Catheter ablation for atrial fibrillation (AF) has emerged as a popular procedure. The purpose of this study was to examine whether there exist differences or disparities in ablation utilization across gender, socioeconomic class, insurance, or race. Using the Nationwide Inpatient Sample (2000 to 2012), we identified adults hospitalized with a principal diagnosis of AF by ICD 9 code 427.31 who had catheter ablation (ICD 9 code–37.34). We stratified patients by race, insurance status, age, gender, and hospital characteristics. A hierarchical multivariate mixed-effect model was created to identify the independent predictors of AF ablation. Among an estimated total of 3,508,122 patients (extrapolated from 20% Nationwide Inpatient Sample) hospitalized with a diagnosis of AF in the United States from the year 2000 to 2012, 102,469 patients (2.9%) underwent catheter ablations. The number of ablations was increased by 940%, from 1,439 in 2000 to 15,090 in 2012. There were significant differences according to gender, race, and health insurance status, which persisted even after adjustment for other risk factors. Female gender (0.83 [95% CI 0.79 to 0.87; p <0.001]), black (0.49 [95% CI 0.44 to 0.55; p <0.001]), and Hispanic race (0.64 [95% CI 0.56 to 0.72; p <0.001]) were associated with lower likelihoods of undergoing an AF ablation. Medicare (0.93, 0.88 to 0.98, <0.001) or Medicaid (0.67, 0.59 to 0.76, <0.001) coverage and uninsured patients (0.55, 0.49 to 0.62, <0.001) also had lower rates of AF ablation compared to patients with private insurance. In conclusion we found differences in utilization of catheter ablation for AF based on gender, race, and insurance status that persisted over time.
Atrial fibrillation (AF) is a growing epidemic. Over the past decade, a 23% increase in hospitalizations for AF was reported across the United States. As the population ages, it is estimated that there exists a 1 in 4 lifetime risk of developing AF. Catheter ablation has emerged as a commonly performed therapeutic option for management of AF. In patients with symptomatic AF, catheter ablation has reduced AF recurrence and improved quality of life as compared with antiarrhythmic therapy. The current national guidelines recommend catheter ablation in patients with symptomatic, paroxysmal AF, who have not responded to or tolerated antiarrhythmic medications. AF ablation may even be a reasonable first-line treatment for some patients with symptomatic paroxysmal or persistent AF. Recent recognition of disparities of care has led to efforts for reducing these disparities. Previous studies reported that gender and racial disparities existed in the use of innovative or costly cardiovascular technologies such as implantable cardioverter–defibrillator implantation. A single-site study showed that AF ablation was performed preferentially in white males. A recent study using a Medicare data set demonstrated race and gender differences in access to care for a diagnosis of AF, including utilization of AF ablation. It is unknown whether differences in AF ablation utilization rates exist in the general population, not just the Medicare-insured patients. Furthermore, it is unclear whether there are other socioeconomic factors associated with AF ablation. Therefore, the primary objective was to identify differences in AF catheter ablation utilization based on gender, racial, health insurance, and other socioeconomic factors using data from the Nationwide Inpatient Sample (NIS).
Methods
The NIS is a part of the Agency for Health Care Research and Quality’s Healthcare Cost and Utilization Project. The NIS (Nationwide) is the largest all-payer inpatient care database in the United States, containing data on >7 million hospital stays. It contains data related to hospital admission and discharge from a 20% stratified sample of community hospitals in the United States. The NIS database has been described in the past.
Our target population consisted of patients aged ≥18 years with a principal diagnosis of AF from the year 2000 to 2012, identified by ICD 9 code 427.31. Among these patients, those who underwent catheter ablation were identified by ICD 9 code–37.34. We excluded observations with missing data on age, gender, and mortality. We also excluded observations with index admission related to pregnancy, newborn, or trauma. To avoid inclusion of patients undergoing ablation for other indications, we further excluded patients with concomitant secondary diagnoses of atrial flutter, Wolff–Parkinson–White syndrome, atrioventricular nodal reentry tachycardia, paroxysmal supraventricular tachycardia, paroxysmal ventricular tachycardia, and/or ventricular premature complexes. In addition, to avoid inclusion of patients undergoing atrioventricular junction ablation, we excluded patients with diagnostic or procedural codes indicating previous or current implantation of a pacemaker or implantable cardioverter–defibrillator. Records with surgical ablations during the hospitalization were also excluded. Similar method has been used previously to identify patients undergoing AF ablation from large administrative databases. The ICD-9-CM codes used to identify each of these diagnoses and procedures are listed in online-only data Supplementary Table 1 . We defined the severity of co-morbid conditions using Deyo modification of the Charlson Co-morbidity Index, which contains 17 co-morbid conditions with differential weights. The scores range from 0 to 33, with higher scores corresponding to a greater burden of co-morbid diseases ( See Supplementary Table 2 ).
