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BMC Research Notes

, 12:398 | Cite as

Temporal trends and factors associated with increased mortality among atrial fibrillation weekend hospitalizations: an insight from National Inpatient Sample 2005–2014

  • Dinesh C. VorugantiEmail author
  • Ghanshyam Shantha
  • Sushma Dugyala
  • Naga Venkata K. Pothineni
  • Deobrat Chandra Mallick
  • Abhishek Deshmukh
  • Ala Mohsen
  • Stephanie S. Colello
  • Mohammed Saeed
  • Rakesh Latchamsetty
  • Krit Jongnarangsin
  • Frank Pelosi
  • Ryan M. Carnahan
  • Michael Giudici
Open Access
Research note

Abstract

Objective

Atrial fibrillation (AF) weekend hospitalizations were reported to have poor outcomes compared to weekday hospitalizations. The relatively poor outcomes on the weekends are usually referred to as ‘weekend effect’. We aim to understand trends and outcomes among weekend AF hospitalizations. The primary purpose of this study is to evaluate the trends for weekend AF hospitalizations using Nationwide Inpatient Sample 2005–2014. Hospitalizations with AF as the primary diagnosis, in-hospital mortality, length of stay, co-morbidities and cardioversion procedures have been identified using the international classification of diseases 9 codes.

Results

Since 2005, the weekend AF hospitalizations increased by 27% (72,216 in 2005 to 92,220 in 2014), mortality decreased by 29% (1.32% in 2005 to 0.94% in 2014), increase in urban teaching hospitalizations by 72% (33.32% in 2005 to 57.64% in 2014), twofold increase in depression and a threefold increase in the prevalence of renal failure were noted over the period of 10 years. After adjusting for significant covariates, weekend hospitalizations were observed to have higher odds of in-hospital mortality OR 1.17 (95% CI 1.108–1.235, P < 0.0001). Weekend AF hospitalizations appear to be associated with higher in-hospital mortality. Opportunities to improve care in weekend AF hospitalizations need to be explored.

Keywords

Atrial fibrillation Weekend hospitalization In-hospital mortality 

Abbreviations

AF

atrial fibrillation

NIS

National Inpatient Sample

AHRQ

Agency for Health Care Quality and Research

ICD-9 CM

International Classification of Diseases-9, Clinical Modifications

LOS

length of stay

OR

odds ratio

Introduction

Atrial fibrillation (AF), the most common sustained arrhythmia in clinical practice had an estimated worldwide prevalence of 33.5 million in 2010 [1]. AF weekend hospitalizations were previously reported to have higher mortality and lower rates of cardioversion [2]. Subsequent studies in this population have demonstrated improved mortality and rates of cardioversion [3, 4]. To date, there has been no temporal trend analysis showing this effect. We sought to investigate the outcomes in the years 2005–2014 through a publicly available national inpatient sample database (NIS).

Main text

Methods

A description of NIS database has been elaborated in prior studies [5, 6, 7]. The NIS is one of the largest, all-payer database for the United States in-patient hospitalizations, and it is maintained by the Agency for Health Care Quality and Research (AHRQ). The NIS includes a 20% random sample of all inpatient hospitalizations from 46 states in the United States. Each observation represents a hospitalization with one primary diagnosis, up to 29 secondary diagnoses and 15 procedure diagnosis with International Classification of Disease, 9th revision, clinical modification (ICD-9-CM) codes.

NIS hospitalizations have 2 sampling strategies. Before 2012, all hospitalizations were from a random sample of 20% of acute care hospitals in the United States, stratified by bed size, region, and location. Starting in 2012, the NIS included a random sample of 20% of discharges from all acute care hospitals in the United States; this effort reduced the margin of error by 50%, and national estimates decreased by 4.3%. From 1998 to 2011, discharge weights are provided by the AHRQ after a validation process, and they are used to calculate national estimates. To account for changes in the sampling strategies, the variable “trend weights” have been used for 2011 and all preceding years to facilitate trend analysis from 1998 to 2014 as recommended by AHRQ [8].

