We performed a retrospective cohort study using the TriNetX US Collaborative Network, a federated database comprising deidentified electronic health records of over 120 million patients across 69 health care organizations. ^, Participating organizations include academic centers, community hospitals, and ambulatory offices. Queries and analyzes performed in this study used the platform’s data on demographics, diagnoses based on the International Statistical Classification of Diseases and Related Health Problems, Ninth and Tenth Revision, Clinical Modification (ICD-9-CM and ICD-10-CM), medication prescriptions based on RxNorm codes, and laboratory values based on TriNetX curated codes. The deidentified data within the TriNetX platform is compliant with the standard defined in section 164.514(a) of the Health Insurance Portability and Accountability Act Privacy Rule. Accordingly, the absence of human subject interaction exempted this study from review by the local institutional review board since no informed consent was required. The study adhered to the tenets of the Declaration of Helsinki and was in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines.

We identified patients with ATTR-CM by querying the TriNetX database for adult patients from October first, 2015 to January first, 2024 with a diagnosis of amyloidosis and heart failure, excluding those with a diagnosis of light chain amyloidosis, multiple myeloma, other known diseases associated with light chain deposition, or those who received treatments for light chain amyloidosis. A patient’s index event was defined as the point in time in which the patient met the above inclusion criteria. Patients were subdivided based on the presence of ICD-10-CM codes associated with “high-risk” SDOH (ICD-10-CM Z55-65) from 1 year prior to the index event to any time after the index event; individuals without these Z-codes were considered to have “low-risk” SDOH. The Z-codes capture adverse conditions related to education and literacy, employment, environmental exposures, housing, food insecurity, economic instability, and social support and upbringing. Demographics, body mass index, medical comorbidities, smoking history, prescribed medications, laboratory findings, and echocardiographic data were collected from any time prior to the index event. All pertinent codes used for querying patients and collecting baseline characteristics are provided in Supplementary Tables S1 and S2 .

Understanding that Black American patients with ATTR-CM have a higher prevalence of the V142I that promotes a more aggressive form of the disease, ^, the high- and low-risk SDOH cohorts were further stratified by self-identified Black or White race, forming 4 subgroups: (1) Black and high-risk SDOH, (2) Black and low-risk SDOH, (3) White and high-risk SDOH, and (4) White and low-risk SDOH. Figure 1 and the Central Illustration offer a visualization of patient selection for the corresponding cohorts. Pairwise subgroup comparisons were conducted among the 4 subgroups as detailed below.

Flow chart of patient selection. ATTR-CM = transthyretin amyloid cardiomyopathy; SDOH = social determinants of health.

Among patients with ATTR-CM, Z-codes documenting high-risk SDOH were associated with higher rates of a composite endpoint of hospitalization or death, even after propensity score matching for several baseline characteristics. Among the subgroup with high-risk SDOH, patients who identified as Black versus White race experienced similar rates of event-free survival. However, among patients with presumed low-risk SDOH, Black patients had significantly higher adverse clinical events, driven primarily by higher rates of hospitalizations. ATTR-CM: transthyretin cardiac amyloidosis; SDOH: social determinants of health.

The follow-up period was from 1 day to 3 years following the index event. The primary endpoint was defined as a composite of all-cause mortality and hospitalization. Secondary endpoints were individual components of the primary composite endpoint. We also identified the number of patients within subgroups who received ATTR amyloidosis-specific treatments, including tafamidis or patisiran after the index event.

Categorical variables are represented as n (%). Continuous data is expressed as mean (standard deviation [SD]). For pairwise comparisons of baseline characteristics, Chi-square tests and independent sample t tests were used for categorical variables and continuous variables, respectively. Cox proportional hazards models were performed for subgroup comparisons after adjusting for covariates to determine hazard ratios (HRs) and associated 95% confidence intervals (CI). To allow for inclusion of patients in the setting of multiple measurements or missing data, relevant continuous covariables were assigned into clinically relevant cutoffs. ^,,

Propensity-score matching (PSM) was then performed for baseline characteristics using a 1:1 greedy nearest neighbor algorithm. Variables used for PSM included age, sex, body mass index, essential hypertension, hyperlipidemia, chronic kidney disease, type 2 diabetes mellitus, atrial fibrillation or flutter, chronic obstructive pulmonary disease, and tobacco use. Propensity score density curves between groups were assessed using absolute standardized mean differences (SMDs), with a SMD <0.1 being used to define well-matched characteristics. Survival analysis between matched cohorts was assessed via Kaplan-Meier curves and log-rank test to determine differences in median event-free survival of the composite outcome and individual outcomes. The incidence of ATTR amyloidosis-specific medication use was compared using Chi square test. Findings were considered significant if p < 0.05. All statistical analyzes were conducted using the TriNetX Platform Analytics tool.

