Ex) Article Title, Author, Keywords
Ex) Article Title, Author, Keywords
R Clin Pharm 2024; 2(1): 27-37
Published online June 30, 2024 https://doi.org/10.59931/rcp.24.0004
Copyright © Asian Conference On Clinical Pharmacy.
Saskia McGrane , Renae Lloyd
, Cassandra Potts
, Ru Jing Ang
Correspondence to:Saskia McGrane
E-mail Saskia.McGrane@sa.gov.au
ORCID
https://orcid.org/0009-0002-6478-8426
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: An Australian consensus statement on the pharmacological prevention and management of heart failure, released in August 2022, recommended initiation of four medications for treating heart failure with reduced ejection fraction (HFrEF): angiotensin- converting enzyme inhibitors or angiotensin receptor-neprilysin inhibitors, heart failure beta blockers, mineralocorticoid receptor antagonists and sodium glucose co-transporter 2 inhibitors. These four medications are classified as the guideline-directed medical therapy (GDMT). Intravenous administration of ferric carboxymaltose (IFC) in the hospital, following an episode of acute heart failure in patients with iron deficiency and an ejection fraction of less than 50% was also recommended to reduce heart failure related hospital admissions as demonstrated in the AFFIRM-AHF trial.
Methods: A study conducted a retrospective audit of patients with HFrEF discharged by the Cardiology and the General Medicine teams of a tertiary hospital between two time periods, October to December 2021, and October to December 2022. Information including patient demographics, medication allergies and iron studies was collected. Discharge medication lists were extracted, reviewed for GDMT prescription and audited to check the accuracy of heart failure medication documentation. SPSS v29 was used to perform the statistical analysis.
Results: A greater proportion of patients were discharged on optimal GDMT across both study periods by the Cardiology department compared with the General Medicine department (37% versus 4% respectively, p<0.001). More accurate recording of heart failure medications in discharge summaries was performed by the Cardiology department compared with the General Medicine department (82% versus 68% respectively, p=0.015). Lastly, eligible Cardiology patients were more likely to receive IFC compared with General Medicine patients (71% versus 28%, p=0.003).
Conclusion: Overall, the audits performed in this study demonstrated that improvements in HFrEF GDMT prescribing adherence, documentation accuracy and IFC administration are warranted since the current practice is suboptimal, particular in General Medicine teams. The barriers to GDMT adherence at the prescribers’ end will require further examination and the appropriate quality improvement tools need to be implemented to ensure that all patients with HFrEF have equal access to optimal healthcare.
KeywordsClinical pharmacy; Heart failure; Medicines; Cardiology; Internal medicine; Iron
Heart failure (HF) is a clinical syndrome characterised by debilitating symptoms such as breathlessness, ankle swelling and fatigue as well as poor prognosis [1]. Heart failure with reduced ejection fraction (HFrEF) is defined by the European Society of Cardiology (ESC) a left ventricular ejection fraction less than 40% [1], whilst the National Heart Foundation of Australia (NHFA) and Cardiac Society of Australia and New Zealand (CSANZ) define it as less than 50% [2]. The burden of HF in Australia is reflected by its high prevalence, affecting 2.1% of adults and accounting for 1.6% of hospitalisations annually [2].
The implementation of the most recent evidence-based pharmacotherapies for the treatment of HFrEF is paramount to maintaining patient quality of life and maximising survival [1]. The basis for implementation can be sourced from guidelines such as that from the European Society of Cardiology (ESC). In this 2021 guideline the ESC made a new recommendation to initiate all four first line pharmacotherapies as soon as clinically possible (see therapies listed below) [1]. In comparison, earlier guidelines recommended the stepwise initiation of the first three listed pharmacotherapies [3].
1. Renin-angiotensin aldosterone system inhibitors (RAASi)
• ACEI-inhibitor (ACEI)
• Angiotensin receptor-neprilysin inhibitor (ARNI)
2. Heart failure beta blocker (HFBB)
3. Mineral-corticoid antagonist (MRA)
4. Sodium and glucose co-transport 2 inhibitor (SGLT2i)
The NHFA and CSANZ released a consensus statement in 2022 which aligned with the recommendations made by the ESC for the management of HFrEF [4]. The importance of prescribers adhering to this guideline can be derived from a comparative analysis which found that HFrEF patients treated with an ARNI, HFBB, MRA, SGLT2i had 1.4 (80-year-old) to 6.3 (55-year-old) additional years of survival compared to those treated with ACEI or angiotensin II receptor blocker (ARB) and HFBB [5]. The importance of timely up-titration of HF therapies was displayed in the STRONG-HF trial which followed patients discharged from hospital after being admitted with acute heart failure [6]. Superior outcomes were displayed for patients with high intensity care (up-titration of RAASi, HFBB and MRA to 100% of recommended doses within 2 weeks post discharge and frequent outpatient monitoring) as HF hospital readmission or all-cause death up to day 180 occurred in 15.2% of high intensity care patients compared to 23.3% in the usual care group [6].
Irrespective of the recent recommendations, these drugs are underutilised as highlighted by observations that HFrEF patients are being discharged from hospital either not prescribed these medications at all or not at the recommended target doses. Evidence of this can be drawn from the QUALIFY international survey which quantified physician adherence to the 2012 ESC HFrEF GDMT recommendations and included patients across 36 countries who discharged from hospital for a period between 2013–14 [7]. This study demonstrated that good adherence to GDMT only occurred for 23% of HFrEF patients [7]. Good adherence referred to a patient who was prescribed all the indicated GDMTs (i.e., ACEIs/ARBs, BBs, MRAs and ivabradine) at a dose of ≥50% of the target dose [7]. Likewise, an American retrospective study focusing on patients hospitalised with HFrEF from 2013–18 determined the following percentage of BB, ACEi/ARB/ARNI, and MRA prescribed at discharge was 73.4%, 55.9% and 13.8% [8]. Post the release of the ESC 2021 HF guidelines, the EVOLUTION-HF study conducted across Japan, Sweden and the United States determined that mean times for initiation of novel GDMTs (dapagliflozin and ARNI) post patient hospitalization for heart failure were longer compared to other GDMTs, suggesting poor prescriber uptake of the latest recommendations [9].
Few studies have sought to demonstrate the differences between HF guideline-based prescribing across specialities. A Polish study determined that Cardiologists were more likely to prescribe MRA and HFBB than general practitioners in an outpatient setting (nil difference in RAASi prescribing) [10]. However, evidence directly comparing adherence to HFrEF guideline-based prescribing between Cardiologists and General Medicine practitioners in a hospital setting is lacking.
Various implementation tools presented in literature have sought to improve GDMT prescriber adherence. As discussed previously, the STRONG-HF trial displayed that intensive outpatient follow up for HF patients discharged from hospital compared to standard practice resulted in a greater proportion of GDMTs titrated to full doses 90 days post discharge i.e., RAASi (55% vs. 2%), BB (49% vs. 4%); and MRA (84% vs. 46%) [6]. A 2024 systematic review of 28 studies intended to determine the effectiveness of various interventions aimed to optimise GDMT [11]. The success of interdisciplinary titration clinics was reinforced as patients prescribed GDMT at target doses increased from 2% in usual care group to 53% in the intervention group [11]. Other interventions such as clinician and patient education, audits and alerts integrated into electronic health record system had inconsistent results across the various studies [11].