The study was exempted by the University of Iowa, Iowa City, institutional review board as it includes only de-identified, publicly available data. For our analysis, we only used NIS data from 2005 to 2014. Similar to previous studies, we used the ICD-9-CM code 427.31 to identify hospitalizations involving hospitalizations with principal diagnosis (dx1) of AF [9]. The variables for hospitalization demographics were provided in the dataset (example: age, gender, length of stay). The weekend hospitalizations (Saturday–Sunday) were identified using ‘AWEEKEND’ variable. Hospitalizations with anticoagulation were identified using the ICD-9-CM code ‘V58.61’. ICD-9-procedure codes 9961, 9962, 9969 and 3734 were used to identify hospitalizations with cardioversion/ablation.

We used survey analysis methods to account for the clustering and stratification of encounters for all continuous and categorical variables. SAS 9.4 (SAS Institute Inc., Cary, North Carolina) software were used to perform statistical analysis. We used sampling weights to estimate trends and national estimates to account for the change in sampling design as recommended by the AHRQ. For the demographics, co-morbid diseases, and weekend hospitalizations within each year were compared using Student’s t test for continuous variables and the Chi square test for categorical variables. Multivariate logistic regression method was used in SAS (proc surveylogistic) to evaluate the association between weekend hospitalizations and in-hospital mortality after including the other variables for potential confounders. C-statistic was used for goodness of the model fit for a binary outcome. Like previous studies, trends in demographics, co-morbid diseases, weekend hospitalizations involving AF hospitalizations, length of hospitalization, in-hospital mortality were evaluated using the survey logistic models after creating dummy variables for each outcome of interest. A P-value < 0.05 was considered statistically significant. The checklist provided by NIS was used to ensure the appropriateness of data analysis as recommended by AHRQ [10].

Results

From January 2005–December 2014, we identified 4,520,409 weighted national estimated AF hospitalizations from NIS 2005–2014 database. Of these, there were 874,944 weekend hospitalizations. The mean age (years) ± standard deviation was slightly lower in the weekday group vs. the weekend group (69.85 ± 13.90 vs. 70.02 ± 14.74, P < 0.0001). The AF weekend group had lower elective admissions, relatively higher female hospitalizations, significantly lower utilization rates of cardioversion (14.17% vs. 23.62%, P < 0.0001), and lower cost of hospitalization (mean USD) 7479 vs. 8414 (p < 0.0001) (Table 1).
Table 1

Demographic characteristics of hospitalizations with atrial fibrillation on weekend and weekday

 

AF on weekday

AF on weekend

P value

Unweighted index admissions

752,845

180,573

 

Weighted index admissions

3,645,465

874,944

 

Age in years at admission

  

Mean age (in years), standard deviation

69.85, 13.90

70.02, 14.74

< 0.0001

 18 to 34

1.48%

2.08%

< 0.0001

 35 to 49

6.54%

7.37%

 

 50 to 64

24.39%

22.73%

 

 65 to 79

39.63%

37.23%

 

 Greater than 80

27.85%

30.49%

 

Died during hospitalization

 

< 0.0001

 Did not die

99.11%

98.92%

 

 Died

0.89%

1.08%

 

Disposition

< 0.0001

 Routine

76.44%

72.63%

 

 Transfer to short-term hospital

2.37%

2.73%

 

 Transfer other: includes Skilled Nursing Facility (SNF), Intermediate Care Facility (ICF), and another type of facility

9.51%

11.94%

 

 Home Health Care (HHC)

10.01%

10.55%

 

 Against medical advice (AMA)

0.76%

1.05%

 

 Died in hospital

0.89%

1.08%

 

 Discharged alive, destination unknown

0.02%

0.02%

 

Elective vs. non-elective admission

< 0.0001

 Non-elective

82.70%

95.35%

 

 Elective

17.30%

4.65%

 

Indicator of sex

< 0.0001

 Male

50.26%

47.74%

 

 Female

49.74%

52.26%

 

Length of hospital stay

  

Mean length of stay (days) ± standard deviation

3.48 ± 3.72

3.52 ± 3.79

< 0.0001

 0–3 days

65.66%

64.67%

< 0.0001

 4 to 6 days

22.56%

25.13%

 

 7 to 9 days

7.45%

5.38%

 

 10 to 12 days

2.15%

2.84%

 

 > 12 days

2.15%

1.98%

 

Primary expected payer

 

< 0.0001

 Medicare

65.49%

81.93%

 

