Incidence, 30-day readmission rates and predictors of readmission after new onset atrial fibrillation who underwent transcatheter aortic valve replacement

Machine Learning of Cardiac Anatomy and the Risk of New-Onset Atrial Fibrillation After TAVR

BACKGROUND

New-onset atrial fibrillation (NOAF) occurs in 5% to 15% of patients who undergo transfemoral transcatheter aortic valve replacement (TAVR). Cardiac imaging has been underutilized to predict NOAF following TAVR.

OBJECTIVES

The objective of this analysis was to compare and assess standard, manual echocardiographic and cardiac computed tomography (cCT) measurements as well as machine learning-derived cCT measurements of left atrial volume index and epicardial adipose tissue as risk factors for NOAF following TAVR.

METHODS

The study included 1,385 patients undergoing elective, transfemoral TAVR for severe, symptomatic aortic stenosis. Each patient had standard and machine learning-derived measurements of left atrial volume and epicardial adipose tissue from cardiac computed tomography. The outcome of interest was NOAF within 30 days following TAVR. We used a 2-step statistical model including random forest for variable importance ranking, followed by multivariable logistic regression for predictors of highest importance. Model discrimination was assessed by using the C-statistic to compare the performance of the models with and without imaging.

RESULTS

Forty-seven (5.0%) of 935 patients without pre-existing atrial fibrillation (AF) experienced NOAF. Patients with pre-existing AF had the largest left atrial volume index at 76.3 ± 28.6 cm3/m2 followed by NOAF at 68.1 ± 26.6 cm3/m2 and then no AF at 57.0 ± 21.7 cm3/m2 (P < 0.001). Multivariable regression identified the following risk factors in association with NOAF: left atrial volume index ≥76 cm2 (OR: 2.538 [95% CI: 1.165-5.531]; P = 0.0191), body mass index <22 kg/m2 (OR: 4.064 [95% CI: 1.500-11.008]; P = 0.0058), EATv (OR: 1.007 [95% CI: 1.000-1.014]; P = 0.043), aortic annulus area ≥659 mm2 (OR: 6.621 [95% CI: 1.849-23.708]; P = 0.004), and sinotubular junction diameter ≥35 mm (OR: 3.891 [95% CI: 1.040-14.552]; P = 0.0435). The C-statistic of the model was 0.737, compared with 0.646 in a model that excluded imaging variables.

CONCLUSIONS

Underlying cardiac structural differences derived from cardiac imaging may be useful in predicting NOAF following transfemoral TAVR, independent of other clinical risk factors.

Risk Factors for Hospital Readmission Post-Transcatheter Aortic Valve Implantation in the Contemporary Era: A Systematic Review

Background

Despite transcatheter aortic valve implantation (TAVI) becoming a widely accepted therapeutic option for the management of aortic stenosis, post-procedure readmission rates remain high. Rehospitalization is associated with negative patient outcomes, as well as increased healthcare costs, and has therefore been identified as an important target for quality improvement. Strategies to reduce the post-TAVI readmission rate are needed but require the identification of patients at high risk for rehospitalization. Our systematic review aims to identify predictors of post-procedure readmission in patients eligible for TAVI.

Methods

We conducted a comprehensive search of the MEDLINE, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) databases for the time period from 2015 to the present for articles evaluating risk factors for rehospitalization post-TAVI with a follow-up period of at least 30 days in adults age ≥ 70 years with aortic stenosis. The quality of included studies was evaluated using the Newcastle-Ottawa Scale. We present the results as a qualitative narrative review.

Results

We identified 49 studies involving 828,528 patients. Post-TAVI readmission is frequent, and rates vary (14.9% to 54.3% at 1 year). The most-frequent predictors identified for both 30-day and 1-year post-TAVI readmission are atrial fibrillation, lung disease, renal disease, diabetes mellitus, in-hospital life-threatening bleeding, and non-femoral access.