In addition, the ESC and NHFA CSANZ guidelines recommend periodic screening of iron deficiency and intravenous ferric carboxymaltose (IFC) administration for iron deficient HFrEF patients to improve quality of life and HF symptoms [2]. Furthermore, the AFFIRM-AHF trial conducted in 2020 demonstrated that the administration of IFC in hospital after an episode of acute heart failure in patients with HFrEF was safe and reduced the risk of heart failure hospitalisations [12].
The optimisation of HFrEF pharmacotherapy is multi-faceted involving;
• Prescribers adhering to guideline-directed medication therapy (GDMT) [1],
• Prescribers creating a comprehensive multidisciplinary understanding of the individual patient’s medication regime through accurate medication documentation [13],
• Iron deficiency surveillance and management of iron deficiency with IFC [1].
This study had three main aims. Firstly, to describe and compare the prescribing patterns of HFrEF GDMT between Cardiology and General Medicine teams across two study periods (2021 and 2022) at a hospital network. Secondly, to determine the accuracy of HFrEF therapy in discharge medication lists documented in the medical discharge summary and to identify any difference in accuracy between Cardiology and General Medicine teams across the two study periods. Lastly, to determine the proportion of hospitalised HFrEF patients; screened for iron deficiency, eligible for IFC and administered IFC and if there was any difference in iron deficiency screening and IFC administration between Cardiology and General Medicine patients across the two study periods.
The study design was a retrospective cross-sectional audit of patients discharged from a tertiary hospital over two 3-month study periods; October to December 2021 and October to December 2022. The rationale behind the two chosen study periods includes time at which certain heart failure guidelines were released and updates to GDMT access on the Pharmaceutical Benefit Scheme (PBS). The 2021 ESC guideline for the “Diagnosis and Treatment of Acute and Chronic Heart Failure” was the first to include the new GDMT recommendations worldwide [1]. This guideline was released late August 2021, hence the first study period commenced in October 2021 to allow a month for prescribers to become familiar with the new evidence and recommendations before auditing their adherence [1]. SGLT2is were listed on the PBS for HFrEF in January 2022, removing the economic barrier that was suspected to affect SGLT2i prescribing in the first study period [14]. The release of the Australian consensus on the current pharmacological prevention and management of heart failure by NHFA & CSANZ occurred early August 2022, thus the second study period commenced in October 2022 to allow prescribers to become familiar with the new Australian GDMT recommendations [4].
The research was segregated into three sections which all shared the same initial study population. Patients were identified from the SUNRISE® electronic medical record (EMR) system using case-mix extraction and ICD-10 codes (International Classification of Diseases 10th revision). For a patient’s hospital visit to be extracted from the SUNRISE® EMR system, they had to meet the following inclusion criteria: discharged from Flinders Medical Centre (FMC) or Noarlunga Hospital (NH) from October to December 2021 or from October to December 2022, discharged from a General Medicine or Cardiology heart failure team, heart failure as a primary admission diagnosis or secondary admission diagnosis or complication. The study population was further refined by applying the following exclusion criteria: heart failure with a left ventricular ejection fraction >49%, transferred to another hospital or rehabilitation facility or died during the hospital admission. Additional exclusion criteria were applied to the three sub studies to produce a tailored study population for each study aim (Fig. 1).
The following information was extracted from the case notes, medication orders, flowsheets and results sections of the SUNRISE® electronic medical record (EMR) system; patient demographics, height, weight, laboratory test results (SeCr, CrCL), clinical observations (HR,BP), allergies and intolerances, iron studies, ferric carboxymaltose administration, haemoglobin, left ventricular ejection fraction and length of stay.
To determine the list of medications on discharge, the medical discharge summaries, pharmacist discharge notes (where available), inpatient medication administration charts and discharge prescriptions for each patient were accessed using the SUNRISE® EMR system.
Medical discharge summaries were reviewed for a discharge medication list. The HFrEF medications (RAASi, HFBB, MRA, SGLT2i and loop diuretic) were reviewed for accuracy. To determine the accuracy of the list of medications in the medical discharge summary, the pharmacist discharge notes (where available), the inpatient medication administration charts and the discharge prescriptions for each patient were accessed using the SUNRISE® EMR system to determine the medication name, dose, route, and frequency. A discharge medication listed in the medical discharge summary was considered 100% accurate if the name, dose and frequency were accurate. If one factor was incorrect then this was defined as <100% accuracy.
The following criteria was used to define iron deficiency and eligibility for IFC based on local hospital protocol; ferritin <100 ng/mL or 100–299 ng/mL with transferrin saturation <20%, haemoglobin <135 g/L for females or <150 g/L males. A second IFC dose was indicated for patients with a haemoglobin <100 g/L and the dosing was based on ideal body weight. Administration of IFC was extracted from the SUNRISE® EMR system. Patient discharge summaries were reviewed to determine the accuracy of IFC documentation and the presence of a plan for a second IFC dose if required.
The data analysis was conducted using the SPSS v29 statistical analysis software. This software produced measures of central tendency as well as hypothesis testing to compare two independent study groups interpreted using a p-value and odds risk ratio.
To assess if any inherent differences existed between patients discharged from Cardiology versus General Medicine teams, certain factors were compared using a T-test (for continuous variables) and Chi-Squared test (for nominal variables). The independent T-test was utilised for continuous data as each factor in the two independent study groups (General Medicine versus Cardiology) had no significant outliers seen when box and whisker plots were created, the data was normally distributed for each group as verified using the Shapiro-Wilk test of normality and there was homogeneity of variances which was confirmed by performing the Levene’s test. The two nominal factors (Sex and T2DM) were assessed using the Chi-squared test as the variables were categorical, all observations were independent and the cells in the contingency table were mutually exclusive. Furthermore, when the contingency table was built and the Chi-squared test was actioned, greater than 80% of the expected value of cells in the contingency tablet were greater than 5, hence meeting the tests assumptions.
Using the SPSS v29 software, 2×2 contingency tables were built to compare patients from Cardiology or General Medicine and whether optimal GDMT was prescribed at discharge. The proportion of patients prescribed all 4 GDMT as compared to 3 or less therapies was determined and compared between the Cardiology and General Medicine groups. This was analysed separately for the 2021 and 2022 groups and as a combined group. Individual medication classes were also analysed to determine the proportion of patients prescribed each of the 4 GDMT across both study periods and this was compared across the 2 study groups. Due to the small sample size the Fisher’s exact test was used to compare the prescribing of individual GDMTs as well as the prescribing of all 4 GDMTs. Furthermore, when analysing GDMT prescribing between medical teams and study periods, in some instances less than 80% of the expected value of cells in the contingency table were greater than 5 (for example prescribing of optimal GDMT between medical teams in the first study period). Hence, the Chi-squared test could not be used as the test assumes that greater than 80% of the expected value of cells in the contingency table are greater than 5, instead the Fisher’s exact test was used. For continuity, the Fisher’s exact test was used in all instances when analysing GDMT prescribing.
Further tables were built using the software to compare the accuracy (100% or <100%) of the documented HFrEF medications between Cardiology and General Medicine teams and the two study periods (2021 and 2022). The Pearson’s chi squared test was used to compare the two specialities across the two study periods as well as overall as each study group meet the assumptions.
To assess whether iron tests were conducted and IFC administration occurred across the two specialities and study periods, 2×2 contingency tables were derived. The Fisher’s exact test was used to compare the variables due to the small sample size. The Chi-squared test could not be utilised for this data as it did not meet the tests assumptions.