The Stata IC 11.0 (StataCorp, College Station, Texas) and SAS 9.4 (SAS Institute Inc, Cary, North Carolina) were used for the analyses, accounting for the complex survey design and clustering, that takes into account regional differences in patient race and/or insurance status. We used the weighted values provided in the NIS database to generate national estimates of the number of annual admissions in the United States. The chi-square test was used to compare categorical variables between patients with and without ablation. The Wilcoxon rank-sum test was used to assess nonnormally continuous variables such as length of stay. Two-level hierarchical mixed-effects logistic regression models were generated to identify the independent multivariate predictors of catheter ablations for the study cohort. In the multivariate models, we included hospital-level variables such as hospital bed size, hospital region (Northeast, South, Midwest with West as referent), urban teaching versus nonteaching/rural hospital, and patient-level variables such as age, gender, race, admission type, Deyo modification of the Charlson Co-morbidity Index, median household income, and primary payer (with private considered as referent). Hospital academic status was obtained from the American Hospital Association Annual Survey of Hospitals. Hospital identification was incorporated as a random effect in the model to account for the effect of hospital clustering (meaning that patients treated at the same hospital may experience similar outcomes as a result of other processes of care).
Because NIS represents a 20% stratified random sample of US hospitals, the population at risk forming the denominator was 20% of the US census population of adults aged >18 years for any given year. We obtained utilization rates by dividing the number of catheter ablations in the NIS data set in a given year divided by 20% of the US census population >18 years age for that year and was also divided into various subgroups. To determine the annual change in categorical variables like ablation rate, a chi-square test of trend for proportions was used based on the Cochrane Armitage test through the “ptrend” command in Stata. For continuous variables like length of stay and the cost of hospitalization, we used a nonparametric test for trend by Cuzick (which is similar to the Wilcoxon rank-sum test) running the “nptrend” command in Stata. A p value of <0.05 was considered significant.
Results
In our NIS cohort, a total of 735,394 patients were admitted with a primary diagnosis of AF after accounting for the exclusion criteria. Of those, 21,382 patients (2.91%) underwent AF ablation. Weighted estimates of the total US population suggest that a total of approximately 3,508,124 patients were admitted to hospitals with a primary diagnosis of AF during the study period from 2000 to 2012. Furthermore, after accounting for our various exclusion criteria, 102,469 patients (2.9%) from the weighted sample underwent AF ablation. Baseline characteristics of the study population are demonstrated in Table 1 . The number of AF ablations increased by 940%, from 1,439 in the year 2000 to 15,090 in 2012 ( Supplementary Figure 1 ), which corresponds to 5.4 ablations/1 US million population in 2000 and 48.5 ablations/1 million US population in 2012, respectively. Most of these catheter ablations were performed in men (63.3%) and whites (71.4%). The mean age of patients undergoing AF ablation was 63.6 ± 0.1 years, with approximately 40% of the ablations performed in the age group of 65 to 79 years. From 2000 to 2012, the mean age of patients undergoing AF ablation trended down from 2000 to 2005 and, subsequently, there was a small uptick in age from 2005 to 2012 ( Table 2 ; p value <0.001). Most of the ablations were done in patients with Medicare (48%), followed by patients with private insurance (46%). A majority of these ablations were done in large (80%), urban teaching hospitals (74%), and in the southern region (34%; Table 1 ).
