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. 2021 Oct 5;10(19):e021973.
doi: 10.1161/JAHA.121.021973. Epub 2021 Sep 24.

Outcomes of Elderly Patients Undergoing Left Atrial Appendage Closure

Affiliations

Outcomes of Elderly Patients Undergoing Left Atrial Appendage Closure

Shubrandu S Sanjoy et al. J Am Heart Assoc. .

Abstract

Background Elderly patients have a higher burden of comorbidities that influence clinical outcomes. We aimed to compare in-hospital outcomes in patients ≥80 years old to younger patients, and to determine the factors associated with increased risk of major adverse events (MAE) after left atrial appendage closure. Methods and Results The National Inpatient Sample was used to identify discharges after left atrial appendage closure between October 2015 and December 2018. The primary outcome was in-hospital MAE defined as the composite of postprocedural bleeding, vascular and cardiac complications, acute kidney injury, stroke, and death. A total of 6779 hospitalizations were identified, of which, 2371 (35%) were ≥80 years old and 4408 (65%) were <80 years old. Patients ≥80 years old experienced a higher rate of MAE compared with those aged <80 years old (6.0% versus 4.6%, P=0.01), and this difference was driven by a numerically higher rate of cardiac complications (2.4% versus 1.8%, P=0.09) and death (0.3% versus 0.1%, P=0.05) among individuals ≥80 years old. In patients ≥80 years old, higher odds of in-hospital MAE were observed in women (1.61-fold), and those with preprocedural congestive heart failure (≈2-fold), diabetes (≈1.5-fold), renal disease (≈2.6-fold), anemia (≈2.7-fold), and dementia (≈5-fold). In patients <80 years old, a higher risk of in-hospital MAE was encountered among women (≈1.4-fold) and those with diabetes (≈1.3-fold), renal disease (≈2.6-fold), anemia (≈2-fold), and dyslipidemia (≈1.2-fold). Conclusions Patients ≥80 years old had higher rates of in-hospital MAE compared with patients aged <80 years old. Female sex and the presence of heart failure, diabetes, renal disease, and anemia were factors associated with in-hospital MAE among both groups.

Keywords: anticoagulation; atrial fibrillation; comorbidities; elderly; left atrial appendage closure; octogenarians; stroke.

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Conflict of interest statement

Dr Holmes is on the Advisory Board for Boston Scientific, unpaid. The remaining authors have no disclosures to report.

Figures

Figure 1
Figure 1. Proportion of components in Charlson Comorbidity Index.
Because of the very low proportions, mild liver disease and moderate–severe liver disease were pooled, leading to 16 variables instead of 17. COPD indicates chronic obstructive pulmonary disease; and TIA, transient ischemic attack.
Figure 2
Figure 2. Proportion of components in Elixhauser Comorbidity Score.
Because of the very low proportions, deficiency anemia and blood loss anemia were pooled, leading to 29 variables instead of 30.
Figure 3
Figure 3. Proportion of components in CHA2DS2‐VASc score.
CHA2DS2‐VASc indicates Congestive heart failure, Hypertension, Age ≥75 years, Diabetes, prior Stroke or transient ischemic attack, Vascular disease (including previous myocardial infarction), Age 65 to 74 years, Sex category; and TIA, transient ischemic attack.
Figure 4
Figure 4. Temporal trends in left atrial appendage closure procedures performed quarterly and in‐hospital complications from 2015 to 2018 according to age ≥80 years old and <80 years old.
Cochran‐Armitage trend test shows statistically significant decrease in complication rates over time among ≥80 year‐old patients. LAAC indicates left atrial appendage closure.
Figure 5
Figure 5. Multilevel multivariable logistic regression analyses of factors associated with in‐hospital MAE.
A, Whole cohort, (B) ≥80 years old, and (C) <80 years old. AIC indicates Akaike's information criterion (lower values indicate better fit of the model); AUC, area under receiver operating characteristic curve; MAE, major adverse events; and OR, odds ratio. For continuous variables, the OR are per unit of increase in each of the predictive factors. *Lower values (close to 0) indicate better calibration of the model.

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