Abstract
Introduction
Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive, often fatal disease. Tafamidis demonstrated efficacy in ATTR-CM clinical trials; however, real-world disease outcomes are not thoroughly characterized. We examined real-world outcomes among patients with wild-type (ATTRwt-CM) and variant (ATTRv-CM) ATTR-CM treated with tafamidis, the only approved treatment at the time of the study.
Methods
This retrospective observational study analyzed Komodo Healthcare Map® data (1/1/2016‒6/30/2024) for tafamidis-treated patients with ATTR-CM. Outcomes included all-cause hospitalization, cardiovascular-related hospitalization (CVH), heart failure (HF)-related hospitalization, outpatient worsening HF (OWHF) with oral diuretic intensification, and mortality. Subgroup analyses examined outcomes by ATTR-CM type and N-terminal pro-B-type natriuretic peptide (NT-proBNP)/B-type natriuretic peptide (BNP) baseline levels.
Results
Among 3239 tafamidis-treated patients (mean age 77.2 years; 75.9% male; 83.0% ATTRwt-CM; 11.7% ATTRv-CM), the cumulative incidence of first all-cause hospitalization was 22% at 6 months and 36% at 12 months, and that of first CVH was 22% and 35%, respectively. Median time to first CVH was 699 days. The cumulative incidence of OWHF with oral diuretic intensification was 22% at 6 months and 33% at 12 months. Mortality was 12.0% over the 5-year follow-up, and 6.2% at 12 months. The cumulative incidence of the composite endpoint (CVH, OWHF, or death) was 37% within 6 months and 53% within 12 months. In the subgroup with NT-proBNP/BNP baseline measurements (n = 412), patients with high NT-proBNP (> 3000 pg/mL, or BNP > 600 pg/mL) had worse outcomes, including a higher cumulative incidence of first CVH (51% vs. 27%) and higher mortality (9.7% vs. 4.1%) at 12 months.
Conclusions
In this large real-world cohort of tafamidis-treated patients, the cumulative incidences of hospitalization and worsening HF were substantial regardless of ATTR-CM subtype. Elevated NT-proBNP/BNP at baseline was associated with worse outcomes. These findings characterize the burden of disease outcomes in tafamidis-treated patients and underscore ongoing unmet needs in ATTR-CM management.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40119-025-00443-3.
Keywords: ATTR-CM, Cardiovascular hospitalization, Heart failure, NT-proBNP, Real-world evidence, Tafamidis, Transthyretin amyloid cardiomyopathy
Key Summary Points
| Why carry out this study? |
| Tafamidis improves outcomes in transthyretin amyloid cardiomyopathy (ATTR-CM) clinical trials, but comprehensive characterization of real-world outcomes remains incomplete. |
| To better understand treatment outcomes across a broad range of patients with ATTR-CM, this study evaluated a large, diverse, real-world, USA-based cohort of tafamidis-treated patients with ATTR-CM, specifically the association between N-terminal pro-B-type natriuretic peptide (NT-proBNP)/B-type natriuretic peptide (BNP) levels and outcomes for mortality and hospitalizations. |
| What was learned from the study? |
| Within 12 months of tafamidis initiation, approximately one-third of tafamidis-treated patients with ATTR-CM experienced cardiovascular hospitalization (CVH) or outpatient worsening of heart failure requiring diuretic intensification, and 6.2% died. |
| Elevated baseline NT-proBNP (> 3000 pg/mL, or BNP > 600 pg/mL) was associated with markedly worse outcomes relative to low baseline levels (≤ 3000 pg/mL, or BNP ≤ 600 pg/mL), with nearly doubled 12-month CVH cumulative incidence (51% vs. 27%) and more than twice the mortality (9.7% vs. 4.1%) at 12 months. |
| Despite the clinical benefits of tafamidis, these real-world findings highlight the remaining burden of disease outcomes in some patients, underscoring the need for earlier diagnosis and additional treatment options for ATTR-CM. |
Introduction
Transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) is a progressive and often life-threatening disease [1, 2]. ATTR-CM is caused by dissociation of unstable TTR tetramers that dissociate into monomers, which misfold and aggregate into toxic amyloid precursors that deposit as insoluble amyloid fibrils in the heart [3]. ATTR-CM is broadly categorized into the following two forms: variant (ATTRv-CM), resulting from a pathogenic variant in the TTR gene, and wild type (ATTRwt-CM), where the cause is unknown [4]. While historically considered a rare disease, advances in cardiovascular imaging, disease awareness, and the availability of disease-modifying therapies have led to increases in the diagnosis of ATTR-CM, and a number of diagnostic guidelines are available [5, 6]. Until recently, the TTR stabilizer tafamidis was the only approved therapy for ATTR-CM, indicated for treatment of the cardiomyopathy of ATTRwt-CM and ATTRv-CM in adults to reduce cardiovascular mortality and cardiovascular-related hospitalization (CVH) [7]. Clinical trials have shown that tafamidis decreases the risk for all-cause mortality and reduces the occurrence of CVH [8, 9]. However, patients with more advanced disease (New York Heart Association class III or National Amyloidosis Centre [NAC] stage III) experienced less favorable outcomes than those with earlier-stage ATTR-CM [8, 9].
Real-world data from the USA are lacking to understand disease progression in patients with ATTR-CM treated with tafamidis [10]. In a global cohort from the Transthyretin Amyloidosis Outcomes Survey (THAOS) (N = 1441), survival at 30 months was 84% in tafamidis-treated patients versus 70% in untreated patients, exceeding rates observed in the ATTR-ACT trial [11]. However, THAOS had limited representation of patients with ATTRv-CM (8.2% of treated patients) and primarily focused on overall survival without detailed examination of other disease outcomes or analyses stratified by known disease-progression biomarkers such as baseline N-terminal pro-B-type natriuretic peptide (NT-proBNP) [12]. Similarly, the US real-world TriNetX research network study (N = 842) demonstrated that patients with ATTRwt-CM and heart failure (HF) receiving tafamidis experienced significantly fewer HF exacerbations and lower all-cause mortality over 12 months than untreated patients, but did not examine outcomes such as outpatient worsening HF (OWHF) requiring diuretic intensification, which is also an indicator of disease progression [13, 14]. The analysis also excluded patients with ATTRv-CM, resulting in limited power to fully characterize outcomes across ATTR-CM subtypes [13]. These limitations underscore the need for more comprehensive real-world analyses to better understand disease outcomes and identify predictive factors across a broader patient population.
The present study was conducted to examine multiple disease outcomes in a diverse population of patients with ATTRwt-CM and ATTRv-CM treated with tafamidis in a real-world setting, with specific attention to analyses stratified by NT-proBNP/B-type natriuretic peptide (BNP).
Methods
Study Design
This was a retrospective observational study of patients with ATTR-CM in the USA using the Komodo Healthcare Map® with records from January 1, 2016 to June 30, 2024 (Supplementary Material Fig. S1). The Komodo Healthcare Map integrates medical and prescription claims data from diverse sources, leveraging proprietary partnerships with > 150 key national payers and consortiums that represent > 150 million covered lives to provide a representative view of US commercial-, Medicare-, and Medicaid-insured populations.
Because the Komodo Healthcare Map database contains anonymized, de-identified data that complies with the Health Insurance Portability and Accountability Act, and this study did not involve the collection, use, or transmittal of individually identifiable data, the study did not require ethics committee review or informed consent. Permission was obtained to access and analyze data.
Patient Population
Patients were included if they had ≥ 2 claims with an amyloidosis diagnosis code (E85.0, E85.1, E85.2, E85.4, E85.82) occurring on separate days AND ≥ 2 claims for a cardiac-related International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) diagnosis code (Supplementary Material Table S1) OR ≥ 1 claim for tafamidis based on National Drug Codes. Additional inclusion criteria required patients to be ≥ 50 years of age at diagnosis (defined as the earlier of ATTR-CM diagnosis or tafamidis claim date), have ≥ 6 months of continuous closed enrollment before diagnosis, and have ≥ 2 tafamidis claims during the identification period with ≥ 6 months of continuous closed enrollment on and before the index date (date of first tafamidis prescription). Patients with ≥ 2 claims for multiple myeloma or light chain amyloidosis (E85.81) or evidence of stem cell transplant during the study period were excluded.
All eligible patients were included in the primary analysis. For all outcomes of interest, an additional subpopulation analysis was conducted for patients with an NT-proBNP measurement within 90 days before or after the index date. Baseline NT-proBNP levels were categorized as high (> 3000 pg/mL, or BNP > 600 pg/mL if NT-proBNP was unavailable) or low (≤ 3000 pg/mL, or BNP ≤ 600 pg/mL) based on NAC staging criteria and a validated conversion formula [15, 16].
