Abstract
Background
There are limited data on the impact of frailty in patients undergoing alcohol septal ablation (ASA) for the treatment of hypertrophic obstructive cardiomyopathy (HOCM).
Objectives
The objective of the study was to evaluate the impact of frailty on long-term clinical outcomes in patients undergoing ASA for HOCM.
Methods
Using the United States Collaborative Network (2005-2025), we identified patients with HOCM undergoing ASA and stratified them into frail and nonfrail groups based on the Johns Hopkins Adjusted Clinical Group frailty-defining diagnosis. Propensity-score matching (1:1) was applied to adjust for baseline differences in demographics, comorbidities, medications, and labs. Kaplan-Meier analysis and Cox proportional hazards regression were used to estimate HRs using the built-in R-computing software (v3.2 to 3). The primary outcome was all-cause mortality at various follow-ups post-ASA (1-, 3-, 5-, and 10 years).
Results
Among 39,063 patients undergoing ASA, 2,264 (5.8%) were frail. Post-propensity-score matching, 2,219 patients were matched per group. Frail patients demonstrated higher all-cause mortality at all follow-ups (10-year HR: 1.40; 95% CI: 1.26–1.55; P < 0.001). Frail patients also demonstrated a higher risk of major adverse cardiac and cerebrovascular event (HR: 1.49; 95% CI: 1.30–1.70; P < 0.001), ischemic stroke (HR: 1.57; 95% CI: 1.24–2.00; P < 0.001), heart failure exacerbation (HR: 1.09; 95% CI: 1.01–1.19; P = 0.024), major bleeding (HR: 1.61; 95% CI: 1.38–1.88; P < 0.001), and all-cause readmission (HR: 1.33; 95% CI: 1.24–1.43; P < 0.001) compared with nonfrail patients at 1-year post-ASA. No differences were noted for acute myocardial infarction, sudden cardiac arrest, antiarrhythmic drugs initiation/escalation, or electrical cardioversion.
Conclusions
Frail patients undergoing ASA for HOCM exhibit higher risks of long-term mortality and adverse clinical outcomes compared with their nonfrail counterparts.
Key words: alcohol septal ablation, frailty, hypertrophic cardiomyopathy, outcomes, risk stratification, septal reduction
Central Illustration
Introduction
Hypertrophic cardiomyopathy (HCM) is the most prevalent inherited cardiac disorder, affecting approximately 1 in 500 individuals. Approximately two-thirds of patients with HCM exhibit a significant left ventricular outflow tract (LVOT) gradient, either at rest, during provocation, or with exercise, categorizing them as having obstructive HCM (HOCM). First-line management involves the use of negative inotropic agents, including beta-blockers, nondihydropyridine calcium channel blockers, and disopyramide, to alleviate obstruction and symptoms. Second-line therapy includes the newer cardiac myosin inhibitors.1 However, about 5% to 10% of patients remain symptomatic despite optimal medical therapy and require septal reduction interventions, such as surgical septal myectomy or alcohol septal ablation (ASA).2, 3, 4, 5, 6 Indications for septal reduction therapy include an LVOT obstruction gradient ≥50 mm Hg, moderate to severe symptoms (NYHA functional class III–IV) (Class I, Level of Evidence: C), and/or recurrent exertional syncope (Class IIa, Level of Evidence: C) despite maximally tolerated drug therapy.5,7,8 Prior studies have compared ASA and surgical myectomy in HCM, particularly in elderly patients and specific anatomic groups.9,10
Frailty is a multidimensional clinical syndrome characterized by reduced physiological reserve and diminished resilience to stressors, leading to increased vulnerability to adverse clinical outcomes.11 Frailty has emerged as a critical determinant of procedural risk, influencing morbidity, mortality, and recovery following procedures such as transcatheter valve replacement and percutaneous coronary intervention.12,13 Despite its established prognostic significance in various cardiac cohorts, frailty remains underexplored in patients undergoing ASA for HOCM. Frail patients who are high-risk for surgery are often referred preferentially for ASA. Understanding its prevalence and impact in this context is essential for optimizing patient selection, tailoring periprocedural management, and improving long-term outcomes. Hence, using a large, multicentric representative database, this study aimed to evaluate the impact of frailty on long-term outcomes in patients undergoing ASA for HOCM.
Material and methods
This study was conducted and reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational studies (Figure 1, Supplemental Table 1).14
Figure 1.
Study Profile and Cohort Selection Flowchart
ASA = alcohol septal ablation; HOCM = hypertrophic obstructive cardiomyopathy.
Data source
This study used data from the United States Collaborative Network of the TriNetX database, which is an extensive multicenter federated health research network that offers access to inpatient and outpatient electronic health records (EHRs) of approximately 130 million patients across 72 health care organizations (HCOs) in the United States. The data cover diverse geographic regions—39% South, 22% Northeast, 16% Midwest, 13% West, and 10% unspecified. Patient data are derived in an aggregate form and statistical summaries, containing only deidentified data, as per the deidentification standards defined in the Health Insurance Portability and Accountability Act privacy rule.15 The data included information on demographic, socioeconomic status, diagnoses (using International Classification of Diseases-10th revision [ICD-10]), procedures (using Current Procedural Terminology [CPT] codes), medications (Medical Prescription Normalized codes [RxNorm]), genomics, and laboratory values (standardized using Logical Observation Identifiers Names and Codes). TriNetX anonymizes EHR data from a network of HCOs, mostly comprising large academic institutions with diverse inpatient and outpatient services, including both insured and noninsured patients across the United States.