When data collection was complete the spreadsheet was reviewed for unusual values to identify errors in data entry and potential errors were reviewed and rectified. The data collection process was systematic following a workflow to minimise the chance of errors or potential individual bias from the various people collecting data. Missing data integral to the study aim such as nil classification of HF type or ejection fraction were excluded when determining the study population. However, missing patient characteristics such as blood pressure or patient height to determine CrCL were still included in the study but was taken into consideration when interpreting results as well as being accounted for in Table 1 in the Supplementary material where there is a column with the number of missing values for each patient characteristic.
The differences in patient factors amongst patients discharged from Cardiology and General Medicine teams is shown in Table 1 and 2. The key findings were that General Medicine patients were older with worse renal function, whilst Cardiology patients had lower ejection fractions and a shorter length of stay. Although statistical differences exist between systolic blood pressure and heart rate at the time of discharge, these do not appear clinically significant. There was no difference in Type 2 diabetes mellitus (T2DM) diagnosis between the two teams (p>0.05), which was an important consideration for patient eligibility on the PBS for SGLT2is in 2021 (Table 2).
Table 1 Patient characteristics across cardiology and general medicine teams (continuous variables)
Mean | Standard deviation | Mean difference | p-value | |
---|---|---|---|---|
Ejection fraction (%) | ||||
Total (n=203) | 34.4 | 10.2 | –6.18 | <0.001 |
Cardiology (n=100) | 31.4 | 10.4 | ||
General medicine (n=103) | 37.5 | 9 | ||
Age (years) | ||||
Total (n=203) | 74.4 | 14.2 | –14.0 | <0.001 |
Cardiology (n=100) | 67.3 | 14 | ||
General medicine (n=103) | 81.3 | 10.5 | ||
CrCL (mL/min) on discharge | ||||
Total (n=168) | 42.4 | 21.4 | 6.68 | 0.045 |
Cardiology (n=73) | 46.1 | 23.8 | ||
General medicine (n=95) | 39.5 | 19 | ||
Length of stay (days) | ||||
Total (n=203) | 6.86 | 7.06 | –1.20 | <0.001 |
Cardiology (n=100) | 6.25 | 5.14 | ||
General medicine (n=103) | 7.44 | 8.51 | ||
Systolic BP on discharge | ||||
Total (n=202) | 121 | 17.7 | –5.83 | 0.018 |
Cardiology (n=99) | 118 | 19 | ||
General medicine (n=103) | 124 | 15.9 | ||
Heart rate (BPM) on discharge | ||||
Total (n=202) | 76.5 | 13 | –3.8 | 0.019 |
Cardiology (n=99) | 74.6 | 12.7 | ||
General medicine (n=103) | 78.4 | 13.1 |
Table 2 Patient characteristics across cardiology and general medicine teams (nominal variables)
Total (n=203) | Cardiology (n=100) | General medicine (n=103) | p-value | |
---|---|---|---|---|
T2DM diagnosis | 32% (n=65) | 32% (n=32) | 32% (n=33) | >0.05 |
Sex as % of males | 67% (n=136) | 69% (n=69) | 65% (n=67) | >0.05 |
Cardiology discharged a greater proportion of patients on optimal GDMT than General Medicine across both study periods (37% versus 4% respectively, p<0.001), however the prescribing of GDMT across both teams remained unsatisfactory (Fig. 2). There was a statistically significant increase in the prescribing of optimal GDMT overall from 2021 to 2022 (7% to 33%, p<0.001). The four cornerstone HFrEF therapies were individually assessed, and Cardiology prescribed more of every class than General Medicine (Fig. 3). SGLT2i prescribing increased between 2021 to 2022 (8% to 25%), see Supplementary material Fig. 1 and 2.
Cardiology commenced 153 individual GDMTs across both study periods whilst General Medicine commenced 55. The medication classes which had the greatest total number of new commencements in hospital were MRAs (n=67), followed by HFBBs (n=66) as seen in Table 3. However, of those patients prescribed SGLT2is on discharge, 70% were commenced during their hospital stay. Therefore, SGLT2is had the greatest proportion of in hospital initiation out of all the GDMTs (Table 3).
Table 3 Percentage of GDMT prescribed which were initiated during the hospital admission across both study periods
GDMT prescribing | Cardiology (n=100) | General medicine (n=103) | Total (n=203) |
---|---|---|---|
RAASi (n) New RAASi | 80 38% (30) | 50 20% (10) | 130 31% (40) |
HF BB (n) New HF BB | 91 49% (45) | 76 28% (21) | 167 40% (66) |
MRA (n) New MRA | 86 57% (49) | 54 33% (18) | 140 48% (67) |
SGLT2i (n) New SGLT2i | 39 74% (29) | 11 55% (6) | 50 70% (35) |
The GDMT most frequently ceased during hospital admission was the RAASi, as a total of 18 RAASis were ceased during a hospital admission (Table 4). Overall, 14 patients experienced a medication changeover from an ACEi/ARB to an ARNI during their admission (Cardiology=9, General Medicine=5).
Table 4 Number of GDMT ceased during a patient admission across both study periods
Cardiology (n=100) | General medicine (n=103) | Total (n=203) | |
---|---|---|---|
CEASED RAASI (n) | 10 | 8 | 18 |
CEASED HF BB (n) | 0 | 6 | 6 |
CEASED MRA (n) | 1 | 4 | 5 |
CEASED SGLT2i (n) | 2 | 1 | 3 |
Cardiology was found to record heart failure medications more accurately on discharge summaries than General Medicine (82% versus 68% respectively, p=0.015) as seen in Fig. 4. Although there was no difference between the two medical teams in 2022 (p>0.05). There was no change in accuracy of medication documentation overall between the two study periods (p>0.05).
There was no difference in the proportion of patients with iron studies (referred to as iron surveillance) between the two medical teams, but overall iron surveillance was poor as seen in Fig. 5. There was also no difference in iron surveillance from 2021 to 2022 (p>0.05).
Of all the patients with iron studies conducted, 62% of patients were eligible for IFC (47 Cardiology patients and 17 General Medicine patients). This patient cohort was used to assess IFC administration. As seen in Fig. 6, the Cardiology patients were more likely to be administered IFC than the General Medicine patients. Overall, only 63% of patients eligible for IFC received therapy. IFC administration increased across both teams from 2021 (50%) to 2022 (77%) (p=0.036) (Fig. 6). Of all the patients indicated for IFC therapy who didn’t receive any, only 8% had a documented reason why it wasn’t given in the discharge summary. One patient received IFC who was not indicated for therapy (haemoglobin was too high to meet IFC administration criteria).
Of the 40 IFC administrations captured in the data, 98% was the correct dose per the local hospital guideline. Only 11 patients required a follow up dose and of those only 3 patients (27%) had a documented plan on discharge for a second dose.
