Adherence to Guideline-Directed Medical Therapy in Heart Failure with Reduced Ejection Fraction: A Comparison Between Residents and Attending Physicians

Adherence to Guideline-Directed Medical Therapy in Heart Failure with Reduced Ejection Fraction: A Comparison Between Residents and Attending Physicians | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adherence to Guideline-Directed Medical Therapy in Heart Failure with Reduced Ejection Fraction: A Comparison Between Residents and Attending Physicians Abbas Mohammadi, Ibrahim Youssef, Iryna Kobita, Nazanin Hazhir-Karzar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5649645/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Heart failure with reduced ejection fraction (HFrEF) requires adherence to guideline-directed medical therapy (GDMT) for optimal outcomes. This study aimed to assess GDMT adherence at a teaching hospital and compare discharge prescriptions by residents and attending physicians. Methods A retrospective cohort study was conducted at Spring Valley Hospital from August 2023 to March 2024. Patients with HFrEF and decompensated heart failure as the primary reason for admission were identified through electronic health records. Adherence to GDMT at discharge was evaluated based on 2022 American Heart Association (AHA) guidelines, focusing on beta-blockers (BB), angiotensin receptor-neprilysin inhibitors (ARNI), mineralocorticoid receptor antagonists (MRA), and sodium-glucose cotransporter 2 inhibitors (SGLT2i). The primary outcome was comparing GDMT adherence between residents and attending physicians. Secondary outcomes included adherence to individual medications, combination regimens, and reasons for non-adherence. Results Among 243 patient charts reviewed, 80 met inclusion criteria (33 residents, 47 attendings). Adherence to beta-blockers was significantly higher than to other GDMT medications (p<0.0001). Residents showed slightly higher adherence to BB, MRA, and SGLT2i, though differences were not statistically significant. Double and triple therapies were prescribed more often than quadruple therapy (p=0.002, p=0.01). Residents demonstrated higher adherence to double therapy with BB and MRA (55% vs. 28%, p=0.02). Conclusion Adherence to GDMT for HFrEF was comparable between residents and attending physicians. Improving adherence to key medications can further enhance HFrEF management and patient outcomes. Heart Failure with Reduced Ejection Fraction (HFrEF) Guideline-Directed Medical Therapy (GDMT) Medication Adherence Resident Physicians Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Heart Failure with Reduced Ejection Fraction (HFrEF) is a prevalent and debilitating condition that poses a significant healthcare burden worldwide 1 . The clinical management of HFrEF has evolved significantly over the past few decades with the introduction of guideline-directed medical therapy (GDMT) to improve patient outcomes 1 . GDMT includes a combination of medications such as angiotensin-converting enzyme inhibitors (ACEi), angiotensin II receptor blockers (ARB), beta-blockers (BB), mineralocorticoid receptor antagonists (MRAs), and newer agents like angiotensin receptor-neprilysin inhibitors (ARNI) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) 1 – 5 . Despite the proven benefits of GDMT, adherence to these therapies remains suboptimal 2 , 3 . Low adherence to GDMT after hospital discharge contributes to adverse outcomes, frequent exacerbations, repeat hospitalizations, and increased healthcare costs 1 , 3 , 6 , 7 . After adhering well to GDMT, patients with an LVEF below 30% exhibited similar mortality rates to those with an LVEF of 50% or higher 3 . Various barriers influence medication adherence, including patient-related factors (e.g., age, comorbidities, socioeconomic status), healthcare provider-related factors (e.g., knowledge, attitudes, and prescribing patterns), and systemic factors (e.g., healthcare access, medication cost). Understanding these factors and improving adherence to GDMT is crucial for optimizing the management of HFrEF 7 . Residents in many US teaching hospitals play a vital role in managing patient discharges. These early-career physicians are often responsible for prescribing discharge medications, educating patients, and ensuring continuity of care 8 . However, the impact of resident involvement on adherence to GDMT compared to the usual discharging group (attending physicians) has not been thoroughly studied. Investigating this aspect is essential, given that residents' prescribing practices and their adherence to clinical guidelines may differ from those of more experienced attending physicians. Our study aims to evaluate the impact of a new residency program on adherence to GDMT for HFrEF at Spring Valley Hospital, a tertiary care center. By comparing the discharge medications of patients managed by residents with those managed by attending physicians; we seek to identify potential gaps in adherence and areas for improvement. Methods Study Design A retrospective cohort study was conducted at Spring Valley Hospital, analyzing data from August 2023 to March 2024. Discharge records were reviewed and patients’ records extracted by a hospital data analysts to identify patients with HFrEF using the keywords "heart failure" and "pulmonary edema," to ensure accuracy and completeness. After extracting all the patients’ data with aforementioned keywords, they were given to residents to either include or exclude. Patients whose primary diagnosis on discharge was not heart failure decompensation were excluded from the initial screen. Patients were assigned to either the resident or attending group based on the physician who signed the discharge summary. If the discharge summary was signed by a resident, the patient was included in the resident group; if signed by an attending, the patient was included in the attending group. A total of 243 patients were identified with the final diagnosis of heart failure decompensation (Fig. 1 ). These records were distributed among four residents for chart review to confirm inclusion criteria and discharge medications. Eligible patients were those with an ejection fraction (EF) ≤ 40%, aged over 18, survived to discharge, and hospitalized with decompensated HFrEF as the primary diagnosis. Exclusion criteria included patients with heart failure with preserved ejection fraction (HFpEF), those who were deceased, those with no echocardiogram within the last six months, and those who left against medical advice (AMA). The primary outcome of this study was the comparison of overall adherence to GDMT for HFrEF at hospital discharge between residents and attending physicians. Adherence was assessed for each medication class, including beta-blockers, ACEi/ARB/ARNI, MRA, and SGLT2 inhibitors, in accordance with the 2022 American Heart Association (AHA) guidelines 9 . Secondary outcomes included adherence to individual GDMT medications, adherence to combination regimens (e.g., double, triple, and quadruple therapy), overall rates of GDMT adherence at discharge, and documented reasons for non-adherence, such as contraindications, patient preferences, intolerance, or other common prescribing barriers. Contraindications were carefully evaluated to ensure clinical justification for any deviations from GDMT. Contraindications for starting specific medications were as follows: ACE inhibitors or ARBs were not prescribed if there was a history of angioedema, pregnancy, renal artery stenosis, or potassium levels > 5.5 mEq/L. MRAs were avoided if potassium levels exceeded 5.5 mEq/L or eGFR was < 30 mL/min/1.73m². Beta-blockers were not prescribed for heart rate 5.5 mEq/L, use of ACE/ARB, or severe hepatic impairment (Child-Pugh class C). SGLT2 inhibitors were not used for type 1 diabetes, DKA, or eGFR < 30 mL/min/1.73m². These criteria were rigorously applied to ensure appropriate medication use. In our internal medicine residency program, residents are taught daily about different