Abstract
Background
Alcohol septal ablation (ASA) effectively treats select patients with hypertrophic obstructive cardiomyopathy (HOCM). Our study analyzes socioeconomic and geographic factors' influence on post-ASA outcomes to improve patient care and accessibility.
Methods
Using the National Inpatient Sample (2016-2021) and International Classification of Diseases 10th Revision codes, we identified patients with a primary diagnosis of HOCM who underwent ASA. Study populations were categorized into 3 groups based on the poverty income ratio. The primary outcome was in-hospital complications following the procedure. Secondary outcomes included length of stay, hospitalization costs, and disposition status.
Results
Of 8585 patients who underwent ASA, 45.3% were from low SES backgrounds. Medicare was the primary payer, with treatments predominantly occurring in urban teaching hospitals for elective procedures in the southern region. Low and middle-SES patients showed higher rates of in-hospital mortality, sudden cardiac arrest, and increased pacemaker placements compared to high-SES groups. They experienced more extended hospital stays, which was associated with higher hospitalization costs and more transfers to skilled nursing facilities than high SES patients (all P < .05). However, other complications, such as acute stroke and acute kidney injury, showed no significant differences among the groups.
Conclusions
Lower and middle socioeconomic HOCM patients who underwent ASA face higher in-hospital mortality, more sudden cardiac arrests, increased pacemaker placements, and more extended hospital stays compared to higher socioeconomic patients, highlighting the need for standardized outcomes for all ASA patients.
Keywords: alcohol septal ablation, hypertrophic cardiomyopathy, length of stay, mortality, socioeconomic status
Introduction
Hypertrophic cardiomyopathy (HCM) is a genetic disorder characterized by abnormal heart muscle thickening, particularly affecting the left ventricle.1, 2, 3 The Coronary Artery Risk Development in Young Adults (CARDIA) study estimates the prevalence of HCM to be approximately 1 in 500 individuals.4 This condition can be classified into 2 main types: nonobstructive HCM and hypertrophic obstructive cardiomyopathy (HOCM). Symptoms of HCM vary widely, ranging from mild to severe, and may include irregular heartbeats, heart failure, and sudden cardiac death.2,5 Notably, 60% to 70% of individuals diagnosed with HCM present with HOCM, commonly exhibiting symptoms such as chest pain, syncope, palpitations, and dyspnea.1,6 Recent advancements in managing HOCM have led to the development of various treatment strategies. These encompass pharmacological therapies designed to provide symptomatic relief for patients experiencing left ventricular outflow tract obstruction, as well as surgical options such as alcohol septal ablation (ASA) and septal myectomy for individuals who do not respond adequately to medical treatment.1,7,8
ASA is a minimally invasive procedure that decreases the thickness of the heart muscle through controlled myocardial infarction (MI). This approach is appropriate for patients with obstructive HCM who exhibit persistent symptoms, do not respond to medication, and show resting or provoked pressure gradients of ≥50 mm Hg.9, 10, 11, 12 Although ASA has proven effective in alleviating symptoms and improving quality of life, it is essential to acknowledge that socioeconomic status (SES) disparities may significantly affect access to and outcomes of this procedure.
SES significantly influences health care outcomes, posing considerable challenges for individuals from lower socioeconomic backgrounds in accessing advanced health care services, including specialized centers for HCM offering ASA.13 Previous studies have highlighted sex disparities in diagnostic and therapeutic considerations for patients with HOCM.14 Moreover, extensive research conducted in the United States and Finland has demonstrated that low-income populations face an increased risk of sudden cardiac death, even after controlling for factors such as smoking and alcohol use.15 Considering the growing evidence surrounding disparities in cardiovascular disease, our objective is to examine the socioeconomic and geographic disparities and their impact on post-ASA outcomes to improve patient care and accessibility.
Materials and methods
Study design
The study specifically targeted patients admitted to the hospital for ASA as a treatment for HOCM. ASA is often necessary for these patients to alleviate left ventricular outflow obstruction and enhance cardiac output. The analysis utilized data from the National Inpatient Sample (NIS) database, spanning period 2016-2021.