 Medicaid

4.08%

8.25%

 

 Private insurance

25.29%

5.76%

 

 Self-pay

2.83%

1.47%

 

 No charge

0.32%

0.46%

 

 Other

1.99%

2.13%

 

Race

  

< 0.0001

 White

83.39%

81.93%

 

 Black

7.49%

8.25%

 

 Hispanic

5.13%

5.76%

 

 Asian

1.35%

1.47%

 

 Pacific Islander

0.51%

0.46%

 

 Other

2.13%

2.13%

 

Cost of hospitalization in USD-(mean)

8414.7 ± 10,343

7479 ± 8785.9

< 0.0001

Bed size of the hospital

 

< 0.0001

 Small

13.52%

14.27%

 

 Medium

24.91%

26.12%

 

 Large

61.57%

59.61%

 

Location/teaching status of the hospital

< 0.0001

 Rural

13.62%

14.80%

 

 Urban-non-teaching

41.30%

43.86%

 

 Urban-teaching

45.08%

41.34%

 

Cardioversion

23.62%

14.17%

< 0.0001

Univariate and multivariate logistic regression analyses were performed. In multivariate analysis, the weekend hospitalizations were associated with higher odds of in-hospital mortality (OR 1.170, 95% CI 1.108–1.125, P < 0.0001) (Table 2). Apart from the weekend admission status, acute respiratory failure, congestive heart failure, renal failure and urban hospital admission (teaching and non-teaching) were found to be strong predictors of in-hospital mortality.
Table 2

Multivariate logistic regression analysis showing the adjusted odds ratio’s predicting the in-hospital mortality for atrial fibrillation (AF) hospitalizations

Effect

Odds ratio

95% confidence limits

P value

Weekend hospitalization

1.170

1.108

1.235

< 0.0001

Length of stay

1.026

1.02

1.031

< 0.0001

AGE

1.054

1.052

1.057

< 0.0001

Hospital region-north east

Reference group

   

Hospital region-midwest

0.729

0.673

0.791

< 0.0001

Hospital region-south

0.885

0.825

0.95

7E−04

Hospital region-west

0.791

0.723

0.865

< 0.0001

Hypertension

0.569

0.542

0.596

< 0.0001

Uncomplicated diabetes

0.975

0.92

1.034

0.401

Complicated diabetes

1.194

1.067

1.338

0.002

Congestive heart failure

1.668

1.354

2.054

< 0.0001

Valvular heart disease

1.305

0.97

1.755

0.079

Renal failure

1.957

1.847

2.075

< 0.0001

Obesity

0.641

0.585

0.703

< 0.0001

Female sex

0.937

0.894

0.982

0.007

Small sized hospital

Reference group

   

Medium sized hospital

1.11

1.023

1.204

0.012

Large sized hospital

1.092

1.015

1.176

0.018

Rural hospital

Reference group

   

Urban non-teaching hospital

0.861

0.799

0.927

< 0.0001

Urban teaching hospital

0.887

0.823

0.955

0.002

Acute respiratory failure

21.2

19.997

22.476

< 0.0001

The adjusted odds ratio’s, 95% confidence intervals and their P-values represent the odds of in-hospital mortality after adjusting for the covariates listed in the table

Over the period of 10 years, we noticed increasing number of weekend hospitalizations with AF (72,216 in 2005 to 92,220 in 2014) (Table 3). In-hospital mortality has gradually decreased (1.32% in 2005 vs 0.94% in 2014, P trend < 0.0001), decreasing mean LOS (3.66 days in 2005 to 3.49 days in 2014, P trend < 0.0001), higher prevalence of depression (5.47% in 2005 vs 9.72% in 2014, P trend < 0.0001), increased rates of cardioversion (11.49% in 2005 vs 17.34% in 2014, P trend < 0.0001), twofold increase in rates of anti-coagulation (9.52% in 2005 vs 17.09% in 2014, P trend < 0.0001).
Table 3

Trends of hospitalization for atrial fibrillation admitted over the weekend 2005–2014

Years

2005

2006

2007

2008

2009

 

Total number of hospitalization (weighted)

72,216

75,822

77,539

88,127

90,013

 

Age in years

 

 18 to 34

2.33%

2.54%

2.36%

2.03%

2.21%

 