Conclusions

This systematic review identifies the most-common predictors for 30-day and 1-year readmission post-TAVI, including comorbidities and potentially modifiable procedural approaches and complications. These predictors can be used to identify patients at high-risk for readmission who are most likely to benefit from increased support and follow-up post-TAVI.

Valve-in-valve transcatheter aortic valve implantation versus repeat surgical aortic valve replacement in patients with a failed aortic bioprosthesis

BACKGROUND

Limited data are available regarding clinical outcomes of valve-in-valve (ViV) transcatheter aortic valve implantation (TAVI) following the United States Food and Drug Administration approval of ViV TAVI in 2015.

AIMS

The aim of this study was to evaluate in-hospital, 30-day, and 6-month outcomes of ViV TAVI versus repeat surgical aortic valve replacement (SAVR) in patients with a failed aortic bioprosthetic valve.

METHODS

This retrospective cohort study identified patients who underwent ViV TAVI or repeat SAVR utilising the Nationwide Readmission Database from 2016 to 2018. Primary outcomes were all-cause readmission (at 30 days and 6 months) and in-hospital death. Secondary outcomes were in-hospital stroke, pacemaker implantation, 30-day/6-month major adverse cardiac events (MACE), and mortality during readmission. Propensity score-matching (inverse probability of treatment weighting) analyses were implemented.

RESULTS

Out of 6,769 procedures performed, 3,724 (55%) patients underwent ViV TAVI, and 3,045 (45%) underwent repeat SAVR. ViV TAVI was associated with lower in-hospital all-cause mortality (odds ratio [OR] 0.42, 95% confidence interval [CI]: 0.20-0.90, p=0.026) and a higher rate of 30-day (hazard ratio [HR] 1.46, 95% CI: 1.13-1.90, p=0.004) and 6-month all-cause readmission (HR 1.54, 95% CI: 1.14-2.10, p=0.006) compared with repeat SAVR. All secondary outcomes were comparable between the two groups.

CONCLUSIONS

ViV TAVI was associated with lower in-hospital mortality but higher 30-day and 6-month all-cause readmission. However, there was no difference in risk of in-hospital stroke, post-procedure pacemaker implantation, MACE, and mortality during 30-day and 6-month readmission compared with repeat SAVR, suggesting that ViV TAVI can be performed safely in carefully selected patients.

Incidence of Early Atrial Fibrillation After Transcatheter versus Surgical Aortic Valve Replacement: A Meta-Analysis of Randomized Controlled Trials

Background

Post-operative atrial fibrillation (POAF) is common after aortic valve replacement (AVR) and is associated with worse outcomes. We performed a meta-analysis of randomized controlled trials comparing Surgical Aortic Valve Replacement (SAVR) and Transcatheter Aortic Valve Replacement (TAVR) for incidence of POAF at 30 days.

Methods

We searched databases from 1/1/1990 to 1/1/2020 for randomized studies comparing TAVR and SAVR. POAF was defined as either worsening or new-onset atrial fibrillation. Random effects model was used to estimate the risk of POAF with TAVR vs SAVR in all trials, and in subgroups (low, intermediate, high risk, and in self-expandable vs balloon expandable valves). Sensitivity analysis was performed including only studies reporting new-onset atrial fibrillation.

Results

Seven RCTs were identified that enrolled 7,934 patients (3,999 to TAVR and 3,935 to SAVR). The overall incidence of POAF was 9.7% after TAVR and 33.3% after SAVR. TAVR was associated with a lower risk of POAF compared with SAVR (OR 0.21 [0.18-0.24]; P < 0.0001). Compared with SAVR, TAVR was associated with a significantly lower risk of POAF in the high-risk cohort (OR 0.37 [0.27-0.49]; P < 0.0001), in the intermediate-risk cohort (OR 0.23 [0.19-0.28]; P < 0.0001), low-risk cohort (OR 0.13 [0.10-0.16]; P < 0.0001). Sensitivity analysis of 4 trials including only new-onset POAF showed similar summary estimates (OR 0.21, 95% CI [0.18-0.25]; P< 0.0001).