TriNetX performs comprehensive data preprocessing to minimize missing values and harmonizes all information into a standardized clinical data model, ensuring consistency of query results across diverse data sources. Variables are formatted as binary, categorical (further expanded into multiple binary columns), or continuous. Age data are universally available. When sex information is missing, it is recorded as “unknown sex,” whereas missing race or ethnicity information is categorized as “unknown race” or “unknown ethnicity.” For other variables, including medical conditions, procedures, laboratory results, and social determinants of health, data entries are treated as either present or absent, and records with missing values are excluded from analysis. These tools facilitate comparison of baseline characteristics before the index event, enable longitudinal outcome analyses, and allow adjustment for potential confounding variables. To ensure patient anonymity, TriNetX obfuscates patient counts <10.
Study population and cohort selection
This analysis included data from January 2005 to October 2025 on adult patients (≥18 years) undergoing ASA for HOCM. The identified population was further stratified into 2 cohorts based on the presence of frailty as frail and nonfrail. Frailty status was determined using the Johns Hopkins Adjusted Clinical Groups frailty-defining diagnoses, a validated claims-based frailty indicator derived from administrative and EHR data. This algorithm identifies frailty based on diagnostic clusters reflecting functional decline and physiologic vulnerability, including dementia, malnutrition, social support needs, severe vision impairment, urinary incontinence, decubitus ulcers, fecal incontinence, weight loss, difficulty in walking, and falls (Figure 2).16 The Johns Hopkins Adjusted Clinical Group cluster has been previously validated as a tool for assessing frailty status and is widely used in large administrative database analyses where direct frailty assessments are not available.17,18 The index date for the cohort’s follow-up was the date of the ablation in each group. The baseline characteristics of study participants were assessed within 20 years preceding the index event. The specific CPT and ICD-10 codes used to define study cohorts and time windows are provided in Supplemental Tables 2 and 3.
Figure 2.
Johns Hopkins Adjusted Clinical Group Frailty-Defining Diagnoses.
Outcome measures
The primary outcome was all-cause mortality at 1-, 3-, 5-, and 10-years postablation. Secondary outcomes were evaluated at 1-year postablation and included sudden cardiac arrest, electrical cardioversion, antiarrhythmic drugs initiation/escalation, major adverse cardiac and cerebrovascular event (MACCE—a composite of mortality, acute myocardial infarction, and ischemic stroke), heart failure (HF) exacerbation, major bleeding, and all-cause readmission. The status of death was based on the vital status code “deceased” that TriNetX imports from the Social Security Death Index. We adhered to validated methodologies for outcome evaluation within the TriNetX platform.
To reduce the possibility of biased outcomes and residual confounding in observational studies, falsification outcomes are usually reported as negative controls. The absence of statistical significance for these unrelated falsification outcomes was used to confirm the strength of the actual endpoints/outcomes.19 We used myopia, folliculitis, hypoglycemia, colorectal cancer, nail disorders, hypothyroidism, and migraine as negative controls because these are unrelated to the intervention and exposure. Outcomes definitions using ICD-10 and CPT codes are provided in Supplemental Table 4.
Statistical analysis
Continuous variables are presented as mean ± SD and were compared using the independent-sample Student’s t-tests. Categorical variables are presented as frequencies or percentages and were compared using the Rao-Scott chi-square test for between-group comparisons. For the matched group, the paired t-test was used to compare continuous variables, and the McNemar test was used to compare categorical variables. To mitigate the effect of probable measured confounders, we used 1:1 propensity-score matching (PSM), including the following types of covariates: demographics (age and race), diagnoses, medications, and laboratory domains (Supplemental Table 5). The TriNetX platform generates propensity scores for each participant by performing logistic regression analyses using input matrices composed of user-specified covariates. PSM using the “greedy nearest-neighbor matching” with a caliper of 0.1 pooled SD of the linear propensity scores to control for differences in the cohorts. TriNetX randomizes the order of rows to mitigate bias introduced by nearest-neighbor matching algorithms. These variables were specifically chosen because of their impact on overall and cardiovascular outcomes. The standardized mean difference (SMD) is a quantitative variable used to denote the difference between the means of 2 groups in terms of SD units to evaluate the balance in measured variables in the population weighted by the inverse probability of treatment. Covariates with SMDs < 0.1 following matching indicated no significant covariate imbalance. The data set does not allow for 1:2 or 1:3 PSM.
Data were represented as HRs and 95% CIs on matched cohorts using Kaplan-Meier analysis using the log-rank test. All-cause mortality was considered as a competing risk when analyzing nonfatal outcomes. The TriNetX platform calculates HRs with CIs using R’s Survival package v3.2 to 3 (R Foundation for Statistical Computing). For generating HRs, TriNetX sets the parameter robust = FALSE using the R survival package. This represents a limitation of the platform, as it does not account for potential clustering of participants within HCOs or specific geographic regions—partly due to TriNetX’s data privacy requirement to restrict visibility by HCO source. The proportional hazards assumption was assessed qualitatively, because the TriNetX platform does not provide Schoenfeld residual diagnostics or time-dependent coefficient testing. We visually inspected the Kaplan-Meier survival curves to ensure there were no major crossings or diverging slopes that would indicate nonproportional hazards.20 All statistical analyses were conducted within the TriNetX platform. Statistical significance was set at a 2-tailed P value <0.05.
Ethical consideration
The TriNetX platform is compliant with §164.514 of the Health Insurance Portability and Accountability Act Privacy Rule; hence, this study was exempt from the Western Institutional Review Board approval.
Results
Baseline characteristics
Between January 01, 2005, and October 31, 2025, we identified a total of 39,063 patients undergoing ASA for HCM. Of these, 2,264 (5.8%) were frail. Before matching, frail patients were more often older (77 years vs 68 years) and females (44% vs 39%) and were more comorbid than nonfrail individuals. Following 1:1 matching, we identified 4,438 patients (2,219 matched pairs). The mean age was 77 years, with 43% being females and 76% being of the White race. Both cohorts had broadly comparable baseline characteristics (SMDs <0.1) and propensity score density function (Table 1, Supplemental Table 6).