protocols and guidelines. While residents work under attending supervision, they are responsible for their own prescriptions on discharge day. Given the high turnover in the hospital, there is minimal chance for attendings to oversee all resident prescriptions during this critical time, ensuring that residents' decisions are relatively independent. Moreover, the study employed rigorous quality control measures, with four residents cross-checking medication lists extracted from EHR to ensure accuracy. Statistical analyses were performed to compare adherence rates between resident and attending groups, focusing on both overall and medication-specific adherence. The study protocol was reviewed and approved by the Touro University Nevada Institutional Review Board (IRB) under reference number TUNIRB000288. Statistical Analysis The study cohort's baseline characteristics are presented as median (IQR) or percentages, as appropriate. The Mann-Whitney test was employed to analyze continuous variables, while categorical variables were assessed using Chi-square tests. Data visualizations were created using GraphPad Prism v.7. Results Demographic and Clinical Characteristics Table 1 presents the demographic and clinical characteristics of patients discharged by residents (n = 33) and non-residents (n = 47) at Spring Valley Hospital with a diagnosis of HFrEF. The mean age of patients was slightly higher in the resident group (68 years) than in the attending group (63 years), but this difference was not statistically significant. The racial distribution was similar across both groups, with the majority being White, followed by Black, Asian, and Other. The gender distribution was also comparable, with a higher percentage of males in both groups. Notably, there was a significant difference in the history of cerebrovascular accidents (CVA), with a higher percentage in the resident group (18%) compared to the attending group (4%) (p = 0.04). Other characteristics, such as prior heart failure diagnosis, hypertension, coronary artery bypass grafting (CABG) history, chronic kidney disease (CKD), and atrial fibrillation, showed no significant differences between the groups. Table 1 Patient Characteristics: Residents vs. Non-Residents Discharges. This table shows demographic and Clinical Characteristics of Patients Discharged by Residents or Non-residents program from Spring Valley Hospital with a Diagnosis of Heart Failure with Reduced Ejection Fraction. Chi-square without Yates correction and Mann–Whitney U tests were used retrospectively for categorical and continuous variables comparisons. Characteristic Resident group (n = 33) Attending group (n = 47) P-value Age (years) (mean) 67.6 63.2 ns Race/ethnicity (%) White % 42 53 ns Black % 30 21 Asian % 9 9 Other % 18 17 Sex, number (%) Male 85 75 ns Prior HF diagnosis (%) 88 72 ns History of Hypertension (%) 72 74 ns History of CABG (%) 24 20 ns History of CVA (%) 18 4 0.04 History of CKD (%) 36 25 ns History of A-fib (%) 51 30 ns NTproBNP (pg/mL) (mean) 7329 10361 ns Ejection Fraction (%) 21 24 ns GFR (mL/min/1.73 m 2 ) (mean) 62 63 ns ns: not significant, HF: heart failure, CABG: Coronary artery bypass graft, CVA: Cerebrovascular attack, CKD: chronic kidney disease, A-fib: atrial fibrillation, GFR: Glomerular filtration rate. Adherence to Guideline-Directed Medical Therapy Adherence to BB was significantly higher compared to ACEi/ARB/ARNI, MRA, and SGLT2 inhibitors (p = 0.0005, p = 0.0001 and p = 0.0001, respectively) (Fig. 2 a). ACEi/ARB/ARNI and MRA adherence were notably higher than that for SGLT2i (p = 0.0001 and p = 0.03, respectively) (Fig. 2 a). When comparing discharge prescriptions by attendings and residents, adherence to BB, MRA and SGLT2i inhibitors were slightly higher among residents, but these differences were not statistically significant (Residents: 94%, 55%, and 33%; Attendings: 89%, 38%, and 26%, respectively) (Fig. 2 b). Overall, no significant differences were observed in adherence across all medication classes (Fig. 2 b). Table 2 details the distribution of prescribed medications between residents and attending groups. Medications such as Losartan (Resident: 9%, Attending: 17%) and Dapagliflozin (Resident: 12%, Attending: 19%) were more commonly prescribed by attendings. In contrast, Lisinopril (Resident: 18%, Attending: 17%), Spironolactone (Resident: 52%, Attending: 38%), ARNI (Resident: 27%, Attending: 21%) and Empagliflozin (Resident: 27%, Attending: 11%) were more frequently prescribed by residents. This distribution underscores the variations in medication prescription patterns between the two groups. Table 2 Distribution of Prescribed Medications Among Resident and Non-Resident Patient Groups. This table presents the prescribed medication distribution in resident (n = 33) and non-resident (n = 47) patient groups. It shows the percentage of patients within each group who were prescribed each medication. Class of medication Name Resident group (n = 33) Attending group (n = 47) P-value ACEi Lisinopril (%) 18 17 ns ARB Losartan (%) 9 17 ns Telmisartan (%) 3 0 ns ARNI Sacubitril-valsartan (%) 27 21 ns BB Metoprolol (%) 52 42 ns Carvedilol (%) 45 45 ns Nebivolol (%) 3 0 ns MRA Spironolactone (%) 52 38 ns Eplerenone (%) 0 2 ns Loop Diuretic Furosemide (%) 58 60 ns Bumetanide (%) 6 4 ns Torsemide (%) 6 13 ns SGLT2i Empagliflozin (%) 27 11 ns Dapagliflozin (%) 12 19 ns Abbreviations. ACEi: Angiotensin-converting enzyme inhibitor, ARB: Angiotensin II receptor blocker, ARNI: angiotensin receptor neprilysin inhibitor, BB: Beta-blocker, MRA: Mineralocorticoid receptor antagonist, Loop Diuretic: High-ceiling diuretic, SGLT2i: Sodium-glucose cotransporter-2 inhibitor. Combination therapy adherence to Guideline-Directed Medical Therapy Furthermore, an additional analysis compared heart failure medication classes in terms of mono, double, triple, and quadruple therapy. Discharge prescriptions from both groups showed that double and triple therapies were prescribed more frequently than quadruple therapy, with p-values of 0.002 and 0.01, respectively (Fig. 3 a). No significant differences were found between residents and attendings regarding combination therapy, though triple therapy and quadruple therapy were more frequent among residents (37.5% vs. 24.4% and 15.6% vs. 11.1%, respectively), while monotherapy and double therapy were more common among attendings (22.2% vs. 18.8% and 42.2% vs. 28.1%, respectively) (Fig. 3 b). Additionally, our data showed that triple therapy was more frequently prescribed to black patients at discharge compared to white patients (50% vs. 15.4%, p = 0.004). In comparison, monotherapy was more commonly prescribed to white patients than to black patients (38.4% vs. 10%, p = 0.02) (Fig. 3 c). To further investigate a potential confounding factor influencing the lower rates of combination therapy between the two groups, we analyzed the data based on age. As shown in the correlation plot, there was no significant difference between the groups. Both groups demonstrated a strong negative correlation between combination therapy and age (Spearman r = -0.4, p < 0.0001) (Fig. 4 ). Discussion This study highlights notable variations in adherence to different GDMT medications for patients with HFrEF. However, the rates of adherence were similar between residents and attending physicians. Despite slightly higher adherence among residents, the absence of a statistically significant difference between the two groups suggests that residents' non-inferiority in discharging patients with HFrEF compared to attending physicians is the primary outcome of our research. This finding has not been previously studied. High adherence to BB in our study aligns with existing literature emphasizing their strong recommendation in heart failure management due to mortality benefits 4 , 10 . Kocabaş et al (2020) found similarly high adherence rates, attributing this to robust supporting evidence and fewer side effects than ACEi/ARB 10 . It is also noteworthy that 