Findings Across the Ejection Fraction Subgroups in Hospitalised Heart Failure Patients: The First MY-HF Registry Study

Heart failure (HF) remains a leading public health concern that is rapidly growing and presents a significant burden of morbidity and mortality on a global scale.¹ As HF is the end stage of most heart diseases, it represents an important cause of hospital admissions and readmissions, including in Malaysia.² These are compounded by the widespread ageing population and burden of comorbidities, including hypertension, diabetes and chronic kidney disease (CKD), which are intricately linked to the risk of HF development.^3,4 Classifying patients by left ventricular ejection fraction (LVEF) subgroups is important because of differences in prognosis and treatment response.¹ Findings from the ASIAN-HF Registry have shown that the risk of 1-year all-cause mortality or HF admission in both HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF) patients was twofold higher in the presence of comorbid diabetes and CKD than in patients with neither diabetes nor CKD.⁴

Despite these growing concerns, the data available on HF in Asian regions remain limited compared with those in the West.^3,5 Furthermore, there are several gaps in the management of HFpEF, which is a growing subset of HF among ageing populations in Malaysia and other low-to-middle-income Asian countries.^6,7 Although symptomatic treatment and prevention of comorbidities are essential, significant barriers in patient, healthcare professional and policy levels may impede optimal clinical management.³ The Malaysian National Heart Failure (MY-HF) Registry, initiated by the National Heart Association of Malaysia, is the first prospective, observational study of patients who were admitted for HF (New York Heart Association [NYHA] classes II–IV), and it seeks to address these knowledge gaps in the country. We previously reported on the baseline characteristics, treatment modalities and in-hospital patient outcomes, pinpointing areas requiring improvement within the country’s HF services.⁸ The present study aims to further elucidate these findings across the ejection fraction (EF) subgroups, namely, HFpEF, HFrEF and HF with mildly reduced EF (HFmrEF). Predictors of 30-day and 1-year outcomes were also investigated.

Methods

Study Design

The MY-HF Registry study design and methodology have been published previously.⁸ In brief, this 3-year prospective, observational study investigated patients admitted to 18 tertiary hospitals in Malaysia for acute HF with NYHA classes II–IV between August 2019 and December 2020 (Supplementary Table 1). All patients included in the study were required to meet the following criteria: age ≥18 years, with a documented diagnosis of HF by the attending in-patient physician at enrolment; visited the hospital because of acute decompensated HF (ADHF) (readmission for worsening HF or de novo HF) during the enrolment period; had LVEF assessment performed at index admission or within the first 6 months of hospital admission; and required a minimum hospitalisation duration of 24 hours for stabilisation before being discharged. Patients were excluded if they had severe pulmonary disease or pulmonary arterial hypertension (pulmonary systolic pressure >50 mmHg), had life-threatening comorbidity with a life expectancy of <1 year or were unable to give informed consent. Of the total patients enrolled, 2,651 were included in this 1-year follow-up analysis and categorised by LVEF type, namely, HFrEF (LVEF ≤40%), HFmrEF (LVEF 41–49%) and HFpEF (LVEF ≥50%). Only patients with a history of HF at admission (n=1,556) were analysed for medication prescribed at admission (Figure 1). Excluding those who died in the hospital, 2,575 patients were analysed for medication prescribed at discharge. The MY-HF Registry protocol was approved by the relevant ethics committees of participating medical centres and conducted according to the ethical standards in the International Conference on Harmonisation guidelines and the Declaration of Helsinki. All participants gave informed consent before data collection. Ethical approval for this study was also obtained from the Medical Research and Ethics Committee, Ministry of Health Malaysia (NMRR-19-1530-47610).

Study Endpoints

The primary endpoints of this study were all-cause mortality and hospital readmission for ADHF at 1 year. The secondary endpoints were in-hospital all-cause mortality, cardiovascular mortality during index hospitalisation and readmission and all-cause mortality at 30 days. This report highlights the baseline findings, preadmission and discharge medications and outcomes (30-day and 1-year outcomes) by LVEF subgroups.