Table 1.
Baseline Characteristics of the Study Cohort Before and After Propensity Score Matching
| Before Propensity-Score Matching |
After Propensity-Score Matching |
|||||||
|---|---|---|---|---|---|---|---|---|
| Frail (n = 2,264) | Not Frail (n = 36,799) | P Value | Std. Difference | Frail (n = 2,219) | Not Frail (n = 2,219) | P Value | Std. Difference | |
| Age, y | 76.9 ± 14.3 | 68.3 ± 18.6 | <0.01 | 0.518 | 76.9 ± 14.3 | 77.2 ± 12.3 | 0.45 | 0.023 |
| Age at index, y | 66.3 ± 15.4 | 55.7 ± 19.3 | <0.01 | 0.607 | 66.3 ± 15.5 | 66.6 ± 13.4 | 0.53 | 0.019 |
| Sex | ||||||||
| Male | 1,263 (56.2) | 21,848 (60.7) | <0.01 | 0.091 | 1,243 (56) | 1,289 (58.1) | 0.16 | 0.042 |
| Female | 984 (43.8) | 14,143 (39.3) | <0.01 | 0.091 | 976 (44) | 930 (41.9) | 0.16 | 0.042 |
| Race | ||||||||
| White | 1,675 (74.5) | 27,017 (75.1) | 0.58 | 0.012 | 1,660 (74.8) | 1,715 (77.3) | 0.05 | 0.058 |
| African American | 333 (14.8) | 3,113 (8.6) | <0.01 | 0.193 | 322 (14.5) | 302 (13.6) | 0.39 | 0.026 |
| Asian | 71 (3.2) | 1,222 (3.4) | 0.55 | 0.013 | 70 (3.2) | 67 (3) | 0.80 | 0.008 |
| Comorbidities | ||||||||
| Hypertension | 1,775 (79) | 15,751 (43.8) | <0.01 | 0.776 | 1,748 (78.8) | 1,793 (80.8) | 0.09 | 0.051 |
| Coronary artery disease | 1,324 (58.9) | 9,926 (27.6) | <0.01 | 0.667 | 1,296 (58.4) | 1,318 (59.4) | 0.50 | 0.020 |
| Acute myocardial infarction | 432 (19.2) | 2,217 (6.2) | <0.01 | 0.400 | 422 (19) | 411 (18.5) | 0.67 | 0.013 |
| Peripheral vascular disease | 342 (15.2) | 1,332 (3.7) | <0.01 | 0.401 | 324 (14.6) | 308 (13.9) | 0.49 | 0.021 |
| Aortic aneurysm and dissection | 129 (5.7) | 824 (2.3) | <0.01 | 0.176 | 126 (5.7) | 138 (6.2) | 0.45 | 0.023 |
| Heart failure | 1,243 (55.3) | 7,318 (20.3) | <0.01 | 0.773 | 1,215 (54.8) | 1,214 (54.7) | 0.98 | 0.001 |
| Atrial fibrillation | 1,603 (71.3) | 19,963 (55.5) | <0.01 | 0.334 | 1,578 (71.1) | 1,606 (72.4) | 0.35 | 0.028 |
| Nonrheumatic mitral valve disorders | 763 (34) | 5,982 (16.6) | <0.01 | 0.407 | 743 (33.5) | 718 (32.4) | 0.43 | 0.024 |
| Nonrheumatic aortic valve disorders | 476 (21.2) | 2,949 (8.2) | <0.01 | 0.373 | 459 (20.7) | 425 (19.2) | 0.20 | 0.038 |
| Nonrheumatic tricuspid valve disorders | 380 (16.9) | 2,384 (6.6) | <0.01 | 0.323 | 362 (16.3) | 333 (15) | 0.23 | 0.036 |
| Nonrheumatic pulmonary valve disorders | 122 (5.4) | 757 (2.1) | <0.01 | 0.175 | 114 (5.1) | 101 (4.6) | 0.36 | 0.027 |
| Prior PCI | 81 (3.6) | 389 (1.1) | <0.01 | 0.167 | 78 (3.5) | 84 (3.8) | 0.63 | 0.014 |
| Prior PPM/ICD | 310 (13.8) | 1,423 (4) | <0.01 | 0.351 | 297 (13.4) | 311 (14) | 0.54 | 0.018 |
| Prior CABG | 31 (1.4) | 241 (0.7) | <0.01 | 0.071 | 31 (1.4) | 33 (1.5) | 0.80 | 0.008 |
| Prior valve surgery | 68 (3) | 328 (0.9) | <0.01 | 0.153 | 67 (3) | 59 (2.7) | 0.47 | 0.022 |
| Cardiomyopathy | 781 (34.8) | 6,552 (18.2) | <0.01 | 0.382 | 760 (34.2) | 750 (33.8) | 0.75 | 0.010 |
| Paroxysmal tachycardia | 1,036 (46.1) | 10,982 (30.5) | <0.01 | 0.325 | 1,014 (45.7) | 1,015 (45.7) | 0.98 | 0.001 |
| Stroke | 243 (10.8) | 907 (2.5) | <0.01 | 0.337 | 228 (10.3) | 216 (9.7) | 0.55 | 0.018 |
| Pericardial effusion | 45 (2) | 108 (0.3) | <0.01 | 0.160 | 38 (1.7) | 34 (1.5) | 0.64 | 0.014 |
| Dyslipidemia | 1,532 (68.2) | 13,053 (36.3) | <0.01 | 0.674 | 1,508 (68) | 1,512 (68.1) | 0.90 | 0.004 |