51% of patients discharged by residents and 30% discharged by attending physicians had atrial fibrillation. The concomitant need for beta-blockers (BB) to treat atrial fibrillation may have contributed to the high adherence rate to BB in both groups 11 . In our study, adherence to quadruple therapy was notably lower than that to triple or double therapy. A study by D'Amario et al. (2023) reported that only 2.6% of patients received quadruple therapy, although 46.2% could have been initiated on it 5 . In our study, the rates of quadruple therapy prescriptions were higher, with 15% for residents and 11% for attendings. Although higher, it was still suboptimal. These findings highlight the need for targeted interventions to educate residents on guidelines, including the indications and contraindications of each GDMT medication, to improve compliance and ensure better patient outcomes upon discharge. Although there was no significant difference in medication adherence between the two groups, both residents and attending physicians exhibited similar trends in prescribing combination therapies (Fig. 4 ). Interestingly, as patients' ages increased, the number of medications prescribed decreased in both groups. This decline may explain, in part, the preference for combination therapy in older patients, as fewer individual medications are typically prescribed. Chronic kidney disease (CKD) and acute kidney injury (AKI) are common concomitant comorbidities in patients with HFrEF 12 , 13 . In our study, 36% of patients in the residents' group and 25% in the attending group had a history of CKD. The higher adherence to BB prescriptions, compared to other GDMT medications, may be attributed to concerns about renal complications associated with these other treatments. The comparison between patients discharged by residents and attending physicians revealed no significant differences in most parameters, except for a higher incidence of cerebrovascular accidents (CVA) in the residents' group. No significant differences existed in other comorbidities, age, race, or prior heart failure diagnosis. This similarity suggests that these factors likely did not influence the choice of discharge medications. Our retrospective cohort study differs from other research on medication adherence in heart failure in several key ways. While we analyzed existing data from a single tertiary care center, other studies found them in the literature, such as Kocabaş et al. (2020) and Lee et al. (2023). They used prospective designs and multi-center settings, enhancing the generalizability of their findings 7 , 10 . Data collection in our study relied on hospital records and discharge summaries, whereas other studies utilized registry data and standardized protocols 9 . These methodological differences highlight the need for future research tackling the question of GDMT adherence to adopt more robust designs, comprehensive compliance measures, and detailed outcome assessments to improve the understanding of medication adherence in heart failure 14 . This study has several limitations that should be considered when interpreting the results. First, the retrospective design limits the ability to establish causal relationships between the physician group (residents vs. attendings) and adherence to guideline-directed medical therapy (GDMT). Second, the use of a single-center, community hospital cohort may limit the generalizability of the findings to other institutions with different practices, patient populations, or organizational structures. Third, there may have been variability in the accuracy and completeness of data extracted from electronic health records, despite efforts to ensure thorough data collection. Additionally, the study focused exclusively on patients with decompensated heart failure with reduced ejection fraction (HFrEF), which may limit the applicability of the findings to patients with other forms of heart failure, such as heart failure with preserved ejection fraction (HFpEF). Finally, unmeasured confounding factors may exist, as patient characteristics and clinical decision-making processes could vary beyond the scope of the data collected. Expanding the study to a prospective design in multiple centers could enhance generalizability and explore variations in practice patterns across different healthcare settings for managing HFrEF 3 , 4 . Developing and testing interventions, such as resident educational programs, enhanced patient engagement strategies, and close monitoring protocols, could improve adherence to GDMT. Further research is needed to understand specific barriers to ACEi/ARB/ARNI and SGLT2i adherence and develop targeted strategies to overcome these challenges 14 . Evaluating the cost-effectiveness of newer therapies like SGLT2i and ARNI can also affect clinical decision-making and policy development. Additionally, comparing resident and attending discharges provides valuable insights into the impact of residency programs on medication adherence. Conclusions Adherence to GDMT is similar between the resident and attending groups. Interventions aimed at enhancing adherence to all medications are crucial for optimizing outcomes in patients with HFrEF. Strengthening education and training for both residents and attendings, along with ensuring close monitoring of therapies, could help address these adherence gaps. Abbreviations HFrEF - Heart Failure with Reduced Ejection Fraction GDMT - Guideline-Directed Medical Therapy ACEi - Angiotensin-Converting Enzyme Inhibitors ARB - Angiotensin II Receptor Blockers BB - Beta-Blockers MRA - Mineralocorticoid Receptor Antagonists ARNI - Angiotensin Receptor-Neprilysin Inhibitors SGLT2i - Sodium-Glucose Cotransporter-2 Inhibitors LVEF - Left Ventricular Ejection Fraction AHA - American Heart Association CVA - Cerebrovascular Accidents CKD - Chronic Kidney Disease CABG - Coronary Artery Bypass Grafting EF - Ejection Fraction eGFR - Estimated Glomerular Filtration Rate AMA - Against Medical Advice AKI - Acute Kidney Injury COPD - Chronic Obstructive Pulmonary Disease Declarations Ethics Statement : The protocol was approved by the Touro University Nevada Institutional Review Board (TUNIRB) in accordance with the ethical standards set forth by the Declaration of Helsinki. Informed Consent : This study involved a chart review, and informed consent was waived by the Touro University Nevada Institutional Review Board (IRB) for this retrospective analysis. Data Availability Statement: The datasets generated and/or analyzed during the current study are available from the corresponding author on request. Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Acknowledgments: We appreciate all the patients who chose our hospital to receive treatment and the Valley Health System for supporting this project. Statement of competing interests: None declared. Competing interests: The authors declare no competing interests. Author contributions: Abbas Mohammadi conceptualized and designed the study, supervised data collection, analyzed the data and drafted the manuscript. Ibrahim Youssef, Iryna Kobita, Nazanin Hazhir-Karzar contributed equally to data collection and interpretation. Hossein A Akhondi and Leo Spaccavento assisted in the design and provided critical revisions to the References Cotter, G., et al. Optimization of Evidence-Based Heart Failure Medications After an Acute Heart Failure Admission: A Secondary Analysis of the STRONG-HF Randomized Clinical Trial. JAMA Cardiology 9, 114–124 (2024). Ho, D., Virani, S., Moghaddam, N. & Hawkins, N. ADHERENCE TO GUIDELINE-DIRECTED MEDICAL THERAPY AMONG PATIENTS FOLLOWED AT AN AMBULATORY HEART FUNCTION CLINIC. Canadian Journal of Cardiology 38, S154 (2022). Chen, X., Kang, Y., Dahlström, U. & Fu, M. Impact of adherence to guideline-directed therapy on risk of death in HF patients across an ejection fraction spectrum. ESC Heart Fail 10, 3656–3666 (2023). Greene, S.J., et al. Medical Therapy for Heart Failure With Reduced Ejection Fraction: The CHAMP-HF Registry. J Am Coll Cardiol 72, 351–366 (2018). D'Amario, D., et al. Eligibility for the 4 Pharmacological Pillars in Heart Failure With Reduced Ejection Fraction at Discharge. Journal of the American Heart Association 12, e029071 (2023). Al-Tamimi, M.A., Gillani, S.W., Abd Alhakam, M.E. & Sam, K.G. Factors Associated With Hospital Readmission of Heart Failure Patients. Front Pharmacol 12, 732760 (2021). Azizi, Z., et al. Challenge of Optimizing Medical Therapy in Heart Failure: Unlocking the Potential of Digital Health and Patient Engagement. Journal of the American Heart Association 13, e030952 (2024). Daraz, Y., et al. Resident-led Quality Improvement Initiative To Increase GDMT Prescribing Prior To Hospital Discharge In Vulnerable Patients With Heart Failure. Journal of Cardiac Failure 29, 711 (2023). 