The NIS, a component of the Healthcare Cost and Utilization Project (HCUP) supported by the Agency for Healthcare Research and Quality (AHRQ), offers deidentified information regarding in-hospital outcomes, procedures, and discharge data.16 Patient-specific information, including comorbidities and procedures, was recorded using the International Classification of Diseases 10th Revision, Clinical Modification (ICD-10-CM) codes, while the International Classification of Diseases 10th Revision, Procedural Coding System (ICD-10-PCS) was employed to identify procedures (Supplemental Tables S1 and S2).
This database aggregates data from all nonfederal, short-term general hospitals and specialized facilities across the United States, excluding rehabilitation and long-term acute care hospitals. The deidentified patient information encompasses demographics, discharge diagnoses, comorbidities, procedures, outcomes, and hospitalization costs. Notably, all states participating in HCUP contribute data to the NIS, representing over 95% of the US population. The design of the NIS is based on a 20% sample of discharges from participating hospitals, thus minimizing the margin of error in estimates and facilitating the provision of stable and precise outcomes. The study was deemed exempt from institutional review board approval as the HCUP-NIS database contains only deidentified patient information, which is publicly available.
Inclusion and exclusion criteria
We selected patients aged ≥18 years diagnosed with HOCM who underwent ASA, identified by the ICD-10-CM codes. Patients were then categorized by SES. We excluded those <18 years of age and those without confirmed HOCM.
Study outcomes
The study included a comparative analysis of primary and secondary outcomes among patients categorized by SES. The primary outcome examined was inpatient mortality, whereas secondary outcomes included pacemaker implantation, implantable cardioverter-defibrillator (ICD) implantation, acute stroke, newly developed acute kidney injury, sudden cardiac arrest, postprocedural bleeding, MI, major adverse cardiac and cerebrovascular events, as well as various discharge outcomes such as length of stay and total costs. The evaluated hospital metrics comprised length of stay, adjusted total charges, and overall costs. Mortality data were sourced directly from the NIS. The poverty income ratio (PIR) remains consistent across survey years due to annual adjustments for inflation in income thresholds.17 Participants were categorized into 3 distinct groups: low-income adults, defined as those with incomes at or below the federal poverty level (PIR ≤ 1); middle-income adults, with a PIR between 1 and 4 (1 < PIR < 4); and high-income adults, classified as those with a PIR of 4 or above (PIR ≥ 4).
Statistical analysis
We conducted a comprehensive survey analysis to classify encounters based on continuous and categorical variables. The statistical analysis was executed utilizing Stata version 18.0 (StataCorp LLC). Our focus was on a cohort of patients with HOCM who underwent ASA. By HCUP data protection guidelines, we excluded encounters with variables and outcome frequencies <10 and percentages <1%. Given that our study utilized deidentified national data, it was exempt from review by the institutional review board.
We employed the χ2 test to identify differences in categorical and continuous variables. Histogram analysis indicated a normal distribution for constant variables, whereas continuous variables exhibited a leptokurtic negative distribution. Consequently, we reported these variables using the mean and SD.
We applied a logistic regression model to calculate the odds ratio (OR) for primary and secondary outcomes. This analysis was followed by a multivariate analysis to adjust for potential confounders, including comorbidities, to minimize bias. A P value < .05 was deemed statistically significant.
The multivariate analysis accounted for significant baseline characteristics such as age, race, hospital bed size, primary payer, hospital region, teaching status of the hospital, and various comorbid conditions, including hypertension, hyperlipidemia, obesity, smoking history, hypothyroidism, anemia, respiratory diseases, pneumonia, liver disease, valvular disease, diabetes mellitus, renal failure, peptic ulcers, alcohol abuse, fluid and electrolyte imbalances, drug abuse, psychosis, and depression.
To mitigate bias due to skewness and inconsistent confounders, we conducted propensity score matching using the PSM-2 logit model. The propensity scores were plotted for raw and matched data to assess causal effects and associations, employing χ2 statistics on the matched propensity score cohort. Additionally, the discharge weights variable was aligned with the survey set to represent the national cohort accurately according to NIS statistical guidelines.