Statistical Analyses

Data were analysed using Stata software (version 16.0, StataCorp LLC). Descriptive statistics summarised demographics, clinical characteristics, laboratory evaluations and outcomes in 30 days and 1 year. Continuous variables were reported as mean ± SD or median with minimum and maximum range, while discrete variables were presented as frequencies and percentages. The complete case analysis approach was employed to handle the missing data. The Pearson χ² test was used to examine the association between the proportion of ADHF patients receiving HF medication and LVEF subgroups. Predictors of 30-day and 1-year outcomes were analysed using univariate and multivariate logistic regression. All p-values <0.05 were considered statistically significant.

Results

Demographics and Basal Characteristics

In this MY-HF Registry study, 2,651 patients with LVEF data were included in the analysis. Their clinical characteristics were stratified by LVEF subgroups as defined in the National Clinical Practice Guidelines for the Management of HF, namely HFrEF (LVEF ≤40%), HFmrEF (LVEF 41−49%) and HFpEF (LVEF ≥50%) (Table 1).⁹ Overall, patients with HFrEF represented the largest proportion of this cohort (66.2%), followed by HFpEF (22.1%) and HFmrEF (11.7%). Patients in the total cohort had a mean age of 60.1 ± 13.7 years; 66.6% were male. Those aged ≥60 years represented 52.4% of all admitted patients. When stratified by LVEF subgroups, patients with HFpEF were older (65.3 ± 13.3 years); 69.7%, 63.1% and 44.8% of patients with HFpEF, HFmrEF and HFrEF, respectively, were aged ≥60 years. The proportion of females with HFpEF (53.5%) was higher than those with HFrEF (25.1%) or HFmrEF (42.7%).

At the time of admission, more patients had a medical history of HF than de novo HF diagnosis, not only in the overall cohort (58.7% versus 34.7%, respectively), but also across all LVEF subgroups (HFrEF, 63.1% versus 31.4%; HFmrEF, 51.1% versus 39.8%; HFpEF 49.6% versus 41.9%). More patients with HFpEF than HFrEF were admitted with de novo HF (41.9% versus 31.4%). Over half of patients presented with NYHA classes III–IV at the time of admission across LVEF subgroups (HFrEF, 55.6%; HFmrEF, 57.3%; HFpEF, 56.0%). More patients in the HFpEF subgroup than in the HFrEF subgroup had comorbidities, namely, hypertension (76.8% versus 68.6%), CKD (35.6% versus 28.6%), AF/atrial flutter (21.0% versus 11.3%) and sleep-disordered breathing (2.6% versus 0.9%); however, ischaemic heart disease (IHD) was more prevalent in the HFrEF subgroup (44.3% versus 59.0%).

Only 360 patients of the total cohort had N-terminal pro B-type natriuretic peptide (NT-proBNP) biomarker assessed; of these, 95.5% of patients with HFrEF and approximately 80% in both the HFmrEF and HFpEF groups had elevated NT-proBNP levels of ≥1,000 pg/ml (Supplementary Table 2). Mean estimated glomerular filtration rates across LVEF subgroups were 59.87 (HFrEF), 52.51 (HFmrEF) and 55.75 (HFpEF) ml/min/1.73 m² (Supplementary Table 2). Mean levels of glycated haemoglobin (HbA1c; HFrEF, 7.61%; HFmrEF, 7.61%; HFpEF, 7.41%) and low-density lipoprotein (LDL; HFrEF, 2.58 mmol/l; HFmrEF, 2.87 mmol/l; HFpEF, 2.61 mmol/l) were consistent across LVEF subgroups.

