Nationwide patterns of cardiac-related mortality in amyloidosis cases: an epidemiologic study

Abstract

Introduction

Cardiac amyloidosis is frequently overlooked, leading to delayed diagnosis and underestimation of its true impact on cardiac mortality.

Methods

Using CDC WONDER (1999–2020), we identified U.S. decedents aged ≥ 25 years with amyloidosis (ICD-10 E85) as the underlying cause of death and at least one cardiac-related condition as a contributing cause. Age-adjusted mortality rates (AAMRs) per 100,000 population were calculated, and trends were analyzed using Joinpoint regression and autoregressive integrated moving average (ARIMA) forecasting through 2040.

Results

Among 16,673 deaths, the AAMR rose from 0.261 in 1999 to 0.608 in 2020, with inflection points in 2012 and 2017 and an overall annual increase of 3.96%. Mortality was higher in men (0.537 vs. 0.210) and Black individuals (0.83 vs. 0.32), with a sharp post-2017 rise, especially among Black decedents. Geographic disparities were also observed, with the highest rates in the Northeast and urban areas. Most deaths occurred in hospitals (43.2%) or at home (31.4%), and mortality rates peaked in those aged 85 and older. Forecasting models project continued increases in AAMR through 2040, reaching approximately 2.0 overall, with especially elevated rates among Black (≈ 5.9) and male (≈ 3.8) decedents.

Conclusion

Cardiac mortality in amyloidosis has risen sharply since 2017, likely driven by improved ATTR-CM detection through non-biopsy imaging and guideline adoption. Disparities by sex, race, and geography persist, reflecting uneven diagnostic capacity concentrated in urban and Northeastern centers. Forecasts project continued increases, particularly among Black and male individuals, underscoring the need for more equitable access to diagnosis and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-025-05353-9.

Introduction

Cardiac amyloidosis, primarily comprising light-chain (AL) and transthyretin (ATTR) subtypes, represents a significant contributor to cardiovascular morbidity and mortality. Historically underdiagnosed, its recognition has markedly improved with the advent and increased utilization of advanced imaging modalities such as technetium-labeled scintigraphy and cardiac magnetic resonance imaging. Moreover, the therapeutic landscape has evolved substantially with the introduction of disease-modifying agents—such as tafamidis for transthyretin amyloid cardiomyopathy (ATTR-CM) and daratumumab-based regimens for AL amyloidosis—offering improved clinical outcomes. These advancements underscore the necessity for comprehensive epidemiological surveillance to assess trends in population-level mortality associated with cardiac amyloidosis [1–4].

National vital statistics provide a robust framework for monitoring mortality trends and identifying disparities over time when analyzed using standardized epidemiological methods. The use of age-adjusted mortality rates (AAMRs), standardized to the 2000 U.S. population, facilitates valid comparisons across demographic groups and temporal periods. Furthermore, Joinpoint regression analysis allows for the detection of statistically significant changes in trend slopes, offering insights into temporal inflection points [5, 6].

In this context, we performed a comprehensive, nationwide analysis of cardiac-related mortality among decedents with amyloidosis in the United States from 1999 to 2020, utilizing data from the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) database. Amyloidosis was identified as the underlying cause of death, with cardiac involvement defined by a prespecified set of multiple-cause-of-death codes related to cardiac conditions. We computed AAMRs, delineated trend segments using Joinpoint regression, and examined disparities stratified by sex, race, ethnicity, geographic region, urbanization level, and place of death. Additionally, we employed machine learning techniques to forecast future AAMRs, with the aim of informing public health planning and advancing strategic care delivery for cardiac amyloidosis.

Methods

Study design and data source

We conducted a nationwide, population-based observational study (1999–2020) using CDC WONDER Multiple Cause of Death data for U.S. decedents aged ≥ 25 years. Amyloidosis deaths were identified when amyloidosis was coded as the underlying cause of death (UCD: ICD-10 E85). Cardiac involvement was defined when ≥ 1 prespecified multiple-cause-of-death (MCD) cardiac code was present: cardiomyopathy (I42.0, I42.5, I42.8, I42.9); heart failure/ill-defined heart disease (I50, I51.5, I51.6, I51.8, I51.9); conduction and arrhythmias (I44, I45, I47, I48, I49); ischemic heart disease (I20–I25); pericardial disease (I31.0, I31.1, I31.3, I31.8, I31.9); and valvular disease seen in cardiac amyloidosis (I34.0, I35.0, I36.1).