Results
Demographic and baseline characteristics
A total of 8585 patients were admitted to the hospital for ASA. The baseline characteristics of patients undergoing ASA illustrate notable socioeconomic disparities (Table 1). The sex distribution shows a more significant proportion of men within the higher socioeconomic group (45.2%) in comparison to those in the middle (40.7%) and lower (36.8%) socioeconomic groups (P = .019). Regarding racial composition, White patients are significantly more represented in the higher-income group (87.5%) than in the middle (82.4%) and lower (82.2%) groups. In contrast, Black and Hispanic patients are more prevalent in the lower socioeconomic strata (P = .01). Furthermore, insurance coverage differs significantly, with private insurance being most common in the higher-income group (48.6%). In contrast, Medicaid utilization diminishes as SES rises (P = .01), underscoring disparities in health care access and financial resources. (Table 1). The age distribution was comparable across the groups; however, a slight predominance of younger patients was noted in the higher SES cohort. The age distribution is detailed in Table 1 and illustrated in Figure 1. The Central Illustration summarizes the study's characteristics and findings.
Table 1.
Baseline characteristics.
| Characteristic | Lower SES (n = 3895) | Middle SES (n = 2310) | Higher SES (n = 2380) | P value |
|---|---|---|---|---|
| Year of admission | .837 | |||
| 2016 | 760 (19.5) | 455 (19.7) | 410 (17.2) | |
| 2017 | 850 (21.8) | 430 (18.6) | 560 (23.5) | |
| 2018 | 635 (16.3) | 395 (17.1) | 420 (17.6) | |
| 2019 | 610 (15.7) | 425 (18.4) | 345 (14.5) | |
| 2020 | 500 (12.8) | 265 (11.5) | 280 (11.8) | |
| 2021 | 540 (13.9) | 340 (14.7) | 365 (15.3) | |
| Age, y | 62 ± 6.2 | 64 ± 7.1 | 60 ± 4.8 | |
| Sex | .019 | |||
| Male | 1435 (36.8) | 940 (40.7) | 1075 (45.2) | |
| Female | 2460 (63.2) | 1370 (59.3) | 1305 (54.8) | |
| Race | <.001 | |||
| White | 2910 (82.2) | 1620 (82.4) | 1750 (87.5) | |
| Black | 430 (12.1) | 185 (9.4) | 90 (4.5) | |
| Hispanic | 155 (4.4) | 125 (6.4) | 85 (4.3) | |
| Asian/Pacific Islander | 40 (1.1) | 35 (1.8) | 75 (3.8) | |
| Native American | 5 (0.1) | 0 | 0 | |
| Type of insurance | <.001 | |||
| Medicare | 1915 (50.6) | 1065 (46.7) | 1110 (47.3) | |
| Medicaid | 385 (10.2) | 145 (6.4) | 85 (3.6) | |
| Private | 1380 (36.5) | 1045 (45.8) | 1140 (48.6) | |
| Self-pay | 105 (2.8) | 20 (0.9) | 10 (0.4) | |
| No charge | 0 | 5 (0.2) | 0 | |
| Elective | .45 | |||
| Nonelective | 650 (16.7) | 345 (15) | 335 (14.1) | |
| Elective | 3235 (83.3) | 1955 (85) | 2035 (85.9) | |
| Hospital bed size (values vary by region and control) | .002 | |||
| Small | 255 (6.5) | 145 (6.3) | 110 (4.6) | |
| Medium | 580 (14.9) | 300 (13) | 525 (22.1) | |
| Large | 3060 (78.6) | 1865 (80.7) | 1745 (73.3) | |
| Hospital location | .01 | |||
| Rural | 55 (1.4) | 15 (0.6) | 0 | |
| Urban | 3840 (98.6) | 2295 (99.4) | 2380 (100) | |
| Hospital teaching status | .24 | |||
| Nonteaching | 180 (4.7) | 100 (4.4) | 70 (2.9) | |
| Teaching | 3660 (95.3) | 2195 (95.6) | 2310 (97.1) | |
| Hospital region | <.001 | |||
| Northeast | 565 (14.5) | 450 (19.5) | 840 (35.3) | |
| Midwest | 1365 (35) | 795 (34.4) | 755 (31.7) | |
| South | 1470 (37.7) | 650 (28.1) | 420 (17.6) | |
| West | 495 (12.7) | 415 (18) | 365 (15.3) | |
