonstrated significantly higher average baseline Pro-BNP levels (12,150 pg/mL) in HFiEF versus HFsEF (6,671 pg/mL) (p=0.01). Angiotensin receptor-neprilysin inhibitor (ARNI) orders on index visit were more likely to be seen in the HFiEF group, but no other significant differences in GDMT were identified. Despite similar Elixhauser Comorbidity Measure (ECM) scores, ECM categorical analysis revealed that HFwEF was more likely to have an established diagnosis of depression (p=0.04) and a spectrum of psychiatric illnesses inclusive of diagnosis codes such as schizoaffective disorder and bipolar disorder (p=0.04) on preliminary visit compared to HFsEF or HFiEF. HFsEF was less likely to have an established diagnosis of blood loss anemia versus HFiEF or HFwEF (n=874, p=0.04).
Conclusions: Salient differences in baseline Pro-BNP levels as well as psychiatric and hematologic comorbidity patterns were observed across the EF class conversion spectrum despite similar baseline comorbidity burden, GDMT, and laboratory data. Findings demonstrate biomarker patterns associated with future EF improvement and targetable comorbid conditions which likely play a role in EF class conversion. Dedicated study evaluating measurements related to quality of life, functionality, hospital readmissions, and mortality are next steps in this field of HF.
Introduction: Temporal conversions among ejection fraction (EF) classes can occur across the heart failure (HF) spectrum reflecting amended structural and functional outcomes unaccounted for by current taxonomy. This retrospective study aims to investigate the differences in laboratory biomarkers, guideline-directed medical therapy (GDMT), and comorbidity burden across EF conversion groups.
Methods: HF patients at least 18-year-old who obtained at least two echocardiograms between January 2018 and January 2020 were identified using ICD-10 codes. Analysis of variance, chi-square test, and analysis of means for proportions were used as appropriate to identify associations with class conversion groups.
Results: A total of 874 patients who underwent 1,748 echocardiograms on unique visits were categorized according to initial EF as HF with preserved EF (HFpEF) (n=531, 61%), HF with midrange EF (HFmrEF) (n=132, 15%), or HF with reduced EF (HFrEF) (n=211, 24%). In accordance with follow-up EF, class conversions were categorized into HF with improved EF (HFiEF) (n=143, 16%), HF with worsened EF (HFwEF) (n=171, 20%), or HF with stable EF (HFsEF) (n=560, 64%). The average age was 75±13 years old; 54% were male, 85% were Caucasian, 11% were African American, and 4% other. Mean time between EF assessments was 208.6±170.2 days. Biomarker analysis demonstrated significantly higher average baseline Pro-BNP levels (12,150 pg/mL) in HFiEF versus HFsEF (6,671 pg/mL) (p=0.01). Angiotensin receptor-neprilysin inhibitor (ARNI) orders on index visit were more likely to be seen in the HFiEF group, but no other significant differences in GDMT were identified. Despite similar Elixhauser Comorbidity Measure (ECM) scores, ECM categorical analysis revealed that HFwEF was more likely to have an established diagnosis of depression (p=0.04) and a spectrum of psychiatric illnesses inclusive of diagnosis codes such as schizoaffective disorder and bipolar disorder (p=0.04) on preliminary visit compared to HFsEF or HFiEF. HFsEF was less likely to have an established diagnosis of blood loss anemia versus HFiEF or HFwEF (n=874, p=0.04).
Conclusions: Salient differences in baseline Pro-BNP levels as well as psychiatric and hematologic comorbidity patterns were observed across the EF class conversion spectrum despite similar baseline comorbidity burden, GDMT, and laboratory data. Findings demonstrate biomarker patterns associated with future EF improvement and targetable comorbid conditions which likely play a role in EF class conversion. Dedicated study evaluating measurements related to quality of life, functionality, hospital readmissions, and mortality are next steps in this field of HF.
Methods: HF patients at least 18-year-old who obtained at least two echocardiograms between January 2018 and January 2020 were identified using ICD-10 codes. Analysis of variance, chi-square test, and analysis of means for proportions were used as appropriate to identify associations with class conversion groups.
Results: A total of 874 patients who underwent 1,748 echocardiograms on unique visits were categorized according to initial EF as HF with preserved EF (HFpEF) (n=531, 61%), HF with midrange EF (HFmrEF) (n=132, 15%), or HF with reduced EF (HFrEF) (n=211, 24%). In accordance with follow-up EF, class conversions were categorized into HF with improved EF (HFiEF) (n=143, 16%), HF with worsened EF (HFwEF) (n=171, 20%), or HF with stable EF (HFsEF) (n=560, 64%). The average age was 75±13 years old; 54% were male, 85% were Caucasian, 11% were African American, and 4% other. Mean time between EF assessments was 208.6±170.2 days. Biomarker analysis demonstrated significantly higher average baseline Pro-BNP levels (12,150 pg/mL) in HFiEF versus HFsEF (6,671 pg/mL) (p=0.01). Angiotensin receptor-neprilysin inhibitor (ARNI) orders on index visit were more likely to be seen in the HFiEF group, but no other significant differences in GDMT were identified. Despite similar Elixhauser Comorbidity Measure (ECM) scores, ECM categorical analysis revealed that HFwEF was more likely to have an established diagnosis of depression (p=0.04) and a spectrum of psychiatric illnesses inclusive of diagnosis codes such as schizoaffective disorder and bipolar disorder (p=0.04) on preliminary visit compared to HFsEF or HFiEF. HFsEF was less likely to have an established diagnosis of blood loss anemia versus HFiEF or HFwEF (n=874, p=0.04).
Conclusions: Salient differences in baseline Pro-BNP levels as well as psychiatric and hematologic comorbidity patterns were observed across the EF class conversion spectrum despite similar baseline comorbidity burden, GDMT, and laboratory data. Findings demonstrate biomarker patterns associated with future EF improvement and targetable comorbid conditions which likely play a role in EF class conversion. Dedicated study evaluating measurements related to quality of life, functionality, hospital readmissions, and mortality are next steps in this field of HF.
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