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure. J Card Fail 28, e1-e167 (2022). Kocabaş, U., et al. Adherence to guideline-directed medical and device Therapy in outpAtients with heart failure with reduced ejection fraction: The ATA study. Anatol J Cardiol 24, 32–40 (2020). Desta, L., et al. Adherence to beta-blockers and long-term risk of heart failure and mortality after a myocardial infarction. ESC Heart Fail 8, 344–355 (2021). Chahal, R.S., Chukwu, C.A., Kalra, P.R. & Kalra, P.A. Heart failure and acute renal dysfunction in the cardiorenal syndrome. Clin Med (Lond) 20, 146–150 (2020). Jankowski, J., Floege, J., Fliser, D., Böhm, M. & Marx, N. Cardiovascular Disease in Chronic Kidney Disease. Circulation 143, 1157–1172 (2021). Jarjour, M. & Ducharme, A. Optimization of GDMT for patients with heart failure and reduced ejection fraction: can physiological and biological barriers explain the gaps in adherence to heart failure guidelines? Drugs Context 12(2023). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5649645","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":400064808,"identity":"e6fa4600-b533-4a46-9913-3f9e79c30458","order_by":0,"name":"Abbas Mohammadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie3QMWvCQBTA8XcE7BLJeiGfQbgiXJGm+EG6WIRzSfALSLkgJFN2+0VKx5SDTKGuJzoogrNuupS+aAtFyHXtcP/pEfLj8Q7AZvuHtah7GTyHZPuDrEeSFCbi/RA/m5bkRQJFIo3En30TVpXCaZ8JgJGwZf5+OMJqDDri24e38LmTKdwyCR8byepjGLiw6yWz6K4bV4Ly6glJKWLZRHTEAgDFHIpDnCrKCyREqkbS11H3dETSqkmvJvONmeAWjo+mmOuWIiA10X9swVvEPd7C6M1U3eap8F81bhmYblnmaoEvxvqKJOtTGnp8Ptqs95OwkZwjn8XVl4Hp90vXxGaz2Wy/+gJI5mZcBMnKTwAAAABJRU5ErkJggg==","orcid":"","institution":"Valley Health System","correspondingAuthor":true,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Mohammadi","suffix":""},{"id":400064809,"identity":"fead0d7e-50b6-4a0a-8acb-206f6c021a3e","order_by":1,"name":"Ibrahim Youssef","email":"","orcid":"","institution":"Valley Health System","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Youssef","suffix":""},{"id":400064810,"identity":"c9b4d54b-3069-4ac1-9c40-cc2949d36ffa","order_by":2,"name":"Iryna Kobita","email":"","orcid":"","institution":"Valley Health System","correspondingAuthor":false,"prefix":"","firstName":"Iryna","middleName":"","lastName":"Kobita","suffix":""},{"id":400064811,"identity":"3a9a94af-f969-4abb-96e0-fc2da5386be0","order_by":3,"name":"Nazanin Hazhir-Karzar","email":"","orcid":"","institution":"Valley Health System","correspondingAuthor":false,"prefix":"","firstName":"Nazanin","middleName":"","lastName":"Hazhir-Karzar","suffix":""},{"id":400064812,"identity":"92c44f09-d79f-44e0-9d58-eae30196459a","order_by":4,"name":"Hossein A Akhondi","email":"","orcid":"","institution":"Valley Health System","correspondingAuthor":false,"prefix":"","firstName":"Hossein","middleName":"A","lastName":"Akhondi","suffix":""},{"id":400064813,"identity":"81410589-df5b-4d35-ba22-746e07483173","order_by":5,"name":"Leo Spaccavento","email":"","orcid":"","institution":"Advanced Heart Care Associates","correspondingAuthor":false,"prefix":"","firstName":"Leo","middleName":"","lastName":"Spaccavento","suffix":""}],"badges":[],"createdAt":"2024-12-16 01:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5649645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5649645/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73515979,"identity":"5ba914df-66dd-4277-9ad3-45f8b34d01ce","added_by":"auto","created_at":"2025-01-10 17:45:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient Selection Flowchart. \u003c/strong\u003eThis flowchart illustrates the selection process for the study. After reviewing patient charts, a portion were excluded based on specific criteria. The remaining patients met the inclusion criteria, with a subset discharged by residents and the rest by attendings. Abbreviations; HFrEF: Heart Failure with Reduced Ejection Fraction, HFpEF: Heart Failure with Preserved Ejection Fraction, EF: Ejection Fraction, AMA: Against Medical Advice, GDMT: Guideline-Directed Medical Therapy.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5649645/v1/db1aa2b5b7658905204100f7.png"},{"id":73515977,"identity":"09db8d06-959f-4da3-b44f-e4d80617ac8d","added_by":"auto","created_at":"2025-01-10 17:45:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdherence to GDMT between different classes of heart failure medications: Resident and non-resident discharges prescription\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003ea. The figure is a bar graph that compares the adherence to GDMT (guideline-directed medical therapy) between different classes of heart failure medications. The x-axis of the graph shows the categories of adherence, labeled as \"ACEi/ARBi/ARNI”, “MRAs\", \"BB\", and \"SGLT2i\". The y-axis shows the percentage of patients adhering to each treatment category, ranging from 0% to 100%. b. Bar graph comparing adherence to GDMT between patients discharged by residents and non-residents across five medication classes: ACEi/ARBi/ARNI, MRAs, BB, and SGLT2i.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5649645/v1/205a4402445b3379c028ecb6.png"},{"id":73517422,"identity":"1449fd03-55c8-418a-bf41-5bd076d3bfe4","added_by":"auto","created_at":"2025-01-10 17:53:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Prescribed Medications in Resident and Non-Resident Groups\u003c/strong\u003e. a. This bar chart illustrates the overall prescription rates for various therapy types on discharge for patients with HFrEF at our hospital. The therapy types include mono, double, triple, and quadruple therapies. b. The pie chart presents the distribution of medications prescribed for the management of both resident and non-resident patient groups. A chi-square test was performed. c. The bar graph shows the percentage of people from different ethnicities who were prescribed HFrEF medication upon discharge from the hospital. The ethnicities listed are White, Black, Asian, Hispanic, and Other. The y-axis indicates the number of HFrEF medications prescribed at discharge. The number of people in each ethnic group is displayed in parentheses next to the ethnicity label.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5649645/v1/a2c60e3acab6328558be8cbd.png"},{"id":73515994,"identity":"ca211809-d8ed-412c-a64a-2a5cc52af4eb","added_by":"auto","created_at":"2025-01-10 17:45:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship Between Patient Age and Number of HFrEF Medications at Discharge. \u003c/strong\u003eThis scatter plot shows the relationship between patients' age and the number of HFrEF medications (mono, double, triple, or quadruple therapy) they were discharged on. Each point on the scatter plot represents one patient. The x-axis shows the patient’s age, and the y-axis shows the number of medications.