Valvular codes were included a priori because mitral/tricuspid regurgitation is common in cardiac amyloidosis, and wild-type transthyretin amyloidosis frequently coexists with degenerative aortic stenosis in older adults, particularly in TAVR/SAVR cohorts [7, 8].

Because the CDC WONDER dataset is publicly available and fully de-identified, this study was exempt from Institutional Review Board (IRB) review. The study adhered to the principles of the Declaration of Helsinki and followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [9].

Outcome variable

The primary outcome was the AAMR of cardiac-related deaths among amyloidosis decedents with UCD E85; deaths were counted as cardiac-related if any of the above MCD cardiac codes appeared on the death certificate.

Data extraction and variables

Records for individuals aged ≥ 25 years were extracted from the CDC WONDER Multiple Cause of Death database for the years 1999–2020. Stratification was performed by sex (male, female), race (White, Black, Asian/Pacific Islander, American Indian/Alaska Native), Hispanic origin (Hispanic, non-Hispanic), and age (categorized into seven strata: <35, 35–44, 45–54, 55–64, 65–74, 75–84, and ≥ 85 years).

Geographic variables included state of residence, U.S. Census region (Northeast, Midwest, South, West), and urbanization level, based on the 2013 National Center for Health Statistics (NCHS) classification: large central metro, large fringe metro, medium metro, small metro, micropolitan, and noncore (rural). Place of death was also extracted and categorized as: inpatient facility, home, nursing home/long-term care, and others. Because the CDC mortality database reports age in predefined groupings (e.g., 15–24 years) and does not provide data specifically for individuals aged 18 years and older, the analysis was restricted to decedents aged 25 years and older.

Age-adjusted mortality rates (AAMRs) per 100,000 population were calculated using the direct standardization method to the 2000 U.S. standard population. Denominator estimates were based on U.S. Census intercensal population estimates.

Statistical analysis

Crude and age-adjusted mortality rates were computed per 100,000 population, with 95% confidence intervals (CIs). Demographic and geographic variables, including sex, race/ethnicity, urbanization level, and census region stratified analyses. All trend and forecasting models (Joinpoint and ARIMA) were restricted to 1999–2020 for internal consistency.

Temporal trends in AAMRs from 1999 to 2020 were assessed using the Joinpoint Regression Program (version 4.9.1.0; National Cancer Institute). Log-linear segmented models with up to four joinpoints were selected using the Bayesian Information Criterion (BIC). Annual percent changes (APCs) and average annual percent changes (AAPCs) were calculated, with empirical-quantile 95% confidence intervals derived from 5,001 bootstrap resamples. Statistical significance was defined as a two-sided p-value < 0.05. In compliance with CDC data privacy rules, any cell count with fewer than 20 deaths was suppressed.

To project future trends, we applied overall, race- and sex-specific Autoregressive Integrated Moving Average (ARIMA) models to annual AAMR data using data from 1999 to 2020. ARIMA [1, 2] models were fitted using maximum likelihood estimation. Model adequacy was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and residual diagnostics, including the Ljung–Box test for autocorrelation and the Jarque–Bera test for normality. All statistical analyses and visualizations were conducted in Python using the Google Collaborator environment. Mortality forecasts were extended through 2040 with 95% forecast intervals. Due to the transition from bridged-race to single-race reporting in CDC WONDER beginning in 2021, data from 2021 to 2023 were excluded from the Joinpoint regression analysis to preserve methodological consistency. Accordingly, both Joinpoint and ARIMA models were limited to data from 1999 to 2020 to avoid reader confusion.

Results

Overall mortality

Between 1999 and 2020, there were 16,673 deaths among U.S. decedents aged ≥ 25 years with amyloidosis as the underlying cause (ICD-10 E85) and at least one cardiac multiple-cause code. The overall AAMR was 0.35 (95% CI: 0.34–0.35), increasing from 0.26 in 1999 to 0.61 in 2020; men had a higher AAMR than women (0.54 vs. 0.21).