Both the overall population and NT-proBNP/BNP subpopulation were further stratified by ATTR-CM disease type (ATTRv-CM or ATTRwt-CM), determined using ICD-10-CM diagnosis codes from the identification period. Patients were classified as having ATTRv-CM if they had ≥ 2 ATTRv-CM codes on separate days; all others with ATTRwt-CM codes or < 2 ATTRv-CM codes were classified as having ATTRwt-CM. If a patient had only a tafamidis claim and no relevant ATTR-CM codes during the identification period or had one ATTRv-CM code and no ATTRwt-CM codes (with a tafamidis claim), then the patient was labeled as “unknown.”
Objectives and Outcomes
The primary objectives were to describe the demographic and clinical characteristics of tafamidis-treated patients with ATTR-CM and to quantify real-world outcomes in these patients. Outcomes of interest (Table 1) included occurrence of first hospitalization by type (all-cause, CVH, and HF related), median time from index date to first hospitalization, OWHF with oral diuretic intensification, and mortality. Events were assessed for the entire follow-up period and at the prespecified time points of 6 and 12 months after tafamidis initiation. Median time to first event was calculated for each outcome.
Table 1.
Outcome definitions and measurement details
| Outcome | Definition | Measurement details |
|---|---|---|
| Hospitalization outcomes | ||
| All-cause hospitalization | Inpatient admission for any cause |
Identified in any claim position Reported for entire post-index period and at 6 and 12 months after tafamidis initiation Median time to first event calculated |
| CVH | Inpatient admission with a medical claim for a cardiovascular-related diagnosis |
Identified by ICD-10-CM diagnosis codes in any position Reported for entire post-index period and at 6 and 12 months after tafamidis initiation Median time to first event calculated |
| HF-related hospitalization | Inpatient admission with a medical claim for a HF-related diagnosis |
Identified by ICD-10-CM diagnosis codes in any position Reported for entire post-index period and at 6 and 12 months after tafamidis initiation Median time to first event calculated |
| Disease progression | ||
| OWHF with oral diuretic intensification | Evidence of newly prescribed or increased dose of loop diuretic |
Identified using NDC, HCPCS, and CPT codes Events defined on the basis of evidence of use in the pre-index perioda: • For patients with no prior diuretic use: any newly prescribed loop diuretic • For patients with prior diuretic use: any increase in dose of loop diuretic Loop diuretic conversion (if needed): 1 mg bumetanide = 20 mg torsemide = 80 mg furosemide (oral); 1 mg bumetanide = 20 mg torsemide = 40 mg furosemide (IV) [35, 36] Reported for entire post-index period and at 6 and 12 months Median time to first event calculated |
| Mortality | Patient death |
Defined as binary variable (yes/no) Defined as time-to-event variable (from index date to death or end of follow-up) Reported for entire follow-up period and at 6 and 12 months |
| Other outcomes | ||
| NT-proBNP changes | Change in NT-proBNP levels from baseline |
Reported only in the subpopulation of patients with multiple NT-proBNP measurements Calculated as difference between index value and post-index values at 6 and 12 months Values measured within ± 60 days of target date, inclusive; closest value used if multiple values available Included patients who achieved > 700 pg/mL AND > 30% increase [37, 38] Values sourced from laboratory data in Komodo database enhanced with Quest lab data using LOINC codes Biologically implausible results excluded |
| Heart transplant | Evidence of heart transplantation procedure |
Defined using ICD-10-CM and CPT codes Included only patients with no history of heart transplantation during pre-index period Recorded as binary variable (yes/no) based on post-index period claims |
| Cardiovascular interventions | Evidence of specific cardiovascular procedures |
Included LVAD implantation, TAVR, implantable cardioverter defibrillator placement, and pacemaker implantation Summarized for patients with no history of these interventions in pre-index period Measured using HCPCS and CPT codes Recorded as binary variables (yes/no) |
| Anti-arrhythmia medication use | Evidence of anti-arrhythmia medication claims |
For patients with no pre-index claim: binary (yes/no) for any post-index claim For patients with pre-index claim: binary (yes/no) for post-index claim with dose increase or drug change Defined using NDC codes |
CPT Current Procedural Terminology, CVH cardiovascular-related hospitalization, HCPCS Healthcare Common Procedure Coding System, HF heart failure, ICD-10-CM International Classification of Diseases, 10th revision, Clinical Modification, IV intravenous, LOINC Logical Observation Identifiers Names and Codes, LVAD left ventricular assistance device, NDC National Drug Code, NT-proBNP N-terminal pro-B-type natriuretic peptide, OWHF outpatient worsening heart failure, TAVR transcatheter aortic valve replacement
aA 1-year lookback period before the tafamidis index date was used to search for diuretic events and determine initial reference dose
Additional outcomes included changes in NT-proBNP from baseline (absolute change and the proportion of patients experiencing significant magnitude of change) for those with multiple measurements during follow-up and the proportion of patients experiencing other cardiovascular outcomes and interventions (heart transplant, left ventricular assistance device implantation, transcatheter aortic valve replacement, implantable cardioverter defibrillator placement, pacemaker implantation, and use of anti-arrhythmia medications and beta blockers) (Table 1).
Statistical Analysis
Demographics, outcomes, and measures of interest were reported in the overall population and in the subpopulation of patients with an NT-proBNP/BNP measurement within 90 days before or after the index date. All measures and outcomes were summarized using descriptive statistics. Kaplan–Meier estimates assessed the cumulative incidence of first all-cause hospitalization, CVH, HF-related hospitalization, OWHF with oral diuretic intensification, and outpatient urgent HF (OUHF), and Kaplan–Meier curves were plotted to visualize cumulative incidence of first CVH and mortality. Median time to event and cumulative incidence of event occurrence with corresponding 95% confidence interval (CI) were evaluated at two time points (6 months [182 days] and 12 months [365 days]). R statistical software (version 4.2.1) was used for analytics [17].
To identify predictors of disease outcomes in patients treated with tafamidis, Cox proportional hazards regression with the Coxnet method using least absolute shrinkage and selection operator/elastic net penalties was employed for variable selection [18, 19]. Time-to-event outcomes included CVH, OWHF with oral diuretic intensification, and death, as well as the composite endpoint of these three outcomes. Clinical factors evaluated as potential predictors included ATTR-CM type, NT-proBNP/BNP level at baseline, age by 10-year increment, region, race, Charlson Comorbidity Index (CCI), and baseline use of mineralocorticoid inhibitors (MRAs) and sodium-glucose cotransporter 2 (SGLT2) inhibitors. To avoid variable redundancy, individual comorbidities included in the CCI calculation (HF, chronic kidney disease, dementia, Alzheimer’s disease, cognitive impairment, and diabetes) were not included as separate variables in the Cox models. Five-fold cross-validation with grid search was used to optimize Coxnet model parameters and prevent overfitting. This approach divides data into five subsets, trains the model on four subsets, and tests on the remaining subset, repeating this process to identify the best-performing model configuration. Model performance was evaluated using Harrell’s C-index, a measure of how well the model distinguishes between patients with different risks of experiencing events, with values > 0.7 indicating good discriminatory ability. Cox proportional hazard model was used with hazard ratio, 95% CIs, and p values reported.
Results
Overall Population
A total of 3239 tafamidis-treated patients were identified (Supplementary Material Table S2). Patients had a mean (standard deviation [SD]) age of 77.2 years (8.4) and were predominantly male (75.9%), white (57.6%), and enrolled in Medicare (77.8%; Table 2). At index, 19.2% of patients were prescribed tafamidis meglumine 80 mg, and the remaining were prescribed tafamidis free acid 61 mg (bioequivalent to tafamidis meglumine 80 mg). Most patients had ATTRwt-CM (83.0%); 11.7% had ATTRv-CM. When stratified by ATTR-CM type, notable differences in demographic characteristics were observed for age, age grouping, and race/ethnicity distributions. Patients with ATTRv-CM were younger (mean [SD] 72.3 years [8.8]) than patients with ATTRwt-CM (77.8 [8.1]). In addition, a substantially larger proportion of patients with ATTRv-CM were Black or African American compared with ATTRwt-CM (55.0% vs. 27.5%). At baseline, 33.9% were using MRAs and 20.3% of patients were using SGLT2 inhibitors. The mean (SD) CCI score was 3.8 (2.0); 28.7% of patients had a score of 0–2, 41.0% had a score of 3–4, and 30.3% had a score of 5 or higher. The median (interquartile range [IQR]) follow-up time was 12.4 months (5.8‒23.7).
Table 2.
