Sex Disparities in Longitudinal Utilization and Intensification of Guideline Directed Medical Therapy among Patients with Newly Diagnosed Heart Failure with Reduced Ejection Fraction

Abstract

Background:

Guideline-directed medical therapies (GDMT) are the mainstay of treatment for heart failure with reduced ejection fraction (HFrEF), but they are underutilized. Whether sex differences exist in the initiation and intensification of GDMT for newly diagnosed HFrEF is not well-established.

Methods:

Patients with incident HFrEF from the 2016–2020 Optum de-identified Clinformatics^® Data Mart Database, which is derived from a database of administrative health claims for members of large commercial and Medicare Advantage health plans. The primary outcome was the utilization of optimal GDMT within 12 months of HFrEF diagnosis. Consistent with the guideline recommendations during the time period of the study, optimal GDMT was defined as ≥50% of the target dose of evidence-based beta-blocker plus ≥ 50% of the target dose of angiotensin-converting enzyme inhibitor, angiotensin receptor blocker, or any dose of angiotensin receptor neprilysin inhibitor, plus any dose of mineralocorticoid receptor antagonist. The probability of achieving optimal GDMT on follow-up and predictors of optimal GDMT were evaluated using time-to-event analysis with adjusted Cox proportional hazard models.

Results:

The study cohort included 63,759 patients (mean age: 71.3 years, 15.2% Non-Hispanic Black race, and 56.6% male). Optimal GDMT utilization was achieved by 6.2% of patients at 12 months post-diagnosis. Female (vs. male) patients with HFrEF had lower utilization across every GDMT class and lower utilization of optimal GDMT at each time point on follow-up. In an adjusted Cox model, female sex was associated with a 23% lower probability of achieving optimal GDMT post-diagnosis (HR 0.77 [95% CI, 0.71–0.83], P<0.001). The sex disparities in GDMT utilization following HFrEF diagnosis were most pronounced among patients with commercial insurance (female vs. males, HR: 0.66 [95% CI, 0.58–0.76]) compared with Medicare (HR: 0.85 [95% CI, 0.77–0.92]), P_interaction sex*insurance status = 0.005 ) and for younger patients (Age < 65 years: 0.65 [95% CI, 0.58–0.74]) than older patients (Age ≥ 65 years: HR: 87 [95% CI 80–96]) Sex*age P_interaction = 0.009).

Conclusions:

Overall utilization of optimal GDMT following HFrEF diagnosis was low, with significantly lower utilization among female (vs. male) patients. These findings highlight the need for implementation efforts directed at improving GDMT initiation and titration.

Introduction:

Heart failure (HF) affects nearly 6.5 million American adults, with 960,000 new HF diagnoses annually.¹ Among patients with HF with reduced ejection fraction (HFrEF), guideline-directed medical therapy (GDMT), comprised of 1) angiotensin-converting enzyme inhibitors (ACEi), angiotensin receptor blockers (ARB), or angiotensin receptor neprilysin inhibitors (ARNI), 2) evidence-based beta-blocker (e.g. bisoprolol, carvedilol, or metoprolol succinate), and 3) mineralocorticoid receptor antagonists (MRA) reduce the risk of all-cause mortality and hospitalization.² Recently, sodium-glucose cotransporter 2 inhibitors (SGLT2i) were recommended by the 2022 AHA/ACC/HFSA HF guidelines to reduce the risk of HF hospitalization and cardiovascular mortality.² HF GDMT benefits are incremental, accrue quickly, and can be appreciated within weeks after HF hospitalization. All current U.S. guidelines recommend early initiation and up-titration of multiple GDMTs after HFrEF diagnosis.² However, poor utilization of these therapies persists due to low prescription and poor rates of dose up-titration to maximally tolerated guideline-recommended treatment doses.³

While male and female patients have similar lifetime risks of clinical HF, there are substantial sex differences in the type of HF, risk factor burden, and HF clinical presentation.^4–6 Female (vs. male) patients are less likely to have HFrEF than HF with preserved ejection fraction and are more likely to be older at the time of diagnosis.^7–9 Females (vs. males) are less likely to receive evidence-based device therapy or to undergo coronary revascularization and are more likely to receive treatments that could worsen HF.¹⁰ Furthermore, females (vs. males) with HF have a higher burden of symptoms, worse health-related quality of life, higher long-term risk of readmission, and greater loss of survival time following HF hospitalization.^9,11–13