| Baseline table | Overall | Catheter Ablation | P-Value | |
|---|---|---|---|---|
| No | Yes | |||
| Total no. (Unweighted no.) | 735394 | 714012 (97.1%) | 21382 (2.9%) | |
| Total no. (Weighted no.) | 3508122 | 3405652 (97.1%) | 102469 (2.9%) | |
| Age – mean ± standard error (years) | 70.1 ± 0.02 | 70.3 ± 0.02 | 63.6 ± 0.08 | <.0001 |
| <0.001 | ||||
| 18-34 | 1.8% | 1.8% | 1.6% | |
| 35-49 | 7.2% | 7.1% | 10.9% | |
| 50-64 | 22.4% | 21.9% | 38.3% | |
| 65-79 | 39.6% | 39.6% | 39.4% | |
| ≥80 | 28.9% | 29.5% | 9.8% | |
| <.0001 | ||||
| Male | 46.2% | 45.7% | 63.3% | |
| Female | 53.8% | 54.3% | 36.7% | |
| <.0001 | ||||
| White | 66.7% | 66.5% | 71.4% | |
| Black | 5.4% | 5.5% | 2.9% | |
| Hispanic | 3.5% | 3.6% | 2.4% | |
| Others | 1.1% | 1.0% | 1.2% | |
| Missing ∗ | 23.4% | 23.4% | 22.0% | |
| <.0001 | ||||
| Charlson/deyo comorbidity index † | <.0001 | |||
| 0 | 43.6% | 43.2% | 58.3% | |
| 1 | 29.5% | 29.7% | 25.4% | |
| ≥2 | 26.9% | 27.2% | 16.3% | |
| Obesity ‡ | 8.9% | 8.8% | 10.6% | <.0001 |
| Hypertension(history) § | 52.8% | 52.8% | 53.6% | <.0001 |
| Diabetes Mellitus § | 18.8% | 18.9% | 16.1% | <.0001 |
| Heart failure § | 0.4% | 0.4% | 0.5% | <.0001 |
| Chronic pulmonary disease § | 17.2% | 17.3% | 13.0% | <.0001 |
| Peripheral vascular disease § | 4.8% | 4.8% | 3.6% | <.0001 |
| Fluit-electrolute abnormalities & Renal failure § | 19.7% | 20.0% | 9.9% | <.0001 |
| Neurological disorder or paralysis § | 5% | 5.1% | 2.3% | <.0001 |
| Anemia or coagulopathy § | 10.3% | 10.4% | 5.3% | <.0001 |
| Solid Tumors or Metastatic Cancers or Lymphoma § | 3.8% | 3.9% | 1.2% | <.0001 |
| Depression, psychosis or substance abuse § | 7.9% | 7.9% | 5.1% | <.0001 |
| Primary Payer | <.0001 | |||
| Medicare | 65.7% | 66.2% | 48.0% | |
| Medicaid | 3.4% | 3.4% | 2.3% | |
| Private including HMOs & PPOs | 25.7% | 25.1% | 46.3% | |
| Other/Self-pay/No charge | 5% | 5.1% | 3.2% | |
| Median household income category for patient’s zip code ¶ | <.0001 | |||
| 1. 0-25th percentile | 21.1% | 21.2% | 16.9% | |
| 2. 26-50th percentile | 26.3% | 26.4% | 23.6% | |
| 3. 51-75th percentile | 24.6% | 24.6% | 26.2% | |
| 4. 76-100th percentile | 25.9% | 25.8% | 31.4% | |
| Hospital characteristics | ||||
| Hospital bed size | <.0001 | |||
| Small | 13.5% | 13.7% | 5.7% | |
| Medium | 25% | 25.4% | 14.0% | |
| Large | 61.1% | 60.6% | 79.5% | |
| Hospital Region | <.0001 | |||
| Northeast | 23.9% | 23.8% | 27% | |
| Midwest or North Central | 26.3% | 26.3% | 25.4% | |
| South | 40.8% | 41.0% | 34.5% | |
| West | 8.4% | 8.3% | 12.9% | |
| Hospital Teaching status | <.0001 | |||
| Rural | 17.7% | 18.2% | 1.8% | |
| Urban non-teaching | 43.2% | 43.8% | 23.2% | |
| Urban teaching | 38.7% | 37.7% | 74.3% | |
| Admission types | <.0001 | |||
| Emergent/Urgent | 86.1% | 87.7% | 31.7% | |
| Elective admission | 13.9% | 12.3% | 68.2% | |
| Admission day | <.0001 | |||
| Weekdays | 80% | 79.6% | 95.4% | |
| Weekend | 20% | 20.4% | 4.6% | |
| Disposition | <.0001 | |||
| Home | 77.3% | 76.9% | 93.0% | |
| Transfer to Short-term Hospital/other facilities/Home Health Care | 20.9% | 21.3% | 6.7% | |
| AMA | 0.8% | 0.8% | 0.1% | |
| Died in Hospital | 1% | 1.1% | 0.2% | |
| Length of Stay – mean ± standard error (days) | 3.4 ± 0.004 | 3.4 ± 0.004 | 2.6 ± 0.02 | <.0001 |
∗ Race was missing in 23.4% of the population.
† Charlson/Deyo co-morbidity index was calculated as per Deyo classification.