Urbanization and regional variation

AAMRs by 2013 NCHS urbanization: Large Central Metro 0.39; Large Fringe Metro 0.35; Medium Metro 0.31; Small Metro 0.32; Micropolitan (non-metro) 0.31; Noncore (non-metro) 0.28. By U.S. Census region, rates were highest in the Northeast and Midwest and lowest in the South (Northeast 0.40; Midwest 0.40; West 0.36; South 0.27). These patterns are illustrated in Fig. 1.

Fig. 1.

Geographic variation in age-adjusted cardiac-related mortality rates among amyloidosis decedents (1999–2020), by U.S. Census region

Racial and ethnic disparities

AAMRs: Black or African American 0.83; White 0.32; Asian or Pacific Islander 0.18; American Indian or Alaska Native 0.14. The Black-to-White rate ratio was 2.59. By ethnicity, non-Hispanic individuals had a higher AAMR than Hispanic individuals (0.35 vs. 0.21).

Geographic variation by state

State-level AAMRs varied widely. Highest: District of Columbia 0.93; Vermont 0.82; Minnesota 0.73; Rhode Island 0.64; Washington 0.55. Lowest: Arkansas 0.14; Louisiana 0.15; Nevada 0.16; Alabama 0.17; Mississippi 0.18. Figure 2 illustrates the ten states with the highest and lowest AAMRs, highlighting the geographic disparities in mortality outcomes.

Fig. 2.

State-level age-adjusted cardiac-related mortality rates per 100,000 population among amyloidosis decedents (1999–2020), showing the ten states with the highest rates and the ten with the lowest rates

Age-Specific crude mortality rates

Crude mortality rates increased steeply with age. Among individuals aged 25–34 years, the rate was 0.00, rising to 0.02 in the 35–44 age group, 0.10 in the 45–54 group, and 0.32 among those aged 55–64. The rate continued to climb in older groups, reaching 0.86 in the 65–74 age group, 1.92 among those aged 75–84, and peaking at 2.46 for individuals aged 85 and older.

Place of death

Among the 16,673 deaths examined, 43.20% occurred in inpatient medical facilities, followed by 31.40% at home and 9.80% in nursing homes or long-term care facilities. An additional 15.5% of deaths occurred in other places.

Table 1 presents the demographic characteristics of decedents with cardiac amyloidosis (Table 1).

Table 1.

Baseline characteristics of amyloidosis decedents

Characteristic	Category	Rate*
Sex	Female	0.21
	Male	0.54
Race	American Indian or Alaska Native	0.14
	Asian or Pacific Islander	0.18
	Black or African American	0.83
	White	0.32
Hispanic Origin	Hispanic or Latino	0.21
	Not Hispanic or Latino	0.35
Urbanization	Large central metro	0.39
	Large fringe metro	0.35
	Medium metro	0.31
	Small metro	0.32
	Micropolitan (nonmetro)	0.31
	Noncore (nonmetro)	0.28
Census Region	Northeast	0.4
	Midwest	0.4
	South	0.27
	West	0.36
Age Group	< 35 years	0.002
	35–44 years	0.024
	45–54 years	0.10
	55–64 years	0.32
	65–74 years	0.86
	75–84 years	1.92
	85 + years	2.46
Place of Death	Medical Facility – Inpatient	7,206 (43.2%)
	Decedent’s home	5,242 (31.4%)
	Nursing home/long-term care	1,639 (9.8%)
	Others	2,586 (15.5%)

*Rate represents the age-adjusted mortality rate (AAMR), except for age groups where it represents the crude mortality rate, and for place of death, where it represents n (%)

Temporal trends: overall joinpoint analysis

Joinpoint regression identified two inflection points in the overall AAMR at 2012 (95% CI: 2010–2013) and 2017 (95% CI: 2013–2018). From 1999 to 2012, the AAMR increased non-significantly by 0.62% per year (95% CI: −0.97 to 1.66; p = 0.20). Between 2012 and 2017, the annual percent change accelerated to 7.41% (95% CI: 0.12 to 9.58; p = 0.04). From 2017 to 2020, the rate increased sharply at 13.43% per year (95% CI: 9.85 to 17.97; p < 0.01). The average annual percent change (AAPC) for the full study period (1999–2020) was 3.96% (95% CI: 3.59–4.32; p < 0.01), indicating a sustained upward trajectory, with a marked surge after 2017.