Sex differences in cardiovascular pharmacotherapies among patients with coronary artery disease,^14,15 hyperlipidemia,¹⁶ and atrial fibrillation^17,18 are well described. In a pragmatic multicenter trial of patients hospitalized for HF in Canada, females (vs. males) receiving ‘usual care’ had a lower uptake of renin-angiotensin system inhibitors (RASi) and MRAs and a greater uptake of beta-blockers at 30 days post-discharge.⁹ However, the sex differences in longitudinal patterns of GDMT utilization in a contemporary population of patients with HFrEF in the US are not well-established. This represents a significant knowledge gap considering the known sex differences in long-term outcomes among HF patients. Accordingly, this study aimed to estimate sex differences in optimal GDMT (RASi, beta-blockers, and MRA) utilization within one year of HFrEF diagnosis using a large, contemporary national insurance database.

Methods:

Data used for the study are covered under a data use agreement with Optum^® and are not available for distribution by the authors but may be obtained with an approved data use agreement.

Database

The current study utilized the Optum de-identified Clinformatics^® Data Mart Database. This database includes administrative inpatient, outpatient, and pharmacy claims data among patients with commercial insurance and Medicare Advantage. Each unique patient has associated coverage enrollment data, which provides demographic information such as age, race/ethnicity, region of residence, and dates of enrollment and disenrollment. All claims are linked to the International Classification of Diseases Codes (ICD) 9^th and 10^th editions.

Study Population

The cohort of interest included patients with incident HFrEF between 2016 and 2020 (Figure 1). Consistent with prior literature, patients were first identified as having HF with either one inpatient or two outpatient claims for HF in the primary diagnosis position.¹⁹ The complete list of ICD codes used to derive the cohort is provided in Table S1. Two outpatient claims in separate quarters were used to ensure HF diagnosis.²⁰ The date of the first HF claim during the study period was defined as the date of diagnosis. To exclude prevalent HF, we excluded patients who had an HF diagnosis code in any place in the preceding 12 months of the claim date or any patients receiving HF-specific therapy before the index diagnosis date (Figure 1). Patients without 12 months of continuous follow-up in the preceding 12 months of the HF diagnosis date were also excluded to ensure a full 12-month retrospective period. Next, to identify patients with HFrEF, patients without an HFrEF-specific ICD-10 (I50.2, I50.4) code in the primary or secondary diagnosis position within the first six months of HF diagnosis were excluded.^21,22 Patients were followed for up to 12 months post-HF diagnosis. Patients without 12 months of follow-up time, either due to a change in coverage, loss to follow-up, or death, were right-censored at the time of the last known coverage date. Patients with end-stage kidney disease, prior heart transplantation, left ventricular assist device implantation, pregnancy, or unknown sex were excluded.

Figure 1:

Cohort derivation for the study

Outcome of Interest

The primary outcome was time to achieve optimal GDMT on follow-up. Consistent with the guideline recommendations during the time period of the study, optimal GDMT was defined as ≥50% target dose of ACEI or ARB or any dose of ARNI, ≥50% dose of beta-blockers, and any dose of MRA, as previously established from a secondary analysis of the GUIDE-IT study.²³ SGLT2i was not included in this analysis because this class was approved for HFrEF in 2020. The definitions for target doses were derived from the CHAMP-HF study and are displayed in Table S2.³ GDMT utilization was derived through pharmacy claims data. We extracted the drug name, medication dosage, and fill date for GDMT after the new HFrEF diagnosis. As our time-to-event primary outcome required medication fills of three separate drug classes, the outcome time was defined as the number of days after HF diagnosis required for a patient to fill all three medication classes at optimal dosages. The maximum follow-up period was 12 months post-HFrEF diagnosis. Our secondary outcomes included class-specific utilization at target doses and class-specific utilization at any dose across each 3-month interval.