Heart Failure Medication Prior to Hospital Admission and at Discharge

HF medications analysed included angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), angiotensin receptor/neprilysin inhibitors (ARNIs), β-blockers, mineralocorticoid receptor antagonists (MRAs) and ivabradine. The use of HF medications increased at discharge (93.6% for patients discharged alive) compared with prior to admission (83.9% had at least one HF medication; Supplementary Table 3). β-blockers were the most prescribed class of medication prior to admission (69.2%), followed by renin–angiotensin–aldosterone system inhibitors (RAASis [i.e., ACEIs, ARBs, or ARNis], 52.4%) and MRAs (33.3%; Supplementary Table 3). Trends for HF medications prescribed at discharge were similar, being highest for β-blockers (81.2%), followed by RAASis (61.6%) and MRAs (43.3%). The dual combination of β-blockers in addition to either ACEIs or ARBs was the most prescribed combination therapy for all patients prior to admission (35.0%) and at discharge (39.7%), with a higher proportion in patients with HFrEF than with HFpEF (37.2% versus 27.5% prior to admission and 40.3% versus 36.7% at discharge, respectively). Similar patterns were observed across other combination therapies, including the dual combination of ARNIs and β-blockers and the triple combination therapies of ACEIs/ARBs/ARNIs plus both β-blockers and MRAs.

Thirty-day and 1-year Patient Outcomes

Figure 2 shows differences in in-hospital, 30-day and 1-year outcomes by LVEF subgroups. While in-hospital and 30-day mortality rates were consistent across the subgroups, significant differences were observed in 1-year mortality rates. Patients with HFpEF had a lower 1-year mortality rate (26.6%) than those with HFrEF (33.8%) or HFmrEF (32.0%). Significant differences were also observed in 30-day readmission rates, but not at 1 year. Specifically, more patients with HFmrEF were readmitted at 1 month after discharge (14.7%) compared with HFrEF (11.7%) and HFpEF (8.6%).

Figure 3 illustrates the factors affecting 30-day and 1-year hospital readmission rates. Being aged >60 years was associated with a significantly higher risk of hospital readmission at 1 year. Presentation of NYHA classes III–IV at admission also showed an association with an increased risk of 1-year readmission. The presence of comorbidities (i.e., hypertension, CKD or diabetes) was associated with an increased risk of 1-year readmission but not 30-day readmission. Conversely, some variables were associated with a lower risk of 30-day and/or 1-year readmission. Patients with HFpEF had a lower risk of readmission at 30 days than those with HFrEF or HFmrEF. Patients hospitalised due to de novo HF had a lower risk of hospital readmission at 30 days and 1 year than hospitalised patients with a medical history of HF. In terms of HF medication, receiving RAASi at discharge was associated with a reduced risk of 30-day hospital readmission. A dual combination of ACEI/ARBs and β-blockers at discharge was associated with a reduced risk of 1-year readmission. Other analysed risk factors are shown in Supplementary Tables 4 and 5.

Figure 4 illustrates the factors affecting 30-day and 1-year all-cause mortality rates. Having CKD was associated with increased 30-day and 1-year all-cause mortality. However, although pulmonary hypertension increased the 1-year mortality risk, hypertension was associated with a reduced risk of 30-day mortality. Having NYHA classes III–IV at admission was statistically significantly associated with 1-year mortality risk. In terms of HF medication, several medical regimens were associated with a lower risk of 30-day and 1-year mortalities. Hospitalised HF patients prescribed RAASis or a dual combination of ARNIs and β-blockers at discharge had a lower risk of 30-day mortality. Likewise, RAASis prescribed at discharge also reduced 1-year mortality risk. Other analysed risk factors are included in Supplementary Tables 6 and 7.

Discussion

Baseline Characteristics of Patients with Heart Failure across Left Ventricular Ejection Fraction Subgroups

The 3-year prospective, observational MY-HF Registry study aimed to describe the characteristics, management and outcomes in Malaysian patients with HF.⁸ We previously reported that the total patient population (i.e., mean age ~60 years) in the MY-HF Registry was consistent with the younger ‘Asian HF phenotype’ characterised in other registries, including the ASIAN-HF Registry.^9–12 In this follow-up analysis, we further elucidated these findings across the range of LVEF subgroups.