Temporal trends by race

Among Black or African American decedents, two inflection points were observed in 2007 (95% CI: 2002–2011) and 2018 (95% CI: 2012–2018). The AAMR remained relatively stable between 1999 and 2007, with an APC of − 0.52% (95% CI: −8.79 to 2.40; p = 0.61). From 2007 to 2018, mortality rose at a rate of 6.08% per year (95% CI: 3.00 to 8.19; p = 0.03), followed by a steep increase of 20.24% per year between 2018 and 2020 (95% CI: 9.32 to 26.80; p < 0.01). The AAPC for Black individuals from 1999 to 2020 was 4.76% per year (95% CI: 3.88–5.58; p < 0.01). Among White decedents, a single joinpoint was identified in 2014 (95% CI: 2014–2016). The APC during 1999–2014 showed a non-significant increase of 0.71% per year (95% CI: −0.37 to 1.63; p = 0.15), followed by a significant uptick of 11.39% per year from 2014 to 2020 (95% CI: 8.93 to 16.78; p < 0.01). The AAPC for White individuals over the entire period was 3.65% (95% CI: 3.16–4.23; p < 0.01). These patterns parallel the aggregate trends, with higher long-term growth among Black individuals.

Temporal trends by sex

For female decedents, joinpoints were detected in 2011 (95% CI: 2001–2014) and 2017 (95% CI: 2013–2018). From 1999 to 2011, AAMRs declined slightly at − 1.06% per year (95% CI: −4.55 to 0.31; p = 0.06). Between 2011 and 2017, there was a modest but non-significant increase of 3.65% per year (95% CI: −0.84 to 7.35; p = 0.10), followed by a sharp rise of 13.56% per year between 2017 and 2020 (95% CI: 8.08 to 20.90; p < 0.01). The overall AAPC for females from 1999 to 2020 was 2.25% (95% CI: 1.70–2.74; p < 0.01). Among males, joinpoints were identified in 2012 (95% CI: 2001–2013) and 2017 (95% CI: 2013–2018). From 1999 to 2012, the increase in AAMR was not statistically significant at 1.62% per year (95% CI: −1.36 to 6.30; p = 0.12), while between 2012 and 2017 the rate rose significantly at 8.32% per year (95% CI: 0.06 to 10.70; p = 0.05). This was followed by an even steeper rise of 14.54% per year from 2017 to 2020 (95% CI: 10.52 to 19.80; p < 0.01). The AAPC for males was 4.95% over the full study period (95% CI: 4.50–5.46; p < 0.01), indicating a steeper long-run slope than in females.

Figure 3 presents the results of all joinpoint regression analyses conducted in the study.

Fig. 3.

Multiple Joinpoint Models of Age-Adjusted Cardiac Mortality Rates by Race and Sex (1999–2020). Panel A displays race-specific Joinpoint models for Black or African American and White decedents and the overall cohort. Panel B shows sex-specific Joinpoint models for female and male decedents and the overall cohort

Forecasting

Using ARIMA [1, 2] on annual AAMR, the overall rate was 0.6 per 100,000 in 2020 and is projected to rise to 1.3 (95% CI, 0.8–1.8) in 2030 and 2.0 (0.7–3.2) in 2040. By race, White individuals increase from 0.5 in 2020 to 1.2 (0.8–1.7) in 2030 and 1.9 (0.7–3.1) in 2040, while Black or African American individuals increase from 1.5 to 3.7 (1.8–5.7) and 5.9 (0.8–11.0), respectively. By sex, males rise from 1.0 in 2020 to 2.4 (1.5–3.3) in 2030 and 3.8 (1.4–6.3) in 2040, whereas females rise from 0.3 to 0.6 (0.3–0.9) and 0.9 (0.3–1.5).

Figure 4 visualizes all forecasting projections.

Fig. 4.