‡ Obesity is defined as body mass index >30.
§ Variables are AHRQ (Agency for Healthcare Research and Quality) co-morbidity measurements.
¶ Median household income category for patient’s zip code: This represents a quartile classification of the estimated median household income of residents in the patient’s ZIP Code. These values are derived from ZIP Code-demographic data obtained from Claritas. The quartiles are identified by values of 1 to 4, indicating the poorest to wealthiest populations. Because these estimates are updated annually, the value ranges vary by year. http://www.hcupus.ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp .
| Catheter Ablation | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | overall | Chisquare p-value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total No. of Hospitalizations for Afib (weighted) | 230729 | 252362 | 244094 | 240511 | 234608 | 242645 | 262665 | 258103 | 287959 | 298762 | 295082 | 316534 | 344070 | 3508124 | |
| Total No. of AF Ablations performed (weighted) | 1439 | 2182 | 2257 | 3569 | 4432 | 7992 | 7896 | 8957 | 11399 | 11444 | 11247 | 14566 | 15090 | 102470 | |
| % of AF hospitalizations | 0.6 | 0.9 | 0.9 | 1.5 | 1.9 | 3.3 | 3.0 | 3.5 | 4.0 | 3.8 | 3.8 | 4.6 | 4.4 | 2.9 | <0.001 |
| Age (years) | 68.3± 0.8 | 65.8 ± 0.7 | 66.0 ± 0.7 | 62.0 ± 0.5 | 63.1 ± 0.4 | 61.1 ± 0.3 | 63.0 ± 0.3 | 63.2 ± 0.3 | 64.2 ± 0.3 | 63.1 ± 0.2 | 63.8 ± 0.3 | 64.0 ± 0.2 | 64.5 ± 0.2 | 63.6 ± 0.1 | <0.001 |
| 18-34 | 0.9 | 1.3 | 0.9 | 2.6 | 1.4 | 3.6 | 2.7 | 3.2 | 2.7 | 3.1 | 3.1 | 3.6 | 3.8 | 2.6 | |
| 35-49 | 0.7 | 1.3 | 1.7 | 3.1 | 3.8 | 6.7 | 5.2 | 5.2 | 5.5 | 5.7 | 5.4 | 6.5 | 5.3 | 4.4 | |
| 50-64 | 0.6 | 1.3 | 1.2 | 2.4 | 3.3 | 6.0 | 5.1 | 6.0 | 6.7 | 6.9 | 6.4 | 7.5 | 6.9 | 5.0 | |
| 65-79 | 0.7 | 0.8 | 0.9 | 1.3 | 1.6 | 2.7 | 2.8 | 3.3 | 4.1 | 3.7 | 4.1 | 5.2 | 5.2 | 2.9 | |
| ≥80 | 0.5 | 0.5 | 0.6 | 0.6 | 0.9 | 0.9 | 1.1 | 1.2 | 1.4 | 1.2 | 1.1 | 1.3 | 1.2 | 1.0 | |
| <0.001 | |||||||||||||||
| Male | 0.7 | 0.9 | 1.1 | 2.0 | 2.6 | 4.9 | 4.2 | 4.8 | 5.4 | 5.4 | 5.3 | 6.3 | 5.7 | 4.0 | |
| Female | 0.6 | 0.8 | 0.8 | 1.0 | 1.3 | 1.9 | 2.0 | 2.3 | 2.7 | 2.5 | 2.6 | 3.1 | 3.2 | 2.0 | |
| <0.001 | |||||||||||||||
| White | 0.7 | 0.9 | 1.0 | 1.6 | 2.1 | 3.5 | 3.0 | 4.0 | 4.4 | 3.5 | 3.9 | 4.7 | 4.5 | 3.1 | |
| Black | 0.1 | 0.5 | 0.4 | 0.9 | 0.7 | 0.8 | 1.0 | 1.8 | 2.0 | 2.0 | 2.1 | 2.0 | 3.2 | 1.6 | |