Demographic and clinical characteristics at index date
| Characteristic | Overalla, N = 3239 | ATTRv-CM, n = 378 | ATTRwt-CM, n = 2687 | High NT-proBNP/BNPb, n = 144 | Low NT-proBNP/BNPb, n = 268 |
|---|---|---|---|---|---|
| Age, years | |||||
| Mean (SD) | 77.2 (8.4) | 72.3 (8.8) | 77.8 (8.1) | 79.7 (6.7) | 76.3 (8.4) |
| Median (Q1, Q3) | 79.0 (72.0, 84.0) | 73.0 (66.0, 79.0) | 79.0 (73.0, 84.0) | 81.0 (76.0, 85.0) | 78.0 (71.0, 83.0) |
| Min, max | 50.0, 89.0 | 50.0, 89.0 | 50.0, 89.0 | 58.0, 89.0 | 54.0, 89.0 |
| Age group, n (%) | |||||
| 50‒59 years | 116 (3.6) | 36 (9.5) | 79 (2.9) | 2 (1.4) | 10 (3.7) |
| 60‒69 years | 484 (14.9) | 99 (26.2) | 366 (13.6) | 12 (8.3) | 50 (18.7) |
| 70‒79 years | 1112 (34.3) | 155 (41.0) | 907 (33.8) | 47 (32.6) | 93 (34.7) |
| 80+ years | 1527 (47.1) | 88 (23.3) | 1335 (49.7) | 83 (57.6) | 115 (42.9) |
| Sex, n (%) | |||||
| Male | 2459 (75.9) | 263 (69.6) | 2070 (77.0) | 108 (75.0) | 208 (77.6) |
| Female | 657 (20.3) | 100 (26.5) | 517 (19.2) | 31 (21.5) | 50 (18.7) |
| Unknown | 123 (3.8) | 15 (4.0) | 100 (3.7) | 5 (3.5) | 10 (3.7) |
| Race/ethnicity, n (%) | |||||
| White | 1866 (57.6) | 115 (30.4) | 1639 (61.0) | 81 (56.3) | 176 (65.7) |
| Black or African American | 988 (30.5) | 208 (55.0) | 740 (27.5) | 55 (38.2) | 63 (23.5) |
| Asian or Pacific Islander | 55 (1.7) | 6 (1.6) | 44 (1.6) | 2 (1.4) | 2 (0.7) |
| Hispanic or Latino | 145 (4.5) | 16 (4.2) | 122 (4.5) | 3 (2.1) | 10 (3.7) |
| Other | 56 (1.7) | 9 (2.4) | 44 (1.6) | 2 (1.4) | 12 (4.5) |
| Missing/unknown | 129 (4.0) | 24 (6.3) | 98 (3.6) | 1 (0.7) | 5 (1.9) |
| Region, n (%) | |||||
| Northeast | 1386 (42.8) | 153 (40.5) | 1165 (43.4) | 81 (56.3) | 157 (58.6) |
| Midwest | 803 (24.8) | 104 (27.5) | 664 (24.7) | 18 (12.5) | 21 (7.8) |
| South | 724 (22.4) | 89 (23.5) | 584 (21.7) | 30 (20.8) | 57 (21.3) |
| West | 321 (9.9) | 31 (8.2) | 270 (10.0) | 15 (10.4) | 32 (11.9) |
| Missing/unknown | 5 (0.2) | 1 (0.3) | 4 (0.1) | 0 | 1 (0.4) |
| Insurance type, n (%) | |||||
| Commercial | 483 (14.9) | 93 (24.6) | 377 (14.0) | 8 (5.6) | 47 (17.5) |
| Medicaid | 71 (2.2) | 12 (3.2) | 55 (2.0) | 3 (2.1) | 7 (2.6) |
| Medicare | 2521 (77.8) | 250 (66.1) | 2127 (79.2) | 128 (88.9) | 201 (75.0) |
| Mixed/partial | 157 (4.8) | 23 (6.1) | 123 (4.6) | 5 (3.5) | 13 (4.9) |
| Missing/unknown | 7 (0.2) | 0 | 5 (0.2) | 0 | 0 |
| Type of ATTR-CM, n (%) | |||||
| Wild-type (ATTRwt-CM) | 2687 (83.0) | 0 | 2687 (100) | 118 (81.9) | 227 (84.7) |
| Variant (ATTRv-CM) | 378 (11.7) | 378 (100) | 0 | 23 (16.0) | 33 (12.3) |
| Unknownc | 174 (5.4) | 0 | 0 | 3 (2.1) | 8 (3.0) |
| NT-proBNP/BNP levels, n (%) | |||||
| High (NT-proBNP > 3000 pg/mL or BNP > 600 pg/mL) | 144 (35.0)d | 23 (41.1) | 118 (34.2) | 144 (100) | 0 |
| Low (NT-proBNP ≤ 3000 pg/mL or BNP ≤ 600 pg/mL) | 268 (65.0)d | 33 (58.9) | 227 (65.8) | 0 | 268 (100) |
| NTpro-BNP or BNP measure used, n (%) | |||||
| NTpro-BNP (prioritized) | 326 (79.1) | 45 (80.4) | 273 (79.1) | 114 (79.2) | 212 (79.1) |
| BNP | 86 (20.9) | 11 (19.6) | 72 (20.9) | 30 (20.8) | 56 (20.9) |
| Baseline medication use, n (%) | |||||
| SGLT2 inhibitor | 658 (20.3) | 54 (14.3) | 554 (20.6) | 37 (25.7) | 55 (20.5) |
| Mineralocorticoid receptor antagonist | 1099 (33.9) | 134 (35.4) | 908 (33.8) | 63 (43.8) | 90 (33.6) |
| Charlson Comorbidity Index (CCI), n (%) | |||||
| Mean (SD) | 3.8 (2.0) | 3.8 (2.2) | 3.9 (1.9) | 4.3 (1.9) | 3.8 (2.2) |
| Median (Q1‒Q3) | 4.0 (2.0–5.0) | 3.5 (2.0–5.0) | 4.0 (2.0–5.0) | 4.0 (3.0–5.5) | 3.0 (2.0–5.0) |
| Min‒max | 0.0–14.0 | 0.0–13.0 | 0.0–14.0 | 0.0–10.0 | 0.0–11.0 |
| CCI categories, n (%) | |||||
| 0–2 | 930 (28.7) | 125 (33.1) | 706 (26.3) | 23 (16.0) | 96 (35.8) |
| 3–4 | 1328 (41.0) | 139 (36.8) | 1144 (42.6) | 59 (41.0) | 92 (34.3) |
| 5+ | 981 (30.3) | 114 (30.2) | 837 (31.1) | 62 (43.1) | 80 (29.9) |
ATTR-CM transthyretin amyloid cardiomyopathy, ATTRv-CM variant transthyretin amyloid cardiomyopathy, ATTRwt-CM wild-type transthyretin amyloid cardiomyopathy, BNP B-type natriuretic peptide, CCI Charlson Comorbidity Index, Q quartile, NT-proBNP N-terminal pro-B-type natriuretic peptide, SD standard deviation, SGLT2 sodium-glucose cotransporter 2
aWild-type and variant do not add up to the overall population, as some patients’ ATTR-CM type is unknown (unknown ATTR-CM type, n = 174)
bBaseline characteristics for NT-proBNP/BNP subgroups (n = 412) should be interpreted with caution due to limited sample size
cIf a patient had only a tafamidis claim and no relevant ATTR-CM codes during the identification period or had one ATTRv-CM code and no ATTRwt-CM codes (with tafamidis claim), then the patient was labeled as “unknown.” Additionally, if a patient had one ATTRv-CM code and no ATTRwt-CM codes (with tafamidis claim), the patient was also labeled as “unknown”
dFor 2827 patients, values were unable to be calculated (missing both values at baseline)
The median time to first all-cause hospitalization was approximately 2 years (678 days), with Kaplan–Meier estimates showing a 22% cumulative incidence of hospitalization at 6 months and 36% cumulative incidence at 12 months, suggesting early clinical worsening in a subset of patients, with others experiencing later events (Table 3). The median time to first CVH was 699 days, with Kaplan–Meier estimates showing a 22% cumulative incidence of first CVH at 6 months and 35% cumulative incidence at 12 months. The median time to first HF-related hospitalization (908 days) was longer than that of all-cause hospitalization or CVH; the cumulative incidence of first HF-related hospitalization was 19% at 6 months and 30% at 12 months. Hospitalization cumulative incidence was generally similar between genotypes, with patients with ATTRv-CM having modestly shorter median time to HF-related hospitalization compared with patients with ATTRwt-CM (774 vs. 874 days).
Table 3.