Sex-, race-, and overall age-adjusted mortality rates (1999–2020) with ARIMA [1, 2] forecasts through 2040. Panels display observed AAMRs (solid orange line), point forecasts (blue dashed line), and 95% forecast intervals (shaded area)

Discussion

The temporal pattern in our study, a gradual rise in age-adjusted mortality through the 2000 s followed by a sharper increase after 2017, likely reflects a confluence of improved case recognition and delayed diffusion of effective therapies. Beginning in the mid-to-late 2010 s, bone-avid tracer scintigraphy enabled reliable non-biopsy diagnosis of transthyretin cardiac amyloidosis (ATTR-CM) in patients without a monoclonal gammopathy, which widened the diagnostic funnel and plausibly increased the recording of amyloidosis on death certificates that already listed cardiomyopathy, heart failure, or arrhythmias [10]. Non-biopsy diagnosis became increasingly established during this period, coinciding with early reports of tafamidis’ efficacy in hereditary ATTR polyneuropathy (ATTR-PN) in 2012, which preceded later efficacy data in ATTR cardiomyopathy reported in 2018 [3, 4, 11]. The timing of this diagnostic shift, first defined by Gillmore and colleagues and subsequently reinforced in practice recommendations and updated algorithms, is concordant with the joinpoints we detected [3].

Recent studies of elderly patients referred for transcatheter aortic valve replacement (TAVR) report a prevalence of ATTR in approximately 13–16%, often referred to as “AS-amyloid.” Early outcomes suggest that TAVR provides comparable benefits in these patients relative to those with isolated AS, reinforcing the rationale for routine ATTR screening in severe AS care pathways because identifying ATTR-CM does not preclude the benefits of TAVR — it enables more personalized care without denying effective treatment. Increased screening for ATTR within AS programs during the late 2010 s may therefore have contributed modestly to the observed mortality uptick by increasing diagnostic ascertainment rather than disease lethality per se [7, 8, 12].

Therapeutic advances emerged during the same era but would be expected to influence national mortality only with a lag. Tafamidis reduced the composite of all-cause mortality and cardiovascular hospitalization in ATTR-ACT and, more recently, attenuated deterioration in ventricular function [4]; however, payer coverage, cost, and real-world uptake after approval likely limited the near-term population impact before 2020 [13]. Newer agents have since expanded options; the stabilizer acoramidis improved a hierarchical outcome incorporating mortality, hospitalizations, biomarkers, and functional capacity; gene-silencing therapies preserved function at 12 months with patisiran and lowered risks of death and recurrent cardiovascular events with vutrisiran over longer follow-up [14–17]. For immunoglobulin light-chain (AL) amyloidosis with cardiac involvement, daratumumab-CyBorD produced deeper hematologic and organ responses and is now a standard first-line regimen [18]. Although recognition outpaced therapeutic reach in our study window, the therapeutic landscape is still advancing [19].

Observed disparities by race were large and consistent with known biology and health-system factors. The transthyretin V122I (p.Val142Ile) variant, present in approximately 3–4% of individuals of African ancestry and associated with late-onset ATTR-CM, likely contributes to the higher age-adjusted mortality in Black decedents [20]. When this inherited predisposition is layered onto historically uneven access to nuclear imaging, amyloidosis expertise, and high-cost disease-modifying drugs, a steeper increase among Black individuals in the late 2010 s is biologically and structurally understandable [21]. Our findings, therefore, strengthen calls for ancestry-attuned case-finding in older adults with increased wall thickness, conduction disease, or heart failure phenotypes compatible with amyloid [22].

Geographic and urbanicity gradients also align with differences in diagnostic capacity and referral networks. The higher rates in large central metropolitan counties and in the Northeast/Midwest likely track the concentration of amyloidosis centers, nuclear cardiology programs, and advanced imaging, rather than pure differences in disease biology [23]. These spatial and demographic patterns echo those observed for LVF mortality over the same era, suggesting shared structural drivers of detection and access rather than disease-specific biology alone [24]. It is noteworthy that the 2013 National Center for Health Statistics (NCHS) urban–rural classification used in our analyses formally distinguishes large central and large fringe metropolitan areas; this framework is well-suited for mortality surveillance but assigns urbanization by county of residence, not site of care, which can obscure cross-border referral patterns.

Place-of-death distributions in our cohort, mostly inpatient, about one-third at home, and relatively small hospice fractions, are consistent with clinical series of cardiac amyloidosis characterized by heart failure, arrhythmias, and late recognition [2]. These patterns likely reflect both the natural history of advanced amyloid cardiomyopathy and historically slower hospice penetration compared with oncology and general heart failure [25].

