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Incidence and predictors of sudden cardiac arrest in cardiac amyloidosis: a 5-year retrospective analysis
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Abstract
Background
Despite recent advances in treatment, cardiac amyloidosis (CA) portends a poor outcome. Clinical manifestations of CA are variable and often nonspecific, which makes the diagnosis challenging. Failure to recognize early cardiac involvement may result in devastating complications such as sudden cardiac arrest (SCA). Electrocardiography (ECG) is a widely available and affordable modality that can aid with risk stratification.
Purpose
We aimed to study patient demographics and identify clinical and electrocardiographic (ECG) predictors of SCA in patients with CA.
Methods
We queried the Nationwide Inpatient Sample database between 2016–2020 to identify hospitalizations for CA who were then subcategorized based on SCA, using the ICD-10 diagnosis code. The primary outcome was to assess clinical and ECG predictors of SCA. Both groups were compared for association using the chi-square test, following which multivariate regression analysis was performed to identify predictors of SCA. In addition, we performed a temporal trends analysis of CA hospitalizations and SCA during this study period.
Results
A total of 103,620 weighted hospitalizations for CA were identified of which 1935(1.9%) suffered a SCA. Baseline characteristics and comorbidities were largely equivalent except for a history of heart failure which was significantly higher in the SCA group(63.3% vs 46.8%, p < 0.001). Interestingly, traditional cardiac risk factors like smoking, hypertension, diabetes, and obesity did not correlate with increased risk of SCA. African American (AA) race had a higher incidence of SCA(30.2% vs 23.4%, p < 0.001). A multivariate regression analysis was performed to identify predictors of SCA and account for potential clinical as well as demographic confounders. Higher rates of SCA were observed in male sex, age < 40 years, and AA race as outlined in figure 1. The presence of heart failure with reduced ejection fraction (HFrEF), heart failure with preserved ejection fraction (HFpEF), and tachyarrhythmias such as atrial fibrillation, atrial flutter, ventricular tachycardia and ventricular fibrillation were associated with significantly higher odds of SCA. In addition, right bundle branch block, left bundle branch block, first-degree AV block, complete heart block and fascicular blocks were independently predictive of higher rates of SCA. As expected, the rates of ICD implantation were higher in the SCA group (7.2% vs 0.9%, p < 0.001. There is an increasing trend in hospitalizations for cardiac amyloidosis from 2017 to 2020 as highlighted in figure 2.
Conclusion
Among patients with cardiac amyloidosis, young age, AA race, ECG evidence of AV nodal dysfunction, distal conduction disease, and tachyarrhythmias is associated with an elevated risk of SCA. These findings could improve risk stratification, prompt further targeted evaluation with advanced imaging modalities and help in early identification of amyloid patients with cardiac involvement.Figure 1Figure 2
Title: Incidence and predictors of sudden cardiac arrest in cardiac amyloidosis: a 5-year retrospective analysis
Description:
Abstract
Background
Despite recent advances in treatment, cardiac amyloidosis (CA) portends a poor outcome.
Clinical manifestations of CA are variable and often nonspecific, which makes the diagnosis challenging.
Failure to recognize early cardiac involvement may result in devastating complications such as sudden cardiac arrest (SCA).
Electrocardiography (ECG) is a widely available and affordable modality that can aid with risk stratification.
Purpose
We aimed to study patient demographics and identify clinical and electrocardiographic (ECG) predictors of SCA in patients with CA.
Methods
We queried the Nationwide Inpatient Sample database between 2016–2020 to identify hospitalizations for CA who were then subcategorized based on SCA, using the ICD-10 diagnosis code.
The primary outcome was to assess clinical and ECG predictors of SCA.
Both groups were compared for association using the chi-square test, following which multivariate regression analysis was performed to identify predictors of SCA.
In addition, we performed a temporal trends analysis of CA hospitalizations and SCA during this study period.
Results
A total of 103,620 weighted hospitalizations for CA were identified of which 1935(1.
9%) suffered a SCA.
Baseline characteristics and comorbidities were largely equivalent except for a history of heart failure which was significantly higher in the SCA group(63.
3% vs 46.
8%, p < 0.
001).
Interestingly, traditional cardiac risk factors like smoking, hypertension, diabetes, and obesity did not correlate with increased risk of SCA.
African American (AA) race had a higher incidence of SCA(30.
2% vs 23.
4%, p < 0.
001).
A multivariate regression analysis was performed to identify predictors of SCA and account for potential clinical as well as demographic confounders.
Higher rates of SCA were observed in male sex, age < 40 years, and AA race as outlined in figure 1.
The presence of heart failure with reduced ejection fraction (HFrEF), heart failure with preserved ejection fraction (HFpEF), and tachyarrhythmias such as atrial fibrillation, atrial flutter, ventricular tachycardia and ventricular fibrillation were associated with significantly higher odds of SCA.
In addition, right bundle branch block, left bundle branch block, first-degree AV block, complete heart block and fascicular blocks were independently predictive of higher rates of SCA.
As expected, the rates of ICD implantation were higher in the SCA group (7.
2% vs 0.
9%, p < 0.
001.
There is an increasing trend in hospitalizations for cardiac amyloidosis from 2017 to 2020 as highlighted in figure 2.
Conclusion
Among patients with cardiac amyloidosis, young age, AA race, ECG evidence of AV nodal dysfunction, distal conduction disease, and tachyarrhythmias is associated with an elevated risk of SCA.
These findings could improve risk stratification, prompt further targeted evaluation with advanced imaging modalities and help in early identification of amyloid patients with cardiac involvement.
Figure 1Figure 2.
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