| Median household income for patient ZIP Code | <.001 | |||
| Quartile 1 | 1750 (44.9) | 0 | 0 | |
| Quartile 2 | 2145 (55.1) | 0 | 0 | |
| Quartile 3 | 0 | 2310 (100) | 0 | |
| Quartile 4 | 0 | 0 | 2380 (100) | |
| Transferred in | .15 | |||
| Not transferred | 3655 (94.3) | 2180 (95) | 2255 (95.6) | |
| Transferred | 165 (4.3) | 105 (4.6) | 100 (4.2) | |
| Unknown | 55 (1.4) | 10 (0.4) | 5 (0.2) | |
| Transfer out indicator | .60 | |||
| 0 | 3490 (89.6) | 2045 (88.5) | 2170 (91.2) | |
| 1 | 20 (0.5) | 5 (0.2) | 10 (0.4) | |
| 2 | 385 (9.9) | 260 (11.3) | 200 (8.4) | |
| Day of admission | .52 | |||
| Monday to Friday | 3775 (96.9) | 2235 (96.8) | 2330 (97.9) | |
| Saturday/Sunday | 120 (3.1) | 75 (3.2) | 50 (2.1) | |
| Disposition of patient (uniform) | .05 | |||
| Routine | 2435 (62.5) | 1385 (60) | 1370 (57.6) | |
| Transfer to a short-term hospital | 20 (0.5) | 5 (0.2) | 10 (0.4) | |
| Transfer other: SNF, ICF, etc. | 385 (9.9) | 260 (11.3) | 200 (8.4) | |
| Home health care | 965 (24.8) | 575 (24.9) | 765 (32.1) | |
| Against medical advice | 0 | 0 | 5 (0.2) | |
| Died | 90 (2.3) | 85 (3.7) | 30 (1.3) | |
Values are n (%) or mean ± SD.
HCM, hypertrophic cardiomyopathy; ICF, intermediate care facility; PI, pacific islander; SES, socioeconomic status; SNF, skilled nursing facility.
Figure 1.
Age distribution of high and low socioeconomic groups: A histogram analysis.
Central Illustration.
Socioeconomic disparities in patients undergoing alcohol septal ablation for hypertrophic cardiomyopathy (2016-2021). A total of 8585 patients were categorized into lower (45.4%), middle (26.9%), and higher (27.7%) socioeconomic groups. Demographic characteristics, including sex, race, payer type, regional distribution, hospital bed size, and elective admission status, are compared across socioeconomic categories. Trends from 2016 to 2021 indicate changing utilization patterns. Comparative clinical outcomes across groups, including inpatient mortality, pacemaker implantation, implantable cardioverter-defibrillator (ICD) placement, acute ischemic stroke, acute kidney injury (AKI), sudden cardiac arrest, postprocedure bleeding, myocardial infarction, and major adverse cardiovascular and cerebrovascular events (MACCE), are presented with odds ratios (OR), 95% CI, and corresponding P values.
Hospital-related factors further highlight these socioeconomic differences. Patients from lower-income groups are more often admitted to rural hospitals (1.4% vs 0% in the higher-income group, P = .01) and smaller hospital settings (6.5% vs 4.6%, P = .002). Although a higher proportion of elective admissions is noted across all groups, there are no statistically significant differences (P = .45). Regional variations are also substantial, with higher-income patients more likely to receive treatment in the Northeast (35.3%), whereas lower-income patients predominantly reside in the South (37.7%) (P = .01).
Notably, obesity (P = .004) and smoking (P = .04) were significantly more prevalent in lower-income populations, whereas a trend indicating increased prevalence of valvular disease was observed in higher-income patients (P = .05). Hypertension and hyperlipidemia were commonly found across all socioeconomic groups, showing no significant differences. Additionally, the occurrence of psychoses displayed a low yet statistically significant variation (P = .03). (Table 2).