 35 to 49

8.16%

8.55%

8.44%

7.72%

7.78%

 

 50 to 64

22.09%

22.36%

21.89%

21.75%

22.29%

 

 65 to 79

37.35%

36.99%

37.27%

37.26%

37%

 

 Greater than 80

29.89%

29.4%

29.92%

31.06%

30.62%

 

Indicator of sex

 

 Male

47.68%

47.99%

47.16%

47.22%

47.75%

 

 Female

52.32%

52.01%

52.84%

52.78%

52.25%

 

Died during hospitalization

 

 Did not die

98.68%

98.87%

98.85%

98.88%

98.92%

 

 Died

1.32%

1.13%

1.15%

1.12%

1.08%

 

Race

 

 White

85.34%

82.48%

81.55%

82.28%

82.23%

 

 Black

6.27%

7.71%

8.54%

7.6%

7.43%

 

 Hispanic

5.06%

6.25%

5.81%

5.39%

5.5%

 

 Asian or Pacific Islander

1.31%

1.31%

1.66%

1.55%

1.45%

 

 Native American

0.17%

0.46%

0.47%

0.58%

0.56%

 

 Other

1.85%

1.78%

1.96%

2.61%

2.83%

 

Length of stay (LOS)

 

 0 to 3

62.84%

63.52%

63.99%

64.09%

63.81%

 

 4 to 6

26.36%

25.36%

25.32%

25.52%

25.81%

 

 7 to 9

5.59%

5.53%

5.79%

5.47%

5.54%

 

 10 to 12

2.91%

3.09%

2.98%

3.03%

2.95%

 

 Greater than 12

2.31%

2.49%

1.91%

1.9%

1.9%

 

 Mean LOS (days)

3.66

3.65

3.55

3.56

3.53

 

Hospital location and teaching status

 

 Rural

16.83%

15.84%

16.66%

15.74%

14.83%

 

 Urban non teaching

49.85%

43.73%

45.48%

46.67%

47.46%

 

 Urban teaching

33.32%

40.43%

37.86%

37.59%

37.71%

 

Comorbidities

 

 Alcohol abuse

4.05%

4.56%

4.42%

4.07%

4.48%

 

 Congestive heart failure

0.39%

0.36%

0.26%

0.31%

0.5%

 

 Depression

5.47%

6.18%

6.59%

7.57%

7.28%

 

 Diabetes with chronic complications

2.39%

2.24%

2.61%

2.71%

2.96%

 

 Hypertension (combine uncomplicated and complicated)

56.07%

59.4%

61.12%

63.5%

65.14%

 

 Liver disease

0.97%

0.97%

1%

1.38%

1.49%

 

 Obesity

7.03%

7.77%

9.53%

10.41%

11.93%

 

 Peripheral vascular disorder

4.66%

5.31%

5.59%

6.04%

6.01%

 

 Psychoses

1.58%

1.93%

1.74%

2.38%

2.13%

 

 Renal failure

5.02%

8.19%

9.51%

9.74%

11.33%

 

 Uncomplicated diabetes

17.43%

18.7%

19.37%

20.11%

21.02%

 

 Cardioversion rates

11.49%

12.21%

12.02%

12.55%

13.63%

 

 Anticoagulation

9.52%

10.55%

12.07%

12.13%

14.37%

 

 Cost of hospitalization (in US dollars)

6260.13

6807.54

7060.35

7287.86

7332.45

 

Years

2010

2011

2012

2013

2014

P value (trend)

Total number of hospitalization (weighted)

89,600

97,140

97,174

95,089

92,220

 

Age in years

 18 to 34

1.92%

1.99%

2.07%

1.71%

1.82%

< 0.0001

 35 to 49

7.32%

6.76%

6.7%

6.69%

6.19%

< 0.0001

 50 to 64

23.74%

23.03%

23.37%

22.9%

23.45%

< 0.0001

 65 to 79

36.04%

37%

36.89%

37.91%

38.53%

0.0449

 Greater than 80

30.88%

31.15%

30.89%

30.69%

29.93%

0.0177

Indicator of sex

0.0082

 Male

48.36%

47.21%

47.93%

47.89%

48.13%

 

 Female

51.64%

52.79%

52.07%

52.11%

51.87%

 