The patient distribution across LVEF subgroups in our study revealed a notable prevalence of HFpEF in hospitalised HF patients; it accounts for 22.1% of the cohort and is a growing burden in Asia.¹³ This observation is partly attributed to an older population, with a higher incidence among females – an observation also reported in the OPTIMIZE-HF Registry.¹⁴ In our study, the HFpEF subgroup comprised predominantly older patients, with a higher representation of females than the HFrEF group (mean age 65 years versus 58 years and 53.5% versus 25.1% females, respectively). These findings echo those reported in the ASIAN-HF Registry.⁷ Another finding in our study was the relatively low usage of natriuretic peptide (NP) biomarkers in HF diagnostics across the LVEF subgroups (12.6–18.3%). This is notable given the role of NP as biomarkers in non-invasive HF diagnostics, as highlighted by the Malaysian HFpEF Working Group for timely HFpEF diagnosis.⁶

Multimorbidity is common among patients with HF globally, and its prevalence in Southeast Asia is gradually increasing.^15,16 The MY-HF cohort highlights the complexity of HF in Asia, demonstrating a number of comorbidities, such as diabetes, CKD and hypertension. In our study, these comorbidities showed similarities to that of the ASIAN-HF Registry, with hypertension emerging as the most prevalent comorbidity across all LVEF subgroups, followed by diabetes, IHD, obesity and AF/atrial flutter.⁷

Patients with HFpEF have more major comorbidities than patients with HFrEF; this – along with older age – exacerbates the risk of HFpEF.¹⁷ In our study, we likewise found a higher prevalence of comorbidities in patients with HFpEF than in those with HFrEF; these included hypertension, CKD, AF and sleep-disordered breathing. We also previously highlighted the high prevalence of hypoalbuminaemia in HF patients in our study – likely due to the presence of diabetes and severe congestion at admission – and showed that it was associated with poorer in-hospital outcomes in univariate analysis but not in multivariate analysis.⁸

Consistent with the literature, there is a frequent association between the aforementioned comorbidities and HFpEF in our study.^17,18 Hypertension, in particular, is considered one of the most prevalent comorbidities in this LVEF subgroup.¹⁸ This is important because it is common for patients with HFpEF to be hospitalised for non-cardiovascular causes. However, the primary reason for hospitalisation may vary depending on the population.¹⁷ The increased incidence of AF in patients with HFpEF at admission aligns with findings from the SWEDE-HF Registry – which had similar enrolment criteria to that of the ASIAN-HF Registry – and the European Society of Cardiology (ESC)-HF Long-Term Registry, where increasing EF correlated with an increased prevalence of AF.^19,20 This may have an impact on prognosis, including all-cause mortality and HF hospitalisation rates, especially in patients with HFpEF.²⁰ Conversely, IHD is more common in patients with HFrEF. It is among the most common comorbid conditions in patients with HF and is also a prevalent factor contributing to patterns of multimorbidity in Asia, including Malaysia, as reported by the ASIAN-HF Registry.^1,3 Our study also showed that diabetes was the second most prevalent comorbidity after hypertension, and that it was higher among patients with HFmrEF than those with HFrEF or HFpEF (66.7%, 58.3% and 59.6%, respectively). Findings from the ASIAN-HF Registry showed that diabetes and CKD were both predictors of worse patient outcomes.⁴

Patterns in Mortality and Hospital Readmission Rates Across Left Ventricular Ejection Fraction Subgroups

In-hospital mortality rates were comparable across LVEF subgroups. This trend extended to the 30-day mortality rates, indicating a consistent pattern in the short-term outcomes after hospitalisation, regardless of LVEF subgroup. However, notable differences emerged in the 1-year mortality rates. Patients with HFrEF exhibited higher 1-year mortality rates than those with HFpEF, which is consistent with findings from other multicentre registries, such as the ASIAN-HF (10.6% versus 5.4%) and ESC-HF Long-Term Registries (8.8% versus 6.3%).^7,21 This finding may provide insights into the long-term prognosis of patients with HF, based on their LVEF status. The 1-year readmission rates in our study were uniformly high regardless of LVEF subgroup (58.7% in HFrEF; 66.3% in HFmrEF; 66.3% in HFpEF). This finding echoes that of the GWTG-HF Registry: 59.6% in HFrEF, 63.2% in HFmrEF and 62.5% in HFpEF.²²