Table 2.
Baseline comorbidities.
| Comorbidities | Lower SES (n = 3895) | Middle SES (n = 2310) | Higher SES (n = 2380) | P value |
|---|---|---|---|---|
| Hyperlipidemia | 55.1 | 60.1 | 58.4 | .18 |
| Hypertension | 78.2 | 75.5 | 74.6 | .30 |
| Obesity | 34.4 | 31.6 | 26.1 | .004 |
| Smoker | 40.1 | 41.1 | 33.6 | .04 |
| Hypothyroid | 14 | 13.4 | 11.8 | .50 |
| Anemia | 3.2 | 2.2 | 4 | .30 |
| Pulmonary disease | 13.2 | 11.9 | 9.2 | .09 |
| Pneumonia | 1.9 | 1.9 | 1.1 | .45 |
| Liver disease | 2.4 | 1.9 | 2.1 | .83 |
| Valvular disease | 57.8 | 63 | 64.5 | .05 |
| Pulmonary circulation disorders | 15 | 14.5 | 13.7 | .80 |
| Uncomplicated diabetes | 9.6 | 10.6 | 7.1 | .17 |
| Diabetes with complications | 12.6 | 11 | 12.6 | .68 |
| Renal failure | 14.9 | 14.1 | 14.7 | .92 |
| Peptic ulcer disease excluding bleeding | 0.4 | 0.2 | 0.2 | .81 |
| Fluid and electrolyte disorders | 33.9 | 39.2 | 39.5 | .09 |
| Alcohol abuse | 1.8 | 1.5 | 2.1 | .80 |
| Drug abuse | 3.1 | 1.7 | 1.3 | .09 |
| Psychoses | 0.0 | 0.6 | 0.0 | .03 |
| Depression | 13 | 14.7 | 11.6 | .39 |
| Complicated hypertension | 37.6 | 36.8 | 34.9 | .62 |
Values are %.
Study outcomes
The mean length of stay was most significant in the middle-income group, averaging 8.07 ± 9.4 days, compared to the lower-income group at 7.06 ± 6.8 days and the higher-income group at 7.02 ± 5.8 days. Additionally, hospitalization costs were highest for the middle-income group, amounting to $51,355 ± 64,906 (P < .05). Our unmatched crude analysis shows elevated mortality rates in the middle-income group at 3.7% compared to 2.3% for the lower-income group and 1.3% for the higher-income group (P = .05). We found no clinically significant differences in pacemaker implantation, acute strokes, and MI among the groups (Table 3).
Table 3.
Outcomes.
| Outcomes | Lower SES (n = 3895) | Middle SES (n = 2310) | Higher SES (n = 2380) | P value |
|---|---|---|---|---|
| Length of stay | 7.06 ± 6.8 | 8.07 ± 9.4 | 7.02 ± 5.8 | |
| Hospitalization cost, $ | 40,204 ± 38,004 | 51,355 ± 64,906 | 47,778 ± 43,753 | |
| Death during hospitalization | 2.3 | 3.7 | 1.3 | .05 |
| Pacemaker implant | 11.8 | 14.1 | 12.4 | .57 |
| ICD implantation | 5.9 | 5.4 | 6.7 | .64 |
| Acute stroke | 3.9 | 4.3 | 2.9 | .53 |
| Acute kidney injury | 13.5 | 15.6 | 12.6 | .39 |
| Sudden cardiac arrest | 16.3 | 16.7 | 17.4 | .87 |
| Postprocedural bleeding | 2.2 | 2.6 | 2.1 | .86 |
| Myocardial infarction | 2.2 | 2.2 | 1.7 | .82 |
| Major cardiac and cerebrovascular event | 7.8 | 9.5 | 5.9 | .14 |
Values are mean ± SD or %.
ICD, implantable cardioverter-defibrillator.