Conclusions

TAVR is associated with a significantly lower risk of post-operative atrial fibrillation compared with SAVR in all strata. Further studies are needed to identify the contribution of post-operative atrial fibrillation to the differences in clinical outcomes after TAVR and SAVR.

Readmission for cardiac and non-cardiac causes among adults with epilepsy or multiple sclerosis - A nationwide analysis

PURPOSE

The aim of this study was to determine proportions of 30-day cardiac readmissions in adults with epilepsy compared to multiple sclerosis (MS) or those with neither condition. Predictors and causes of readmissions were also examined.

METHODS

We used the 2014 Nationwide Readmissions Database and ICD-9-CM codes to identify people with epilepsy, MS, and without epilepsy or MS. Multinomial logistic regressions were fitted to: (1) examine association between 30-day readmissions and epilepsy, MS or neither, and (2) to describe causes and predictors of 30-day readmission for cardiac readmissions in epilepsy.

RESULTS

Out of 6,870,508 adults admitted in 2014, 202,938 (2.98%) had epilepsy and 29,556 (0.45%) had MS. The proportion of 30-day readmission for epilepsy and MS were, respectively: (1) due to cardiac causes (0.17% vs. 0.13%); (2) due to other causes (13.89% vs. 10.61%). The odds of 30-day cardiac readmission in those with epilepsy and MS were lower compared to those without either condition (OR = 0.64, 95% CI 0.57-0.73, p < 0.0001; OR = 0.60, 95% CI 0.43-0.84, p = 0.003). Among those with epilepsy, increasing age (OR = 1.03, 95% CI 1.02-1.04, p < 0.0001) and a Charlson comorbidity index ≥1 (OR = 1.79, 95% CI 1.24-2.60, p = 0.002) were associated with higher odds of 30-day cardiac readmission. A higher proportion of those with epilepsy readmitted within 30-days due to cardiac causes died in hospital (10.09%) compared to those with MS (not reportable due to cell frequency <10) or without epilepsy or MS (5.61%).

CONCLUSION

Those admitted to a hospital and living with epilepsy had a higher proportion of cardiac readmissions and death in hospital when compared to those living with MS, and the determinants are likely multifactorial. These findings are important and need to be further explored to identify strategies to prevent readmissions due to any cause and treatments that could reduce mortality.

National rates and trends of tobacco and substance use disorders among atrial fibrillation hospitalizations

BACKGROUND

Atrial Fibrillation (AF) has been associated with various behavioral risk factors such as tobacco, alcohol, and/or substances abuse.

OBJECTIVE

The main objective is to describe the national trends and burden of tobacco and substance abuse in AF hospitalizations. Also, this study identifies potential population who are more vulnerable to these substance abuse among AF hospitalizations.

METHODS

The National Inpatient Sample database from 2007 to 2015 was utilized and the hospitalizations with AF were identified using the international classification of disease, Ninth Revision, Clinical Modification code. They were stratified into without abuse, tobacco use disorder (TUD), substance use disorder (SUD), alcohol use disorder (AUD) and drug use disorder (DUD).

RESULTS

Of 3,631,507 AF hospitalizations, 852,110 (23.46%) had TUD, 1,851,170 (5.1%) had SUD, 155,681 (4.29%) had AUD and 42,667 (1.17%) had DUD. The prevalence of TUD, SUD, AUD, and DUD was substantially increased across all age groups, races, and gender during the study period. Female sex was associated with lower odds TUD, SUD, AUD, and DUD. Among AF hospitalizations, the black race was associated with higher odds of SUD, and DUD. The younger age group (18-35 years), male, Medicare/Medicaid as primary insurance, and lower socioeconomic status were associated with increased risk of both TUD and SUDs.

CONCLUSION

TUD and SUD among AF hospitalizations in the United States mainly affects males, younger individuals, white more than black, and those of lower socioeconomic status which demands for the development of preventive strategies to address multilevel influences.