Use of Heart Failure Medication across Left Ventricular Ejection Fraction Subgroups and Its Association with Patient Outcomes

The need for guideline-directed medical therapy (GDMT) in managing HF is emphasised in guidelines as the use of the recommended HF medications, including RAASis, has been associated with improved patient outcomes.^1,23 Thus, optimising GDMT usage is crucial in HF management to reduce patient morbidity and mortality.²⁴ Our analysis identified several associations between HF medication use and short- and long-term outcomes in patients with HF. In the MY-HF cohort, we previously reported lower prescription rates of RAASis and β-blockers at discharge compared with the western and Asia-Pacific regions.^8,10,25,26 A similar finding was observed in a local single-centre retrospective study of Malaysian patients with HF, where the proportions of RAASis and β-blockers at discharge were lower than in the GWTG-HF Registry.^24,27

The proportion of patients with HFpEF in our study who did not receive discharge HF medication was approximately twice as high as in the HFrEF subgroup (10.5% versus 4.7%) (Supplementary Table 3). Furthermore, the HFpEF subgroup received the least HF therapy combinations at discharge, including both triple (e.g. ACEI/ARB, β-blocker and MRA) and dual combination therapies (e.g. ARNI and β-blocker), ranging from 1.6% to 36.7%. This is because many of the HF medications were not in the guidelines for HFpEF at the time of the study. Despite the guidelines not mandating the use of β-blockers and ACEIs in the management of HFpEF, a good proportion of patients in this group received β-blockers and ACE inhibitors for the management of their comorbid condition. Despite the difference in the number of patient records on HF medications prior to admission versus at discharge, we observed an overall increase in the prescriptions of HF medications at discharge compared with preadmission across all LVEF subgroups. This indicates that hospitalisation presents a crucial opportunity for the timely diagnosis and optimisation of HF therapy, benefitting patients with HFpEF, HFmrEF and HFrEF. Nevertheless, potential areas of improvement remain, for example, in the usage of RAASi at discharge, which was lower than the rates reported in the GWTG-HF Registry (49.0–65.1% versus 94.5%), although this gap was less pronounced for β-blockers (75.9–83.2% versus 89.9%).²⁷

Our findings also highlight the positive correlation of HF medication usage at discharge with improved patient outcomes. The association of HF medications with improved mortality rates was notable with the prescribing of RAASis or dual combinations of ARNIs and β-blockers at discharge. Indeed, it has been shown that HF medications – particularly in combination and when prescribed to patients with HFrEF – improve clinical outcomes, such as lower mortality risk, hospitalisation and emergency department visits.²⁸ In our study, prescribing RAASis or a dual combination of ACEI/ARBs and β-blockers at discharge was associated with lower 30-day or 1-year hospital readmission rates. These outcomes should be cautiously interpreted, as there was insufficient statistical power because of the smaller sample size for a detailed predictor analysis.

Risk Factors for 30-day and 1-year Outcomes of Heart Failure

The analysis of predictors for mortality and hospital readmissions yielded several key insights for HF management, particularly in the Asian context. Among the most notable findings was the significant association of NYHA classes III–IV presentation at admission with the 1-year mortality and readmission rates. It is well established that patients presenting with more severe NYHA classes had an increased risk for all-cause mortality and hospital readmissions – a finding consistently observed across numerous HF registries globally.^7,29–31 This highlights the importance of early intervention in patients showing advanced HF symptoms.