Summary of hospitalization after tafamidis initiation
| Parameter | Overall, N = 3239 | ATTRv-CM, n = 378 | ATTRwt-CM, n = 2687 | High NT-proBNP/BNP, n = 144 | Low NT-proBNP/BNP, n = 268 |
|---|---|---|---|---|---|
| All-cause hospitalization | |||||
| Median (95% CI) time to all-cause hospitalization, days | 678 (624‒746) | 658 (521‒830) | 646 (588‒715) | 347 (251‒461) | 1169 (830‒1639) |
| Cumulative incidence (95% CI) of all-cause hospitalization by: | |||||
| 6 months | 22% (21‒24%) | 24% (19‒28%) | 23% (21‒25%) | 35% (26‒43%) | 20% (15‒25%) |
| 12 months | 36% (34‒37%) | 36% (30‒41%) | 37% (35‒39%) | 52% (41‒61%) | 27% (21‒33%) |
| CVH | |||||
| Median (95% CI) time to CVH, days | 699 (651‒784) | 688 (532‒913) | 674 (606‒754) | 347 (251‒461) | 1169 (830‒1639) |
| Cumulative incidence (95% CI) of CVH by: | |||||
| 6 months | 22% (20‒23%) | 23% (19‒28%) | 23% (21‒24%) | 35% (26‒43%) | 19% (14‒24%) |
| 12 months | 35% (33‒37%) | 35% (30‒40%) | 36% (34‒38%) | 51% (40‒59%) | 27% (21‒33%) |
| HF-related hospitalization | |||||
| Median (95% CI) time to HF-related hospitalization, days | 908 (813‒1033) | 774 (637‒1044) | 874 (792‒1033) | 403 (262‒544) | 1471 (1169‒NA) |
| Cumulative incidence (95% CI) of HF-related hospitalization by: | |||||
| 6 months | 19% (18‒21%) | 21% (17‒25%) | 20% (18‒22%) | 31% (23‒38%) | 15% (11‒20%) |
| 12 months | 30% (28‒32%) | 32% (27‒37%) | 31% (29‒33%) | 45% (35‒54%) | 23% (17‒28%) |
ATTRv-CM variant transthyretin amyloid cardiomyopathy, ATTRwt-CM wild-type transthyretin amyloid cardiomyopathy, BNP B-type natriuretic peptide, CI confidence interval, CVH cardiovascular-related hospitalization, HF heart failure, NA not available, NT-proBNP N-terminal pro-B-type natriuretic peptide
Cardiovascular interventions, including heart transplant, occurred infrequently (Supplementary Material Table S3). Half of patients (50.4%) had evidence of anti-arrhythmic medication use (not including beta blockers) at baseline. During follow-up, 21.4% of those who were not using these medications at baseline initiated anti-arrhythmic therapy, and 25.0% of those with baseline use had dose increases or drug changes. Regarding beta blockers, more than two-thirds (69.5%) had evidence of use at baseline. During follow-up, 17.6% of patients without baseline beta blocker use initiated therapy, and 18.5% of those with baseline use had dose increases or drug changes. Patients with ATTRv-CM had higher rates of medication changes compared with patients with ATTRwt-CM for both anti-arrhythmic medications (32.6% vs. 24.8%) and beta blockers (24.4% vs. 18.2%).
The median time to first OWHF with oral diuretic intensification was 872 days (95% CI 799‒1048), with cumulative incidences of 22% within 6 months and 33% within 12 months (Table 4). Most patients (81.0%) had a diuretic prescription either at baseline or at 1 year of follow-up, with no substantial differences observed between ATTR-CM types. The cumulative incidences of OUHF were 5% and 8% within 6 months and 12 months of tafamidis initiation, respectively.
Table 4.
Summary of HF endpoints
| Parameter | Overall, N = 3239 | ATTRv-CM, n = 378 | ATTRwt-CM, n = 2687 | High NT-proBNP/BNP, n = 144 | Low NT-proBNP/BNP, n = 268 |
|---|---|---|---|---|---|
| OWHF | |||||
| Median (95% CI) time to OWHF event, days | 872 (799‒1048) | 783 (629‒1167) | 874 (799‒1069) | 319 (241‒1048) | 827 (690‒NA) |
| Cumulative incidence (95% CI) of an OWHF event by: | |||||
| 6 months | 22% (21‒24%) | 23% (18‒27%) | 23% (21‒24%) | 36% (27‒44%) | 24% (18‒29%) |
| 12 months | 33% (31‒35%) | 33% (27‒37%) | 33% (31‒35%) | 52% (42‒61%) | 32% (26‒38%) |
| OUHF | |||||
| Median (95% CI) time to OUHF event, days | NR (NR‒NR) | NR (NR‒NR) | NR (NR‒NR) | NR (1119‒NR) | NR (1668‒NR) |
| Cumulative incidence (95% CI) of an OUHF event by; | |||||
| 6 months | 5% (4‒6%) | 5% (3‒ S7%) | 5% (4‒ 6%) | 10% (4‒14%) | 6% (3‒ 9%) |
| 12 months | 8% (7‒10%) | 9% (6‒12%) | 9% (7‒10%) | 18% (10‒26%) | 9% (6‒13%) |
ATTRv-CM variant transthyretin amyloid cardiomyopathy, ATTRwt-CM wild-type transthyretin amyloid cardiomyopathy, BNP B-type natriuretic peptide, CI confidence interval, HF heart failure, NT-proBNP N-terminal pro-B-type natriuretic peptide, NR not reached, OUHF outpatient urgent heart failure, OWHF outpatient worsening heart failure
Mortality in the overall cohort was 12.0% (n = 390) over 5 years of total follow-up time, with 2.7% (n = 86) of deaths occurring within 6 months of tafamidis initiation and 6.2% (n = 200) within 12 months. Mortality rates were similar between ATTR-CM types through 12 months of follow-up.
The cumulative incidence of the composite endpoint of CVH, OWHF, or death was 37% within 6 months of tafamidis initiation and 53% within 12 months, with median time to first composite endpoint event of 315 days (95% CI 292‒340) (Table 5). When stratified by ATTR-CM type, composite endpoint rates were similar between variant and wild-type groups within 6 months (38% each) and 12 months (53% vs. 54%), with comparable median times to event (333 vs. 305 days, respectively).
Table 5.
Summary of the composite outcome of CVH, OWHF, or death
| Parameter | Overall, N = 3239 | ATTRv-CM, n = 378 | ATTRwt-CM, n = 2687 | High NT-proBNP/BNP, n = 144 | Low NT-proBNP/BNP, n = 268 |
|---|---|---|---|---|---|
| Median (95% CI) time to composite event, days | 315 (292‒340) | 333 (273‒446) | 305 (277‒328) | 117 (71‒200) | 412 (279‒629) |
| Cumulative incidence (95% CI) of a composite event by: | |||||
| 6 months | 37% (36‒39%) | 38% (33‒43%) | 38% (36‒40%) | 55% (46‒63%) | 36% (30‒42%) |
| 12 months | 53% (51‒55%) | 53% (47‒58%) | 54% (52‒56%) | 75% (65‒82%) | 49% (42‒55%) |
ATTRv-CM variant transthyretin amyloid cardiomyopathy, ATTRwt-CM wild-type transthyretin amyloid cardiomyopathy, BNP B-type natriuretic peptide, CI confidence interval, CVH cardiovascular hospitalization, NT-proBNP N-terminal pro-B-type natriuretic peptide, OWHF outpatient worsening heart failure
Patients with NT-proBNP/BNP Measurements
A total of 412 patients (12.7%) had NT-proBNP/BNP measurements at baseline and comprised the NT-proBNP/BNP subpopulation. This subpopulation did not differ from the overall cohort, though patients with high NT-proBNP/BNP were more likely to be ≥ 80 years of age and Black or African American (Table 2).
Patients with high baseline NT-proBNP/BNP demonstrated worse outcomes than those with low baseline levels. The 12-month cumulative incidence of first all-cause hospitalization was approximately twice as high in patients with high versus low NT-proBNP/BNP (52% vs. 27%), with markedly shorter median times to events in those with high vs. low NT-proBNP/BNP (347 vs. 1169 days, respectively) (Table 3). The 12-month cumulative incidence of first CVH was also higher in patients with high versus with low NT-proBNP/BNP (51% vs. 27%) (Fig. 1). The 12-month estimated cumulative incidence of OWHF with oral diuretic intensification was also higher in patients with high versus low NT-proBNP/BNP (52% vs. 32%) (Table 4). Cardiovascular interventions occurred infrequently in both groups (Supplementary Material Table S3). Mortality at 12 months was more than double in patients with high baseline NT-proBNP compared to those with low levels (9.7% vs. 4.1%) (Fig. 2). Patients with high baseline NT-proBNP/BNP were also more likely to experience the composite endpoint of CVH, OWHF, or death than those with low levels (75% vs. 49% by 12 months) (Table 5). Changes from baseline in NT-proBNP levels over time among patients with multiple measurements during follow-up are shown in Table 6.