| Diabetes mellitus | 883 (39.3) | 5,279 (14.7) | <0.01 | 0.578 | 861 (38.8) | 848 (38.2) | 0.69 | 0.012 |
| Thyroid disorders | 627 (27.9) | 4,098 (11.4) | <0.01 | 0.425 | 618 (27.9) | 600 (27) | 0.55 | 0.018 |
| Osteoarthritis | 715 (31.8) | 3,702 (10.3) | <0.01 | 0.548 | 697 (31.4) | 687 (31) | 0.75 | 0.010 |
| Sleep apnea | 564 (25.1) | 4,459 (12.4) | <0.01 | 0.330 | 550 (24.8) | 540 (24.3) | 0.73 | 0.010 |
| Chronic kidney disease | 701 (31.2) | 2,835 (7.9) | <0.01 | 0.615 | 674 (30.4) | 664 (29.9) | 0.74 | 0.010 |
| Liver diseases | 372 (16.6) | 1,493 (4.1) | <0.01 | 0.416 | 357 (16.1) | 335 (15.1) | 0.36 | 0.027 |
| Gastroesophageal reflux disease | 917 (40.8) | 5,579 (15.5) | <0.01 | 0.586 | 899 (40.5) | 904 (40.7) | 0.88 | 0.005 |
| Gastritis and duodenitis | 262 (11.7) | 1,031 (2.9) | <0.01 | 0.344 | 254 (11.4) | 263 (11.9) | 0.67 | 0.013 |
| Chronic pulmonary diseases | 875 (38.9) | 5,492 (15.3) | <0.01 | 0.553 | 854 (38.5) | 852 (38.4) | 0.95 | 0.002 |
| Iron deficiency anemia | 365 (16.2) | 1,192 (3.3) | <0.01 | 0.446 | 349 (15.7) | 342 (15.4) | 0.77 | 0.009 |
| Neoplasms | 982 (43.7) | 6,366 (17.7) | <0.01 | 0.588 | 964 (43.4) | 986 (44.4) | 0.51 | 0.020 |
| Smoking | 415 (18.5) | 2,909 (8.1) | <0.01 | 0.310 | 406 (18.3) | 415 (18.7) | 0.73 | 0.010 |
| Alcohol use disorder | 194 (8.6) | 1,055 (2.9) | <0.01 | 0.246 | 188 (8.5) | 204 (9.2) | 0.40 | 0.025 |
| Baseline medications | ||||||||
| Beta blockers | 1,819 (81) | 20,076 (55.8) | <0.01 | 0.562 | 1,791 (80.7) | 1,786 (80.5) | 0.85 | 0.006 |
| Antiarrhythmics | 1,557 (69.3) | 15,117 (42) | <0.01 | 0.571 | 1,531 (69) | 1,545 (69.6) | 0.65 | 0.014 |
| Antilipemic agents | 1,389 (61.8) | 13,253 (36.8) | <0.01 | 0.516 | 1,362 (61.4) | 1,375 (62) | 0.69 | 0.012 |
| Diuretics | 1,428 (63.6) | 11,104 (30.9) | <0.01 | 0.693 | 1,401 (63.1) | 1,403 (63.2) | 0.95 | 0.002 |
| Calcium channel blockers | 1,206 (53.7) | 10,451 (29) | <0.01 | 0.517 | 1,185 (53.4) | 1,204 (54.3) | 0.57 | 0.017 |
| Angiotensin II inhibitor | 492 (21.9) | 4,527 (12.6) | <0.01 | 0.249 | 476 (21.5) | 453 (20.4) | 0.40 | 0.025 |
| Antianginals | 842 (37.5) | 5,196 (14.4) | <0.01 | 0.545 | 819 (36.9) | 822 (37) | 0.93 | 0.003 |
| ACE inhibitors | 1,041 (46.3) | 9,019 (25.1) | <0.01 | 0.455 | 1,022 (46.1) | 1,058 (47.7) | 0.28 | 0.033 |
| Midodrine | 63 (2.8) | 129 (0.4) | <0.01 | 0.197 | 57 (2.6) | 48 (2.2) | 0.37 | 0.027 |
| Milrinone | 95 (4.2) | 276 (0.8) | <0.01 | 0.223 | 85 (3.8) | 79 (3.6) | 0.63 | 0.014 |
| ARNI | 42 (1.9) | 77 (0.2) | <0.01 | 0.164 | 34 (1.5) | 22 (1) | 0.11 | 0.048 |
| Analgesics | 1,970 (87.7) | 20,986 (58.3) | <0.01 | 0.701 | 1,942 (87.5) | 1,963 (88.5) | 0.33 | 0.029 |
| Antidepressants | 980 (43.6) | 5,988 (16.6) | <0.01 | 0.615 | 959 (43.2) | 949 (42.8) | 0.76 | 0.009 |
| Sedatives | 1,541 (68.6) | 13,490 (37.5) | <0.01 | 0.656 | 1,514 (68.2) | 1,524 (68.7) | 0.75 | 0.010 |
| Anticoagulants | 1,751 (77.9) | 18,133 (50.4) | <0.01 | 0.600 | 1,724 (77.7) | 1,762 (79.4) | 0.17 | 0.042 |
| Platelet aggregation inhibitors | 1,537 (68.4) | 14,782 (41.1) | <0.01 | 0.571 | 1,512 (68.1) | 1,496 (67.4) | 0.61 | 0.015 |
| Aspirin | 1,509 (67.2) | 14,452 (40.2) | <0.01 | 0.562 | 1,485 (66.9) | 1,471 (66.3) | 0.66 | 0.013 |