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5649645/v1/c713b31aa6252c6a625cec2b.jpg"},{"id":75985044,"identity":"ec6b6d9e-960f-4528-bb2c-71f11f16b572","added_by":"auto","created_at":"2025-02-11 08:17:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1249414,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5649645/v1/0abdc488-a0a2-4d24-afe4-627aeaa9eaa4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adherence to Guideline-Directed Medical Therapy in Heart Failure with Reduced Ejection Fraction: A Comparison Between Residents and Attending Physicians","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart Failure with Reduced Ejection Fraction (HFrEF) is a prevalent and debilitating condition that poses a significant healthcare burden worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The clinical management of HFrEF has evolved significantly over the past few decades with the introduction of guideline-directed medical therapy (GDMT) to improve patient outcomes \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. GDMT includes a combination of medications such as angiotensin-converting enzyme inhibitors (ACEi), angiotensin II receptor blockers (ARB), beta-blockers (BB), mineralocorticoid receptor antagonists (MRAs), and newer agents like angiotensin receptor-neprilysin inhibitors (ARNI) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the proven benefits of GDMT, adherence to these therapies remains suboptimal \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Low adherence to GDMT after hospital discharge contributes to adverse outcomes, frequent exacerbations, repeat hospitalizations, and increased healthcare costs \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. After adhering well to GDMT, patients with an LVEF below 30% exhibited similar mortality rates to those with an LVEF of 50% or higher \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Various barriers influence medication adherence, including patient-related factors (e.g., age, comorbidities, socioeconomic status), healthcare provider-related factors (e.g., knowledge, attitudes, and prescribing patterns), and systemic factors (e.g., healthcare access, medication cost). Understanding these factors and improving adherence to GDMT is crucial for optimizing the management of HFrEF \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResidents in many US teaching hospitals play a vital role in managing patient discharges. These early-career physicians are often responsible for prescribing discharge medications, educating patients, and ensuring continuity of care \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, the impact of resident involvement on adherence to GDMT compared to the usual discharging group (attending physicians) has not been thoroughly studied. Investigating this aspect is essential, given that residents' prescribing practices and their adherence to clinical guidelines may differ from those of more experienced attending physicians.\u003c/p\u003e \u003cp\u003eOur study aims to evaluate the impact of a new residency program on adherence to GDMT for HFrEF at Spring Valley Hospital, a tertiary care center. By comparing the discharge medications of patients managed by residents with those managed by attending physicians; we seek to identify potential gaps in adherence and areas for improvement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted at Spring Valley Hospital, analyzing data from August 2023 to March 2024. Discharge records were reviewed and patients\u0026rsquo; records extracted by a hospital data analysts to identify patients with HFrEF using the keywords \"heart failure\" and \"pulmonary edema,\" to ensure accuracy and completeness. After extracting all the patients\u0026rsquo; data with aforementioned keywords, they were given to residents to either include or exclude.\u003c/p\u003e \u003cp\u003ePatients whose primary diagnosis on discharge was not heart failure decompensation were excluded from the initial screen. Patients were assigned to either the resident or attending group based on the physician who signed the discharge summary. If the discharge summary was signed by a resident, the patient was included in the resident group; if signed by an attending, the patient was included in the attending group. A total of 243 patients were identified with the final diagnosis of heart failure decompensation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These records were distributed among four residents for chart review to confirm inclusion criteria and discharge medications.\u003c/p\u003e \u003cp\u003eEligible patients were those with an ejection fraction (EF)\u0026thinsp;\u0026le;\u0026thinsp;40%, aged over 18, survived to discharge, and hospitalized with decompensated HFrEF as the primary diagnosis. Exclusion criteria included patients with heart failure with preserved ejection fraction (HFpEF), those who were deceased, those with no echocardiogram within the last six months, and those who left against medical advice (AMA).\u003c/p\u003e \u003cp\u003eThe primary outcome of this study was the comparison of overall adherence to GDMT for HFrEF at hospital discharge between residents and attending physicians. Adherence was assessed for each medication class, including beta-blockers, ACEi/ARB/ARNI, MRA, and SGLT2 inhibitors, in accordance with the 2022 American Heart Association (AHA) guidelines \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Secondary outcomes included adherence to individual GDMT medications, adherence to combination regimens (e.g., double, triple, and quadruple therapy), overall rates of GDMT adherence at discharge, and documented reasons for non-adherence, such as contraindications, patient preferences, intolerance, or other common prescribing barriers. Contraindications were carefully evaluated to ensure clinical justification for any deviations from GDMT.\u003c/p\u003e \u003cp\u003eContraindications for starting specific medications were as follows: ACE inhibitors or ARBs were not prescribed if there was a history of angioedema, pregnancy, renal artery stenosis, or potassium levels\u0026thinsp;\u0026gt;\u0026thinsp;5.5 mEq/L. MRAs were avoided if potassium levels exceeded 5.5 mEq/L or eGFR was \u0026lt;\u0026thinsp;30 mL/min/1.73m\u0026sup2;. Beta-blockers were not prescribed for heart rate\u0026thinsp;\u0026lt;\u0026thinsp;50 bpm, unstable heart failure, severe asthma or COPD, or second/third-degree AV block without a pacemaker. Sacubitril/valsartan was contraindicated in cases of angioedema, pregnancy, potassium\u0026thinsp;\u0026gt;\u0026thinsp;5.5 mEq/L, use of ACE/ARB, or severe hepatic impairment (Child-Pugh class C). SGLT2 inhibitors were not used for type 1 diabetes, DKA, or eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73m\u0026sup2;. These criteria were rigorously applied to ensure appropriate medication use.\u003c/p\u003e \u003cp\u003e In our internal medicine residency program, residents are taught daily about different protocols and guidelines. While residents work under attending supervision, they are responsible for their own prescriptions on discharge day. Given the high turnover in the hospital, there is minimal chance for attendings to oversee all resident prescriptions during this critical time, ensuring that residents' decisions are relatively independent. Moreover, the study employed rigorous quality control measures, with four residents cross-checking medication lists extracted from EHR to ensure accuracy.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed to compare adherence rates between resident and attending groups, focusing on both overall and medication-specific adherence. The study protocol was reviewed and approved by the Touro University Nevada Institutional Review Board (IRB) under reference number TUNIRB000288.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe study cohort's baseline characteristics are presented as median (IQR) or percentages, as appropriate. The Mann-Whitney test was employed to analyze continuous variables, while categorical variables were assessed using Chi-square tests. Data visualizations were created using GraphPad Prism v.7.