Died during hospitalization

< 0.0001

 Did not die

99.01%

98.93%

99.04%

98.86%

99.06%

 

 Died

0.99%

1.07%

0.96%

1.14%

0.94%

 

Race

 White

81.42%

82.03%

81.54%

81.62%

80.32%

< 0.0001

 Black

8.81%

8.32%

8.72%

8.97%

9.03%

< 0.0001

 Hispanic

5.77%

6.05%

5.64%

5.71%

6.25%

< 0.0001

 Asian or Pacific Islander

1.44%

1.29%

1.42%

1.54%

1.66%

< 0.0001

 Native American

0.71%

0.34%

0.44%

0.4%

0.43%

0.0102

 Other

1.85%

1.96%

2.23%

1.76%

2.3%

< 0.0001

Length of stay (LOS)

 0 to 3

63.83%

65.81%

66.2%

66.11%

65.52%

< 0.0001

 4 to 6

25.62%

24.62%

24.32%

24.16%

24.75%

< 0.0001

 7 to 9

5.58%

5.02%

5.19%

5.1%

5.18%

< 0.0001

 10 to 12

2.93%

2.78%

2.66%

2.62%

2.57%

< 0.0001

 Greater than 12

2.04%

1.78%

1.64%

2.01%

1.98%

< 0.0001

 Mean LOS (days)

3.58

3.41

3.4

3.44%

3.49

< 0.0001

Hospital location and teaching status

 Rural

14.83%

14.64%

13.92%

14.08%

11.73%

< 0.0001

 Urban non teaching

46.38%

45.72%

42.51%

42.1%

30.63%

< 0.0001

 Urban teaching

38.79%

39.63%

43.57%

43.82%

57.64%

< 0.0001

Comorbidities

 Alcohol abuse

4.74%

5.08%

5.35%

5.55%

5.83%

< 0.0001

 Congestive heart failure

0.35%

0.4%

0.3%

0.36%

0.33%

0.4994

 Depression

7.95%

8.84%

9.08%

9.2%

9.72%

< 0.0001

 Diabetes with chronic complications

2.81%

3.5%

3.21%

3.48%

3.61%

< 0.0001

 Hypertension (combine uncomplicated and complicated)

66.63%

68.5%

69.08%

69.91%

71.55%

< 0.0001

 Liver disease

1.7%

1.76%

1.86%

1.79%

2.15%

< 0.0001

 Obesity

12.08%

13.01%

15.2%

16.23%

17.62%

< 0.0001

 Peripheral vascular disorder

6.46%

6.94%

7.11%

6.96%

7.55%

< 0.0001

 Psychoses

2.41%

2.69%

2.68%

2.6%

2.94%

< 0.0001

 Renal failure

12.71%

13.5%

14.28%

15.24%

16.15%

< 0.0001

 Uncomplicated diabetes

22.1%

21.65%

22.97%

23.3%

23.95%

< 0.0001

 Cardioversion rates

14.74%

14.55%

15. 7%

15.95%

17.34%

< 0.0001

 Anticoagulation

15.18%

15%

15.48%

15.67%

17.09%

< 0.0001

 Cost of hospitalization (in US dollars)

7568.3

7656.57

7719.1

8143.44

8265.78

< 0.0001

Discussion

The main findings and trends noted in the current study of weekend AF hospitalizations are (1) improving trends in in-hospital mortality over 10 years from 2005 to 2014. (2) Weekend hospitalizations are associated with higher odds of in-hospital mortality. (3) Decreasing the mean length of hospital stay, and (4) increasing trends of utilization rates of cardioversion and anticoagulation.

The ‘weekend effect’ is a concern where the patients are thought to have worse outcomes when admitted to the hospital on a Saturday or a Sunday [11]. The first reports of the weekend hospitalizations having higher mortality appeared in the 1970s. Higher mortality and longer hospital LOS have been reported among AF hospitalizations during the weekend by Deshmukh et al. and Weeda et al. [3, 12] Subsequent study reported no difference in weekend and weekday AF in-hospital mortality [13].

