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Tripathi, Byomesh

0000-0002-7246-7262

Mayo Clinic

St. Luke's-Roosevelt Hospital Center

Citations 6

Abstract 21363: Etiologies and Trends of Readmission in Patients With Takotsubo Cardiomyopathy
Kundoor, Vishwa, Tripathi, Byomesh , Arora, Shilpkumar , Nalluri, Nikhil , Kumar, Varun , Patel, Nirali , Patel, Neil , Patel, Prashant, Badal, Madan, Patel, Nileshkumar , Deshmukh, Abhishek , Lahewala, Sopan , Gopalan, Radha

Circulation | March 19, 2022 | Volume: 136 | Issue: suppl_1 | Pages: No page available

10.1161/circ.136.suppl_1.21363 Get It

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.
Abstract 21168: Heart Failure With Preserved Ejection Fraction (HFpEF) Outcomes After Same Hospital Readmission versus Different Hospital Readmission
Patel, Nilay , Arora, Shilpkumar , Sabhrawal, Sera, Kumar, Varun , Tripathi, Byomesh , Aneja, Ekta, Dave, Mihir, Patel, Neil , Setarah, Sam, Kondoor, Vishwa, Mishra, Abhishek , Bhatia, Subir , Bharadwaj, Bhaskar, Patel, Smit , Malik, Priti, Gidwani, Umesh , Sharma, Kriti , Patel, Neil, Gopalan, Radha

Circulation | March 19, 2022 | Volume: 136 | Issue: suppl_1 | Pages: No page available

10.1161/circ.136.suppl_1.21168 Get It

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 >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<0.001), LOS (Days: -1.14, 95% CI: -1.31 - -0.97, p<0.001), Cost of care ($: -4208, 95% CI: - 4681 - -3734, p<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.
Burden of arrhythmia in hospitalized HIV patients
Abudan, Anas , Vaidya, Vaibhav R. , Tripathi, Byomesh , Noseworthy, Peter A. , DeSimone, Daniel C. , Egbe, Alexander , Arora, Shilpkumar , Sridhar, Haarini , DeSimone, Christopher V. , Mulpuru, Siva K. , Deshmukh, Abhishek , Abudan, Anas A , Mulpuru, Siva K , Deshmukh, Abhishek J , Tripathi, Byomesh , Noseworthy, Peter A , DeSimone, Christopher V , DeSimone, Daniel C

Clinical Cardiology | December 10, 2020 | Volume: 44 | Issue: 1 | Pages: 66-77

10.1002/clc.23506 Get It

AbstractBackgroundThe improved life expectancy observed in patients living with human immunodeficiency virus (HIV) infection has made age‐related cardiovascular complications, including arrhythmias, a growing health concern.HypothesisWe describe the temporal trends in frequency of various arrhythmias and assess impact of arrhythmias on hospitalized HIV patients using the Nationwide Inpatient Sample (NIS).MethodsData on HIV‐related hospitalizations from 2005 to 2014 were obtained from the NIS database using International Classification of Diseases, 9th Revision (ICD‐9) codes. Data was further subclassified into hospitalizations with associated arrhythmias and those without. Baseline demographics and comorbidities were determined. Outcomes including in‐hospital mortality, cost of care, and length of stay were extracted. SAS 9.4 (SAS Institute Inc., Cary, NC) was utilized for analysis. A multivariable analysis was performed to identify predictors of arrhythmias among hospitalized HIV patients.ResultsAmong 2 370 751 HIV‐related hospitalizations identified, the overall frequency of any arrhythmia was 3.01%. Atrial fibrillation (AF) was the most frequent arrhythmia (2110 per 100 000). The overall frequency of arrhythmias increased over time by 108%, primarily due to a 132% increase in AF. Arrhythmias are more frequent among older males, lowest income quartile, and nonelective admissions. Patients with arrhythmias had a higher in‐hospital mortality rate (9.6%). In‐hospital mortality among patients with arrhythmias decreased over time by 43.8%. The cost of care and length of stay associated with arrhythmia‐related hospitalizations were mostly unchanged.ConclusionsArrhythmias are associated with significant morbidity and mortality in hospitalized HIV patients. AF is the most frequent arrhythmia in hospitalized HIV patients.
Causes and Predictors of Readmission in Patients With Atrial Fibrillation Undergoing Catheter Ablation: A National Population‐Based Cohort Study
Arora, Shilpkumar , Lahewala, Sopan , Tripathi, Byomesh , Mehta, Varshil , Kumar, Varun , Chandramohan, Divya , Lemor, Alejandro , Dave, Mihir, Patel, Nileshkumar , Patel, Nilay , Shantha, Ghanshyam Palamaner Subash , Viles-González, Juan F., Deshmukh, Abhishek

Journal of the American Heart Association | June 15, 2018 | Volume: 7 | Issue: 12 | Pages: No page available

