Shah, Harshil

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: Role of therapeutic hypothermia in out of hospital cardiac arrest is well studied but data is sparse for in-hospital cardiac arrest (IHCA). Methods: Nationwide Inpatient Sample (NIS) database from 2011-2014 was utilized to identify ICHA using the ICD-9-CM procedural codes 99.60(CPR) and 99.63(closed cardiac massage) in any procedural fields. Therapeutic hypothermia was identified by 99.81 in any procedural field. Patient with age<18 years and missing information on age, gender and mortality was removed. Primary outcome was hospitalization survival. We used Greedy’s algorithm for propensity score match (1:20) to adjust for extensive list of clinically important confounders. We used simple logistic regression to generate odds ratio on matched cohort. Results: 86,593 patients (Age>65 years: 58.48%, female: 44.12%) with In hospital cardiac arrest was identified,of which 1777 (2.05 %) received therapeutic hypothermia. Overall survival was 30%- with or without hypothermia. After propensity score matching (1:20) we identified 1537 matched pairs (32,277 patients) without any statically significant difference in baseline characteristics between two groups. C- index for the model was 0.714. Worse survival was observed in patients with hypothermia in matched cohort [Survival: (31.82% without hypothermia vs. 28.1% with hypothermia with p-value- 0.002) (OR:0.84, 95% CI:0.75-0.94, p-0.002)]. About a quarter of in-hospital cardiac arrest were Vfib/Vtach arrest and Subgroup propensity score match analysis in Vfib/Vtach arrest showed better survival without hypothermia, similar results were observed in PEA/asystole cardiac arrest. Conclusion: Worst survival was observed with hypothermia in hospital cardiac arrest patients. Risk of hypothermia complications outweighs the benefit of preserving brain in patients who received almost immediate resuscitation. Future clinical trials and prospective studies will be needed to concur our results.

AbstractBackgroundUnderstanding the factors associated with early readmissions following atrial flutter (AFL) ablation is critical to reduce the cost and improving the quality of life in AFL patients.MethodThe study cohort was derived from the national readmission database 2013–2014. International Classification of Diseases, 9th Revision (ICD‐9‐CM) diagnosis code 427.32 and procedure code 37.34 were used to identify AFL and catheter ablation, respectively. The primary and secondary outcomes were 90‐day readmission and complications including in‐hospital mortality. Cox proportional regression and hierarchical logistic regression were used to generate the predictors of primary and secondary outcomes respectively. Readmission causes were identified by ICD‐9‐CM code in primary diagnosis field of readmissions.ResultReadmission rate of 18.19% (n = 1,010 with 1,396 readmissions) was noted among AFL patients (n = 5552). Common etiologies for readmission were heart failure (12.23%), atrial fibrillation (11.13%), atrial flutter (8.93%), respiratory complications (9.42%), infections (7.4%), bleeding (7.39%, including GI bleed–4.09% and intracranial bleed–0.79%) and stroke/TIA (1.89%). Multivariate predictors of 90‐day readmission (hazard ratio, 95% confidence interval, P value) were preexisting heart failure (1.30, 1.13–1.49, P < 0.001), chronic pulmonary disease (1.37, 1.18–1.58, P < 0.001), anemia (1.23, 1.02–1.49, P = 0.035), malignancy (1.87, 1.40–2.49, P < 0.001), weekend admission compared to weekday admission (1.23, 1.02–1.47, P = 0.029), and length of stay (LOS) ≥5 days (1.39, 1.16–1.65, P < 0.001). Note that 50% of readmissions happened within 30 days of discharge.ConclusionCardiac etiologies remain the most common reason for the readmission after AFL ablation. Identifying high risk patients, careful discharge planning, and close follow‐up postdischarge can potentially reduce readmission rates in AFL ablation patients.