From the results in can be concluded that Cardiology consistently better adhered to the medication recommendations outlined by the ESC, where 37% of all Cardiology patients were discharged on all four GDMT compared to only 4% of all General Medicine patients (Fig. 2). If a patient admits to a General Medicine team without the principal diagnosis of heart failure or has multiple active issues in addition to acute HF, optimising GDMT may not be a priority which may contribute to the prescribing differences seen between medical teams. Confounding factors which may have contributed to the disparity in GDMT prescribing across the two groups, include General Medicine patients being older with worse renal function (Table 1) potentially resulting in more cautious prescribing as these patient groups are more prone to the adverse effects of GDMTs. Although noting CrCL could not be calculated for 27 Cardiology patients and 8 General Medicine patients as information to calculate CrCL was not accessible on the EMR for these patients, therefore the mean or median CrCL seen in Table 1 may not be a true representation of the renal function of patients in each medical team. Another limitation was the inclusion of patients with factors that could limit the prescribing of a GDMT such as poor renal function, low heart rate or blood pressure and hyperkalaemia. A stricter inclusion criteria could be determined to exclude such patients to avoid bias in the results, or as an alternative, a sub analysis of patients not on optimal therapy could be performed with physician input to determine if there was an appropriate clinical reason to withhold or not commence a GDMT. The prescribing of all four GDMT amongst the whole study population was inadequate as only 20% patients were discharged on optimal GDMT (Fig. 2). Prescribing of optimal GDMT had increased from 7% in 2021 to 33% in 2022 (Fig. 2). A contributor to this result was the poor prescribing of SGLT2is across both study periods, where 38% of Cardiology patients were discharged on a SGLT2i compared to 12% of General Medicine patients (Fig. 5). In the first study period (2021), the prescribing of SGLT2is was likely affected by PBS restrictions, as the SGLT2is only became accessible on the PBS for HFrEF regardless of type 2 diabetes mellitus (T2DM) comorbidity in 2022 [14]. Considering the first study period was late 2021, prescribers may have been reluctant to prescribe a SGLT2i due to the economic burden on non-diabetic patients as it could only be prescribed privately. Differences in SGLT2i prescribing between Cardiology and General Medicine teams in 2021 was unlikely to be associated with T2DM co-morbidity as no differences in T2DM diagnosis was found between the groups (p>0.05). However, for the second study period (2022) the PBS was not a barrier to prescribing and it remained suboptimal as only 25% of patients were discharged on an SGTL2i (Supplementary material Fig. 2). This suggests presence of other barriers to the prescribing of SGLT2is. One limitation may be prescribers lack of awareness of the new guidelines available for HFrEF particularly in General Medicine where the prescribing rate was lower. The frequency of use of GDMT in this study was greater than that described in a study of patients with HFrEF discharged from another Australian hospital which was published in 2017. That study identified the prescribing rates of ACEI/ARB, BB and MRA to be 52%, 49%, and 15% respectively [15]. The most significant difference in prescribing rates compared to their study was for MRAs (69% vs. 15%), followed by BBs (82% vs. 49%) [15]. Furthermore, the outcome of this research determined that a similar proportion of patients were prescribed optimal GDMT as compared to the QUALIFY international survey (20% vs. 23%) [7]. This may suggest a stagnation in prescriber adherence to GDMT considering the first study period commenced 5 years after the QUALIFY survey was conducted [7]. However, noting a key difference with the QUALIFY study was good adherence also considered the dose of the GDMT not just whether the drug was prescribed. This may suggest prescriber GDMT adherence is poorer in 2021–22 compared to 2013–16, as optimal GDMT seemed easier to achieve in this study, hence a greater proportion of optimal GDMT in 2021–22 would be expected (which wasn’t the case). The poor prescribing of SGLT2is on discharge confirms the results of the EVOLUTION-HF study which found delays to commencement of dapagliflozin post HF related hospitalisation [9]. This indicates that the poor uptake of SGLT2i prescribing is not isolated to Australia but is a global issue. The suboptimal prescribing suggests that an implementation tool may be appropriate particular in General Medicine teams to improve the prescribing rates of HFrEF medications. Furthermore, it reiterates the importance of education for prescribers about the new recommendations to initiate SGLT2i.
Cardiology was more successful at accurately recording HFrEF medications in 2021 where 86% of Cardiology discharge summaries contained accurate documentation compared to only 60% General Medicine (Fig. 4). However, in 2022 no difference in the accuracy of discharge summaries between the two medical teams was observed. When directly comparing the accuracy of discharge summaries for both teams from 2021 to 2022 there was also no statistically significant difference between the two study periods. Overall, there is still a proportion of patient from both teams discharged without accurate documentation of their HF medications, which reflects suboptimal continuity of care. Poor documentation makes it difficult for GPs to effectively continue HFrEF treatment and therefore be able to up-titrate and optimise therapy, which as displayed in the STRONG-HF trial is integral to reducing heart failure hospital readmissions and all-cause mortality [6]. Being able recognise the current level of documentation accuracy can act as impetus for further research into how this affects patient outcomes.
The rate of administration of IFC for HFrEF patients during hospitalisation for acute heart failure was lower than expected for both medical teams, given iron deficiency is expected in up to 80% of these patients [1]. Whilst Cardiology patients were more likely to receive IFC than General Medicine patients, overall, the administration of IFC was still suboptimal (Fig. 6). The increase in IFC administration in 2022 when compared to 2021 may be reflective of prescribers becoming more familiar with the local hospital protocol for IFC administration in HFrEF patients. However, further prescriber education is likely required considering the results of this audit. Poor documentation of the second IFC dose (when indicated) may suggest patients are being underdosed. Lack of screening for iron deficiency is a major barrier to IFC prescribing, given 62% of those screened were found to be deficient. Our results were comparable to a similar audit which found that 74% of patients were screened for iron deficiency and 65% of iron deficient patients were administered IFC [16]. The barriers to IFC administration and iron surveillance within a hospital will need to be further explored.
Overall, these audits showed that improvements in HFrEF GDMT prescribing adherence, documentation accuracy and IFC administration are needed as current practice is suboptimal, especially within General Medicine teams. A follow-up study could be conducted to assess whether an intervention could improve GDMT prescribing rates such as such as clinician-decision support software alerts integrated into the EMR. In addition, this research reinforces the importance of intensive HF clinics post hospital discharge after a HF exacerbation, as this intervention has already been established as an effective tool to improve GDMT prescribing. The barriers to prescriber adherence to GDMT will need to be further investigated and the appropriate quality improvement tools implemented to ensure all patients with HFrEF have equal access to optimal health care.
None.
None.
No potential conflict of interest relevant to this article was reported.
Supplementary materials can be found via https://doi.org/10.59931/rcp.24.0004.
rcp-2-1-27-supple.pdfR Clin Pharm 2024; 2(1): 27-37
Published online June 30, 2024 https://doi.org/10.59931/rcp.24.0004
Copyright © Asian Conference On Clinical Pharmacy.
Saskia McGrane , Renae Lloyd
, Cassandra Potts
, Ru Jing Ang
SA Pharmacy, Flinders Medical Centre, Bedford Park, SA, Australia
Correspondence to:Saskia McGrane
E-mail Saskia.McGrane@sa.gov.au
ORCID
https://orcid.org/0009-0002-6478-8426
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: An Australian consensus statement on the pharmacological prevention and management of heart failure, released in August 2022, recommended initiation of four medications for treating heart failure with reduced ejection fraction (HFrEF): angiotensin- converting enzyme inhibitors or angiotensin receptor-neprilysin inhibitors, heart failure beta blockers, mineralocorticoid receptor antagonists and sodium glucose co-transporter 2 inhibitors. These four medications are classified as the guideline-directed medical therapy (GDMT). Intravenous administration of ferric carboxymaltose (IFC) in the hospital, following an episode of acute heart failure in patients with iron deficiency and an ejection fraction of less than 50% was also recommended to reduce heart failure related hospital admissions as demonstrated in the AFFIRM-AHF trial.
Methods: A study conducted a retrospective audit of patients with HFrEF discharged by the Cardiology and the General Medicine teams of a tertiary hospital between two time periods, October to December 2021, and October to December 2022. Information including patient demographics, medication allergies and iron studies was collected. Discharge medication lists were extracted, reviewed for GDMT prescription and audited to check the accuracy of heart failure medication documentation. SPSS v29 was used to perform the statistical analysis.
