Patel, Smit

Background: There is sparse comparative data on short-term readmissions following Transcatheter aortic valve replacement (TAVR) and surgical aortic valve replacement (SAVR). Methods: The study cohort was derived from the National Readmission Data (NRD) 2013, a subset of the Healthcare Cost and Utilization Project (HCUP) sponsored by AHRQ. TAVR and SAVR were identified using appropriate ICD-9-CM codes (TAVR - 35.05, 35.06 & SAVR - 35.21, 35.22) as either primary or secondary procedural codes. Variables which could affect treatment assignment, were adjusted with propensity score match analysis and Match (1:1) cohort was generated. Readmission causes were identified by ICD 9 code in primary diagnosis. Results: Our analysis included 3,886 TAVR and SAVR procedures each in the propensity matched cohorts. There was no significant difference in terms of readmission rates amongst patients who underwent TAVR vs. SAVR [21.41% vs. 21.07% (OR: 1.02, 95% CI: 0.92 - 1.14) p = 0.702]. Cardiac etiologies were more likely to be responsible for readmissions following SAVR (42.66% vs. 35.91%, p = 0.006). Atrial fibrillation (AF) was a much more common cause of readmission following SAVR (7.81% vs. 3.37%, p <0.001). Overall vascular (3.24% vs. 1.32%, p = 0.006) and bleeding complications (9% vs. 6.2%, p = 0.025) were more likely to result in readmission in patients undergoing TAVR. Cost of care of index admission and readmission was similar in both groups. Readmission mortality was higher amongst patient with TAVR vs. SAVR (6.49 vs. 3.91, p = 0.02). 50% of TAVR and SAVR readmission occur within 11 days post discharge. Conclusion: In our study from a large nationwide database, there was no significant difference in readmission rate, median readmission rate and resource utilization between TAVR and SAVR. Arrhythmias especially AF accounted for more SAVR readmission and Bleeding/vascular complications were more commonly responsible for readmissions following TAVR.

Introduction: Catheter ablation is widely accepted intervention for atrial fibrillation (AF), but data is limited on contemporary trends in major complications and mortality due to the procedure. Methods: HCUP’s Nationwide inpatient sample (NIS) database was utilized from 2011-14 to identify the AF patients treated with Catheter Ablation using ICD-9- CM codes. χ 2 test was utilized to compare categorical variables, the student’s t-test for continuous variables and hierarchical mixed-effects logistic regression models for multivariate predictors of post-procedural complications. Results: In total 50,969 patients were identified undergoing AF ablation during the study period (37.6% female, 51.4 age ≥ 65). Overall complication rate was 5.5% with relative increase over study period (+56.4%, p < 0.001). In subgroup analysis of complication trends significant relative increase seen in respiratory (+352.7%, p < 0.001), neurological (+43.9%, p = 0.003) and vascular (+38.7%, p = 0.003) complications. While cardiac complications (+9.1%, p = 0.323) and In-hospital mortality (+ 60%, p = 0.173) showed insignificant change. On multivariate analysis of mortality and complication significant increased risk was associated with female gender (OR, 95% CI, p-value) (1.40, 1.17-1.68, p < 0.001), as well as higher comorbidity burden (CCI ≥ 1). While higher hospital volume (≥ 50 procedures) was associated with significantly decreased complication rates. Conclusions: Patients undergoing AF ablation showed an insignificant change in mortality, with increase trends of complication. Which was likely related to higher comorbidity burden over study period.

Introduction: Heart failure with preserved ejection fraction(HFpEF) is a syndromemwith limited treatment modalities. Our control over 30 day readmissions and readmission outcomes is limited. Methods: HCUP’s Nationwide Readmissions Database (NRD) from 2013-14 was used for HFpEF related hospitalizations using ICD-9- CM diagnostic code:428.3x in primary diagnosis field. 30-day readmissions were identified and classified into two groups: same-hospital readmissions and different-hospital readmissions. A comparative analysis was conducted focused on three metrics - mortality, length of stay (LOS) and cost of hospitalization. We used hierarchical two-level modeling and propensity score matching to adjust for confounders. Results: Of the 94,988 readmitted patients (age &gt;65 years: 79%, Females: 61.45%) - 76,665 (80.70%) to the same hospital and rest to different hospitals. After adjusting extensive list of confounders, same hospital readmission was associated with better outcomes including in hospital mortality (OR: 0.73, 95% CI: 0.66 - 0.80, p&lt;0.001), LOS (Days: -1.14, 95% CI: -1.31 - -0.97, p&lt;0.001), Cost of care ($: -4208, 95% CI: - 4681 - -3734, p&lt;0.001). For validation of our results, we ran propensity score matching, showed similar results - in hospital mortality (6.23% vs 8.07%), LOS (6.30 ± 0.08 vs 7.47 ± 0.095), Cost of care (14042.7 ± 215.7 vs 18685.4 ± 279.2). Post hoc high-risk subgroup analysis were conducted and showed similar results (Figure). Number needed to treat (for mortality) was 74 patients. Up to ~ 100 million dollars could have been saved over 2 years. Conclusions: Considering scale of problem and limited option to prevent worse outcomes, Simple interventions such as redirecting patient to same hospital as previous admission would be beneficial.