Following the multivariate regression analysis, we found that SES had no significant impact on inpatient mortality (OR, 0.76; P = .26) or on postprocedural bleeding (OR, 0.76; P = .27). Likewise, pacemaker implantation (OR, 0.94; P = .56), ICD implantation (OR, 1; P = .99), and occurrences of acute stroke (OR, 0.89; P = .52) revealed no statistically significant differences. Similarly, sudden cardiac arrest (OR, 0.87; P = .12) and major adverse cardiovascular and cerebrovascular events (OR, 0.88; P = .34) were also unaffected by socioeconomic factors (Table 4).
Table 4.
Multivariate regression analysis of outcomes.
| Outcomes | Odds ratio | 95% CI | P value |
|---|---|---|---|
| Inpatient mortality | 0.76 | 0.47-1.22 | .26 |
| Pacemaker | 0.94 | 0.77-1.15 | .56 |
| ICD | 1 | 0.75-1.32 | .99 |
| Acute stroke | 0.89 | 0.64-1.26 | .52 |
| Acute kidney injury | 1 | 0.8-1.25 | .97 |
| SCA | 0.87 | 0.73-1.04 | .12 |
| Postprocedural bleed | 0.76 | 0.47-1.23 | .27 |
| MI | 0.73 | 0.40-1.34 | .31 |
| MACCE | 0.88 | 0.67-1.15 | .34 |
ICD, implantable cardioverter-defibrillator; MACCE, major adverse cardiovascular and cerebrovascular events; MI, myocardial infarction; OR, odds ratio; SCA, sudden cardiac arrest.
Following the analysis of propensity score matching, we found that inpatient mortality rates were significantly higher in the middle-income group (3.43%) and the low-income group (3.17%) compared to the high-income group (1.6%) (P = .04). Moreover, the rate of pacemaker implantation was notably higher in the middle-income (15.8%) and low-income (15.3%) groups than in the high-income group (11.11%) (P = .04). In addition, postprocedural bleeding revealed a significant difference (P = .05), with the highest rates observed in the low-income group (4.49%). Similarly, SCA is more prevalent in the low-socioeconomic group compared to others. However, other complications, including acute stroke and acute kidney injury, did not show statistically significant differences among the groups (Table 5 and Figure 2).
Table 5.
Outcomes after propensity score matching analysis.
| Outcomes | Low SES (n = 379) |
Medium SES (n = 379) |
Higher SES (n = 726) |
P value |
|---|---|---|---|---|
| Inpatient mortality | 12 (3.17%) | 13 (3.43%) | 12 (1.6%) | .04 |
| Pacemaker | 58 (15.3%) | 60 (15.8%) | 86 (11.11%) | .04 |
| ICD | 27 (7.12%) | 18 (4.75%) | 48 (6.2%) | .40 |
| Acute stroke | 22 (5.8%) | 20 (5.28%) | 28 (3.62%) | .20 |
| Acute kidney injury | 56 (14.78%) | 56 (14.78%) | 108 (14%) | .90 |
| SCA | 68 (17.94%) | 67 (17.68%) | 128 (16.5%) | < .01 |
| Postprocedural bleeding | 17 (4.49%) | 9 (2.37%) | 16 (2.07%) | .05 |
| MI | 13 (3.43%) | 8 (2.11%) | 14 (1.81%) | .22 |
| MACCE | 38 (10.03%) | 38 (10.03%) | 54 (6.9%) | .10 |
Values are n (%).
ICD, implantable cardioverter-defibrillator; MACCE, major adverse cardiovascular and cerebrovascular events; MI, myocardial infarction; SCA, sudden cardiac arrest; SES, socioeconomic status.
Figure 2.
Propensity score–matched analysis of low and high socioeconomic status patients undergoing alcohol septal ablation.
Discussion
This research seeks to examine the impact of socioeconomic disparities on the postprocedural outcomes of ASA in patients diagnosed with HCM. Utilizing a comprehensive analysis of a substantial national database spanning period of 2016-2021, this study highlights significant socioeconomic-related variations in disease prevalence across the United States. The key clinical findings of this extensive observational study are as follows:
-
1.
Most procedures (95%-97%) were performed in urban teaching hospitals, primarily funded by Medicare, followed by private insurance.