In total, 22,303 adult patients met the criteria for diagnosis of ATTR-CM, of whom 1,874 (8.4%) were documented to have high-risk SDOH. Clinical characteristics, including patient demographics, medical comorbidities, prescribed medications, laboratory values, and echocardiographic data, are compared between both cohorts and reported in Table 1 . Overall, patients from a high-risk SDOH were more likely to be of younger age, female gender, and Black race. These patients exhibited a higher prevalence of several established atherosclerotic risk factors and were more likely to be prescribed renin-angiotensin-aldosterone system inhibitors, beta blockers, sodium-glucose cotransporter-2 inhibitors, calcium channel blockers, and aldosterone antagonists. Laboratory studies demonstrated higher natriuretic peptide levels, and LVEF was slightly lower in the high-risk SDOH subgroup.

Within 3 years of diagnosis, 1,387 (74.0%) patients with high-risk SDOH experienced the composite endpoint, significantly higher than those with low-risk SDOH prior to any matching (11,745 [57.5%]; log-rank Chi-square = 539.56, p < 0.001, Supplementary Figure S1 ).

Using the unmatched high-risk SDOH and low-risk SDOH cohorts, a multivariate Cox proportional hazards model was performed, adjusting for the same baseline characteristics collected in Table 1 . High-risk SDOH was found to be strongly associated with risk of the composite outcome, even after accounting for all other covariates (HR = 1.498, 95% CI [1.411 to 1.591], p < 0.001, Table 2 ).

After matching demographics, body mass index, and comorbidities, there were 1,863 patients each included in the high-risk SDOH and low-risk SDOH subgroups, with no significant difference in baseline characteristics ( Supplementary Table S3 ). The mean follow-up was 556 (385) days and 720 (393) days for high-risk SDOH and low-risk SDOH patients, respectively. Within a 3-year follow up period, there were 1,377 (73.9%) patients with high-risk SDOH who reached the composite outcome, including 1,234 (66.2%) patients who were hospitalized and 547 (29.4%) who expired. Patients in the high-risk SDOH cohort had a median event-free survival of 83 days, significantly lower compared to those in the matched low-risk SDOH cohort (log-rank Chi-square = 92.611, p < 0.001, Figure 2 ). Individual components of the composite endpoint were experienced at significantly higher rates within 3 years in the high-risk SDOH subgroup (hospitalization: log-rank Chi-square = 83.338, p < 0.001; death: log-rank Chi-square = 4.515, p = 0.034). Notably, 215 (11.5%) patients with high-risk SDOH received ATTR amyloidosis-specific medications, which did not significantly differ from the 229 (12.3%) low-risk SDOH patients who received treatment (p = 0.479).

Event-free survival stratified by high- versus low-risk SDOH after propensity score matching. After propensity score matching, patients with high-risk SDOH had significantly higher instances of the composite endpoint, as well as its individual components. SDOH = social determinants of health.

Among the high-risk SDOH cohort, the multivariate Cox proportional hazards model revealed that Black race was not a significant predictor of risk of composite outcome (HR = 1.211, 95% CI [0.944 to 1.325], p = 0.129, Table 3 ). Only tobacco use, diagnosis of chronic kidney disease, chronic pulmonary obstructive disease, BNP value ≥100 pg/dL, and LVEF <50% met statistical significance in this subset analysis.

Table 3

Multivariate Cox proportional hazards model among high-risk SDOH subgroup

Covariates	HR	95% CI	p-value
Black race	1.211	0.944-1.325	0.129
Demographics
Age at index event (years)	1.005	0.997-1.010	0.190
Male	0.994	0.882-1.121	0.924
Medical history
Essential hypertension	0.882	0.706-1.102	0.269
Hyperlipidemia	0.947	0.812-1.103	0.480
Atrial fibrillation or atrial flutter	1.053	0.930-1.193	0.413
Type 2 diabetes mellitus	0.962	0.849-1.089	0.538
Chronic kidney disease	1.406	1.226-1.614	<0.001
Chronic obstructive pulmonary disease	1.178	1.041-1.333	0.009
Tobacco use	1.265	1.082-1.481	0.003
Medications
ACE inhibitors	0.928	0.821-1.051	0.240
Angiotensin II inhibitors	0.932	0.823-1.054	0.260
Beta blockers	0.978	0.802-1.192	0.823
SGLT2 inhibitors	1.017	0.876-1.181	0.823
NDHP calcium channel blockers	1.044	0.923-1.182	0.489
DHP calcium channel blockers	0.974	0.856-1.108	0.686
Potassium sparing diuretics	0.891	0.786-1.009	0.069
Tafamidis	0.805	0.660-0.981	0.032
Patisiran	0.667	0.328-1.358	0.264
Laboratory values
Creatinine
≥1 mg/dL	1.013	0.822-1.249	0.899
<1 mg/dL	1.043	0.906-1.202	0.556
Brain natriuretic peptide
≥100 pg/dL	1.302	1.150-1.475	<0.001
<100 pg/dL	0.968	0.836-1.123	0.671
Pro-brain natriuretic peptide
≥125 mg/dL	1.116	0.986-1.265	0.083
<125 mg/dL	1.032	0.809-1.319	0.795
Left ventricular ejection fraction
≥50%	1.042	0.922-1.177	0.505
<50%	1.167	1.018-1.338	0.027

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