| Clopidogrel | 404 (18) | 2,662 (7.4) | <0.01 | 0.322 | 392 (17.7) | 408 (18.4) | 0.53 | 0.019 |
| Antihemorrhagics | 160 (7.1) | 444 (1.2) | <0.01 | 0.297 | 148 (6.7) | 123 (5.5) | 0.12 | 0.047 |
| Heparin antagonists | 154 (6.9) | 552 (1.5) | <0.01 | 0.268 | 143 (6.4) | 142 (6.4) | 0.95 | 0.002 |
| Antacids | 1,704 (75.8) | 16,317 (45.3) | <0.01 | 0.657 | 1,676 (75.5) | 1,681 (75.8) | 0.86 | 0.005 |
| Antiulcer agents | 589 (26.2) | 2,981 (8.3) | <0.01 | 0.489 | 570 (25.7) | 542 (24.4) | 0.33 | 0.029 |
| Glucocorticoids | 1,173 (52.2) | 7,917 (22) | <0.01 | 0.658 | 1,149 (51.8) | 1,138 (51.3) | 0.74 | 0.010 |
| Insulin | 804 (35.8) | 3,942 (11) | <0.01 | 0.614 | 777 (35) | 774 (34.9) | 0.93 | 0.003 |
| Metformin | 310 (13.8) | 2,582 (7.2) | <0.01 | 0.217 | 306 (13.8) | 305 (13.7) | 0.97 | 0.001 |
| SGLT2i | 55 (2.4) | 128 (0.4) | <0.01 | 0.123 | 46 (2.1) | 34 (1.5) | 0.54 | 0.018 |
| Antineoplastics | 195 (8.7) | 1,179 (3.3) | <0.01 | 0.229 | 193 (8.7) | 186 (8.4) | 0.71 | 0.011 |
| Hemoglobin, g/dL | 12.2 ± 2.1 | 13.5 ± 1.9 | <0.01 | 0.681 | 12.2 ± 2.1 | 12.8 ± 2.0 | <0.01 | 0.295 |
| Hematocrit, % | 37.0 ± 6.0 | 40.4 ± 5.2 | <0.01 | 0.61 | 37.0 ± 6.0 | 38.8 ± 5.7 | <0.01 | 0.295 |
| Prothrombin time, sec | 17.2 ± 7.7 | 17.0 ± 7.9 | 0.57 | 0.014 | 17.1 ± 7.6 | 17.3 ± 8.3 | 0.41 | 0.027 |
| Activated partial thromboplastin time, sec | 38.0 ± 16.8 | 36.4 ± 15.3 | <0.01 | 0.095 | 37.9 ± 16.8 | 37.9 ± 16.7 | 0.95 | 0.002 |
| INR | 1.6 ± 0.8 | 1.6 ± 0.8 | 0.74 | 0.008 | 1.6 ± 0.8 | 1.6 ± 0.8 | 0.32 | 0.032 |
| Cholesterol, mg/dL | 158.7 ± 46.4 | 166.8 ± 42.4 | <0.01 | 0.183 | 159.4 ± 46.3 | 159.7 ± 42.9 | 0.86 | 0.006 |
| LDL, mg/dL | 86.7 ± 35.7 | 94.5 ± 34.7 | <0.01 | 0.223 | 87.2 ± 35.7 | 87.4 ± 34.5 | 0.84 | 0.007 |
| HDL, mg/dL | 47.2 ± 18.1 | 47.0 ± 16.2 | 0.65 | 0.012 | 47.4 ± 18.1 | 46.4 ± 16.9 | 0.14 | 0.054 |
| Triglyceride, mg/dL | 128.5 ± 96.5 | 130.3 ± 94.3 | 0.49 | 0.019 | 129.0 ± 96.8 | 137.0 ± 108.8 | 0.03 | 0.078 |
| Troponin I, ng/mL | 0.8 ± 6.6 | 0.8 ± 10.3 | 0.99 | <0.001 | 0.8 ± 6.7 | 0.6 ± 5.4 | 0.65 | 0.021 |
| NT-proBNP, pg/mL | 5,291.7 ± 9111.9 | 2,948.7 ± 6049.2 | <0.01 | 0.303 | 5,222.1 ± 9,045.3 | 4,449.6 ± 9,458.2 | 0.22 | 0.083 |
| Hemoglobin A1c, % | 6.3 ± 1.4 | 6.3 ± 1.4 | 0.67 | 0.013 | 6.3 ± 1.4 | 6.5 ± 1.5 | <0.01 | 0.112 |
| C-reactive protein, mg/mL | 37.1 ± 61.2 | 22.5 ± 44.9 | <0.01 | 0.272 | 36.4 ± 60.7 | 28.9 ± 52.4 | 0.028 | 0.132 |
| Erythrocyte sedimentation rate, mm/hr | 28.5 ± 27.3 | 21.7 ± 23.3 | <0.01 | 0.265 | 28.0 ± 27.2 | 28.6 ± 27.3 | 0.67 | 0.023 |
| Respiratory rate,/min | 17.3 ± 3.0 | 17.1 ± 3.5 | 0.01 | 0.079 | 17.3 ± 3.0 | 17.2 ± 3.5 | 0.21 | 0.052 |
| Heart rate,/min | 78.9 ± 20.7 | 75.5 ± 19.6 | <0.01 | 0.166 | 78.9 ± 20.7 | 77.6 ± 21.1 | 0.10 | 0.059 |
| Oxygen saturation, % | 88.5 ± 18.3 | 93.6 ± 13.2 | <0.01 | 0.318 | 88.7 ± 18.2 | 89.0 ± 19.0 | 0.71 | 0.018 |
| SBP, mm Hg | 120.8 ± 21.3 | 122.8 ± 19.0 | <0.01 | 0.098 | 120.9 ± 21.2 | 122.4 ± 20.8 | 0.03 | 0.071 |
| DBP, mm Hg | 69.4 ± 13.2 | 72.0 ± 12.3 | <0.01 | 0.201 | 69.4 ± 13.2 | 70.4 ± 13.0 | 0.02 | 0.079 |
| BMI, kg/m2 | 29.0 ± 7.5 | 29.6 ± 7.1 | <0.01 | 0.085 | 29.0 ± 7.5 | 30.2 ± 7.5 | <0.01 | 0.161 |
| LVEF (%) | 46.3 ± 19.2 | 51.9 ± 15.7 | <0.01 | 0.317 | 46.5 ± 19.2 | 49.5 ± 18.1 | 0.11 | 0.157 |
Values mean ± SD or n (%).