Results: A greater proportion of patients were discharged on optimal GDMT across both study periods by the Cardiology department compared with the General Medicine department (37% versus 4% respectively, p<0.001). More accurate recording of heart failure medications in discharge summaries was performed by the Cardiology department compared with the General Medicine department (82% versus 68% respectively, p=0.015). Lastly, eligible Cardiology patients were more likely to receive IFC compared with General Medicine patients (71% versus 28%, p=0.003).
Conclusion: Overall, the audits performed in this study demonstrated that improvements in HFrEF GDMT prescribing adherence, documentation accuracy and IFC administration are warranted since the current practice is suboptimal, particular in General Medicine teams. The barriers to GDMT adherence at the prescribers’ end will require further examination and the appropriate quality improvement tools need to be implemented to ensure that all patients with HFrEF have equal access to optimal healthcare.
Keywords: Clinical pharmacy, Heart failure, Medicines, Cardiology, Internal medicine, Iron
Heart failure (HF) is a clinical syndrome characterised by debilitating symptoms such as breathlessness, ankle swelling and fatigue as well as poor prognosis [1]. Heart failure with reduced ejection fraction (HFrEF) is defined by the European Society of Cardiology (ESC) a left ventricular ejection fraction less than 40% [1], whilst the National Heart Foundation of Australia (NHFA) and Cardiac Society of Australia and New Zealand (CSANZ) define it as less than 50% [2]. The burden of HF in Australia is reflected by its high prevalence, affecting 2.1% of adults and accounting for 1.6% of hospitalisations annually [2].
The implementation of the most recent evidence-based pharmacotherapies for the treatment of HFrEF is paramount to maintaining patient quality of life and maximising survival [1]. The basis for implementation can be sourced from guidelines such as that from the European Society of Cardiology (ESC). In this 2021 guideline the ESC made a new recommendation to initiate all four first line pharmacotherapies as soon as clinically possible (see therapies listed below) [1]. In comparison, earlier guidelines recommended the stepwise initiation of the first three listed pharmacotherapies [3].
1. Renin-angiotensin aldosterone system inhibitors (RAASi)
• ACEI-inhibitor (ACEI)
• Angiotensin receptor-neprilysin inhibitor (ARNI)
2. Heart failure beta blocker (HFBB)
3. Mineral-corticoid antagonist (MRA)
4. Sodium and glucose co-transport 2 inhibitor (SGLT2i)
The NHFA and CSANZ released a consensus statement in 2022 which aligned with the recommendations made by the ESC for the management of HFrEF [4]. The importance of prescribers adhering to this guideline can be derived from a comparative analysis which found that HFrEF patients treated with an ARNI, HFBB, MRA, SGLT2i had 1.4 (80-year-old) to 6.3 (55-year-old) additional years of survival compared to those treated with ACEI or angiotensin II receptor blocker (ARB) and HFBB [5]. The importance of timely up-titration of HF therapies was displayed in the STRONG-HF trial which followed patients discharged from hospital after being admitted with acute heart failure [6]. Superior outcomes were displayed for patients with high intensity care (up-titration of RAASi, HFBB and MRA to 100% of recommended doses within 2 weeks post discharge and frequent outpatient monitoring) as HF hospital readmission or all-cause death up to day 180 occurred in 15.2% of high intensity care patients compared to 23.3% in the usual care group [6].
Irrespective of the recent recommendations, these drugs are underutilised as highlighted by observations that HFrEF patients are being discharged from hospital either not prescribed these medications at all or not at the recommended target doses. Evidence of this can be drawn from the QUALIFY international survey which quantified physician adherence to the 2012 ESC HFrEF GDMT recommendations and included patients across 36 countries who discharged from hospital for a period between 2013–14 [7]. This study demonstrated that good adherence to GDMT only occurred for 23% of HFrEF patients [7]. Good adherence referred to a patient who was prescribed all the indicated GDMTs (i.e., ACEIs/ARBs, BBs, MRAs and ivabradine) at a dose of ≥50% of the target dose [7]. Likewise, an American retrospective study focusing on patients hospitalised with HFrEF from 2013–18 determined the following percentage of BB, ACEi/ARB/ARNI, and MRA prescribed at discharge was 73.4%, 55.9% and 13.8% [8]. Post the release of the ESC 2021 HF guidelines, the EVOLUTION-HF study conducted across Japan, Sweden and the United States determined that mean times for initiation of novel GDMTs (dapagliflozin and ARNI) post patient hospitalization for heart failure were longer compared to other GDMTs, suggesting poor prescriber uptake of the latest recommendations [9].
Few studies have sought to demonstrate the differences between HF guideline-based prescribing across specialities. A Polish study determined that Cardiologists were more likely to prescribe MRA and HFBB than general practitioners in an outpatient setting (nil difference in RAASi prescribing) [10]. However, evidence directly comparing adherence to HFrEF guideline-based prescribing between Cardiologists and General Medicine practitioners in a hospital setting is lacking.
Various implementation tools presented in literature have sought to improve GDMT prescriber adherence. As discussed previously, the STRONG-HF trial displayed that intensive outpatient follow up for HF patients discharged from hospital compared to standard practice resulted in a greater proportion of GDMTs titrated to full doses 90 days post discharge i.e., RAASi (55% vs. 2%), BB (49% vs. 4%); and MRA (84% vs. 46%) [6]. A 2024 systematic review of 28 studies intended to determine the effectiveness of various interventions aimed to optimise GDMT [11]. The success of interdisciplinary titration clinics was reinforced as patients prescribed GDMT at target doses increased from 2% in usual care group to 53% in the intervention group [11]. Other interventions such as clinician and patient education, audits and alerts integrated into electronic health record system had inconsistent results across the various studies [11].
In addition, the ESC and NHFA CSANZ guidelines recommend periodic screening of iron deficiency and intravenous ferric carboxymaltose (IFC) administration for iron deficient HFrEF patients to improve quality of life and HF symptoms [2]. Furthermore, the AFFIRM-AHF trial conducted in 2020 demonstrated that the administration of IFC in hospital after an episode of acute heart failure in patients with HFrEF was safe and reduced the risk of heart failure hospitalisations [12].
The optimisation of HFrEF pharmacotherapy is multi-faceted involving;
• Prescribers adhering to guideline-directed medication therapy (GDMT) [1],
• Prescribers creating a comprehensive multidisciplinary understanding of the individual patient’s medication regime through accurate medication documentation [13],
• Iron deficiency surveillance and management of iron deficiency with IFC [1].
This study had three main aims. Firstly, to describe and compare the prescribing patterns of HFrEF GDMT between Cardiology and General Medicine teams across two study periods (2021 and 2022) at a hospital network. Secondly, to determine the accuracy of HFrEF therapy in discharge medication lists documented in the medical discharge summary and to identify any difference in accuracy between Cardiology and General Medicine teams across the two study periods. Lastly, to determine the proportion of hospitalised HFrEF patients; screened for iron deficiency, eligible for IFC and administered IFC and if there was any difference in iron deficiency screening and IFC administration between Cardiology and General Medicine patients across the two study periods.