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic and clinical characteristics of patients discharged by residents (n\u0026thinsp;=\u0026thinsp;33) and non-residents (n\u0026thinsp;=\u0026thinsp;47) at Spring Valley Hospital with a diagnosis of HFrEF. The mean age of patients was slightly higher in the resident group (68 years) than in the attending group (63 years), but this difference was not statistically significant. The racial distribution was similar across both groups, with the majority being White, followed by Black, Asian, and Other. The gender distribution was also comparable, with a higher percentage of males in both groups. Notably, there was a significant difference in the history of cerebrovascular accidents (CVA), with a higher percentage in the resident group (18%) compared to the attending group (4%) (p\u0026thinsp;=\u0026thinsp;0.04). Other characteristics, such as prior heart failure diagnosis, hypertension, coronary artery bypass grafting (CABG) history, chronic kidney disease (CKD), and atrial fibrillation, showed no significant differences between the groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePatient Characteristics: Residents vs. Non-Residents Discharges.\u003c/b\u003e This table shows demographic and Clinical Characteristics of Patients Discharged by Residents or Non-residents program from Spring Valley Hospital with a Diagnosis of Heart Failure with Reduced Ejection Fraction. Chi-square without Yates correction and Mann\u0026ndash;Whitney U tests were used retrospectively for categorical and continuous variables comparisons.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResident group (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAttending group (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years) (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRace/ethnicity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex, number (%) Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrior HF diagnosis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistory of Hypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistory of CABG (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistory of CVA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistory of CKD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistory of A-fib (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNTproBNP (pg/mL) (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEjection Fraction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGFR (mL/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ens: not significant, HF: heart failure, CABG: Coronary artery bypass graft, CVA: Cerebrovascular attack, CKD: chronic kidney disease, A-fib: atrial fibrillation, GFR: Glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAdherence to Guideline-Directed Medical Therapy\u003c/h3\u003e\n\u003cp\u003eAdherence to BB was significantly higher compared to ACEi/ARB/ARNI, MRA, and SGLT2 inhibitors (p\u0026thinsp;=\u0026thinsp;0.0005, p\u0026thinsp;=\u0026thinsp;0.0001 and p\u0026thinsp;=\u0026thinsp;0.0001, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). ACEi/ARB/ARNI and MRA adherence were notably higher than that for SGLT2i (p\u0026thinsp;=\u0026thinsp;0.0001 and p\u0026thinsp;=\u0026thinsp;0.03, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). When comparing discharge prescriptions by attendings and residents, adherence to BB, MRA and SGLT2i inhibitors were slightly higher among residents, but these differences were not statistically significant (Residents: 94%, 55%, and 33%; Attendings: 89%, 38%, and 26%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Overall, no significant differences were observed in adherence across all medication classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e details the distribution of prescribed medications between residents and attending groups. Medications such as Losartan (Resident: 9%, Attending: 17%) and Dapagliflozin (Resident: 12%, Attending: 19%) were more commonly prescribed by attendings. In contrast, Lisinopril (Resident: 18%, Attending: 17%), Spironolactone (Resident: 52%, Attending: 38%), ARNI (Resident: 27%, Attending: 21%) and Empagliflozin (Resident: 27%, Attending: 11%) were more frequently prescribed by residents. This distribution underscores the variations in medication prescription patterns between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDistribution of Prescribed Medications Among Resident and Non-Resident Patient Groups.\u003c/b\u003e This table presents the prescribed medication distribution in resident (n\u0026thinsp;=\u0026thinsp;33) and non-resident (n\u0026thinsp;=\u0026thinsp;47) patient groups. It shows the percentage of patients within each group who were prescribed each medication.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass of medication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResident group (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAttending group (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLisinopril (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLosartan (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelmisartan (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSacubitril-valsartan (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetoprolol (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarvedilol (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNebivolol (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpironolactone (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEplerenone (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLoop Diuretic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFurosemide (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBumetanide (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTorsemide (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSGLT2i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmpagliflozin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDapagliflozin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eAbbreviations.\u003c/b\u003e ACEi: Angiotensin-converting enzyme inhibitor, ARB: Angiotensin II receptor blocker, ARNI: angiotensin receptor neprilysin inhibitor, BB: Beta-blocker, MRA: Mineralocorticoid receptor antagonist, Loop Diuretic: High-ceiling diuretic, SGLT2i: Sodium-glucose cotransporter-2 inhibitor.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCombination therapy adherence to Guideline-Directed Medical Therapy\u003c/h2\u003e \u003cp\u003eFurthermore, an additional analysis compared heart failure medication classes in terms of mono, double, triple, and quadruple therapy. Discharge prescriptions from both groups showed that double and triple therapies were prescribed more frequently than quadruple therapy, with p-values of 0.002 and 0.01, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). No significant differences were found between residents and attendings regarding combination therapy, though triple therapy and quadruple therapy were more frequent among residents (37.5% vs. 24.4% and 15.6% vs. 11.1%, respectively), while monotherapy and double therapy were more common among attendings (22.2% vs. 18.8% and 42.2% vs. 28.1%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAdditionally, our data showed that triple therapy was more frequently prescribed to black patients at discharge compared to white patients (50% vs. 15.4%, p\u0026thinsp;=\u0026thinsp;0.004). In comparison, monotherapy was more commonly prescribed to white patients than to black patients (38.4% vs. 10%, p\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). To further investigate a potential confounding factor influencing the lower rates of combination therapy between the two groups, we analyzed the data based on age. As shown in the correlation plot, there was no significant difference between the groups. Both groups demonstrated a strong negative correlation between combination therapy and age (Spearman r = -0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights notable variations in adherence to different GDMT medications for patients with HFrEF. However, the rates of adherence were similar between residents and attending physicians. Despite slightly higher adherence among residents, the absence of a statistically significant difference between the two groups suggests that residents' non-inferiority in discharging patients with HFrEF compared to attending physicians is the primary outcome of our research. This finding has not been previously studied.