Kundoor, Vishwa

Introduction: Takotsubo cardiomyopathy (TTC) is diagnosed in 1-2% of the patients presenting with troponin positive suspected acute coronary syndromes(ACS). Rising trends of adverse events in patients with TTC, makes it imperative to explore causes and predictors of readmission in this patient population. Methods: National Readmission Data (NRD) 2013-14 was utilized to select study cohort. International Classification of Diseases, 9th Revision (ICD-9-CM) diagnostic code 429.83 was used to identify takotsubo cardiomyopathy. Admissions within 90 day of index admission were considered as early readmissions. Readmission etiologies were identified by ICD-9-CM code in primary diagnosis field of readmissions. Hierarchical multivariate models were used to evaluate predictors of early readmission. Results: A total of 12255 patients were identified during study period out of which 23.92% (n=2932 with 4224 readmissions) patients were readmitted within 90 days of discharge following index admission. Most common etiologies for readmission were cardiac (18.56%), respiratory (17.20%), GI renal (9.10%), infections (13.12%) and neurological complications (5.88%). Among cardiac complications acute heart failure was the most common etiology (7.48%). Highest number of readmissions happened on first day after discharge (n=125) and within 33 days after discharge, >50% of readmissions took place (n=2136). Conclusions: Patients with TTC are highly likely to be readmitted with first month after discharge, most likely secondary to cardiac or respiratory complications. These findings warrant close post discharge transition to reduce morbidity and improve health care outcomes.

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.