The study design was a retrospective cross-sectional audit of patients discharged from a tertiary hospital over two 3-month study periods; October to December 2021 and October to December 2022. The rationale behind the two chosen study periods includes time at which certain heart failure guidelines were released and updates to GDMT access on the Pharmaceutical Benefit Scheme (PBS). The 2021 ESC guideline for the “Diagnosis and Treatment of Acute and Chronic Heart Failure” was the first to include the new GDMT recommendations worldwide [1]. This guideline was released late August 2021, hence the first study period commenced in October 2021 to allow a month for prescribers to become familiar with the new evidence and recommendations before auditing their adherence [1]. SGLT2is were listed on the PBS for HFrEF in January 2022, removing the economic barrier that was suspected to affect SGLT2i prescribing in the first study period [14]. The release of the Australian consensus on the current pharmacological prevention and management of heart failure by NHFA & CSANZ occurred early August 2022, thus the second study period commenced in October 2022 to allow prescribers to become familiar with the new Australian GDMT recommendations [4].
The research was segregated into three sections which all shared the same initial study population. Patients were identified from the SUNRISE® electronic medical record (EMR) system using case-mix extraction and ICD-10 codes (International Classification of Diseases 10th revision). For a patient’s hospital visit to be extracted from the SUNRISE® EMR system, they had to meet the following inclusion criteria: discharged from Flinders Medical Centre (FMC) or Noarlunga Hospital (NH) from October to December 2021 or from October to December 2022, discharged from a General Medicine or Cardiology heart failure team, heart failure as a primary admission diagnosis or secondary admission diagnosis or complication. The study population was further refined by applying the following exclusion criteria: heart failure with a left ventricular ejection fraction >49%, transferred to another hospital or rehabilitation facility or died during the hospital admission. Additional exclusion criteria were applied to the three sub studies to produce a tailored study population for each study aim (Fig. 1).
The following information was extracted from the case notes, medication orders, flowsheets and results sections of the SUNRISE® electronic medical record (EMR) system; patient demographics, height, weight, laboratory test results (SeCr, CrCL), clinical observations (HR,BP), allergies and intolerances, iron studies, ferric carboxymaltose administration, haemoglobin, left ventricular ejection fraction and length of stay.
To determine the list of medications on discharge, the medical discharge summaries, pharmacist discharge notes (where available), inpatient medication administration charts and discharge prescriptions for each patient were accessed using the SUNRISE® EMR system.
Medical discharge summaries were reviewed for a discharge medication list. The HFrEF medications (RAASi, HFBB, MRA, SGLT2i and loop diuretic) were reviewed for accuracy. To determine the accuracy of the list of medications in the medical discharge summary, the pharmacist discharge notes (where available), the inpatient medication administration charts and the discharge prescriptions for each patient were accessed using the SUNRISE® EMR system to determine the medication name, dose, route, and frequency. A discharge medication listed in the medical discharge summary was considered 100% accurate if the name, dose and frequency were accurate. If one factor was incorrect then this was defined as <100% accuracy.
The following criteria was used to define iron deficiency and eligibility for IFC based on local hospital protocol; ferritin <100 ng/mL or 100–299 ng/mL with transferrin saturation <20%, haemoglobin <135 g/L for females or <150 g/L males. A second IFC dose was indicated for patients with a haemoglobin <100 g/L and the dosing was based on ideal body weight. Administration of IFC was extracted from the SUNRISE® EMR system. Patient discharge summaries were reviewed to determine the accuracy of IFC documentation and the presence of a plan for a second IFC dose if required.
The data analysis was conducted using the SPSS v29 statistical analysis software. This software produced measures of central tendency as well as hypothesis testing to compare two independent study groups interpreted using a p-value and odds risk ratio.
To assess if any inherent differences existed between patients discharged from Cardiology versus General Medicine teams, certain factors were compared using a T-test (for continuous variables) and Chi-Squared test (for nominal variables). The independent T-test was utilised for continuous data as each factor in the two independent study groups (General Medicine versus Cardiology) had no significant outliers seen when box and whisker plots were created, the data was normally distributed for each group as verified using the Shapiro-Wilk test of normality and there was homogeneity of variances which was confirmed by performing the Levene’s test. The two nominal factors (Sex and T2DM) were assessed using the Chi-squared test as the variables were categorical, all observations were independent and the cells in the contingency table were mutually exclusive. Furthermore, when the contingency table was built and the Chi-squared test was actioned, greater than 80% of the expected value of cells in the contingency tablet were greater than 5, hence meeting the tests assumptions.
Using the SPSS v29 software, 2×2 contingency tables were built to compare patients from Cardiology or General Medicine and whether optimal GDMT was prescribed at discharge. The proportion of patients prescribed all 4 GDMT as compared to 3 or less therapies was determined and compared between the Cardiology and General Medicine groups. This was analysed separately for the 2021 and 2022 groups and as a combined group. Individual medication classes were also analysed to determine the proportion of patients prescribed each of the 4 GDMT across both study periods and this was compared across the 2 study groups. Due to the small sample size the Fisher’s exact test was used to compare the prescribing of individual GDMTs as well as the prescribing of all 4 GDMTs. Furthermore, when analysing GDMT prescribing between medical teams and study periods, in some instances less than 80% of the expected value of cells in the contingency table were greater than 5 (for example prescribing of optimal GDMT between medical teams in the first study period). Hence, the Chi-squared test could not be used as the test assumes that greater than 80% of the expected value of cells in the contingency table are greater than 5, instead the Fisher’s exact test was used. For continuity, the Fisher’s exact test was used in all instances when analysing GDMT prescribing.
Further tables were built using the software to compare the accuracy (100% or <100%) of the documented HFrEF medications between Cardiology and General Medicine teams and the two study periods (2021 and 2022). The Pearson’s chi squared test was used to compare the two specialities across the two study periods as well as overall as each study group meet the assumptions.
To assess whether iron tests were conducted and IFC administration occurred across the two specialities and study periods, 2×2 contingency tables were derived. The Fisher’s exact test was used to compare the variables due to the small sample size. The Chi-squared test could not be utilised for this data as it did not meet the tests assumptions.
When data collection was complete the spreadsheet was reviewed for unusual values to identify errors in data entry and potential errors were reviewed and rectified. The data collection process was systematic following a workflow to minimise the chance of errors or potential individual bias from the various people collecting data. Missing data integral to the study aim such as nil classification of HF type or ejection fraction were excluded when determining the study population. However, missing patient characteristics such as blood pressure or patient height to determine CrCL were still included in the study but was taken into consideration when interpreting results as well as being accounted for in Table 1 in the Supplementary material where there is a column with the number of missing values for each patient characteristic.
The differences in patient factors amongst patients discharged from Cardiology and General Medicine teams is shown in Table 1 and 2. The key findings were that General Medicine patients were older with worse renal function, whilst Cardiology patients had lower ejection fractions and a shorter length of stay. Although statistical differences exist between systolic blood pressure and heart rate at the time of discharge, these do not appear clinically significant. There was no difference in Type 2 diabetes mellitus (T2DM) diagnosis between the two teams (p>0.05), which was an important consideration for patient eligibility on the PBS for SGLT2is in 2021 (Table 2).
Table 1 . Patient characteristics across cardiology and general medicine teams (continuous variables).