Fig. 1.
Cumulative incidence of first CVH by baseline NT-proBNP/BNP level. BNP B-type natriuretic peptide, CVH cardiovascular-related hospitalization, High NT-proBNP > 3000 pg/mL (or BNP > 600 pg/mL if NT-proBNP was not available), Low NT-proBNP ≤ 3000 pg/mL (or BNP ≤ 600 pg/mL if NT-proBNP was not available) , NT-proBNP N-terminal pro-B-type natriuretic peptide
Fig. 2.
Cumulative incidence of mortality by baseline NT-proBNP/BNP level. BNP B-type natriuretic peptide, High NT-proBNP > 3000 pg/mL (or BNP > 600 pg/mL if NT-proBNP was not available), Low NT-proBNP ≤ 3000 pg/mL (or BNP ≤ 600 pg/mL if NT-proBNP was not available), NT-proBNP N-terminal pro-B-type natriuretic peptide
Table 6.
NT-proBNP levels over time
| Parameter | Overall, N = 412 | High NT-proBNP/BNP, n = 144 | Low NT-proBNP/BNP, n = 268 | ATTRv-CM, n = 56 | ATTRwt-CM, n = 345 |
|---|---|---|---|---|---|
| NT-proBNP at index date | |||||
| n | 326 | 114 | 212 | 45 | 273 |
| Median (Q1, Q3) | 1908 (827, 3961) | 4843 (3830, 6771) | 1150 (493, 1863) | 2000 (778, 4430) | 1905 (855, 3844) |
| NT-proBNP at 6 months | |||||
| n | 97 | 37 | 60 | 10 | 86 |
| Median (Q1, Q3) | 2136 (885, 3813) | 4052 (3011, 5078) | 1574 (596, 2143) | 4096 (3076, 4880) | 2003 (874, 3611) |
| Change from baseline in NT-proBNP at 6 months | |||||
| n | 95 | 36 | 59 | 10 | 84 |
| Median (Q1, Q3) | 38 (− 427, 810) | − 393 (− 1360, 1098) | 65 (− 78, 548) | − 59.5 (− 801, 1491) | 49.5 (− 381, 782) |
| Increase > 700 pg/mL and > 30%, n (%) | 21 (22.1) | 8 (22.2) | 13 (22.0) | 3 (30.0) | 18 (21.4) |
| NT-proBNP at 12 months | |||||
| n | 99 | 31 | 68 | 18 | 81 |
| Median (Q1, Q3) | 1540 (642, 3409) | 3969 (3163, 6715) | 1092 (453, 1931) | 1962 (1158, 3134) | 1497 (642, 3409) |
| Change from baseline in NT-proBNP at 12 months | |||||
| n | 95 | 31 | 64 | 17 | 78 |
| Median (Q1, Q3) | 5 (− 355, 620) | − 105 (− 2486, 947) | 44.5 (− 139. 601) | − 119 (− 500, 369) | 44.5 (− 251, 620) |
| Increase > 700 pg/mL and > 30%, n (%) | 14 (14.7) | 4 (12.9) | 10 (15.6) | 2 (11.8) | 12 (15.4) |
ATTRv-CM variant transthyretin amyloid cardiomyopathy, ATTRwt-CM wild-type transthyretin amyloid cardiomyopathy, BNP B-type natriuretic peptide, Q quartile, NT-proBNP N-terminal pro-B-type natriuretic peptide
Predictors of Disease Outcomes
Predictors of disease outcomes are shown in Table 7. Higher CCI scores were associated with increased hazard for the individual endpoints of CVH, OWHF with oral diuretic intensification, and mortality, as well as their composite endpoint, with patients having scores of ≥ 5 showing particularly elevated risk. Baseline SGLT2 inhibitor use was associated with reduced hazard for CVH, while baseline MRA use was associated with increased hazard for both CVH and mortality. Advanced age was associated with increased hazard across all outcomes. Among other baseline comorbidities, arrhythmia or conduction disorder and hypertension were associated with increased hazard for CVH and the composite endpoint, while orthostatic hypotension was associated with increased hazard for CVH, mortality, and the composite endpoint. Geographic variations were observed, with some regions showing decreased hazard for specific endpoints. Black race was also associated with increased hazard for CVH, OWHF, and the composite endpoint. Predictors of disease outcomes in the subpopulation of patients with available NT-proBNP measurements are available in Supplementary Material Table S4.
Table 7.
Clinical factors predictive of disease outcomes
| Parameter | N | CVH | Outpatient worsening heart failure | Death | Compositea | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | p value | HR | 95% CI | p value | HR | 95% CI | p value | HR | 95% CI | p value | ||
| ATTR-CM type | |||||||||||||
| Wild type | 2683 | – | – | – | – | – | – | – | – | ||||
| Variant | 377 | 1.07 | 0.90‒1.26 | 0.4 | 1.1 | 0.92‒1.32 | 0.3 | 0.82 | 0.58‒1.16 | 0.3 | 1.07 | 0.92‒1.23 | 0.4 |
| Age (10-year increment) | 3060 | 1.19 | 1.10‒1.29 | < 0.001* | 1.17 | 1.08‒1.28 | < 0.001* | 2.16 | 1.80‒2.59 | < 0.001* | 1.21 | 1.13‒1.29 | < 0.001* |
| Sex | |||||||||||||
| Female | 616 | – | – | – | – | – | – | – | – | ||||
| Male | 2329 | 1 | 0.86‒1.16 | > 0.9 | 0.93 | 0.79‒1.09 | 0.3 | 1.27 | 0.96‒1.68 | 0.092 | 1.02 | 0.90‒1.16 | 0.8 |
| Unknown | 115 | 1.28 | 0.95‒1.73 | 0.1 | 0.97 | 0.69‒1.37 | 0.9 | 1.57 | 0.94‒2.62 | 0.084 | 1.07 | 0.83‒1.40 | 0.6 |
| Region | |||||||||||||
| Northeast | 1318 | – | – | – | – | – | – | – | – | ||||
| Midwest | 768 | 0.98 | 0.85‒1.13 | 0.8 | 0.82 | 0.70‒0.96 | 0.013* | 1.03 | 0.80‒1.33 | 0.8 | 0.87 | 0.77‒0.98 | 0.021* |
| South | 673 | 0.94 | 0.80‒1.09 | 0.4 | 1.03 | 0.88‒1.21 | 0.7 | 1.36 | 1.04‒1.78 | 0.025* | 1.05 | 0.93‒1.19 | 0.4 |
| West | 301 | 0.68 | 0.53‒0.86 | 0.002* | 0.96 | 0.77‒1.20 | 0.7 | 1.09 | 0.72‒1.65 | 0.7 | 0.81 | 0.68‒0.97 | 0.024* |
| Race | |||||||||||||
| White | 1754 | – | – | – | – | – | – | – | – | ||||
| Black | 948 | 1.26 | 1.10‒1.44 | < 0.001* | 1.18 | 1.02‒1.37 | 0.028* | 1.07 | 0.84‒1.37 | 0.6 | 1.2 | 1.07‒1.35 | 0.002* |
| Other | 358 | 1.05 | 0.86‒1.29 | 0.6 | 1.05 | 0.85‒1.28 | 0.7 | 0.54 | 0.33‒0.87 | 0.012* | 1.11 | 0.94‒1.30 | 0.2 |
| Arrhythmia or conduction disorder | |||||||||||||
| No | 309 | – | – | – | – | – | – | – | – | ||||
| Yes | 2751 | 1.33 | 1.06‒1.65 | 0.012* | 1.18 | 0.95‒1.47 | 0.13 | 1.59 | 0.99‒2.58 | 0.057 | 1.29 | 1.08‒1.54 | 0.006* |
| Genitourinary conditions | |||||||||||||
| No | 1940 | – | – | – | – | – | – | – | – | ||||
| Yes | 1120 | 1.08 | 0.96‒1.22 | 0.2 | 1.09 | 0.96‒1.24 | 0.2 | 0.79 | 0.63‒0.99 | 0.037* | 1.07 | 0.97‒1.19 | 0.2 |
| Hypertension | |||||||||||||
| No | 200 | – | – | – | – | – | – | – | – | ||||
| Yes | 2860 | 1.71 | 1.22‒2.39 | 0.002* | 1.2 | 0.90‒1.60 | 0.2 | 2.04 | 1.00‒4.15 | 0.049* | 1.34 | 1.05‒1.71 | 0.018* |
| Anxiety or depression | |||||||||||||
| No | 1874 | – | – | – | – | – | – | – | – | ||||
| Yes | 1186 | 1.11 | 0.99‒1.25 | 0.086 | 0.94 | 0.83‒1.07 | 0.4 | 0.99 | 0.80‒1.22 | 0.9 | 1.03 | 0.93‒1.14 | 0.5 |
| Orthostatic hypotension | |||||||||||||
| No | 2698 | – | – | – | – | – | – | – | – | ||||
| Yes | 362 | 1.36 | 1.16‒1.60 | < 0.001* | 1.05 | 0.87‒1.27 | 0.6 | 1.49 | 1.12‒1.97 | 0.006* | 1.26 | 1.10‒1.45 | 0.001* |
| Pulmonary embolism | |||||||||||||
| No | 2813 | – | – | – | – | – | – | – | – | ||||
| Yes | 247 | 1.19 | 0.98‒1.44 | 0.087 | 1.01 | 0.81‒1.26 | > 0.9 | 1.1 | 0.77‒1.55 | 0.6 | 1.15 | 0.98‒1.36 | 0.095 |
| Pericarditis | |||||||||||||
| No | 2873 | – | – | – | – | – | – | – | – | ||||
| Yes | 187 | 0.94 | 0.72‒1.22 | 0.6 | 0.85 | 0.64‒1.14 | 0.3 | 1.52 | 0.98‒2.34 | 0.061 | 0.94 | 0.76‒1.16 | 0.6 |
| CCI score | |||||||||||||
| 0–2 | 894 | – | – | – | – | – | – | – | – | ||||
| 3–4 | 1002 | 1.47 | 1.25‒1.73 | < 0.001* | 1.36 | 1.15‒1.60 | < 0.001* | 1.39 | 1.01‒1.91 | 0.046* | 1.42 | 1.24‒1.61 | < 0.001* |
| 5+ | 1164 | 2.24 | 1.92‒2.63 | < 0.001* | 1.42 | 1.21‒1.67 | < 0.001* | 2.36 | 1.75‒3.19 | < 0.001* | 1.88 | 1.66‒2.14 | < 0.001* |
| Baseline MRA use | |||||||||||||
| No | 2019 | – | – | – | – | – | – | – | – | ||||
| Yes | 1041 | 1.2 | 1.06‒1.35 | 0.004* | 1.02 | 0.90‒1.17 | 0.7 | 1.24 | 1.00‒1.54 | 0.049* | 1.1 | 0.99‒1.22 | 0.071 |
| Baseline SGLT2i use | |||||||||||||
| No | 2453 | – | – | – | – | – | – | – | – | ||||
| Yes | 607 | 0.83 | 0.70‒0.97 | 0.022* | 1.06 | 0.90‒1.25 | 0.5 | 0.89 | 0.65‒1.23 | 0.5 | 0.93 | 0.81‒1.06 | 0.3 |