Study Covariates

The primary exposure of interest was sex (male or female). Medical comorbidities (prior myocardial infarction, atrial fibrillation, hypertension, depression, peripheral arterial disease, cerebrovascular disease, chronic obstructive pulmonary disease, diabetes, chronic kidney disease, and valvular heart disease) were identified from ICD codes (Table S1). We also identified the year of HF diagnosis, prior utilization of beta-blockers, ACEI/ARB/ARNI, or MRA, and the number of unique medications before diagnosis.

Statistical Analysis

Patient characteristics were compared across sex groups using chi-square tests and t-tests. Overall and sex-specific rates of any GDMT (overall and by individual classes) at any dose, the proportion of patients who achieved optimal GDMT, and the proportion of patients who achieved maximum dosages of all three GDMT classes were assessed at three-month intervals. Cumulative incidence curves were used to visualize the achievement of optimal GDMT over time across sex groups.

Cox proportional hazards model was used to evaluate the association between sex and time-to-optimal GDMT. The model included the following covariates: patient demographics (age, sex, race/ethnicity), year of diagnosis, region of diagnosis, location (inpatient vs. outpatient) of diagnosis, comorbidities (history of myocardial infarction, atrial fibrillation, hypertension, depression, peripheral arterial disease, stroke, chronic obstructive pulmonary disease, diabetes, chronic kidney disease, valvular heart disease, polypharmacy at baseline, baseline use of GDMT therapies prior to HFrEF diagnosis for other indications, and a time-varying covariate of hospitalization during follow-up. The proportional hazards assumption was verified by visual examination of Schoenfield-type residual plots. Interaction analyses were conducted to assess for effect modification by following covariates on the association between sex and time to optimal GDMT: insurance, age, location of diagnosis, and hospitalization for HF during follow-up. Data analyses were performed using SAS v9.4 (SAS Institute, Inc., Cary, NC). Statistical significance was set at p <0.05. The study used de-identified data and was determined to be exempt from Institutional Review Board approval as the research did not involve human subjects.

Results:

The final cohort comprised 63,759 patients (mean age of 71.3 years, 56.6% male, 15.2% Non-Hispanic Black). A total of 43.2% of patients were diagnosed in the inpatient setting. Compared with males, females were diagnosed with HFrEF at an older age, more frequently had Medicare insurance, were more likely to be diagnosed in the inpatient setting and had a lower prevalence of cardiometabolic co-morbidities. (Table 1).

Table 1:

Characteristics of patients from the Optum de-identified Clinformatics^® Data Mart Database with new onset heart failure with reduced ejection fraction stratified by sex.