ACE = angiotensin-converting enzyme; ARNI = angiotensin receptor-neprilysin inhibitor; BMI = body mass index; CABG = coronary artery bypass grafting; DBP = diastolic blood pressure; HDL = high-density lipoprotein; ICD = implantable cardioverter defibrillator; INR = International Normalized Ratio; LDL = low-density lipoprotein; LVEF = left ventricular ejection fraction; NT-proBNP = N-terminal pro–B-type natriuretic peptide; PCI = percutaneous coronary intervention; PPM = permanent pacemaker; SBP = systolic blood pressure; SGLT2i = sodium-glucose cotransporter-2 inhibitor.
All-cause mortality
Matched cohort analysis demonstrated that frail patients have higher all-cause mortality at 1 year (HR: 1.84; 95% CI: 1.49–2.28; P < 0.001), 3 years (HR: 1.62; 95% CI: 1.40–1.87; P < 0.001), 5 years (HR: 1.50; 95% CI: 1.32–1.69; P < 0.001), and 10 years (HR: 1.40; 95% CI: 1.26–1.55; P < 0.001) compared with nonfrail patients following ASA for HOCM (Figure 3). Absolute mortality counts between the 2 cohorts, frail vs nonfrail patients, were 233 vs 130 at 1 year, 483 vs 314 at 3 years, 636 vs 455 at 5 years, and 894 vs 705 at 10 years. Visual inspection of the Kaplan-Meier survival curves did not reveal notable departures from proportional hazards.
Figure 3.
All-Cause Mortality Until 10 Years Following Alcohol Septal Ablation for Hypertrophic Obstructive Cardiomyopathy
Secondary outcomes
Matched cohort analysis demonstrated that frail patients have a higher risk of MACCE (HR: 1.49; 95% CI: 1.30–1.70; P < 0.001), ischemic stroke (HR: 1.57; 95% CI: 1.24–2.00; P < 0.001), HF exacerbation (HR: 1.09; 95% CI: 1.01–1.19; P = 0.024), major bleeding (HR: 1.61; 95% CI: 1.38–1.88; P < 0.001), and all-cause readmission (HR: 1.33; 95% CI: 1.24–1.43; P < 0.001) following ASA for HOCM compared with nonfrail patients at 1-year follow-up. No differences were noted for sudden cardiac arrest, antiarrhythmic drugs initiation/escalation, electrical cardioversion, and acute myocardial infarction between the cohorts (Figure 4).
Figure 4.
Outcomes Between Frail vs Nonfrail Cohorts at 1-Year Following Alcohol Septal Ablation for Hypertrophic Obstructive Cardiomyopathy
MACCE = major adverse cardiac and cerebrovascular event.
Falsification outcomes
No differences in any of the falsification outcomes were noted at 1-year follow-up following ASA, ruling out significant confounding (Table 2).
Table 2.
Falsification Outcomes at 1 year Following Alcohol Septal Ablation for Hypertrophic Obstructive Cardiomyopathy
| Falsification Outcome | HR | 95% CI | P Value |
|---|---|---|---|
| Myopia | 0.63 | 0.36–1.11 | 0.108 |
| Folliculitis | 1.23 | 0.60–2.50 | 0.559 |
| Hypoglycemia | 1.16 | 0.74–1.83 | 0.506 |
| Colorectal cancer | 1.14 | 0.57–2.23 | 0.699 |
| Nail disorders | 1.12 | 0.73–1.72 | 0.595 |
| Hypothyroidism | 1.07 | 0.92–1.25 | 0.352 |
| Migraine | 1.15 | 0.79–1.66 | 0.459 |
Discussion
This analysis of a large, contemporary nationwide cohort demonstrated that frail patients exhibited higher mortality, MACCE, ischemic stroke, HF exacerbation, major bleeding, and readmission compared with nonfrail patients undergoing ASA for HOCM. Both cohorts demonstrated comparable risks for sudden cardiac arrest, antiarrhythmic drugs initiation/escalation, electrical cardioversion, and acute myocardial infarction (Central Illustration). These findings further reinforce the impact of frailty on ASA outcomes in patients with HOCM.
Central Illustration.
Main Study Findings
In this analysis of 4,438 patients undergoing ASA for HOCM, patients with frailty demonstrated significantly higher risks of all-cause mortality, MACCE, ischemic stroke, HF exacerbation, major bleeding, and all-cause readmissions than their nonfrail counterparts. ASA = alcohol septal ablation; HOCM = hypertrophic obstructive cardiomyopathy; MACCE = major adverse cardiac and cerebrovascular events; PSM = propensity-score matching.