-
2.
Patients from lower socioeconomic backgrounds who underwent ASA showed a higher prevalence of risk factors, such as obesity, smoking, and valvular diseases.
-
3.
Middle socioeconomic groups faced a greater risk of mortality and a higher likelihood of pacemaker placements compared to other socioeconomic groups.
-
4.
Patients in the low socioeconomic group had higher risks of postoperative bleeding than those in other groups.
-
5.
Furthermore, individuals from the middle socioeconomic group experienced more extended hospital stays and higher hospitalization costs. They were also more likely to be transferred to skilled nursing facilities than those from higher socioeconomic backgrounds.
We categorized patients into 3 SES groups based on the PIR; however, we recognize that this approach may complicate the interpretation of the data. To clarify the socioeconomic gradient in outcomes, we conducted a sensitivity analysis comparing the high SES group against the combined non–high SES group (low and middle).
We have observed that patients from low and middle socioeconomic backgrounds who underwent ASA experienced less favorable outcomes, including elevated mortality rates, more extended hospital stays, and an increased necessity for transfer to skilled nursing facilities. Our findings are consistent with the existing literature, which has persistently documented higher mortality rates among low socioeconomic patients admitted for acute MI or cardiovascular diseases, both during hospitalization and within 1 year.18,19 Additionally, research indicates that patients from the lowest income quartile are less likely to receive timely cardiac catheterization upon admission for acute MI compared to their counterparts in higher-income quartiles, which correlates with increased mortality rates.20,21 These disparities may be attributed to factors such as delayed access to care, a greater prevalence of comorbidities, and variations in the quality of care administered. The extended hospital stays often seen in low SES patients can frequently be linked to more severe disease presentations and a higher prevalence of underlying health conditions.
Our analysis indicates that ASA for HOCM is primarily conducted as an elective procedure in large urban teaching hospitals. Additionally, our observations reveal a notable demographic trend in which White individuals are the predominant recipients of septal ablation, followed by Black and Hispanic populations across various socioeconomic strata. A study by Wells et al22 highlighted disparities in the diagnosis and timely referral of patients with HOCM, demonstrating that Black patients are significantly underrecognized and underreferred for essential treatments such as ICD and surgical myomectomies. This underrecognition may stem from disparities in health care access or provider bias, raising critical ethical and equity issues within the health care system.
Further examination by Eberly et al23 also brought to light disparities in HCM management, showing that Black patients presented with a higher prevalence of severe heart failure upon admission and had lower rates of genetic testing and access to invasive septal reduction therapies. These findings indicate systemic challenges that must be urgently addressed to ensure equitable access to high-quality patient care. Moreover, a study conducted by Sheikh et al24 in the UK revealed varied phenotypic expressions of HCM among Black patients, which may contribute to delays in diagnosis and treatment. Collectively, these findings underscore the pressing need to confront and rectify the impacts of racial disparities, socioeconomic factors, and diverse clinical presentations on the timely diagnosis and management of HCM.
Our analysis indicates that patients from middle and lower-SES backgrounds are significantly more likely to be transferred to skilled nursing facilities compared to their high-SES counterparts. This trend underscores the disparities in postdischarge support and available resources. Interestingly, individuals in the high SES group experienced elevated hospitalization expenses, likely reflecting an increased utilization of specialized care, advanced diagnostic procedures, or extended monitoring in the intensive care unit.25 Previous studies have shown that individuals with higher SES typically benefit from enhanced social support systems and greater access to outpatient rehabilitation services, which reduces their reliance on institutionalized care.18,26 This disparity highlights the broader systemic challenges in posthospitalization care, where socioeconomic factors critically shape long-term recovery trajectories and health care resource utilization.26,27 Moreover, our findings indicate that middle and higher-SES patients incur substantially higher hospitalization costs than those with lower SES. This may be linked to the increased utilization of diagnostic and therapeutic resources, the need for specialized care, and extended stays in the intensive care unit following medical procedures.