ATTR-CM transthyretin amyloid cardiomyopathy, BNP B-type natriuretic peptide, CCI Charlson Comorbidity Index, CI confidence interval, CVH cardiovascular-related hospitalization, HR hazard ratio, MRA mineralocorticoid receptor antagonist, NT-proBNP N-terminal pro-B-type natriuretic peptide, SGLT2i sodium-glucose cotransporter 2 inhibitor
*Statistically significant p value
aComposite endpoint includes CVH, outpatient worsening heart failure, or death
Discussion
Results from this retrospective observational study of patients with ATTR-CM in the USA using data from the Komodo Healthcare Map demonstrate that many tafamidis-treated patients with ATTR-CM still experience adverse disease outcomes in real-world practice. While both clinical trial and real-world evidence have shown benefits of tafamidis treatment, this analysis of a large, diverse US cohort provides insight into rates of hospitalization and disease outcomes that warrant further examination. Within 6 months of initiating tafamidis, the cumulative incidence of first CVH was 22%, increasing to 35% by 12 months. Similarly, the 12-month cumulative incidence of OWHF with oral diuretic intensification was 33% and mortality occurred in approximately 6% within the first year of therapy. Within 6 months of initiating tafamidis, the cumulative incidence of the composite endpoint event (CVH, OWHF, or death) was 37% and 53% within 12 months; median time to first composite endpoint event was 10.4 months (315 days). Across these three key outcomes and their composite endpoint, stratification by ATTR-CM type revealed no notable differences. As expected, patients with elevated NT-proBNP (> 3000 pg/mL, or BNP > 600 pg/mL) at baseline demonstrated markedly worse outcomes than those with low NT-proBNP in the descriptive analysis, with the cumulative incidence of hospitalizations nearly twice as high and mortality rates reaching 9.7% at 12 months. This finding underscores the prognostic value of NT-proBNP/BNP in the risk stratification of patients with ATTR-CM. Additionally, patients with baseline arrhythmias demonstrated increased hazard for CVH and the composite endpoint, which aligns with general understanding that this comorbidity is associated with poorer outcomes. Regarding baseline medications, SGLT2 use was associated with reduced hazard for CVH, while MRA use was associated with increased hazard, likely reflecting residual confounding by disease severity, as MRA use in real-world settings is often concentrated in patients with more advanced HF [20, 21]. However, it is important to note that in a real-world study examining outcomes of MRAs in elderly patients with HF with reduced ejection fraction and moderately impaired renal function, after adjusting for worsening renal function, MRA use did not increase the overall risk of all-cause mortality [22]. Our analysis was not able to accurately capture potassium dosing and blood levels, which may have affected outcomes.
The demographic characteristics observed in this cohort align with established patterns in ATTR-CM epidemiology. Patients with ATTRv-CM have a variable age of onset (30‒80 years) that is dependent on the mutation, while ATTRwt-CM typically presents around 75 years of age [3]. In two retrospective studies of patients diagnosed with ATTR-CM, approximately 72‒80% of Black or African American patients had ATTRv-CM [23, 24]. The results of our study are consistent, as we report that patients with ATTRv-CM were younger than patients with ATTRwt-CM (mean 72.3 years vs. 77.8 years) and that a larger proportion of patients with ATTRv-CM were Black or African American compared with ATTRwt-CM (55.0% vs. 27.5%). These demographic differences between ATTR-CM subtypes underscore the importance of genetic testing and tailored screening approaches across diverse populations. When we compared this real-world patient population to those of randomized controlled trials (RCTs) for ATTR-CM [8, 25, 26], patients in the Komodo database were similarly aged; however, the database included more women (20% vs. 8‒10%) and Black or African American patients (30% vs. 5‒15%) compared with the RCTs (Supplementary Material Table S5). This aligns with a lack of representation of women and minorities in RCTs, as observed in other publications [27, 28], and highlights a benefit of using real-world data to evaluate groups typically underrepresented. A direct comparison of outcomes between RCTs and the current analysis is not feasible given differences in study design and endpoint selection, and the lack of published data.
Non-invasive diagnosis of ATTR-CM has led to improved recognition at earlier stages, with improvements in survival in the placebo arms of amyloid trials over the past decade [11]. In this context, recent studies reporting real-world outcomes for tafamidis-treated patients with ATTR-CM provide valuable benchmarks for comparison. Previous observational studies have reported survival rates in tafamidis-treated patients of 89.3% at 12 months [13], 72.5–84.4% at 30 months [11, 29], and 76.8% at 42 months [11]; the current analysis found 93.8% survival at 12 months. While differences in study design, patient populations, and the later period of data capture in the present analysis may explain some variation [11, 13, 29], focusing primarily on overall survival may underestimate the complete burden of disease in patients treated with tafamidis.
The patterns of other disease outcomes observed in the current analysis support the generalizability of prior findings in tafamidis-treated patients to settings beyond specialized amyloidosis centers, while revealing potentially higher event rates in broader real-world populations. In a multicenter study from Italy of 683 patients treated with tafamidis, the composite endpoint of death or HF hospitalization requiring intravenous diuretics occurred in 9% of patients with ATTR-CM within 12 months of tafamidis initiation [30]; this analysis was limited to patients with New York Heart Association functional class I and II symptoms, as the Italian Medicines Agency restricts tafamidis use to this subgroup. In a single-center analysis of 238 tafamidis-treated patients with ATTR-CM who were followed for 12 months, 21% had oral diuretic intensification and 11% had NT-proBNP level increases of > 700 pg/mL and > 30% from baseline [14], compared with approximately 30% experiencing OWHF with oral diuretic intensification and 15% having the same threshold of NT-proBNP increases in the present analysis. Similarly, Zeldin et al. reported in a single-center retrospective analysis of 303 tafamidis-treated patients with ATTR-CM (mean follow-up of 3.3 years) that 11.6% experienced hospitalization for HF and 47.9% experienced worsening HF with outpatient diuretic intensification (defined as any initiation or increase in total daily dose of loop diuretic lasting > 30 days) [31]. Despite a shorter follow-up period of 12 months in the current study, the cumulative incidences of first HF-related hospitalizations and OWHF with oral diuretic intensification at 12 months were 30% and 33%, respectively. These findings confirm that worsening cardiac disease despite tafamidis treatment occurs across healthcare settings, while suggesting that patients in broader real-world populations may experience some disease outcomes at higher rates than those treated in specialized care environments.