The presence of CKD is an important determinant of poorer HF outcomes. One study showed that a decrease in estimated glomerular filtration rate (eGFR), alongside no early decline in NT-proBNP levels, was associated with the risk of higher mortality rates in patients with eGFR <60 ml/min/1.73 m².³² Moreover, the combined presence of CKD, with or without diabetes, was associated with increased 1-year mortality and hospitalisation rates, as observed in the ASIAN-HF Registry.⁴ Our finding holds relevance in the Asian populations, given their increasing prevalence of CKD with HF, which can lead to poorer outcomes.⁴ However, it is interesting to note that the association of CKD as a risk factor was not observed in registries from China and Japan, particularly in patients with HFpEF.^33,34 Pulmonary hypertension increases the risk of 1-year mortality. Indeed, secondary pulmonary hypertension is a clinical indicator of advanced HF and can lead to poorer outcomes if specialist management is not promptly sought.¹ Conversely, the association of hypertension with reduced mortality was intriguing, given that it typically exacerbates the risk of HF.¹ One study found that hypertension was not associated with increased mortality in specific groups of patients who are aged over 85 years or are frail at 75–84 years old.³⁵ Patients presenting with hypertension can be started on foundation HF medications (i.e., ACEI/ARB/ARNIs and β-blockers) earlier and titrated to a higher dose. This may explain our study’s reduced risk of mortality, but further investigations are needed.

Nevertheless, our findings highlight the importance of comprehensive HF management in Malaysia, focusing on renal function monitoring and management. Hospitalisation due to de novo HF was another factor associated with improved patient outcomes compared with those who had a history of HF at admission. Findings from the ASCEND-HF study demonstrated that being diagnosed with HF within a month before hospitalisation was independently associated with improved post-discharge outcomes.³⁶ Our analysis also revealed that patients aged over 60 years were at higher risks for 30-day and 1-year hospital readmission, as well as 1-year mortality. Similar observations were made in the ASIAN-HF Registry, where older age in patients with HFrEF or HFpEF correlated with increased HF readmissions.³⁷ An Italian study further supported this, identifying older age as an independent factor for higher 30-day and 1-year mortalities.³⁸ This indicates a need for tailored management strategies for elderly patients with HF, possibly including more intensive monitoring and care.

It would be interesting to see whether the outcomes observed in our study are similar to those of our neighbouring countries that share similar populations and cultural diversity. However, at the time of writing this manuscript, no such literature was available.

Study Limitations

While providing significant insights into HF management in Malaysia, this study has several limitations. Firstly, patient recruitment from large, urban tertiary hospitals (including 17 government-affiliated centres and one private cardiac centre) may have inadvertently led to the underrepresentation of patients from rural areas. This might limit the applicability of our findings to the entire spectrum of HF patients in Malaysia. Imputation was not performed for missing treatment and outcome data due to the percentage of missing data being <5%, except for HbA1c and LDL-cholesterol (Supplementary Table 2) as these data are not routinely collected for all patients with HF but only for patients with specific risk factors on admission. Sodium–glucose cotransporter 2 inhibitors were not investigated in this study because this class of medication had not yet been recommended as a treatment for HF at the time of study commencement.¹

Patients with short life expectancy or severe comorbidities were also excluded from our study, possibly leading to an underestimation of the actual short-term prognosis for patients experiencing acute HF. Other limitations include the absence of HF medication dosage data prior to admission, at discharge and during follow-up (1 month, 6 months and 1 year) and the incidence of adverse events due to limitations in capturing this information in the registry. As the timing of this study coincided with the peak of the COVID-19 pandemic in 2020, there may be an underrepresentation of individuals who succumbed to HF outside the hospital setting because of a reluctance to seek hospital care. Thus, these factors should be considered when interpreting the findings.

Conclusion

The MY-HF Registry study offers valuable insights into HF demographics, comorbidities and GDMT usage and their impact on patient outcomes across all LVEF subgroups. The study identified concerningly high 1-year mortality rates in patients with HF, particularly in those with advanced NYHA class or CKD. While the specific reasons remain unclear, suboptimal medication regimens, medication non-adherence and inadequate comorbidity management may be among the factors associated with poor patient outcomes. Future research exploring these factors and optimal GDMT usage could improve clinical practice and patient outcomes.

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