\u003c/p\u003e \u003cp\u003eHigh adherence to BB in our study aligns with existing literature emphasizing their strong recommendation in heart failure management due to mortality benefits \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Kocabaş et al (2020) found similarly high adherence rates, attributing this to robust supporting evidence and fewer side effects than ACEi/ARB \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. It is also noteworthy that 51% of patients discharged by residents and 30% discharged by attending physicians had atrial fibrillation. The concomitant need for beta-blockers (BB) to treat atrial fibrillation may have contributed to the high adherence rate to BB in both groups \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, adherence to quadruple therapy was notably lower than that to triple or double therapy. A study by D'Amario et al. (2023) reported that only 2.6% of patients received quadruple therapy, although 46.2% could have been initiated on it \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In our study, the rates of quadruple therapy prescriptions were higher, with 15% for residents and 11% for attendings. Although higher, it was still suboptimal. These findings highlight the need for targeted interventions to educate residents on guidelines, including the indications and contraindications of each GDMT medication, to improve compliance and ensure better patient outcomes upon discharge.\u003c/p\u003e \u003cp\u003eAlthough there was no significant difference in medication adherence between the two groups, both residents and attending physicians exhibited similar trends in prescribing combination therapies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Interestingly, as patients' ages increased, the number of medications prescribed decreased in both groups. This decline may explain, in part, the preference for combination therapy in older patients, as fewer individual medications are typically prescribed.\u003c/p\u003e \u003cp\u003eChronic kidney disease (CKD) and acute kidney injury (AKI) are common concomitant comorbidities in patients with HFrEF \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In our study, 36% of patients in the residents' group and 25% in the attending group had a history of CKD. The higher adherence to BB prescriptions, compared to other GDMT medications, may be attributed to concerns about renal complications associated with these other treatments.\u003c/p\u003e \u003cp\u003eThe comparison between patients discharged by residents and attending physicians revealed no significant differences in most parameters, except for a higher incidence of cerebrovascular accidents (CVA) in the residents' group. No significant differences existed in other comorbidities, age, race, or prior heart failure diagnosis. This similarity suggests that these factors likely did not influence the choice of discharge medications.\u003c/p\u003e \u003cp\u003eOur retrospective cohort study differs from other research on medication adherence in heart failure in several key ways. While we analyzed existing data from a single tertiary care center, other studies found them in the literature, such as Kocabaş et al. (2020) and Lee et al. (2023). They used prospective designs and multi-center settings, enhancing the generalizability of their findings \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Data collection in our study relied on hospital records and discharge summaries, whereas other studies utilized registry data and standardized protocols \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These methodological differences highlight the need for future research tackling the question of GDMT adherence to adopt more robust designs, comprehensive compliance measures, and detailed outcome assessments to improve the understanding of medication adherence in heart failure \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the results. First, the retrospective design limits the ability to establish causal relationships between the physician group (residents vs. attendings) and adherence to guideline-directed medical therapy (GDMT). Second, the use of a single-center, community hospital cohort may limit the generalizability of the findings to other institutions with different practices, patient populations, or organizational structures. Third, there may have been variability in the accuracy and completeness of data extracted from electronic health records, despite efforts to ensure thorough data collection. Additionally, the study focused exclusively on patients with decompensated heart failure with reduced ejection fraction (HFrEF), which may limit the applicability of the findings to patients with other forms of heart failure, such as heart failure with preserved ejection fraction (HFpEF). Finally, unmeasured confounding factors may exist, as patient characteristics and clinical decision-making processes could vary beyond the scope of the data collected.\u003c/p\u003e \u003cp\u003eExpanding the study to a prospective design in multiple centers could enhance generalizability and explore variations in practice patterns across different healthcare settings for managing HFrEF \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Developing and testing interventions, such as resident educational programs, enhanced patient engagement strategies, and close monitoring protocols, could improve adherence to GDMT. Further research is needed to understand specific barriers to ACEi/ARB/ARNI and SGLT2i adherence and develop targeted strategies to overcome these challenges \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Evaluating the cost-effectiveness of newer therapies like SGLT2i and ARNI can also affect clinical decision-making and policy development. Additionally, comparing resident and attending discharges provides valuable insights into the impact of residency programs on medication adherence.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAdherence to GDMT is similar between the resident and attending groups. Interventions aimed at enhancing adherence to all medications are crucial for optimizing outcomes in patients with HFrEF. Strengthening education and training for both residents and attendings, along with ensuring close monitoring of therapies, could help address these adherence gaps.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eHFrEF\u003c/strong\u003e - Heart Failure with Reduced Ejection Fraction\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGDMT\u003c/strong\u003e - Guideline-Directed Medical Therapy\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eACEi\u003c/strong\u003e - Angiotensin-Converting Enzyme Inhibitors\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eARB\u003c/strong\u003e - Angiotensin II Receptor Blockers\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBB\u003c/strong\u003e - Beta-Blockers\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMRA\u003c/strong\u003e - Mineralocorticoid Receptor Antagonists\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eARNI\u003c/strong\u003e - Angiotensin Receptor-Neprilysin Inhibitors\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSGLT2i\u003c/strong\u003e - Sodium-Glucose Cotransporter-2 Inhibitors\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e - Left Ventricular Ejection Fraction\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAHA\u003c/strong\u003e - American Heart Association\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCVA\u003c/strong\u003e - Cerebrovascular Accidents\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCKD\u003c/strong\u003e - Chronic Kidney Disease\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCABG\u003c/strong\u003e - Coronary Artery Bypass Grafting\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEF\u003c/strong\u003e - Ejection Fraction\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eeGFR\u003c/strong\u003e - Estimated Glomerular Filtration Rate\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAMA\u003c/strong\u003e - Against Medical Advice\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAKI\u003c/strong\u003e - Acute Kidney Injury\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e - Chronic Obstructive Pulmonary Disease\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e: The protocol was approved by the Touro University Nevada Institutional Review Board (TUNIRB) in accordance with the ethical standards set forth by the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e: This study involved a chart review, and informed consent was waived by the Touro University Nevada Institutional Review Board (IRB) for this retrospective analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe appreciate all the patients who chose our hospital to receive treatment and the Valley Health System for supporting this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of competing interests:\u0026nbsp;\u003c/strong\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eAbbas Mohammadi conceptualized and designed the study, supervised data collection, analyzed the data and drafted the manuscript. Ibrahim Youssef, Iryna Kobita, Nazanin Hazhir-Karzar contributed equally to data collection and interpretation. Hossein A Akhondi and Leo Spaccavento assisted in the design and provided critical revisions to the\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCotter, G., \u003cem\u003eet al.\u003c/em\u003e Optimization of Evidence-Based Heart Failure Medications After an Acute Heart Failure Admission: A Secondary Analysis of the STRONG-HF Randomized Clinical Trial. JAMA Cardiology 9, 114\u0026ndash;124 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo, D., Virani, S., Moghaddam, N. \u0026amp; Hawkins, N. ADHERENCE TO GUIDELINE-DIRECTED MEDICAL THERAPY AMONG PATIENTS FOLLOWED AT AN AMBULATORY HEART FUNCTION CLINIC. Canadian Journal of Cardiology 38, S154 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, X., Kang, Y., Dahlstr\u0026ouml;m, U. \u0026amp; Fu, M. Impact of adherence to guideline-directed therapy on risk of death in HF patients across an ejection fraction spectrum. ESC Heart Fail 10, 3656\u0026ndash;3666 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreene, S.J., \u003cem\u003eet al.\u003c/em\u003e Medical Therapy for Heart Failure With Reduced Ejection Fraction: The CHAMP-HF Registry. J Am Coll Cardiol 72, 351\u0026ndash;366 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Amario, D., \u003cem\u003eet al.\u003c/em\u003e Eligibility for the 4 Pharmacological Pillars in Heart Failure With Reduced Ejection Fraction at Discharge. Journal of the American Heart Association 12, e029071 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Tamimi, M.A., Gillani, S.W., Abd Alhakam, M.E. \u0026amp; Sam, K.G. Factors Associated With Hospital Readmission of Heart Failure Patients. Front Pharmacol 12, 732760 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzizi, Z., \u003cem\u003eet al.\u003c/em\u003e Challenge of Optimizing Medical Therapy in Heart Failure: Unlocking the Potential of Digital Health and Patient Engagement. Journal of the American Heart Association 13, e030952 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaraz, Y., \u003cem\u003eet al.\u003c/em\u003e Resident-led Quality Improvement Initiative To Increase GDMT Prescribing Prior To Hospital Discharge In Vulnerable Patients With Heart Failure. Journal of Cardiac Failure 29, 711 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure. J Card Fail 28, e1-e167 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKocabaş, U., \u003cem\u003eet al.\u003c/em\u003e Adherence to guideline-directed medical and device Therapy in outpAtients with heart failure with reduced ejection fraction: The ATA study. Anatol J Cardiol 24, 32\u0026ndash;40 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesta, L., \u003cem\u003eet al.\u003c/em\u003e Adherence to beta-blockers and long-term risk of heart failure and mortality after a myocardial infarction. ESC Heart Fail 8, 344\u0026ndash;355 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChahal, R.S., Chukwu, C.A., Kalra, P.R. \u0026amp; Kalra, P.A. Heart failure and acute renal dysfunction in the cardiorenal syndrome. Clin Med (Lond) 20, 146\u0026ndash;150 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJankowski, J., Floege, J., Fliser, D., B\u0026ouml;hm, M. \u0026amp; Marx, N. Cardiovascular Disease in Chronic Kidney Disease. Circulation 143, 1157\u0026ndash;1172 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJarjour, M. \u0026amp; Ducharme, A. Optimization of GDMT for patients with heart failure and reduced ejection fraction: can physiological and biological barriers explain the gaps in adherence to heart failure guidelines? \u003cem\u003eDrugs Context\u003c/em\u003e 12(2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heart Failure with Reduced Ejection Fraction (HFrEF), Guideline-Directed Medical Therapy (GDMT), Medication Adherence, Resident Physicians","lastPublishedDoi":"10.21203/rs.3.rs-5649645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5649645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nHeart failure with reduced ejection fraction (HFrEF) requires adherence to guideline-directed medical therapy (GDMT) for optimal outcomes. This study aimed to assess GDMT adherence at a teaching hospital and compare discharge prescriptions by residents and attending physicians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nA retrospective cohort study was conducted at Spring Valley Hospital from August 2023 to March 2024. Patients with HFrEF and decompensated heart failure as the primary reason for admission were identified through electronic health records. Adherence to GDMT at discharge was evaluated based on 2022 American Heart Association (AHA) guidelines, focusing on beta-blockers (BB), angiotensin receptor-neprilysin inhibitors (ARNI), mineralocorticoid receptor antagonists (MRA), and sodium-glucose cotransporter 2 inhibitors (SGLT2i). The primary outcome was comparing GDMT adherence between residents and attending physicians. Secondary outcomes included adherence to individual medications, combination regimens, and reasons for non-adherence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nAmong 243 patient charts reviewed, 80 met inclusion criteria (33 residents, 47 attendings). Adherence to beta-blockers was significantly higher than to other GDMT medications (p\u0026lt;0.0001). Residents showed slightly higher adherence to BB, MRA, and SGLT2i, though differences were not statistically significant. Double and triple therapies were prescribed more often than quadruple therapy (p=0.002, p=0.01). Residents demonstrated higher adherence to double therapy with BB and MRA (55% vs. 28%, p=0.02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nAdherence to GDMT for HFrEF was comparable between residents and attending physicians. Improving adherence to key medications can further enhance HFrEF management and patient outcomes.\u003c/p\u003e","manuscriptTitle":"Adherence to Guideline-Directed Medical Therapy in Heart Failure with Reduced Ejection Fraction: A Comparison Between Residents and Attending Physicians","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 17:45:51","doi":"10.21203/rs.3.rs-5649645/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2194109-b8f2-4085-8964-04fb53b428a0","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-11T08:09:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 17:45:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5649645","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5649645","identity":"rs-5649645","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}