Even with the treatment benefits of tafamidis in ATTR-CM, these findings highlight the importance of recognizing the risk of adverse disease outcomes such as CVH and worsening HF, particularly those with elevated NT-proBNP/BNP at baseline. Our analysis confirmed that certain clinical factors—including high baseline NT-proBNP, older age, presence of HF, chronic kidney disease, and other comorbidities—significantly increase the hazard for adverse outcomes across multiple endpoints. These identified risk factors provide prognostic information that may inform cardiac disease monitoring in tafamidis-treated patients, though their practical utility in guiding management strategies requires prospective validation. It should be noted that while earlier identification and treatment of patients can lead to improved outcomes [5, 33, 34], this is especially important for patients with high-risk disease. Moreover, while the current analysis cannot determine whether alternative therapeutic approaches may benefit patients receiving tafamidis, it underscores the need for additional studies in this area.
Strengths and Limitations
The primary strength of this study is its use of a large, nationally representative real-world database with tokenization to laboratory data, allowing examination of tafamidis outcomes across a diverse ATTR-CM population. Notably, 20% of patients in this analysis were female, which exceeds the proportion of female patients in pivotal clinical trials; women comprised only 8.7% in the ATTR-ACT tafamidis arm and 8.8% in the ATTRibute-CM acoramidis arm (Supplementary Material Table S5) [8, 25]. The sex distribution of this cohort—with male predominance aligning with known ATTR-CM epidemiology but including a larger proportion of female patients—may better reflect real-world practice and enhance generalizability to both sexes.
Several limitations warrant consideration when interpreting these findings. First, this analysis shares limitations intrinsic to medical claims database studies and naturally differs from RCTs in the type of data captured and reported [32]. The study did not account for disease duration before tafamidis initiation, which likely varies among patients and could influence observed outcomes, as newly diagnosed patients may respond differently than those with longer duration or advanced stage of the disease. Additionally, the study lacked clinical staging, functional assessments, and quality of life measurements that would provide a more comprehensive understanding of patient outcomes beyond hospitalization rates and biomarker data.
Data representation limitations include an overrepresentation of patients from the Northeast region (42.8%) with fewer from the West (9.9%), likely because large insurance providers prevalent in Western states (such as Kaiser Permanente) are not captured within the Komodo database. Patients may have had multiple insurers or received tafamidis through patient assistance programs, potentially resulting in misclassification of some treated patients as untreated. Furthermore, the Komodo Healthcare Map includes only individuals who have interacted with the medical system or have insurance coverage, potentially limiting representativeness of the entire ATTR-CM population. This study was conducted during a period that included the COVID-19 pandemic, which substantially disrupted routine healthcare delivery and utilization. During this time, when healthcare encounters were delayed or deferred, the initiation of new tafamidis use may have been reduced during the early months of the pandemic. As a result, some time-to-event outcomes may have been longer, and fewer patients may have initiated tafamidis during the pandemic years. However, because the current analysis spans both prepandemic and postpandemic years, it is expected that most healthcare encounters and patients initiating tafamidis were captured in this analysis.
Methodologic limitations include the lack of genomic data and the small proportion of patients with laboratory data available, restricting NT-proBNP stratification analyses and the ability to classify disease severity at baseline. Additionally, more patients ≥ 80 years of age had elevated NT-proBNP rather than low NT-proBNP; increased CVHs and mortality are inherently higher in older patients [29], potentially confounding the observed associations between NT-proBNP levels and outcomes. The study also relied on ICD-10-CM coding algorithms to classify ATTR-CM subtypes, which may have resulted in misclassification bias. The high proportion of CVH and HF-related hospitalization relative to all-cause hospitalization reflects the use of ICD-10-CM diagnosis codes in any position rather than the primary position only. This approach, while sensitive for capturing cardiovascular events, may overestimate hospitalizations directly caused by cardiovascular disease, as CV diagnoses once established are routinely coded on subsequent admissions, regardless of the primary indication for admission.
Conclusion
As the first approved treatment for ATTR-CM, tafamidis established the therapeutic role of TTR stabilization and continues to improve outcomes for patients with this progressive disease when compared with placebo use or no treatment. However, results from this large, national representative retrospective observational study provide comprehensive real-world evidence of disease outcomes after tafamidis initiation and demonstrate the need for additional therapeutic options, as shown by the continued unmet need of patients treated with tafamidis. Within 6 months of initiating tafamidis, the cumulative incidence of first CVH was 22%, and the 12-month cumulative incidence of OWHF with oral diuretic intensification was 33%. The prognostic significance of baseline NT-proBNP/BNP was confirmed across multiple disease outcomes, with patients having high baseline levels generally experiencing worse outcomes, as expected. As more therapeutic options become available, measuring the real-world effectiveness of therapies for ATTR-CM will be important to help inform treatment decisions.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
Medical Writing/Editorial Assistance
Editorial support was provided by Kavitha Kuppusamy, PhD, and Shweta Rane, PhD, CMPP, of BridgeBio Pharma, Inc. Medical writing and editorial assistance were provided by Robert Schupp, PharmD, CMPP, of The Lockwood Group (Stamford, CT), funded by BridgeBio Pharma, Inc.
Author Contributions
Daniel P. Judge, Margarita Udall, Andrew M. Rosen, Neil Lamarre, Elizabeth Nagelhout, Hanh Dung Dao, and Mathew S. Maurer participated in the conception and design of the study, statistical analysis and interpretation of data, and preparation of the manuscript. All named authors meet the International Committee of Medical Journal Editors criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
Funding
The study was funded by BridgeBio Pharma, Inc. (San Francisco, CA). The journal’s Rapid Service Fee was funded by BridgeBio Pharma, Inc. (San Francisco, CA).
Data Availability
The data that support the findings of this study were used under license from Komodo Health® and derived from the Komodo Healthcare Map®. Due to data use agreements and its proprietary nature, restrictions apply regarding the availability of the data. Further information is available from the sponsor of the study, BridgeBio Pharma, Inc., San Francisco, CA, USA (MedInfo@bridgebio.com).
Declarations
Conflict of Interest
Daniel P. Judge: Consultancy fees from Alnylam Pharmaceuticals, Attralus, Bayer, BridgeBio Pharma, Inc., Cytokinetics, Lexeo Therapeutics, Novo Nordisk, Rocket Pharmaceuticals, and Tenaya Therapeutics. Margarita Udall and Andrew M. Rosen: Employees and stockholders of BridgeBio Pharma, Inc. Margarita Udall is a stockholder of Pfizer, Inc. Neil Lamarre, Elizabeth Nagelhout, and Hanh Dung Dao: Contractors for BridgeBio Pharma, Inc. Mathew S. Maurer: for NIH R01HL139671 and R01AG081582-01, Alnylam Pharmaceuticals, BridgeBio Pharma, Inc. (formerly Eidos Therapeutics), Ionis Pharmaceuticals, Pfizer Inc., and Prothena Biosciences; consultant or advisor for Akcea Therapeutics, Alnylam Pharmaceuticals, AstraZeneca, Attralus, BridgeBio Pharma, Inc. (formerly Eidos Therapeutics), Intellia Therapeutics, Ionis Pharmaceuticals, Novo Nordisk, and Pfizer, Inc.
Ethical Approval
Because the Komodo Healthcare Map database contains anonymized, de-identified data that complies with the Health Insurance Portability and Accountability Act, and this study did not involve the collection, use, or transmittal of individually identifiable data, the study did not require ethics committee review or informed consent. Permission was obtained to access and analyze data.
Footnotes
Prior Presentation: Parts of this manuscript were presented at the European Society of Cardiology‒Heart Failure Congress (May 17–20, 2025; Belgrade, Serbia, & Online; Judge D, et al. Eur J Heart Fail. 2025;27(suppl 2):178).