Mean | Standard deviation | Mean difference | p-value | |
---|---|---|---|---|
Ejection fraction (%) | ||||
Total (n=203) | 34.4 | 10.2 | –6.18 | <0.001 |
Cardiology (n=100) | 31.4 | 10.4 | ||
General medicine (n=103) | 37.5 | 9 | ||
Age (years) | ||||
Total (n=203) | 74.4 | 14.2 | –14.0 | <0.001 |
Cardiology (n=100) | 67.3 | 14 | ||
General medicine (n=103) | 81.3 | 10.5 | ||
CrCL (mL/min) on discharge | ||||
Total (n=168) | 42.4 | 21.4 | 6.68 | 0.045 |
Cardiology (n=73) | 46.1 | 23.8 | ||
General medicine (n=95) | 39.5 | 19 | ||
Length of stay (days) | ||||
Total (n=203) | 6.86 | 7.06 | –1.20 | <0.001 |
Cardiology (n=100) | 6.25 | 5.14 | ||
General medicine (n=103) | 7.44 | 8.51 | ||
Systolic BP on discharge | ||||
Total (n=202) | 121 | 17.7 | –5.83 | 0.018 |
Cardiology (n=99) | 118 | 19 | ||
General medicine (n=103) | 124 | 15.9 | ||
Heart rate (BPM) on discharge | ||||
Total (n=202) | 76.5 | 13 | –3.8 | 0.019 |
Cardiology (n=99) | 74.6 | 12.7 | ||
General medicine (n=103) | 78.4 | 13.1 |
Table 2 . Patient characteristics across cardiology and general medicine teams (nominal variables).
Total (n=203) | Cardiology (n=100) | General medicine (n=103) | p-value | |
---|---|---|---|---|
T2DM diagnosis | 32% (n=65) | 32% (n=32) | 32% (n=33) | >0.05 |
Sex as % of males | 67% (n=136) | 69% (n=69) | 65% (n=67) | >0.05 |
Cardiology discharged a greater proportion of patients on optimal GDMT than General Medicine across both study periods (37% versus 4% respectively, p<0.001), however the prescribing of GDMT across both teams remained unsatisfactory (Fig. 2). There was a statistically significant increase in the prescribing of optimal GDMT overall from 2021 to 2022 (7% to 33%, p<0.001). The four cornerstone HFrEF therapies were individually assessed, and Cardiology prescribed more of every class than General Medicine (Fig. 3). SGLT2i prescribing increased between 2021 to 2022 (8% to 25%), see Supplementary material Fig. 1 and 2.
Cardiology commenced 153 individual GDMTs across both study periods whilst General Medicine commenced 55. The medication classes which had the greatest total number of new commencements in hospital were MRAs (n=67), followed by HFBBs (n=66) as seen in Table 3. However, of those patients prescribed SGLT2is on discharge, 70% were commenced during their hospital stay. Therefore, SGLT2is had the greatest proportion of in hospital initiation out of all the GDMTs (Table 3).
Table 3 . Percentage of GDMT prescribed which were initiated during the hospital admission across both study periods.
GDMT prescribing | Cardiology (n=100) | General medicine (n=103) | Total (n=203) |
---|---|---|---|
RAASi (n) New RAASi | 80 38% (30) | 50 20% (10) | 130 31% (40) |
HF BB (n) New HF BB | 91 49% (45) | 76 28% (21) | 167 40% (66) |
MRA (n) New MRA | 86 57% (49) | 54 33% (18) | 140 48% (67) |
SGLT2i (n) New SGLT2i | 39 74% (29) | 11 55% (6) | 50 70% (35) |
The GDMT most frequently ceased during hospital admission was the RAASi, as a total of 18 RAASis were ceased during a hospital admission (Table 4). Overall, 14 patients experienced a medication changeover from an ACEi/ARB to an ARNI during their admission (Cardiology=9, General Medicine=5).
Table 4 . Number of GDMT ceased during a patient admission across both study periods.
Cardiology (n=100) | General medicine (n=103) | Total (n=203) | |
---|---|---|---|
CEASED RAASI (n) | 10 | 8 | 18 |
CEASED HF BB (n) | 0 | 6 | 6 |
CEASED MRA (n) | 1 | 4 | 5 |
CEASED SGLT2i (n) | 2 | 1 | 3 |
Cardiology was found to record heart failure medications more accurately on discharge summaries than General Medicine (82% versus 68% respectively, p=0.015) as seen in Fig. 4. Although there was no difference between the two medical teams in 2022 (p>0.05). There was no change in accuracy of medication documentation overall between the two study periods (p>0.05).
There was no difference in the proportion of patients with iron studies (referred to as iron surveillance) between the two medical teams, but overall iron surveillance was poor as seen in Fig. 5. There was also no difference in iron surveillance from 2021 to 2022 (p>0.05).
Of all the patients with iron studies conducted, 62% of patients were eligible for IFC (47 Cardiology patients and 17 General Medicine patients). This patient cohort was used to assess IFC administration. As seen in Fig. 6, the Cardiology patients were more likely to be administered IFC than the General Medicine patients. Overall, only 63% of patients eligible for IFC received therapy. IFC administration increased across both teams from 2021 (50%) to 2022 (77%) (p=0.036) (Fig. 6). Of all the patients indicated for IFC therapy who didn’t receive any, only 8% had a documented reason why it wasn’t given in the discharge summary. One patient received IFC who was not indicated for therapy (haemoglobin was too high to meet IFC administration criteria).
Of the 40 IFC administrations captured in the data, 98% was the correct dose per the local hospital guideline. Only 11 patients required a follow up dose and of those only 3 patients (27%) had a documented plan on discharge for a second dose.
From the results in can be concluded that Cardiology consistently better adhered to the medication recommendations outlined by the ESC, where 37% of all Cardiology patients were discharged on all four GDMT compared to only 4% of all General Medicine patients (Fig. 2). If a patient admits to a General Medicine team without the principal diagnosis of heart failure or has multiple active issues in addition to acute HF, optimising GDMT may not be a priority which may contribute to the prescribing differences seen between medical teams. Confounding factors which may have contributed to the disparity in GDMT prescribing across the two groups, include General Medicine patients being older with worse renal function (Table 1) potentially resulting in more cautious prescribing as these patient groups are more prone to the adverse effects of GDMTs. Although noting CrCL could not be calculated for 27 Cardiology patients and 8 General Medicine patients as information to calculate CrCL was not accessible on the EMR for these patients, therefore the mean or median CrCL seen in Table 1 may not be a true representation of the renal function of patients in each medical team. Another limitation was the inclusion of patients with factors that could limit the prescribing of a GDMT such as poor renal function, low heart rate or blood pressure and hyperkalaemia. A stricter inclusion criteria could be determined to exclude such patients to avoid bias in the results, or as an alternative, a sub analysis of patients not on optimal therapy could be performed with physician input to determine if there was an appropriate clinical reason to withhold or not commence a GDMT. The prescribing of all four GDMT amongst the whole study population was inadequate as only 20% patients were discharged on optimal GDMT (Fig. 2). Prescribing of optimal GDMT had increased from 7% in 2021 to 33% in 2022 (Fig. 2). A contributor to this result was the poor prescribing of SGLT2is across both study periods, where 38% of Cardiology patients were discharged on a SGLT2i compared to 12% of General Medicine patients (Fig. 5). In the first study period (2021), the prescribing of SGLT2is was likely affected by PBS restrictions, as the SGLT2is only became accessible on the PBS for HFrEF regardless of type 2 diabetes mellitus (T2DM) comorbidity in 2022 [14]. Considering the first study period was late 2021, prescribers may have been reluctant to prescribe a SGLT2i due to the economic burden on non-diabetic patients as it could only be prescribed privately. Differences in SGLT2i prescribing between Cardiology and General Medicine teams in 2021 was unlikely to be associated with T2DM co-morbidity as no differences in T2DM diagnosis was found between the groups (p>0.05). However, for the second study period (2022) the PBS was not a barrier to prescribing and it remained suboptimal as only 25% of patients were discharged on an SGTL2i (Supplementary material Fig. 2). This suggests presence of other barriers to the prescribing of SGLT2is. One limitation may be prescribers lack of awareness of the new guidelines available for HFrEF particularly in General Medicine where the prescribing rate was lower. The frequency of use of GDMT in this study was greater than that described in a study of patients with HFrEF discharged from another Australian hospital which was published in 2017. That study identified the prescribing rates of ACEI/ARB, BB and MRA to be 52%, 49%, and 15% respectively [15]. The most significant difference in prescribing rates compared to their study was for MRAs (69% vs. 15%), followed by BBs (82% vs. 49%) [15]. Furthermore, the outcome of this research determined that a similar proportion of patients were prescribed optimal GDMT as compared to the QUALIFY international survey (20% vs. 23%) [7]. This may suggest a stagnation in prescriber adherence to GDMT considering the first study period commenced 5 years after the QUALIFY survey was conducted [7]. However, noting a key difference with the QUALIFY study was good adherence also considered the dose of the GDMT not just whether the drug was prescribed. This may suggest prescriber GDMT adherence is poorer in 2021–22 compared to 2013–16, as optimal GDMT seemed easier to achieve in this study, hence a greater proportion of optimal GDMT in 2021–22 would be expected (which wasn’t the case). The poor prescribing of SGLT2is on discharge confirms the results of the EVOLUTION-HF study which found delays to commencement of dapagliflozin post HF related hospitalisation [9]. This indicates that the poor uptake of SGLT2i prescribing is not isolated to Australia but is a global issue. The suboptimal prescribing suggests that an implementation tool may be appropriate particular in General Medicine teams to improve the prescribing rates of HFrEF medications. Furthermore, it reiterates the importance of education for prescribers about the new recommendations to initiate SGLT2i.