Our analysis indicated no significant differences in postprocedure complications between the 2 groups, including those associated with ICD implantation, acute stroke, new-onset acute kidney injury, MI, and major adverse cardiovascular and cerebrovascular events. The absence of variance in complications suggests that increased expenditure does not necessarily correlate with enhanced patient outcomes. This observation emphasizes the necessity for a comprehensive evaluation of the relationship between resource utilization and clinical results, particularly in procedures where technical expertise and timely intervention are paramount.28
Although our findings highlight socioeconomic disparities in the outcomes of ASA, it is crucial to recognize that inequalities related to SES likely extend beyond procedural outcomes to earlier stages, such as diagnosis and referral. Because our cohort is restricted to patients who underwent ASA, it may not fully capture the broader systemic disparities in access to care. In addition, our study focuses on ASA due to the limitations of the NIS database, which does not include outpatient pharmacological therapies such as mavacamten, nor does it facilitate direct comparisons with surgical myectomy. Further research is warranted to explore SES disparities across the broader spectrum of HCM therapies.
It is important to note that ASA continues to be a low-volume procedure in the United States, with our analysis documenting approximately 1400 procedures annually during the period 2016-2021. This trend aligns with broader national data that indicate a gradual reduction in ASA utilization. The relatively limited procedural volume may restrict statistical power for subgroup analyses, particularly when investigating disparities among minor subpopulations.
Limitations
We acknowledge the limitations of our study but have taken extensive measures to ensure the quality and reliability of our findings. Our analysis relies on retrospective data collected from the NIS database between 2016 and 2021, which has inherent limitations regarding data accuracy, unavailable long-term outcomes, and potential coding bias across various institutions. Despite these challenges, we have validated our data to ensure the accuracy of our findings. Moreover, we have accounted for potential confounding variables through propensity-matched analysis; however, this may diminish generalizability and obscure variability related to SES by excluding unmatched patients. Utilizing median ZIP code income as a surrogate for individual SES is constrained by the spatial and socioeconomic diversity within ZIP codes and may not adequately reflect individual-level disparities. Lastly, our study's analysis did not include the possible selection bias concerning the type of treatment for septal reduction in HCM patients.
Furthermore, the present study exclusively evaluates patients who have undergone ASA and does not incorporate comparative data of surgical myectomy or novel pharmacological treatments, such as mavacamten. These therapies signify alternative or complementary strategies for septal reduction in analogous patient populations. The absence of outcome data for these groups constrains the interpretability of ASA outcomes in isolation. Although the study has limitations, it provides significant insights into the clinical and procedural outcomes of interest. We strongly encourage clinicians to consider the socioeconomic disparities in ASA outcomes when making treatment decisions for their patients, as this is a critical aspect that can significantly influence patient outcomes.
Conclusion
Patients with HOCM from lower and middle socioeconomic backgrounds who underwent ASA showed higher mortality rates and longer hospital stays. Our research indicates that these patients also face increased risks of pacemaker placements, sudden cardiac death, and postoperative bleeding compared to those from higher SES backgrounds. These findings emphasize the need for establishing standardized outcome measures for all patients undergoing ASA procedures, regardless of their SES. Addressing socioeconomic disparities is crucial for improving the overall outcomes of ASA interventions, thus highlighting the urgency for further research and targeted efforts in this area.
Acknowledgments
Declaration of competing interest
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding sources
This work was not supported by funding agencies in the public, commercial, or not-for-profit sectors.
Ethics statement and patient consent
The study was deemed exempt from institutional review board approval as the HCUP-NIS database contains only deidentified patient information, which is publicly available. This study was conducted using retrospective, de-identified data from a publicly available or institutional database and did not involve any direct patient contact or intervention. In accordance with institutional policy and the Declaration of Helsinki, ethical approval was obtained/exempted by the Institutional Review Board (IRB)/Ethics Committee. As this was a non-interventional study using anonymized data, written informed consent from patients was not required and was waived by the ethics committee.
Footnotes
To access the supplementary material accompanying this article, visit the online version of the Journal of the Society for Cardiovascular Angiography & Interventions at 10.1016/j.jscai.2025.103788.
Supplementary material
References
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