References
- 1.Brito D, Albrecht FC, de Arenaza DP, et al. World Heart Federation consensus on transthyretin amyloidosis cardiomyopathy (ATTR-CM). Glob Heart. 2023;18(1):59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Porcari A, Razvi Y, Cappelli F, et al. Clinical phenotype and prognosis of asymptomatic patients with transthyretin cardiac amyloid infiltration. JAMA Cardiol. 2025;10(5):437–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ruberg FL, Grogan M, Hanna M, Kelly JW, Maurer MS. Transthyretin amyloid cardiomyopathy: JACC state-of-the-art review. J Am Coll Cardiol. 2019;73(22):2872–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kittleson MM, Ruberg FL, Ambardekar AV, et al. 2023 ACC expert consensus decision pathway on comprehensive multidisciplinary care for the patient with cardiac amyloidosis: a report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2023;81(11):1076–126. [DOI] [PubMed] [Google Scholar]
- 5.Writing Committee, Kittleson MM, Ambardekar AV, et al. Transthyretin cardiac amyloidosis evaluation and management: 2025 ACC concise clinical guidance. J Am Coll Cardiol. 2025. 10.1016/j.jacc.2025.09.004 [DOI] [PubMed]
- 6.Korosoglou G, Giusca S, Andre F, et al. Diagnostic work-up of cardiac amyloidosis using cardiovascular imaging: current standards and practical algorithms. Vasc Health Risk Manag. 2021;17:661–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.VYNDAMAX® (tafamidis) [package insert]. 2023.
- 8.Maurer MS, Schwartz JH, Gundapaneni B, et al.; for the ATTR-ACT Study Investigators. Tafamidis treatment for patients with transthyretin amyloid cardiomyopathy. N Engl J Med. 2018;379(11):1007–16. [DOI] [PubMed] [Google Scholar]
- 9.Damy T, Wang R, Maurer MS, Gillmore JD, Fontana M. Long-term efficacy of tafamidis in patients with transthyretin amyloid cardiomyopathy by National Amyloidosis Centre stage. Eur J Heart Fail. 2025. 10.1002/ejhf.3696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Maurer MS. A question in the era of multiple effective therapies for transthyretin amyloid cardiomyopathy (ATTR-CM): is transthyretin biology a driver of disease progression? J Card Fail. 2025;31(4):746–7. [DOI] [PubMed] [Google Scholar]
- 11.Garcia-Pavia P, Kristen AV, Drachman B, et al.; on behalf of the THAOS investigators. Survival in a real-world cohort of patients with transthyretin amyloid cardiomyopathy treated with tafamidis: an analysis from the Transthyretin Amyloidosis Outcomes Survey (THAOS). J Card Fail. 2025;31(3):525–33. [DOI] [PubMed] [Google Scholar]
- 12.Ioannou A, Cappelli F, Emdin M, et al. Stratifying disease progression in patients with cardiac ATTR amyloidosis. J Am Coll Cardiol. 2024;83(14):1276–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ghoneem A, Bhatti AW, Khadke S, et al. Real-world efficacy of tafamidis in patients with transthyretin amyloidosis and heart failure. Curr Probl Cardiol. 2023;48(6):101667. [DOI] [PubMed] [Google Scholar]
- 14.Bampatsias D, Wardhere A, Zeldin L, et al. Cardiac disease monitoring measures in patients with transthyretin amyloid cardiomyopathy treated with tafamidis. Heart. 2025;111(14):651–6. [DOI] [PubMed] [Google Scholar]
- 15.Gillmore JD, Damy T, Fontana M, et al. A new staging system for cardiac transthyretin amyloidosis. Eur Heart J. 2018;39(30):2799–806. [DOI] [PubMed] [Google Scholar]
- 16.Kasahara S, Sakata Y, Nochioka K, et al. Conversion formula from B-type natriuretic peptide to N-terminal proBNP values in patients with cardiovascular diseases. Int J Cardiol. 2019;280:184–9. [DOI] [PubMed] [Google Scholar]
- 17.R Development Core Team. R Foundation for Statistical Computing; 2010. R: A language and environment for statistical computing. 2010.
- 18.Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22. [PMC free article] [PubMed] [Google Scholar]
- 19.Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw. 2011;39(5):1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Flatt DM, Brown MC, Mizeracki AM, King BJ, Weber KT. Mineralocorticoid receptor antagonists in the management of heart failure and resistant hypertension: a review. JAMA Cardiol. 2016;1(5):607–12. [DOI] [PubMed] [Google Scholar]
- 21.Jhund PS, Talebi A, Henderson AD, et al. Mineralocorticoid receptor antagonists in heart failure: an individual patient level meta-analysis. Lancet. 2024;404(10458):1119–31. [DOI] [PubMed] [Google Scholar]
- 22.Jonsson Holmdahl A, Norberg H, Valham F, Bergdahl E, Lindmark K. Mineralocorticoid receptor antagonists use in patients with heart failure and impaired renal function. PLoS ONE. 2021;16(10):e0258949. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Martyn T, Saef J, Dey AR, et al. Racial and genetic differences in presentation of transthyretin amyloid cardiomyopathy with impaired left ventricular function. JACC Heart Fail. 2022;10(9):689–91. [DOI] [PubMed] [Google Scholar]
- 24.Shankar B, Yanek L, Jefferson A, et al. Race and socioeconomic status impact diagnosis and clinical outcomes in transthyretin cardiac amyloidosis. JACC CardioOncol. 2024;6(3):454–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Gillmore JD, Judge DP, Cappelli F, et al. Efficacy and safety of acoramidis in transthyretin amyloid cardiomyopathy. N Engl J Med. 2024;390(2):132–42. [DOI] [PubMed] [Google Scholar]
- 26.Fontana M, Berk JL, Gillmore JD, et al. Vutrisiran in patients with transthyretin amyloidosis with cardiomyopathy. N Engl J Med. 2025;392(1):33–44. [DOI] [PubMed] [Google Scholar]
- 27.Patel RK, Ioannou A, Razvi Y, et al. Sex differences among patients with transthyretin amyloid cardiomyopathy - from diagnosis to prognosis. Eur J Heart Fail. 2022;24(12):2355–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Spencer-Bonilla G, Njoroge JN, Pearson K, Witteles RM, Aras MA, Alexander KM. Racial and ethnic disparities in transthyretin cardiac amyloidosis. Curr Cardiovasc Risk Rep. 2021. 10.1007/s12170-021-00670-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Masri A, Bhattacharya P, Medoff B, et al. A multicenter study of contemporary long-term tafamidis outcomes in transthyretin amyloid cardiomyopathy. JACC CardioOncol. 2025;7(3):282–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Sinigiani G, Sanna GD, Aimo A, et al. Outcome and disease progression in NYHA functional class I and II patients with wild-type transthyretin cardiomyopathy treated with tafamidis. JACC Heart Fail. 2025;13(8):102549. [DOI] [PubMed] [Google Scholar]
- 31.Zeldin L, Eichler JBS, Teruya SL, et al. Outpatient worsening of heart failure and mortality in transthyretin amyloid cardiomyopathy. Eur J Heart Fail. 2024. 10.1002/ejhf.3540. [DOI] [PubMed] [Google Scholar]
- 32.Tyree PT, Lind BK, Lafferty WE. Challenges of using medical insurance claims data for utilization analysis. Am J Med Qual. 2006;21(4):269–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Garcia-Pavia P, Rapezzi C, Adler Y, et al. Diagnosis and treatment of cardiac amyloidosis: a position statement of the ESC Working Group on Myocardial and Pericardial Diseases. Eur Heart J. 2021;42(16):1554–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Elliott P, Drachman BM, Gottlieb SS, et al. Long-term survival with tafamidis in patients with transthyretin amyloid cardiomyopathy. Circ Heart Fail. 2022;15(1):e008193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Testani JM, Brisco MA, Turner JM, et al. Loop diuretic efficiency: a metric of diuretic responsiveness with prognostic importance in acute decompensated heart failure. Circ Heart Fail. 2014;7(2):261–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Konerman MC, Bozaan DA, Adie S, et al. Michigan Medicine clinical care guidelines. Michigan Medicine inpatient diuretic guideline for patients with acute decompensated heart failure. Ann Arbor: Michigan Medicine University of Michigan © Regents of the University of Michigan; 2022. [PubMed] [Google Scholar]
- 37.Bettencourt P, Azevedo A, Pimenta J, Friões F, Ferreira S, Ferreira A. N-terminal-pro-brain natriuretic peptide predicts outcome after hospital discharge in heart failure patients. Circulation. 2004;110(15):2168–74. [DOI] [PubMed] [Google Scholar]
- 38.Perez-Downes J, Palacio C, Ibrahim S, Shelley P, Miller A, Reddy P. Prognostic utility of NT-proBNP greater than 70,000 pg/mL in patients with end stage renal disease. J Geriatr Cardiol. 2018;15(7):476–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study were used under license from Komodo Health® and derived from the Komodo Healthcare Map®. Due to data use agreements and its proprietary nature, restrictions apply regarding the availability of the data. Further information is available from the sponsor of the study, BridgeBio Pharma, Inc., San Francisco, CA, USA (MedInfo@bridgebio.com).