10.1161/jaha.118.009294 Get It

Background Reducing readmission after catheter ablation ( CA ) in atrial fibrillation (AF) is important. Methods and Results We utilized National Readmission Data ( NRD ) 2010–2014. AF was identified by International Classification of Diseases, Ninth Edition, Clinical Modification (ICD‐9‐CM) diagnostic code 427.31 in the primary field, while first CA of AF was identified via ICD‐9 ‐procedure code 37.34. Any admission within 30 or 90 days of index admission was considered a readmission. Cox proportional hazard regression was used to adjust for confounders. The primary outcomes were 30‐ and 90‐day readmissions and the secondary outcome was AF recurrence. In total, 1 128 372 patients with AF were identified from January 1, 2010 to September 30, 2014. Of which 37 360 (3.3%) underwent CA . Patients aged ≥65 years and female sex were less likely to receive CA for AF . Overall, 10.9% and 16.5% of CA patients were readmitted within 30 and 90 days post‐CA, respectively. Most common causes of readmissions were arrhythmia ( AF , atrial flutter), heart failure, pulmonary causes (pneumonia, chronic obstructive pulmonary disease) and bleeding complications (gastrointestinal bleed, intracranial hemorrhage). Patients with diabetes mellitus, heart failure, coronary artery disease ( CAD ), chronic pulmonary and kidney disease, prior stroke/transient ischemic attack (TIA), female sex, length of stay ≥2 and disposition to the facility were prone to higher 30‐ and 90‐day readmissions post ‐CA . Predictors of increase in AF recurrence post‐ CA were female sex, diabetes mellitus, chronic pulmonary disease, and length of stay ≥2. Trends of 90‐day readmission and AF recurrence were found to improve over the study period. Conclusions We identified several demographic and clinical factors associated with the use of CA in AF , and short‐term outcomes of the same, which could potentially help in the patient selection and improve outcomes.
Temporal trends of in‐hospital complications associated with catheter ablation of atrial fibrillation in the United States: An update from Nationwide Inpatient Sample database (2011–2014)
Arora, Shilpkumar , Kumar, Varun , Lahewala, Sopan , Dave, Mihir, Shah, Mahek , Tan, Bryan E‐Xin , Savani, Sejal, Badheka, Apurva, Gopalan, Radha , Shantha, Ghanshyam Palamaner Subash , Viles-González, Juan F., Deshmukh, Abhishek , Tripathi, Byomesh , Tripathi, Byomesh , Abdel‐Rahman, Mohamed H. , Tan, Bryan E-Xin

Journal of Cardiovascular Electrophysiology | February 25, 2018 | Volume: 29 | Issue: 5 | Pages: 715-724

10.1111/jce.13471 Get It

AbstractBackgroundCatheter ablation is widely accepted intervention for atrial fibrillation (AF) refractory to antiarrhythmic drugs, but limited data are available regarding contemporary trends in major complications and in‐hospital mortality due to the procedure. This study was aimed at exploring the temporal trends of in‐hospital mortality, major complications, and impact of hospital volume on frequency of AF ablation–related outcomes.MethodsThe Nationwide Inpatient Sample database was utilized to identify the AF patients treated with catheter ablation. In‐hospital death and common complications including vascular access complications, cardiac perforation and/or tamponade, pneumothorax, stroke, and transient ischemic attack, were identified using International Classification of Disease (ICD‐9‐CM) codes.ResultIn‐hospital mortality rate of 0.15% and overall complication rate of 5.46% were noted among AF ablation recipients (n = 50,969). Significant increase in complications during study period (relative increase 56.37%, P‐trend < 0.001) was observed. Cardiac (2.65%), vascular (1.33%), and neurological (1.05%) complications were most common. On multivariate analysis (odds ratio [OR]; 95% confidence interval [95% CI]; P value), significant predictors of complications were female sex (OR = 1.40; CI = 1.17–1.68; P value < 0.001), high burden of comorbidity as indicated by Charlson Comorbidity Index ≥2 (OR = 2.84; CI = 2.29–3.52; P value < 0.001), and low hospital volume (< 50 procedures).ConclusionOur study noted a decline in AF ablation–related hospitalizations and complications associated with the procedure. These findings largely reflect shifting trends of outpatient performance of the procedure and increasing safety profile due to improved institutional expertise and catheter techniques.
Short‐term outcomes of atrial flutter ablation
Tripathi, Byomesh , Arora, Shilpkumar , Mishra, Abhishek , Kundoor, Vishwa, Lahewala, Sopan , Kumar, Varun , Shah, Mahek , Lakhani, Dhairya A. , Shah, Harshil , Patel, Nilay , Patel, Nileshkumar , Dave, Mihir, Deshmukh, Abhishek , Sattur, Sudhakar, Gopalan, Radha , Lakhani, Dhairya

Journal of Cardiovascular Electrophysiology | August 11, 2017 | Volume: 28 | Issue: 11 | Pages: 1275-1284

10.1111/jce.13311 Get It

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.