In comparison to the prior studies, ours is the first study analyzing the trends of weekend AF hospitalizations. Our results match the results of Weeda et al. [3] where there is improved mortality among weekend hospitalization with AF. Though the lower utilization of cardioversion has been demonstrated through the years, the rates of cardioversion have significantly been improving, and at the same time, the in-hospital mortality has been decreasing during the same time period. This can be attributed to improved access to life-saving procedures. However, the overall utilization rates of cardioversion continue to be low among the weekend hospitalizations when compared to the weekday hospitalization. This is likely due to staffing issues, the availability of anesthesia, or coverage for a trans-esophageal echocardiogram at some institutions.

In the nationwide US practice, the weekend AF hospitalizations appear to have improved rates of in-hospital mortality, rates of cardioversion utilization and improved utilization of anticoagulation. However, the overall rates of in-hospital mortality continue to be poor in comparison to weekday hospitalizations. Further studies are required to identify the opportunities to improve AF weekend care.

Limitations

Although our study has a large nationally representative database sample, these findings should be interpreted considering the following limitations. First, we identified our cases using ICD-9 discharge diagnosis codes, and details of the initial presentation (for example, emergency room visit) are not available, thereby, limiting the ability to confirm the diagnosis. Secondly, the NIS data does not provide information on important clinical predictors of outcomes such as the duration and the type of AF, left atrial diameter, the presence of thrombus in the left atrium and the baseline functional status, which can potentially influence the outcomes for in-hospital mortality. Third, given the description of ICD-9 codes in the database, it is not possible to differentiate pre-existing comorbidities from complications which have occurred during the hospitalization. Fourth, data regarding specific medical management such as anti-arrhythmic agents are not available in the NIS. And lastly, the diagnostic coding inconsistencies between weekends and weekdays also could not be ruled out. Given these limitations, it would require studies to have rigorous analysis having additional clinical information having a more consistent way of collecting data (such as using consistent diagnostic definitions) and analyzing outcomes considering all the above-mentioned factors [11].

Notes

Acknowledgements

None.

Authors’ contributions

DCV, GS, SD and MG all contributed to the study conception/design and the development of the study protocol. DCV was responsible for seeking ethical approval. NVKP, DCB, AD, and AM were responsible for setting up the study, designing study documentation and data validation. DCV and RC undertook all data analyses. DV, AD, SSC, MS, RL, KJ, and FP were all involved in writing the final manuscript. All authors read and approved the final manuscript.

Funding

Not applicable, this project was unfunded.

Ethics approval and consent to participate

Institutional review board (IRB) has determined that this study does not meet the regulatory definition of human subjects research and did not require review, because at the University of Iowa this activity is limited to analysis of publicly available de-identified data.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Dinesh C. Voruganti
    • 1
    Email author
  • Ghanshyam Shantha
    • 2
  • Sushma Dugyala
    • 3
  • Naga Venkata K. Pothineni
    • 4
  • Deobrat Chandra Mallick
    • 5
  • Abhishek Deshmukh
    • 6
  • Ala Mohsen
    • 7
  • Stephanie S. Colello
    • 8
  • Mohammed Saeed
    • 2
  • Rakesh Latchamsetty
    • 2
  • Krit Jongnarangsin
    • 2
  • Frank Pelosi
    • 2
  • Ryan M. Carnahan
    • 9
  • Michael Giudici
    • 10
  1. 1.Division of Internal Medicine, Roy and Lucille J. Carver College of MedicineUniversity of Iowa Hospitals and ClinicsIowa CityUSA
  2. 2.Division of ElectrophysiologyUniversity of MichiganAnn ArborUSA
  3. 3.Division of Internal MedicineUniversity of AlabamaTuscaloosaUSA
  4. 4.Division of Cardiovascular MedicineUniversity of ArkansasLittle RockUSA
  5. 5.Department of Hospital MedicineSpohn Shoreline HospitalCorpus ChristiUSA
  6. 6.Division of Cardiovascular MedicineMayo ClinicRochesterUSA
  7. 7.Division of Cardiovascular MedicineLouisiana State UniversityNew OrleansUSA
  8. 8.Division of Internal MedicineHospital of the University of PennsylvaniaPhiladelphiaUSA
  9. 9.College of Public HealthUniversity of IowaIowa CityUSA
  10. 10.Division of Cardiovascular Medicine, Electrophysiology, Roy and Lucille J. Carver College of MedicineUniversity of Iowa Hospitals and ClinicsIowa CityUSA

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