	Overall	Female	Male	P Value
N	63759	27684	36075
Age, years, (SD)	71.3 (10.9)	72.2 (10.7)	70.4 (11.0)	<0.001
Race				<0.001
Unknown	3113 (4.9%)	1325 (4.8%)	1788 (5.0%)
Asian	1498 (2.4%)	582 (2.1%)	916 (2.5%)
Black	9688 (15.2%)	4917 (17.8%)	4771 (13.2%)
Hispanic	6539 (10.3%)	2753 (9.9%)	3786 (10.5%)
White	42921 (67.3%)	18107 (65.4%)	24814 (68.8%)
Insurance				<0.001
Commercial	11772 (18.5%)	3935 (14.2%)	7837 (21.7%)
Medicare	51939 (81.5%)	23734 (85.7%)	28205 (78.2%)
Region				<0.001
Midwest	12455 (19.5%)	5189 (18.7%)	7266 (20.1%)
Northeast	7444 (11.7%)	3166 (11.4%)	4278 (11.9%)
Pacific	240 (0.4%)	100 (0.4%)	140 (0.4%)
South	28868 (45.3%)	12936 (46.7%)	15932 (44.2%)
West	14704 (23.1%)	6278 (22.7%)	8426 (23.4%)
Comorbid Conditions
Myocardial Infarction	11275 (17.7%)	4529 (16.4%)	6746 (18.7%)	<0.001
Atrial Fibrillation	24684 (38.7%)	9560 (34.5%)	15124 (41.9%)	<0.001
Hypertension	57929 (90.9%)	25116 (90.7%)	32813 (91.0%)	<0.001
Depression	16504 (25.9%)	9496 (34.3%)	7008 (19.4%)	<0.001
Peripheral Arterial Disease	22054 (34.6%)	9325 (33.7%)	12729 (35.3%)	<0.001
Cerebrovascular Disease	10126 (15.9%	4549 (16.4%)	5577 (15.5%)	<0.001
COPD	21789 (34.2%)	9746 (35.2%)	12043 (33.4%)	<0.001
Diabetes	32228 (50.5%)	13780 (49.8%)	18448 (51.1%)	<0.001
Chronic Kidney Disease	24050 (37.7%)	10144 (36.6%)	13906 (38.6%)	<0.001
Valvular Heart Disease	30549 (47.9%)	13516 (48.8%)	17033 (47.2%)	<0.001
Location of Diagnosis				<0.001
Inpatient	27571 (43.2%)	12246 (44.2%)	15326 (42.5%)
Outpatient	36188 (56.8%)	15438 (55.8%)	20750 (57.5%)
Receipt of Any Dose of Class Prior to HFrEF Diagnosis
ACEI	6024 (9.5%)	2422 (8.8%)	3602 (10.0%)	<0.001
ARB	2654 (4.2%)	1200 (4.3%)	1453 (4.0%)	<0.001
ARNI	0	0	0	-
Beta-Blocker	10930 (17.1%)	4430 (16.0%)	6500 (18.0%)	<0.001
MRA	3956 (6.2%)	1580 (5.7%)	2376 (6.6%)	<0.001
Number of Unique Drugs in 3 Months before HFrEF Diagnosis, median (interquartile range)	2.2 (2.4)	2.2 (2.4)	2.1 (2.4)	<0.001

Abbreviations: SD – standard deviation, COPD – chronic obstructive pulmonary disease, ACE – angiotensin converting enzyme inhibitor, ARB – angiotensin receptor blocker, ARNI - angiotensin receptor neprilysin inhibitor, MRA - mineralocorticoid receptor antagonists.

GDMT Utilization 12 Months after HFrEF Diagnosis

Baseline GDMT utilization at the time of HFrEF diagnosis was low (ACEI/ARB/ARNI 13.7%, beta-blockers 17.1%, MRA 6.2%). The largest increase within each GDMT class occurred between the time of HFrEF diagnosis and three months (ACEI/ARB/ARNI: 55.3%, beta-blockers: 55.5%, MRA: 18.4%). By 12 months, the utilization rate of ACEI/ARB/ARNI, beta-blockers, and MRA reached 65.2%, 64.3%, and 24.7%, respectively. Overall, the proportion of patients who achieved optimal GDMT increased from 3.0% at three months to 6.2% at 12 months. The proportion of patients who reached target doses of all three GDMT classes was also low, with only 1.4% of participants achieving target doses of GDMT at 12 months.

Sex Differences in GDMT Utilization after HFrEF Diagnosis

Sex differences were observed in the utilization of any form of GDMT at any dose (Figure S1). By 12 months of HFrEF diagnosis, 81.1% of female and 84.5% of male patients had received any GDMT (Figure 2). Female (vs. male) patients had a lower uptake of optimal GDMT at every time point within 12 months of HFrEF diagnosis ((5.0% vs. 7.0% at 12 months, Figures 2 and 3). A similar pattern of underutilization was noted among female (vs. male) patients for target doses of the three GDMT classes (1.0% vs. 1.6% at 12 months). Among specific drug classes, the utilization of ACEI/ARB/ARNI, beta-blockers, and MRA at target doses was consistently lower among female vs. male patients at each time point within 12 months of initial diagnosis (Figure S2).

Figure 2:

(A) Utilization of GDMT at any dosage (for any class of therapy) and (B) Utilization of optimal dosage (for each class) within 12 months following the diagnosis of heart failure with reduced ejection fraction.

The optimal dose is defined as at least 50% of the target dose of beta-blocker + 50% of the target dose of ACE/ARB or any dose of ARNI + any dose of MRA.

Abbreviations: ACEi – angiotensin converting enzyme inhibitor, ARB – angiotensin receptor blocker, MRA – mineralocorticoid receptor antagonist, GDMT – guideline-directed medical therapies

Figure 3:

Cumulative incidence of optimal GDMT use within 12 months of heart failure diagnosis among male and female patients with heart failure with reduced ejection fraction.

Abbreviations: GDMT – guideline-directed medical therapies

In fully adjusted Cox proportional hazard models, the probability of achieving optimal GDMT was significantly lower among females vs. males (HR 0.77, 95% CI 0.71–0.83, P<0.0001) with HFrEF (Figure 4). A significant interaction was observed between sex and insurance class (P_interaction = 0.005) and sex and age (P_interaction = 0.009) for the probability of achieving optimal GDMT. There were no statistically significant interactions between sex and other covariates, such as diagnosis location (P_interaction = 0.19) and hospitalizations during follow-up (P_interaction = 0.20; Table S3).

Figure 4:

Predictors of optimal GDMT utilization on follow-up after HFrEF diagnosis as determined by multivariable-adjusted time-to-event analysis.

A Cox proportional hazards model was used to evaluate predictors of optimal GDMT use on follow up. The model included the covariates shown in the figure. Abbreviations: GDMT – guideline-directed medical therapies, HF – heart failure.

Sex Differences in GDMT Utilization by Insurance Type

Commercially insured patients were younger, more likely to live in the Midwest, had fewer comorbidities, and had greater baseline medication utilization of ACEI/ARB/ARNI, beta-blockers, and MRAs compared to patients with Medicare (Table S4). At 12 months, the utilization of optimal GDMT was 11.5% for commercially insured patients and 4.9% for patients with Medicare.

In the commercially insured group, female (vs. male) patients had significantly lower rates of utilization of optimal GDMT at every time point (4.5% vs. 6.6% at three months and 8.8% vs. 12.9% at 12 months, Figure 5A). In contrast, among patients with Medicare insurance, the utilization rates for optimal GDMT were more comparable among females vs. males (2.0% vs. 2.5% at three months and 4.4% vs. 5.3% at 12 months, Figure 5B). In adjusted Cox models, sex differences in the probability of achieving optimal GDMT on follow-up were more apparent for females (vs. males) with commercial insurance (HR 0.66, 95% CI 0.58–0.76, P<0.001) than Medicare (HR 0.85, 95% CI 0.77–0.92, P<0.001, Table S3).

Figure 5:

Sex differences in the optimal GDMT utilization rates following HFrEF diagnosis, stratified by insurance and age. Panel (A) represents those with commercial insurance, Panel (B) represents those with Medicare, Panel (C) represents younger patients, and Panel (D) represents older patients.

Abbreviations: GDMT – guideline-directed medical therapies, HFrEF – heart failure with reduced ejection fraction.

Sex Differences in GDMT Utilization by Patient Age

GDMT utilization following HFrEF diagnosis was lower among older (≥ 65 years of age) patients than younger patients (age <65 years, 4.8% vs. 10.9% by 12 months follow-up). Younger female (vs male) patients had significantly lower rates of utilization of optimal GDMT at every time point (Figure 5C–D). In adjusted Cox models, sex differences in optimal GDMT utilization were more apparent for females (vs males) < 65 years of age (HR 0.65, 95% 0.58–0.74, P<0.001) compared to females (vs males) ≥ 65 years (HR 0.87, 95% CI 0.80–0.96, P=0.004, Table S3).

Other Predictors of Achieving Optimal GDMT

Compared to Non-Hispanic White patients, Non-Hispanic Black patients had a higher probability of achieving optimal GDMT (HR 1.50, 95% CI 1.37–1.64, P<0.0001, Figure 4). The use of either beta-blockers or MRA before HFrEF diagnosis, inpatient location of diagnosis, and HF hospitalization during follow-up were associated with a higher probability of achieving optimal GDMT. In contrast, older age and a higher number of medications prior to diagnosis were independently associated with a lower probability of achieving optimal GDMT.

Discussion:

In the present analysis in the Optum de-identified Clinformatics^® Data Mart Database, we observed several key findings. Optimal GDMT utilization among patients with a new diagnosis of HFrEF was low overall, with only 6.2% of patients receiving optimal GDMT within the first 12 months after diagnosis. There were substantial sex differences, with females (vs. males) being less likely to receive any GDMT or optimal GDMT on follow-up. In adjusted analyses, females (vs. males) had a 23% lower probability of achieving optimal GDMT following the initial diagnosis of HFrEF. Finally, we observed that the sex disparities in GDMT were more evident among patients with private (vs. Medicare) insurance and among younger (vs. older) patients. These findings reveal important sex disparities related to GDMT initiation and intensification within the first year of HFrEF diagnosis.

Our findings highlight a significant gap in the care of patients with HFrEF. Prior studies have reported low GDMT utilization following HFrEF diagnosis across different patient populations. In the EVOLUTION-HF study, Bozkurt et al. reported that only 18% of patients received at least 3 GDMTs three months following a HF hospitalization in the United States.²⁴ Similarly, in a cohort of older patients from Canada, optimal GDMT use was 8.3% within six months of HFrEF diagnosis.²⁵ The present study adds to the existing literature by demonstrating a significant underutilization of GDMT up to 12 months following HFrEF diagnosis. Specifically, we observed that only 6% and 1.4% of patients received the three-drug GDMT regimen at optimal and target doses, respectively, by 12 months of follow-up.

We also observed significant sex disparities in the use of GDMT following HFrEF diagnosis. In the present study, females (vs. males) were less likely to receive optimal GDMT and had lower rates of use of all classes of GDMT following HFrEF diagnosis. Our study findings are consistent with cross-sectional observations from other cohorts of patients with HFrEF. In a recent analysis from the REPORT-HF registry, Tromp et al. demonstrated lower rates of beta-blockers, MRA, and RASi utilization at discharge among females (vs. males) with HFrEF.¹⁰ In a longitudinal study of young U.S. Veterans with HFrEF, Dhruva et al. demonstrated that female (vs. male) patients had 46% lower odds of receiving at least one HF medication on follow-up.²⁶ The current study adds to the existing literature by assessing sex differences in the intensification of GDMT following HFrEF diagnosis in a large, representative cohort of HFrEF patients.

The observed sex disparities in the utilization of GDMT may be related to several factors. In our study, female patients with HFrEF were older and had a lower burden of comorbidities with concurrent indications for individual GDMTs such as atrial fibrillation, coronary artery disease, and type 2 diabetes. Furthermore, female patients also had a greater burden of comorbid depression. These differences in co-morbidities may have contributed to lower GDMT utilization in females (vs males).^27,28 At a health-system level, females are less likely to be referred to cardiologists, are less likely to be referred to HF specialists, and are less likely to receive follow-up visits after the initial consultation, all of which may impact GDMT prescription and uptitration.^29,30 Female patients have also reported that their symptoms were not addressed and reported being told symptoms were related to hormonal fluctuations in the Veteran’s Affairs health system.³¹ Poor physician-patient communication may lead to an inadequate understanding of the risks of sub-optimally treated HF. Finally, some observational studies have demonstrated that female patients with HFrEF may derive maximal benefits from GDMT at less than recommended target doses, which may influence the provider’s approach to treatment intensification among female vs male patients.³²

We observed significantly greater use of optimal GDMT among younger (vs. older) patients and those with commercial insurance (vs. Medicare). This may be related to lower frailty burden, fewer comorbidities, younger age (among commercially insured groups), and greater tolerability to higher doses of GDMT.^33,34 While we account for age and co-morbidity burden in our adjusted analyses, these factors may favorably influence physician inertia leading to higher prescription rates. Furthermore, patients with commercial insurance have higher socioeconomic status and better access to outpatient specialty care. Notably, despite the overall higher utilization rates, we observed more exaggerated sex disparities among commercially insured as well as younger patients. However, females with commercial insurance had higher rates of optimal GDMT use than both female and male patients with Medicare insurance.

Consistent with prior studies, we observed non-Hispanic Black race was associated with a greater likelihood of receiving optimal GDMT.^35,36 This may be related to the younger age of diagnosis and the higher prevalence of hypertension among Black patients. Among other factors, patients diagnosed in the hospital and those with follow-up hospitalization were more likely to achieve optimal GDMT. This is consistent with prior observations and highlights the role of hospital stays as a key opportunity to uptitrate GDMT.^37,38

Our study has important implications for HFrEF care delivery in the U.S. Females with HFrEF have a higher readmission burden and greater survival loss (compared to the U.S. population median) than males.¹¹ A potential driver of these differences in outcomes could be the underutilization of GDMT among females (vs. males). Prior data suggest a 1% increased mortality risk each month of GDMT deferral.³⁹ Thus, there is a need to improve GDMT utilization in all patients who are under-treated for HFrEF. However, implementation strategies that improve GDMT uptake and clinical outcomes in HF remain underutilized.⁴⁰ Leveraging EMR-based decision support and protocolized up-titration for both males and females in the immediate post-diagnosis period may also improve the intensification of GDMT and help to overcome therapeutic inertia.^41–43 In the STRONG-HF trial, a rapid GDMT uptitration strategy had comparable improvements in GDMT use and reductions in adverse outcomes across sex groups.⁴⁴ Improving access to healthcare services that enhance post-discharge follow-up among female patients may also help address the observed sex disparities in GDMT use and clinical outcomes.⁹ Finally, improving the enrollment of females in HF clinical trials is also critical to have more generalizable evidence, as HFrEF clinical trials have under-enrolled females relative to the disease distribution.⁴⁵

Our study has several strengths, including using a large national dataset to identify over 60,000 patients with incident HFrEF between 2016 and 2020. This dataset provides granular information on dosages, fill dates, and receipt of HFrEF medications on follow-up, allowing for an in-depth investigation of GDMT utilization over time among patients with commercial insurance and Medicare Advantage. Our cohort selection process was rigorous, using a well-validated ICD-10 code-based algorithm to identify newly diagnosed HFrEF patients.²⁰ Our study also has several noteworthy limitations. These data are derived through ICD-10 codes and do not contain non-claims clinical data; thus, we could not assess whether measures of clinical severity, hemodynamic stability, and clinical parameters such as LVEF, systolic blood pressure, estimated glomerular filtration rate, or adverse effects affected the overall utilization rate. Furthermore, we could not differentiate prescriber practices, patient nonadherence, or patient tolerability as the primary reason for inadequate GDMT utilization. Patients may have achieved maximum tolerated doses of GDMT despite being on doses that were less than at target. The study period did not allow for the assessment of sex differences in SGLT2i utilization, as this drug class was approved for HF in 2020. Additionally, we could not differentiate patients with new HFrEF following an acute coronary event or identify the proportion of patients who had recovered ejection fraction.

Conclusions

In this national study of insured patients with a new diagnosis of HFrEF, we observed poor utilization of optimal GDMT overall and that compared with male patients, female patients are significantly less likely to be initiated and intensified on GDMT on follow up. Sex differences in GDMT utilization between females and males were most significant among patients with commercial insurance (vs. Medicare). Given that females have higher hospitalization rates and greater survival loss than males, implementation efforts directed at improving GDMT initiation and uptitration are urgently needed.

Clinical Perspectives.

What is New?

• Female patients with HFrEF have lower utilization of GDMT compared to males within 12 months of diagnosis.

• The magnitude of sex differences in GDMT utilization was more evident among patients with private (vs. Medicare) insurance and for younger (vs. older) patients.

What are the Clinical Implications?

• The underutilization of GDMT, particularly among female patients, highlights the need for targeted interventions to improve both overall GDMT prescription while also narrowing the sex disparities in their use.

Abbreviations

HF

heart failure

HFrEF

heart failure with reduced ejection fraction

GDMT

guideline-directed medical therapy

ACEi

angiotensin converting enzyme inhibitor

ARB

angiotensin receptor blocker

ARNI

angiotensin receptor neprilysin inhibitors

MRA

mineralocorticoid receptor antagonists

SGLT2i

sodium-glucose cotransporter 2 inhibitors

RASi

renin angiotensin system inhibitors

ICD

International Classification of Diseases