Prior investigations have largely focused on comparing outcomes between ASA and surgical septal myectomy in patients with HOCM. For example, Sawma et al.10 evaluated septal reduction strategies in elderly patients, while Yang et al.9 compared ASA with extended myectomy in patients with midventricular obstruction. In contrast, the present study evaluates frailty as a patient-level determinant of outcomes following ASA, highlighting the prognostic importance of frailty in this population. Frail patients undergoing ASA for HOCM demonstrate higher mortality compared with nonfrail counterparts, a phenomenon that reflects both diminished physiologic reserve and a constellation of pathophysiologic vulnerabilities that amplify procedural risk and limit recovery.21 Mechanistically, frailty denotes multisystem impairment—reduced cardiac, pulmonary, renal, and metabolic reserve that narrows the margin for compensation after an intentional septal infarct created by ASA; even modest hemodynamic instability, transient ventricular dysfunction, or new conduction disturbances that would be tolerated by robust patients may precipitate progressive HF, persistent hypotension, or fatal arrhythmias in the frail.22,23 Sarcopenia and loss of skeletal muscle mass, cardinal components of the frailty phenotype, further decrease respiratory muscle strength and mobility, increasing the risk of postoperative pulmonary complications, prolonged immobilization, delirium, and secondary infectious complications that are important contributors to short-term and longer-term mortality.24 Concurrent biological processes that characterize frailty—chronic low-grade systemic inflammation (“inflammaging”) and immunosenescence—potentiate an exaggerated or dysregulated inflammatory response to myocardial injury and procedural stress, impair efficient tissue repair and angiogenesis, and favor adverse left ventricular remodeling; these mechanisms increase susceptibility to decompensated HF and systemic complications that raise the mortality risk.25 Endothelial dysfunction, microvascular rarefaction, and autonomic dysregulation, commonly accompanying frailty, also limit coronary and systemic compensatory responses after septal necrosis, increasing ischemic burden and arrhythmogenic substrate formation. The typical multimorbidity of frail patients combined with polypharmacy magnifies periprocedural risks through impaired drug clearance, bleeding and thrombotic risks, and reduced capacity to tolerate contrast, hypotension, or renal injury. From a procedural and systems perspective, frail individuals are often preferentially triaged to ASA rather than surgical myectomy because of perceived operative risk; ASA produces a controlled infarct and carries risks of conduction block, ventricular arrhythmia, and need for pacemaker implantation, events that disproportionately compromise frail patients and are linked to higher morbidity and mortality in real-world series.22 Finally, center and operator volume and experience modify outcomes after septal reduction; frail patients treated in lower-volume settings may face higher complication rates and less optimal periprocedural optimization, widening mortality differences.26 Collectively, these interrelated pathophysiologic and health-system factors explain why frailty independently predicts worse long-term survival after ASA and underscore the need for standardized frailty assessment, multidisciplinary risk stratification, and individualized periprocedural optimization when considering ASA in HOCM.
Frail patients demonstrated higher rates of MACCE (driven by mortality and ischemic stroke), HF exacerbation, despite no signal for acute myocardial infarction. The elevated risk reflects the compounded effects of diminished physiologic reserve, multisystem impairment, and maladaptive responses to procedural stress rather than excess procedural ischemic injury. Frailty is marked by chronic inflammation, endothelial dysfunction, autonomic dysregulation, and sarcopenia, all of which limit hemodynamic adaptability following the controlled septal infarction induced during ASA.22,24 In these patients, even transient hypotension or conduction disturbances can trigger prolonged neurohormonal activation, maladaptive ventricular remodeling, and diastolic dysfunction, precipitating recurrent or worsening HF.27,28 Pre-existing myocardial fibrosis, microvascular disease, and impaired coronary flow reserve further constrain compensatory mechanisms, increasing susceptibility to volume overload and decompensation.29,30 The higher incidence of ischemic stroke in frail individuals arises from systemic and cardiac factors, including endothelial senescence, platelet hyper-reactivity, atrial enlargement, and postprocedural atrial arrhythmias, which collectively promote thromboembolic events.31, 32, 33 In addition, impaired cerebral autoregulation amplifies the impact of transient hypotension or arrhythmia during or after the procedure, further elevating cerebrovascular vulnerability. The constellation of metabolic derangements, renal insufficiency, and reduced skeletal muscle strength commonly observed in frailty worsens procedural tolerance and recovery, contributing to adverse outcomes.
No difference in rates of sudden cardiac arrest, initiation or escalation of antiarrhythmic drug therapy, and need for electrical cardioversion between frail and nonfrail patients after ASA can be explained by a combination of mechanistic, clinical-practice, and statistical factors. Mechanistically, ASA produces a localized, controlled infarct confined to the basal septum; the principal arrhythmogenic consequence is the formation of a septal scar and transient conduction disturbance rather than diffuse proarrhythmic myocardial injury.34 Clinically, decisions to start or uptitrate antiarrhythmic drugs and to perform electrical cardioversion are driven primarily by documented arrhythmia burden (sustained ventricular tachycardia, hemodynamically significant supraventricular tachyarrhythmia, or recurrent symptomatic atrial fibrillation), electrocardiographic findings, and hemodynamic instability rather than by frailty per se; in some cases cardiologists caution in frail patients (avoiding proarrhythmic drugs) may counterbalance any small increase in arrhythmia incidence, yielding net similar treatment rates.35 Competing risks and detection issues also attenuate observed differences: higher competing mortality in frail patients can reduce the at-risk time for manifesting sudden cardiac arrest, and variations in rhythm monitoring intensity across institutions can dilute true associations. Finally, limited event counts for cardiac arrest and institution-level practice patterns (monitoring protocols, thresholds for antiarrhythmic initiation or cardioversion) reduce power to detect modest differences.36 Together, these elements explain why arrhythmic endpoints and corresponding interventions may not differ between frail and nonfrail cohorts after ASA, although continued systematic rhythm surveillance and adequately powered prospective studies are required to exclude smaller but clinically important effects.