This study has several limitations intrinsic to death-certificate surveillance. Death certificate files provide national coverage and allow identification of an underlying cause with up to 20 contributing conditions, which is a key strength for our combined amyloidosis-plus-cardiac definition [26]. Nonetheless, death certificate accuracy is imperfect; hospital audits and systematic reviews document frequent certification errors and variability in how chronic contributors are recorded [27]. Under-ascertainment early in the study period could bias absolute rates downward, while growing awareness and electronic prompts may have increased the likelihood that amyloidosis was specified later, thereby making the rise look steeper. The International Classification of Diseases code E85 does not differentiate AL from ATTR or capture transthyretin genotypes, hematologic stage, or exposure to disease-modifying therapy, limiting mechanistic inference. Because our outcome required ≥ 1 cardiac MCD code in the presence of UCD E85, we cannot ascribe causality to amyloid cardiomyopathy versus coincident cardiac comorbidity; certification practices and coexisting conditions (e.g., ischemic heart disease, aortic stenosis) may influence coding. Finally, the recent transition in CDC WONDER to single-race reporting required analytic care and justifies our restriction of joinpoint and forecasting models to 1999–2020.

Rising and projected increases in cardiac amyloidosis mortality, particularly among Black and male decedents—stress the need to shift from passive detection to structured, equity-focused policy. Health systems should implement ancestry-aware case-finding strategies (e.g., EHR alerts for older adults of African ancestry with wall-thickening heart failure phenotypes) and adopt standardized non-biopsy diagnostic pathways that combine hematologic testing with bone-avid tracer scintigraphy (e.g., Tc-99 m PYP/DPD/HMDP). Expanding regional imaging capacity and reimbursement, and ensuring timely, affordable access to disease-modifying therapies such as tafamidis and RNA silencers (patisiran, vutrisiran), will be essential. Coverage policies and outcomes-based agreements can support equitable access. To address geographic disparities in detection, targeted investments in referral networks and tele-nuclear/echo programs should focus on historically low-reporting areas identified through mortality surveillance. Improving surveillance quality will also require brief, mandated training in death certification, EHR prompts to minimize documentation errors, and linkage of mortality data with clinical registries and claims to clarify amyloidosis subtype, genotype, and treatment exposure. These linkages will enable direct assessment of whether therapies have altered mortality trends post-2020. Implementation studies are needed to evaluate ancestry-informed screening in heart failure and cardiomyopathy clinics, and to quantify effects on diagnostic timing, treatment uptake, and clinical outcomes. Additionally, health services research mapping diagnostic capacity against county-level mortality patterns could identify structural gaps and guide targeted infrastructure investments.

Conclusion

U.S. cardiac amyloidosis mortality has risen steadily since 1999, with a marked acceleration after 2017—likely driven by improved detection following the adoption of non-biopsy bone-tracer scintigraphy and updated clinical guidelines, rather than increased disease lethality. Disparities remain pronounced, particularly among Black individuals and urban populations. ARIMA models project continued increases in age-adjusted mortality rates through 2040, with the highest burden expected among Black and male decedents. While broader access to early diagnosis and emerging therapies may help curb these trends, achieving equity in diagnostic pathways, timely treatment, and death certification is critical. Targeted screening, expanded diagnostic infrastructure, and equitable therapeutic access are essential to reducing mortality and addressing persistent disparities.

Authors’ contributions

AS led the conceptualization, methodology, and formal analysis, and drafted the original manuscript; DO, AK, and YD contributed to writing the original manuscript and conducting the literature review; MSE designed the figures and provided critical review; MTM performed the forecasting and contributed to drafting the results; AE revised the manuscript and performed the literature review; ASA assisted with formal analysis and writing the results; AA contributed to project administration and methodology; MA and MSR contributed to writing the revised manuscript; ZN provided administrative support and coordination; and BA, RD, and YS offered clinical oversight and supervision.

Funding

This research received no specific grant from any funding agency.

Data availability

The datasets analyzed during the current study are publicly available from the CDC WONDER database [http://wonder.cdc.gov](http:/wonder.cdc.gov).

Declarations

Ethics approval and consent to participate

The study used publicly available, de‑identified data and was exempt from institutional review board approval.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable.