Association of Medication Profiles, Including Polypharmacy, Guideline-Directed Medical Therapy, and Potentially Inappropriate Medications, With All-Cause Mortality in Older Adults With Heart Failure, With or Without Frailty and Disability

Yuka Sekiya; Shinya Fujiki; Hiroki Tsuchiya; Takeshi Kashimura; Yuji Okura; Kunio Kodera; Hiroshi Watanabe; Kazuyoshi Takahashi; Shogo Bannai; Taturo Hatano; Takahiro Tanaka; Nobutaka Kitamura; Tohru Minamino; Takayuki Inomata

doi:10.1253/circj.CJ-25-0200

Abstract

Background: Polypharmacy, driven by guideline-directed medical therapy (GDMT) and medications for comorbidities, including potentially inappropriate medications (PIMs), is common in older adults with heart failure (HF). Although medication profiles affect survival, the effects of frailty and disability status remain underexplored.

Methods and Results: This retrospective study assessed polypharmacy (≥5 medications), the use of GDMT, and PIMs based on the Beers Criteria. Frailty and disability status were determined using Japan’s Long-term Care Insurance (LTCI) certification. Patients were stratified according to LTCI, and the prognostic impact of medication profiles was analyzed. The total medication count was correlated with both GDMT and PIM use. Among 1,264 patients, those with LTCI were older, had more severe comorbidities, higher polypharmacy and PIM use, and lower use of GDMT medications. In multivariate Cox regression analysis, regardless of LTCI, GDMT medication use was associated with a favorable prognosis (LTCI: odds ratio [OR] 0.47, 95% confidence interval [CI] 0.258–0.866, P=0.015; no LTCI: OR 0.57, 95% CI 0.400–0.799, P=0.001). PIM use was associated with a poor prognosis only in the no-LTCI group (OR 1.51; 95% CI 1.040–2.203; P=0.030).

Conclusions: Polypharmacy may have both beneficial and harmful effects, with prognostic implications potentially influenced by frailty and disability status. Although GDMT medications were consistently associated with favorable outcomes, the impact of PIMs appeared to differ depending on LTCI.

Heart failure (HF) remains a leading cause of mortality and hospitalization worldwide. However, advances in pharmacological treatments and their implementation have significantly improved clinical outcomes, even in older (age >65 years) adult patients.¹^,² There are many evidence-based treatment options available, particularly for patients with symptomatic HF with reduced ejection fraction (HFrEF) and mildly reduced ejection fraction (HFmrEF). Current guidelines recommend the simultaneous use of several medications as guideline-directed medical therapy (GDMT), including angiotensin-converting enzyme inhibitors (ACEi), angiotensin receptor blockers (ARBs), β-blockers, and mineralocorticoid receptor antagonists (MRAs), as well as sacubitril/valsartan (angiotensin receptor-neprilysin inhibitor), ivabradine, and sodium-glucose cotransporter 2 inhibitors (SGLT2i).³^–⁵

Conversely, older adult patients frequently present with multiple comorbidities,⁶ necessitating the use of medications that are not directly related to symptom relief or the reduction of mortality and hospitalization for HF.⁷ Some of these medications are considered potentially inappropriate medications (PIMs), which may contribute to adverse outcomes. PIMs in HF patients include non-dihydropyridine calcium channel blockers, non-steroidal anti-inflammatory drugs, certain antiplatelet agents, and some antidiabetic drugs.⁸^,⁹

The need for GDMT in addition to the need to manage multiple comorbidities (including the use of PIMs), results in the challenge of polypharmacy. HF patients in particular are known to be highly susceptible to polypharmacy.¹⁰^,¹¹ Furthermore, it has been suggested that the prescription rates of GDMT medications and PIMs, which contribute to polypharmacy, are influenced by a patient’s health status, such as functional disability.¹² However, few studies have classified HF patients according to their frailty and disability status to evaluate the associations of polypharmacy, GDMT medications, and PIMs with clinical outcomes. Therefore, the aim of the present study was to investigate the prevalence and characteristics of polypharmacy in HF patients with and without frailty and disability, with a particular focus on the prescription patterns of GDMT medications and PIMs, and their association with prognosis.

Methods

Study Design and Participants

This study is a secondary analysis of a retrospective observational study which included older (age >65 years) adult patients with symptomatic HF with left ventricular ejection fraction (LVEF) <50%. Details of the study design have been reported elsewhere.¹²^,¹³ Using echocardiography, cardiac function was examined at 7 hospitals in Niigata City between January 2011 and December 2016. All echocardiographic procedures were conducted in clinical laboratories for both outpatients and inpatients, with patients undergoing emergency echocardiograms excluded. Data regarding medical history, comorbidities and medication were extracted from medical charts. Due to the retrospective nature of the present study, it was difficult to accurately assess a patient’s symptoms, so this was determined on the basis of the use of diuretics, which are prescribed to relieve HF symptoms.⁴^,¹⁴ That is, patients using diuretics were considered to have symptomatic HF. Patients with missing echocardiography and medical chart data were excluded from the study.

The severity of comorbidities was assessed using the Charlson Comorbidity Index (CCI; see below). Frailty and disability status were assessed on the basis of Japanese Long-term Care Insurance (LTCI) certification, as detailed below.

Non-Cardiac Comorbidities

The severity of comorbidities was evaluated using the CCI.¹⁵ This index includes 19 pathologies with different scores, and patients are classified into 4 groups according to the CCI score as follows: low, CCI 0; medium, CCI 1–2; high, CCI 3–4; and very high, CCI ≥5. In this study, we defined patients with high and very high CCI scores as having severe comorbidities. Based on the characteristics of the study population, all enrolled patients were considered to have HF. However, using only the medical records, it was challenging to determine the severity of diabetes and hepatic diseases. Therefore, all diagnoses of diabetes were classified as “diabetes without end-organ damage” and all hepatic diseases were categorized as “mild liver disease” when calculating the CCI score. Stroke included brain infarction, brain hemorrhage, and subarachnoid hemorrhage. Connective tissue diseases included rheumatoid arthritis, systemic lupus erythematosus, scleroderma, and other collagen vascular diseases.

Frailty and Disability, and the Japanese LTCI System

In this study we considered patients enrolled in the LTCI system as individuals with frailty and disability. The LTCI system was introduced in Japan in 2000 to support the independence of older adult individuals with frailty and disabilities. Every older (age >65 years) adult requiring public nursing care and financial assistance must register with the LTCI system. Therefore, LTCI enrollment is widely regarded as an indicator of frailty and disability in Japan.¹⁶^,¹⁷

Older adults aged ≥65 years are eligible for LTCI benefits based on physical and cognitive dysfunction. Eligibility is assessed through a 74-item questionnaire evaluating activities of daily living, a home visit report, and a physician’s medical opinion. The final decision is made by a Care Need Certification Committee. The LTCI system categorizes individuals into 7 levels based on their care needs, namely 2 support levels (Support Levels 1 and 2) and 5 care need levels (Care Need Levels 1 [least disabled] to 5 [most disabled]). Services are provided according to the certified level of care required by each insured individual.¹⁸

Polypharmacy, GDMT Medications, and PIMs

Data on patients’ medications were extracted from medical charts. The number of medications was counted and the medications were reviewed to identify any classified as either GDMT or PIM. Polypharmacy was defined as the use of ≥5 oral medications per day.¹⁹ GDMT included ACEi, ARBs, β-blockers, and MRAs. Sacubitril/valsartan, ivabradine, and SGLT2i were not approved or commonly used as therapies for HF in Japan during the study period. Patients were considered as receiving GDMT if they were taking a combination of either β-blockers and ACEi or β-blockers and ARBs.²⁰^,²¹ The number of medications included in GDMT (β-blockers, ACEi/ARBs, and MRAs) was counted. PIMs were defined using the Beers Criteria for HF.⁹ Patients were considered as receiving PIMs if they were taking any of the following medications: cilostazol, non-dihydropyridine calcium channel blockers, non-steroidal anti-inflammatory drugs, dextromethorphan, and thiazolidine. Dronedarone is one of the PIMs included in the Beers Criteria for HF, but has not been approved for use in Japan.

Follow-up and Outcomes

Following echocardiography, patients’ survival status (alive or deceased) was confirmed based on medical records, and survival times were calculated. Patients were censored if they were referred to another hospital or clinic. For deceased patients, the cause of death was classified as either cardiovascular or non-cardiovascular. Cardiovascular deaths were defined as those due to HF, ischemic heart disease, valvular disease, arrhythmia, aortic disease, or other cardiovascular conditions. Non-cardiovascular deaths were defined as deaths due to any other cause. The cause of death was determined based on information extracted from death certificates. If the medical chart contained only the fact of death, and the cause of death was unclear, the patient was classified as having an “unidentified” cause of death.

Statistical Analysis

Patients were divided into 2 groups based on frailty and disability (an LTCI group with frailty and disability and the no-LTCI group without frailty and disability) and polypharmacy status for analysis. Continuous variables are expressed as the mean±SD, whereas categorical variables are presented as frequencies and percentages. Baseline characteristics were compared using the Mann-Whitney U test for continuous variables and Fisher’s exact test for categorical variables. The total number of medications, GDMT medications, and PIMs prescribed to each patient were recorded, and Pearson correlation coefficients were calculated to assess associations among these variables. Kaplan-Meier curves were used to estimate overall survival; differences based on polypharmacy status, GDMT medication use, or PIM use were determined using the log-rank test. Cox regression analysis was used to evaluate the predictive value of polypharmacy, GDMT medication use, and PIM use for all-cause mortality. In addition to these factors, univariate analysis was conducted for age, sex, atrial fibrillation, severe comorbidities (high and very high CCI scores), body mass index, hemoglobin, and creatinine. Variables that were significantly associated with all-cause mortality in the univariate analysis were included in the multivariate analysis. Because polypharmacy was correlated with the use of GDMT medications, it was excluded from the multivariate analysis to avoid collinearity. The effects of polypharmacy, GDMT medication use, and PIM use were assessed within subgroups using interaction terms in the models. All statistical analyses were conducted using the SPSS version 28.0.0.0 (IBM Inc., Chicago, IL, USA).

Ethical Considerations

This study was approved by the Ethics Committee of Niigata University (Approval no. Niigata University 1788), as well as by institutional review boards and independent ethics committees at each participating site. Because of the retrospective nature of this study, patients were given the option to opt-out. This study was registered with the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (Registration no. UMIN-000043255) and was performed in accordance with Japanese clinical research ethics guidelines²² and the Declaration of Helsinki.

Results

Participants and Baseline Characteristics

Of the patients who underwent echocardiography at the participating hospitals, 3,550 had an LVEF of ≤50%. Of these, 2,362 were older adult patients aged ≥65 years. Patients who had not used diuretics (n=962) and those for whom clinical data were lacking (n=136) were excluded from the study. Finally, 1,264 individuals were analyzed, of whom 296 were classified into the LTCI group with frailty and disability (Figure 1). Compared with the no-LTCI group, the LTCI group had a lower proportion of males (50.7% vs. 67.9%; P<0.001), was older (median [interquartile range] age 84.0 [77.0–88.0] vs. 76.0 [70.0–82.0] years; P<0.001), and had higher CCI scores (2.8±2.4 vs. 2.1±2.2; P<0.001). The LTCI group also used a greater total number of medications (8.8±3.4 vs. 8.1±3.2; P<0.001) and had a higher proportion of patients treated with polypharmacy (93.2% vs. 87.0%; P=0.003). In addition, patients in the LTCI group were prescribed fewer GDMT medications (1.3±1.0 vs. 1.6±0.9; P<0.001) and were more likely to be prescribed PIMs (16.6% vs. 12.4%; P=0.066) than patients in the no-LTCI group (Table 1). There were no significant differences in underlying heart disease or LVEF between the LTCI and no-LTCI groups (Table 1).

Figure 1.

Flowchart showing patient disposition. LTCI, Long-term Care Insurance; LVEF, left ventricular ejection fraction.

Table 1.

Baseline Characteristics

	LTCI group (n=296)	No-LTCI group (n=968)	P value
Male sex	150 (50.7)	657 (67.9)	<0.001
Age (years)	84.0 [77.0–88.0]	76.0 [70.0–82.0]	<0.001
Body mass index (kg/m²)	20.3±3.8	22.0±3.9	<0.001
Hemoglobin (g/dL)	11.5±2.6	12.2±2.3	<0.001
Creatinine (mg/dL)	1.09±1.35	1.03±1.40	0.132
Heart disease
Ischemic	126 (42.6)	460 (47.5)	0.135
Non-ischemic	170 (57.4)	508 (52.5)
LVEF (%)	40.0 [33.0–45.0]	40.0 [32.0–46.0]	0.959
CCI score	2.8±2.4	2.1±2.2	<0.001
Severe comorbidity (CCI >3)	132 (44.6)	164 (16.9)	<0.001
Comorbidities
Atrial fibrillation	142 (48.0)	442 (45.7)	0.485
Hypertension	204 (68.9)	623 (64.4)	0.163
Diabetes	107 (36.1)	367 (37.9)	0.583
Dyslipidemia	112 (37.8)	451 (46.6)	0.008
Chronic kidney disease	20 (6.8)	53 (5.5)	0.408
CTD	20 (6.8)	34 (3.5)	0.016
No. medications
Total	8.8±3.4	8.1±3.2	<0.001
GDMT^A	1.3±1.0	1.6±0.9	<0.001
Non-GDMT^A	7.6±3.3	6.5±3.1	<0.001
Type of medications
ACEi/ARB	132 (44.6)	580 (59.9)	<0.001
β-blocker	130 (43.9)	557 (57.5)	<0.001
MRA	110 (37.2)	421 (43.5)	0.054
Digitalis	20 (6.8)	120 (12.4)	0.007
Dihydropyridine CCB	106 (35.8)	275 (28.4)	0.015
Non-dihydropyridine CCB	31 (10.5)	71 (7.3)	0.083
Antiplatelet agent	129 (43.6)	443 (52.6)	0.509
Anticoagulant agent	119 (40.2)	509 (52.6)	<0.001
NSAID	28 (9.5)	65 (6.7)	0.113
Steroid	19 (6.4)	30 (3.1)	0.01
Patients with polypharmacy	276 (93.2)	842 (87.0)	0.003
Patients receiving any GDMT medication^B	69 (23.3)	376 (38.8)	<0.001
Patients receiving any PIMs^C	49 (16.6)	120 (12.4)	0.066
Follow-up period (days)	244±527.3	418±626.3	<0.001
Deaths	99 (33.4)	197 (20.4)	<0.001
Causes of death			0.185
Cardiac	29	77
Non-cardiac	56	93
Unidentified	14	35

Unless indicated otherwise, data are given as the mean±SD, median [interquartile range], or n (%). ^AGDMT medications include ACEi or ARBs, β-blockers, and MRAs. ^BPatients were considered as having receiving GDMT medications if they were taking a combination of β-blockers and ACEi or β-blockers and ARBs. ^CPIMs include cilostazol, NSAIDs, non-dihydropyridine CCBs, thiazolidine, and dextromethorphan. ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CCB, calcium channel blocker; CCI, Charlson Comorbidity Index; CTD, connective tissue disease; GDMT, guideline-directed medical therapy; LTCI, Long-term Care Insurance; LVEF, left ventricular ejection fraction; MRA, mineralocorticoid receptor antagonist; NSAID, non-steroidal anti-inflammatory drug; PIM, potentially inappropriate medication.

Associations Between Total Number of Medications, GDMT Medications, and PIMs

To examine the relationship between polypharmacy and the use of GDMT medications or PIMs, bivariate correlations were analyzed between the total number of oral medications taken per day and the number of medications classified as GDMT or PIM. Regardless of frailty and disability status, the total number of medications was positively correlated with the number of GDMT medications (patients with LTCI: r=0.228, P<0.001; patients without LTCI: r=0.214, P<0.001; Figure 2). Similarly, the total number of medications was positively correlated with the number of PIMs regardless of frailty and disability status (patients with LTCI: r=0.297, P<0.001; patients without LTCI: r=0.163, P<0.001; Figure 3). A comparison of patient characteristics according to polypharmacy status is presented in Supplementary Table; the results show that patients with polypharmacy were more likely to be prescribed GDMT medications and PIMs.

Figure 2.

Relationship between the number of medications and guideline-directed medical therapy (GDMT) medications in the Long-term Care Insurance (LTCI) and no-LTCI groups.

Figure 3.

Relationship between the number of medications and potentially inappropriate medications (PIMs) in the Long-term Care Insurance (LTCI) and no-LTCI groups.

Overall Survival in Patients With or Without Frailty and Disability

In the LTCI group, there were no significant differences in all-cause mortality between patients with and without polypharmacy (log-rank P=0.196; Figure 4). Similarly, there were no significant differences in all-cause mortality between patients who received PIMs and those who did not (log-rank P=0.966); however, patients who received GDMT medications had a better prognosis than those who did not (log-rank P=0.022; Figures 5,6).

Figure 4.

Kaplan-Meier curves for all-cause mortality according to polypharmacy status in the Long-term Care Insurance (LTCI) and no-LTCI groups.

Figure 5.

Kaplan-Meier curves for all-cause mortality according to guideline-directed medical therapy (GDMT) medications status in the Long-term Care Insurance (LTCI) and no-LTCI groups.

Figure 6.

Kaplan-Meier curves for all-cause mortality according to potentially inappropriate medications (PIMs) status in the Long-term Care Insurance (LTCI) and no-LTCI groups.

In the no-LTCI group, patients with polypharmacy and those who received GDMT medications had a better prognosis (log-rank P=0.022 for polypharmacy; log-rank P<0.001 for GDMT medications; Figures 4,5). In addition, in the no-LTCI group, patients who received PIMs had a poorer prognosis (log-rank P=0.003; Figure 6). Results in the no-LTCI group were consistent with those observed in the overall patient population (Supplementary Figures 1–3). Subgroup analysis showed no significant interactions between medication profiles and survival outcomes (Supplementary Figure 4). In the analysis of individual GDMT medications, patients in the no-LTCI group who received ACEi/ARBs and β-blockers had a better prognosis (Supplementary Figures 5–7).

During the observation period, 99 (33.4%) patients in the LTCI group and 197 (20.4%) patients in the no-LTCI group died. Although there was no significant difference in the causes of death between the 2 groups, the proportion of non-cardiac deaths was higher in the LTCI group (56.6% vs. 45.4%; P=0.185; Table 1).

Predictive Value of Polypharmacy, GDMT Medications, and PIMs for All-Cause Mortality

Results of Cox regression analysis for all-cause mortality are presented in Table 2. In univariate analysis, neither polypharmacy (odds ratio [OR] 0.64, 95% confidence interval [CI] 0.321–1.269, P=0.200) nor PIM use (OR 1.01, 95% CI 0.637–1.601, P=0.966) was significantly associated with all-cause mortality in the LTCI group. However, the use of GDMT medications was significantly associated with all-cause mortality in the LTCI group in both univariate (OR 0.53; 95% CI 0.307–0.921; P=0.024) and multivariate (OR 0.47; 95% CI 0.258–0.866; P=0.015) analyses.

Table 2.

Cox Regression Analysis for Factors Predictive of All-Cause Mortality

	LTCI group						No-LTCI group
	Univariate			Multivariate			Univariate			Multivariate
	OR	95% CI	P value	OR	95% CI	P value	OR	95% CI	P value	OR	95% CI	P value
Age	1.03	0.999–1.055	0.059				1.08	1.079–1.101	<0.001	1.06	1.036–1.085	<0.001
Male sex	1.05	0.705–1.556	0.820				0.29	0.869–1.608	0.287
Body mass index	0.90	0.845–0.959	0.001	0.93	0.868–0.994	0.032	0.94	0.910–0.978	0.001	0.96	0.925–0.998	0.041
Hemoglobin	0.80	0.706–0.907	0.001	0.81	0.697–0.937	0.005	0.90	0.838–0.966	0.004	0.99	0.939–1.036	0.578
Creatinine	0.38	0.148–0.964	0.327				1.10	1.010–1.187	0.027	1.06	0.961–1.158	0.264
LVEF	0.97	0.951–0.997	0.026	0.98	0.952–1.004	0.096	0.99	0.976–1.008	0.320
Severe comorbidities	1.54	1.032–2.300	0.035	1.46	0.915–2.337	0.112	1.98	1.501–2.604	<0.001	1.81	1.320–2.492	<0.001
Atrial fibrillation	1.08	0.730–1.608	0.690				1.22	0.930–1.609	0.149
Polypharmacy	0.64	0.321–1.269	0.200				0.65	0.447–0.943	0.023
GDMT medication use	0.53	0.307–0.921	0.024	0.47	0.258–0.866	0.015	0.44	0.323–0.600	<0.001	0.57	0.400–0.799	0.001
PIM use	1.01	0.637–1.601	0.966				1.46	1.035–2.054	0.031	1.51	1.040–2.203	0.030

CI, confidence interval; LTCI, Long-term Care Insurance; OR, odds ratio. Other abbreviations as in Table 1.

In the no-LTCI group, univariate analysis revealed that polypharmacy (OR 0.65; 95% CI 0.447–0.943; P=0.023), the use of GDMT medications (OR 0.44; 95% CI 0.323–0.600; P<0.001), and PIM use (OR 1.46; 95% CI 1.035–2.054; P=0.031) were significant predictors of all-cause mortality. In multivariate analysis, the use of GDMT medications (OR 0.57; 95% CI 0.400–0.799; P=0.001) and PIM use (OR 1.51; 95% CI 1.040–2.203; P=0.030) remained significantly associated with all-cause mortality (Table 2).

Discussion

The major findings of this study are as follows. First, the number of medications was positively correlated with both the number of GDMT medications and the number of PIMs. This indicates that polypharmacy has both positive and negative aspects. It is difficult to determine the appropriateness of medication management based solely on the number of medications in HF patients. A detailed assessment of the medication profile, including the identification of GDMT medications and PIMs, is essential. Second, the association of medication profiles with prognosis was inconsistent according to frailty and disability status. In the no-LTCI group, as reported previously,²³ patients who received GDMT medications had a better prognosis, whereas those who received PIMs had a poorer prognosis. In patients with LTCI, patients who received GDMT medications also had a better prognosis, whereas PIMs were not significant predictors of mortality. These results suggest the applicability and effectiveness of GDMT medications in patients with frailty and disability, who are often excluded from randomized controlled trials. Further investigations are needed to identify medication profiles associated with poor prognosis in the frailty and disability (LTCI) group.

Polypharmacy and GDMT Medications in HF

The rate of GDMT medication prescriptions increased with the number of medications, making polypharmacy an unavoidable aspect of medical therapy for HF. In particular, it is recommended that patients with HFrEF and HFmrEF take β-blockers,³ ACEi/ARBs, angiotensin receptor-neprilysin inhibitor, MRAs, and SGLT2i. In addition, ivabradine or vericiguat may sometimes be considered.³^–⁵ Recent studies have demonstrated additive therapeutic effects of GDMT medications for HF, and the simultaneous use of multiple medications is recommended from the early stages of HF.¹^,²⁴ Indeed, the development of HF medications and improved adherence to guidelines have contributed to an increase in polypharmacy.²⁵ These factors complicate the assessment of polypharmacy in patients with HF. Recently, new approaches to defining polypharmacy in HF have been proposed, such as considering polypharmacy as the use of ≥10 medications²⁶^,²⁷ or counting only the number of medications other than GDMT medications.²⁸ The number of medications used by patients with HF may reflect the extent of GDMT implementation, as suggested by our analysis, regardless of the frailty and disability status.

Clinical Implications of Medication Profiles According to Frailty and Disability Status

In general, HF patients with frailty and disability are older, have more severe comorbidities, and often require medications that are not directly related to HF treatment.⁷^,²⁹^,³⁰ Consistent with this, our study found that the number of medications was higher in the LTCI group, and many medications unrelated to HF treatment, including PIMs, were prescribed. However, in the LTCI group, the use of PIMs was not a significant predictor of prognosis. Various academic societies and experts have proposed different criteria for PIMs, all of which have been associated with worsened outcomes, including hospitalization for HF.⁸^,⁹^,³¹^–³³ However, these reports have not specifically examined outcomes based on frailty and disability status. Our findings suggest that PIM use in this population may primarily serve as a marker of overall health status rather than a direct contributor to adverse outcomes. Furthermore, certain PIMs, such as steroids, play an important role in managing extracardiac diseases.³⁴^,³⁵ In populations with severe comorbidities, discontinuation of these medications solely due to their potential risk of worsening HF may not be appropriate. Although the results of this study suggest that the prognostic impact of PIMs may differ between the no-LTCI and LTCI groups, no significant interaction was observed, making it difficult to draw a definitive conclusion about differences in effect between the 2 groups. Therefore, further validation is necessary.

In addition, in the LTCI group, the frequency of GDMT medication prescriptions was lower. The medication profile in this study was assessed at enrollment, and the rate of GDMT medication prescriptions may have improved during follow-up. Frailty and disability are known to be a major barriers to GDMT implementation, making a significant improvement unlikely.³⁶^–³⁸ However, the use of GDMT medications was associated with a favorable prognosis. In recent years, several studies have re-examined randomized controlled trials of medications already shown to be effective in HF patients, stratifying them by frailty and disability status.³⁹^,⁴⁰ Although the need for the use of GDMT has sometimes been questioned in vulnerable populations where non-cardiac events are common,⁴¹^,⁴² it should be considered in patients with reduced LVEF who are prone to cardiac events,⁴³ even if they are using LTCI.

Study Limitations

This study has several limitations. First, clinical data, including medications, were extracted at enrollment, and prescription patterns may have changed during follow-up. Second, the study data were obtained retrospectively from medical records, which limits the generalizability of our findings. In particular, patients’ symptoms could not be accurately evaluated, the New York Heart Association classification was unclear, and patients included in the analysis were differentiated based on the use of diuretics. Furthermore, B-type natriuretic peptide levels could not be assessed due to a high proportion of missing data. Moreover, because this was a cross-sectional study, it could not determine the direction of causality. Third, regarding the CCI used for evaluating comorbidities, the items “diabetes with end-organ damage (2 points)” and “moderate or severe liver disease (3 points)” could not be assessed due to the limitations of the medical records. Instead, we classified these conditions as “diabetes (1 point)” and “mild liver disease (1 point),” respectively, which may have led to an underestimation of the severity of comorbidity. Fourth, although several studies consider the presence of LTCI certification as an indicator of frailty and disability,⁴⁴ it whether frailty and disability should be determined solely based on LTCI certification needs to be discussed. Fifth, the primary outcome of this study was all-cause mortality. Further research is required to determine whether medication profiles are associated with other clinically relevant outcomes, such as HF-related hospitalizations, non-cardiac hospitalizations, and quality of life, in both the LTCI and no-LTCI groups. Sixth, this was a retrospective observational study, and the sample size was not predetermined, which may have resulted in insufficient statistical power for some analyses. The estimated statistical power for detecting a hazard ratio of ≥1.75 was 29% for polypharmacy in the LTCI group, 67% for GDMT, and 64% for PIM. Further research in an appropriately powered study with a suitable sample size is needed to confirm our findings. Finally, this analysis did not account for the severity of cognitive impairment, which may have influenced the medication profile.

Conclusions

This study highlights the complex relationship between polypharmacy, GDMT medications, and PIMs in older adults with HF, particularly in relation to frailty and disability status. Although the use of GDMT medications was associated with improved survival and PIM use was associated with poorer outcomes in patients without LTCI, these associations were not evident among those with LTCI. A comprehensive assessment of medication appropriateness is important, considering overall health conditions. Although our findings offer meaningful insights, they should be interpreted with caution due to potential confounding factors and limited statistical power, and further research is warranted to validate these observations and optimize treatment strategies.

Acknowledgments

The authors acknowledge Niigata City Welfare Department Nursing Care Insurance Division for their contribution to data extraction for community-dwelling older people. During the preparation of this manuscript, the authors used Chat GTP to correct grammatical mistakes.

Sources of Funding

This research was supported, in part, by a Grant-in-Aid for Community Health from the Medical Association of Niigata City (GC02520183). The funder had no role in the design of the study, in the collection, analysis, and interpretation of data, or in writing the manuscript.

Disclosures

T.M. and T.I. are members of Circulation Journal’s Editorial Team. The remaining authors have no conflicts of interest to disclose.

IRB Information

This study was approved by the Ethics Committee of Niigata University (Approval no. Niigata University 1788).

Data Availability

The deidentified participant data will not be shared.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-25-0200

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