STUDY Limitations
Certain limitations warrant acknowledgment. First, the study population was identified using a diagnostic code-based definition of HOCM, and did not provide detailed phenotype information with advanced cardiac imaging. Second, the validity of ICD diagnostic codes could not be established, as these administrative codes are inherently predisposed to misclassification and confounding. However, we conducted a sensitivity analysis using falsification outcomes, which showed no spurious association. Third, individual risk profiles in terms of sudden cardiac death could not be established as data on various high-risk variables, including late-gadolinium enhancement, variations in ejection fraction over the study period, could not be accounted for. Fourth, the TriNetX dataset captures outcomes only if the individual encounters the same or another participating HCO; hence, this could lead to clinical events being missed or undocumented. Fifth, the statistical associations in this study may be directly or indirectly affected by residual confounding and referral bias. Sixth, the lack of 1:2 or 1:3 matching would have improved statistical precision as there were significant baseline differences in the 2 cohorts before PSM; however, due to the limitations of the platform, only 1:1 PSM is currently allowed. Seventh, the TriNetX platform does not provide residual-based diagnostic tests (eg, Schoenfeld residuals); only a visual assessment of the Kaplan-Meier curves was conducted without formal testing for the proportional hazard assumption. Eighth, frailty was assessed using a claims-based frailty indicator rather than direct physical performance measures, which may not fully capture all dimensions of frailty. Lastly, data on crucial hemodynamic parameters, such as LVOT gradient, severity of mitral regurgitation, and echocardiography measurements, were not available to granularly stratify these patients. Despite these limitations, administrative databases have demonstrated comparable discriminatory ability to traditional clinical databases in evaluating patient outcomes.37,38
Clinical implications
The increased risk observed in frail patients undergoing ASA for HOCM carries important clinical implications for patient selection, procedural planning, and postprocedural care, especially in newer or low-volume centers. Importantly, these findings should not be interpreted as suggesting that alternative septal reduction strategies, such as surgical myectomy, would necessarily confer lower risk in frail individuals. Frailty itself reflects diminished physiological reserve and is associated with increased peri-operative morbidity and mortality across major cardiac surgeries. In many clinical settings, frail or high-surgical-risk patients are preferentially referred for ASA because it represents a less invasive strategy compared with open surgical myectomy. Therefore, the excess risk observed in frail patients likely reflects the underlying biological vulnerability of this population rather than the procedural risk of ASA alone. Future studies directly comparing septal reduction strategies within frail subgroups are needed to determine the optimal therapeutic approach.
A potential frailty-informed care pathway may help operationalize these findings in clinical practice. First, frailty assessment should be incorporated during the initial evaluation of patients using validated tools that capture physical, nutritional, and comorbidity domains, as it provides prognostic information beyond conventional cardiac risk scores. Second, frailty status should be integrated into multidisciplinary heart team discussions involving interventional cardiology, cardiac surgery, geriatrics, and HF specialists to determine the most appropriate therapeutic strategy and whether the potential functional benefits of ASA outweigh the procedural risks. Third, frailty metrics should inform shared decision-making discussions with patients and families by contextualizing procedural risk, expected symptomatic benefit, and long-term prognosis. In selected frail patients, prehabilitation—optimizing nutrition, exercise capacity, and volume status—may improve physiological reserve and procedural tolerance. During ASA, procedural strategies should focus on minimizing myocardial injury, contrast exposure, and hemodynamic instability. Early recognition and management of conduction disturbances, rhythm monitoring, and tailored pharmacotherapy accounting for altered drug metabolism and polypharmacy in frailty are crucial. Postprocedural care should emphasize structured cardiac rehabilitation adapted to reduced functional capacity, meticulous HF management, and close surveillance for arrhythmias and cerebrovascular complications.
At the systems level, frailty status should guide referral to high-volume centers with integrated geriatric and HF services to improve procedural safety and optimize recovery. Future research should focus on developing frailty-guided management pathways, testing prehabilitation protocols, and comparing ASA with surgical myectomy in frail subgroups to generate evidence-based strategies. Overall, integrating frailty into the clinical framework for ASA allows for more precise risk stratification, individualized care, and improved outcomes in this vulnerable population.
Conclusions
This nationwide analysis of a large contemporary database reports frailty to be an independent predictor of long-term mortality, MACCE, ischemic stroke, HF exacerbation, major bleeding, and readmission in HOCM patients undergoing ASA. These findings highlight the importance of considering frailty as a crucial parameter in risk stratification tools and provide individualized care postablation in this high-risk, vulnerable cohort. Larger prospective studies are needed to validate the impact of frailty in patients with HOCM undergoing ASA and elucidate strategies to mitigate these increased risks.
Perspectives.
COMPETENCY IN MEDICAL KNOWLEDGE: Among frail patients undergoing ASA for HOCM, frailty is an independent predictor of long-term mortality, MACCE, ischemic stroke, HF exacerbation, major bleeding, and readmission. These findings highlight the importance of considering frailty as a crucial parameter in risk stratification tools and provide individualized care postablation in this high-risk, vulnerable cohort. In selected frail patients, prehabilitation—optimizing nutrition, exercise capacity, and volume status—may improve physiological reserve and procedural tolerance.
TRANSLATIONAL OUTLOOK: Larger prospective studies are needed to validate the impact of frailty in patients with HOCM undergoing ASA and elucidate strategies to mitigate these increased risks. Overall, integrating frailty into the clinical framework for ASA allows for more precise risk stratification, individualized care, and improved outcomes in this vulnerable population.
Funding support and author disclosures
The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Declaration of Generative Ai and Ai-Assisted Technologies in the Writing Process
The authors report that they have not used any AI tools in composing this manuscript.
Footnotes
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.
Appendix
For a supplemental table, please see the online version of this paper.
Supplementary material
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