Cardiology was more successful at accurately recording HFrEF medications in 2021 where 86% of Cardiology discharge summaries contained accurate documentation compared to only 60% General Medicine (Fig. 4). However, in 2022 no difference in the accuracy of discharge summaries between the two medical teams was observed. When directly comparing the accuracy of discharge summaries for both teams from 2021 to 2022 there was also no statistically significant difference between the two study periods. Overall, there is still a proportion of patient from both teams discharged without accurate documentation of their HF medications, which reflects suboptimal continuity of care. Poor documentation makes it difficult for GPs to effectively continue HFrEF treatment and therefore be able to up-titrate and optimise therapy, which as displayed in the STRONG-HF trial is integral to reducing heart failure hospital readmissions and all-cause mortality [6]. Being able recognise the current level of documentation accuracy can act as impetus for further research into how this affects patient outcomes.
The rate of administration of IFC for HFrEF patients during hospitalisation for acute heart failure was lower than expected for both medical teams, given iron deficiency is expected in up to 80% of these patients [1]. Whilst Cardiology patients were more likely to receive IFC than General Medicine patients, overall, the administration of IFC was still suboptimal (Fig. 6). The increase in IFC administration in 2022 when compared to 2021 may be reflective of prescribers becoming more familiar with the local hospital protocol for IFC administration in HFrEF patients. However, further prescriber education is likely required considering the results of this audit. Poor documentation of the second IFC dose (when indicated) may suggest patients are being underdosed. Lack of screening for iron deficiency is a major barrier to IFC prescribing, given 62% of those screened were found to be deficient. Our results were comparable to a similar audit which found that 74% of patients were screened for iron deficiency and 65% of iron deficient patients were administered IFC [16]. The barriers to IFC administration and iron surveillance within a hospital will need to be further explored.
Overall, these audits showed that improvements in HFrEF GDMT prescribing adherence, documentation accuracy and IFC administration are needed as current practice is suboptimal, especially within General Medicine teams. A follow-up study could be conducted to assess whether an intervention could improve GDMT prescribing rates such as such as clinician-decision support software alerts integrated into the EMR. In addition, this research reinforces the importance of intensive HF clinics post hospital discharge after a HF exacerbation, as this intervention has already been established as an effective tool to improve GDMT prescribing. The barriers to prescriber adherence to GDMT will need to be further investigated and the appropriate quality improvement tools implemented to ensure all patients with HFrEF have equal access to optimal health care.
None.
None.
No potential conflict of interest relevant to this article was reported.
Supplementary materials can be found via https://doi.org/10.59931/rcp.24.0004.
rcp-2-1-27-supple.pdfTable 1 Patient characteristics across cardiology and general medicine teams (continuous variables)
Mean | Standard deviation | Mean difference | p-value | |
---|---|---|---|---|
Ejection fraction (%) | ||||
Total (n=203) | 34.4 | 10.2 | –6.18 | <0.001 |
Cardiology (n=100) | 31.4 | 10.4 | ||
General medicine (n=103) | 37.5 | 9 | ||
Age (years) | ||||
Total (n=203) | 74.4 | 14.2 | –14.0 | <0.001 |
Cardiology (n=100) | 67.3 | 14 | ||
General medicine (n=103) | 81.3 | 10.5 | ||
CrCL (mL/min) on discharge | ||||
Total (n=168) | 42.4 | 21.4 | 6.68 | 0.045 |
Cardiology (n=73) | 46.1 | 23.8 | ||
General medicine (n=95) | 39.5 | 19 | ||
Length of stay (days) | ||||
Total (n=203) | 6.86 | 7.06 | –1.20 | <0.001 |
Cardiology (n=100) | 6.25 | 5.14 | ||
General medicine (n=103) | 7.44 | 8.51 | ||
Systolic BP on discharge | ||||
Total (n=202) | 121 | 17.7 | –5.83 | 0.018 |
Cardiology (n=99) | 118 | 19 | ||
General medicine (n=103) | 124 | 15.9 | ||
Heart rate (BPM) on discharge | ||||
Total (n=202) | 76.5 | 13 | –3.8 | 0.019 |
Cardiology (n=99) | 74.6 | 12.7 | ||
General medicine (n=103) | 78.4 | 13.1 |
Table 2 Patient characteristics across cardiology and general medicine teams (nominal variables)
Total (n=203) | Cardiology (n=100) | General medicine (n=103) | p-value | |
---|---|---|---|---|
T2DM diagnosis | 32% (n=65) | 32% (n=32) | 32% (n=33) | >0.05 |
Sex as % of males | 67% (n=136) | 69% (n=69) | 65% (n=67) | >0.05 |
Table 3 Percentage of GDMT prescribed which were initiated during the hospital admission across both study periods
GDMT prescribing | Cardiology (n=100) | General medicine (n=103) | Total (n=203) |
---|---|---|---|
RAASi (n) New RAASi | 80 38% (30) | 50 20% (10) | 130 31% (40) |
HF BB (n) New HF BB | 91 49% (45) | 76 28% (21) | 167 40% (66) |
MRA (n) New MRA | 86 57% (49) | 54 33% (18) | 140 48% (67) |
SGLT2i (n) New SGLT2i | 39 74% (29) | 11 55% (6) | 50 70% (35) |
Table 4 Number of GDMT ceased during a patient admission across both study periods
Cardiology (n=100) | General medicine (n=103) | Total (n=203) | |
---|---|---|---|
CEASED RAASI (n) | 10 | 8 | 18 |
CEASED HF BB (n) | 0 | 6 | 6 |
CEASED MRA (n) | 1 | 4 | 5 |
CEASED SGLT2i (n) | 2 | 1 | 3 |