| Hispanic | 0.3 | 1.1 | 0.5 | 0.4 | 1.1 | 1.6 | 1.8 | 2.9 | 1.9 | 2.6 | 2.0 | 3.7 | 2.9 | 2.0 | |
| Others | 0.3 | 0.6 | 1.0 | 0.3 | 0.8 | 2.9 | 1.8 | 10.9 | 5.2 | 2.2 | 1.8 | 5.7 | 3.5 | 3.5 | |
| Missing | 0.6 | 0.8 | 0.9 | 1.5 | 1.8 | 3.4 | 3.6 | 2.4 | 3.3 | 5.9 | 4.8 | 5.4 | 5.4 | 2.8 | |
| Primary Payer | <0.001 | ||||||||||||||
| Medicare | 0.7 | 0.8 | 0.8 | 1.0 | 1.3 | 2.0 | 2.1 | 2.4 | 2.9 | 2.7 | 2.8 | 3.4 | 3.5 | 2.1 | |
| Medicaid | 0.3 | 0.8 | 0.6 | 0.8 | 1.0 | 1.7 | 2.8 | 1.9 | 2.2 | 2.9 | 2.8 | 3.8 | 2.1 | 2.0 | |
| Private | 0.6 | 1.3 | 1.3 | 2.9 | 3.7 | 7.0 | 5.7 | 6.5 | 6.9 | 7.2 | 6.9 | 8.4 | 7.9 | 5.3 | |
| Nopay/Selfpay/Others | 0.2 | 0.3 | 0.7 | 0.9 | 1.5 | 2.1 | 1.8 | 1.7 | 2.7 | 2.5 | 2.3 | 2.8 | 2.4 | 1.9 | |
| Hospital Region | <0.001 | ||||||||||||||
| Northeast | 0.7 | 1.3 | 1.1 | 2.3 | 2.3 | 2.8 | 2.5 | 4.7 | 3.7 | 4.4 | 6.5 | 4.8 | 4.6 | 3.3 | |
| Midwest or North Central | 0.6 | 0.7 | 0.5 | 1.5 | 1.6 | 5.1 | 2.7 | 3.1 | 2.8 | 5.0 | 3.6 | 3.7 | 4.3 | 2.8 | |
| South | 0.6 | 0.8 | 1.0 | 1.0 | 1.8 | 2.5 | 3.2 | 2.7 | 3.7 | 2.2 | 2.3 | 4.7 | 3.9 | 2.5 | |
| West | 0.6 | 0.3 | 1.6 | 1.5 | 2.3 | 2.5 | 4.6 | 5.8 | 10.1 | 7.1 | 4.0 | 6.7 | 5.5 | 4.5 | |
| Hospital teaching status | <0.001 | ||||||||||||||
| Rural | 0.0 | 0.1 | 0.0 | 0.1 | 0.2 | 0.0 | 0.1 | 0.4 | 0.2 | 0.9 | 1.0 | 0.3 | 0.6 | 0.3 | |
| Urban non-teaching | 0.2 | 0.5 | 0.7 | 0.6 | 1.2 | 1.9 | 2.5 | 1.9 | 2.6 | 1.7 | 1.6 | 1.9 | 2.4 | 1.6 | |
| Urban Teaching | 1.4 | 1.8 | 1.7 | 3.3 | 3.7 | 6.8 | 4.8 | 6.5 | 7.1 | 7.1 | 7.5 | 9.0 | 7.5 | 5.6 | |
| Admission types (%) | <0.001 | ||||||||||||||
| Emergent/Urgent | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 1.3 | 1.0 | 1.6 | 1.4 | 1.3 | 1.2 | 1.7 | 1.5 | 1.1 | |
| Elective admission | 2.2 | 3.4 | 3.5 | 6.8 | 9.2 | 15.4 | 15.2 | 15.4 | 20.8 | 20.2 | 21.5 | 22.6 | 24.9 | 14.3 | |
| Median household income category for patient’s zip code ∗ | <0.001 | ||||||||||||||
| 1. 0-25th percentile | 0.4 | 1.3 | 1.0 | 1.1 | 0.9 | 1.8 | 2.4 | 2.5 | 2.7 | 2.6 | 2.7 | 3.3 | 3.1 | 2.3 | |
| 2. 26-50th percentile | 0.6 | 0.6 | 0.6 | 1.3 | 1.7 | 3.0 | 2.8 | 3.0 | 3.5 | 3.7 | 3.3 | 4.0 | 4.3 | 2.6 | |
| 3. 51-75th percentile | 0.7 | 0.8 | 0.8 | 1.4 | 2.3 | 3.4 | 3.1 | 3.7 | 4.2 | 4.5 | 4.1 | 5.3 | 4.8 | 3.1 | |
| 4. 76-100th percentile | 0.7 | 1.0 | 1.2 | 2.2 | 2.8 | 5.2 | 3.8 | 5.1 | 5.7 | 4.7 | 5.6 | 6.2 | 5.8 | 3.5 |
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