Patterns of utilization and optimization of guideline-directed medical therapy and associated factors among heart failure patients with reduced ejection fraction in selected hospitals of Addis Ababa, Ethiopia: a cross-sectional study

Patterns of utilization and optimization of guideline-directed medical therapy and associated factors among heart failure patients with reduced ejection fraction in selected hospitals of Addis Ababa, Ethiopia: a cross-sectional study | 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 Patterns of utilization and optimization of guideline-directed medical therapy and associated factors among heart failure patients with reduced ejection fraction in selected hospitals of Addis Ababa, Ethiopia: a cross-sectional study Michael Adamseged, Mekoya Mengistu, Gashaw Solela, Abel Andargie Berhane, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4348655/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 The global burden of heart failure, especially with reduced ejection fraction, is a significant health issue. Current guidelines stress the importance of optimal medication use to maximize patient outcomes. Nevertheless, a notable gap exists in implementing these guidelines worldwide. In Ethiopia, there is limited post-guideline data on the utilization and optimization of medications for patients with heart failure and reduced ejection fraction. This study aims to evaluate the patterns of utilization and drug optimization and associated factors of guideline directed medical therapy among these patients attending cardiac centers at selected public and private hospitals, Addis Ababa, Ethiopia. Methods A facility-based, cross-sectional study was conducted. Data were collected using a pretested, structured checklist. Data were edited and cleaned via Microsoft Excel 2016 and analyzed using SPSS version 26. Baseline demographic and clinical datawere summarized using descriptive statistics. Multiple logistic regression analysis was run to identify association between dependent and independent variables, by computing odds ratio and 95% confidence interval. A p-value < 0.05 was considered significant. Results A total of 404 patients were included in this study, with a response rate of 95.7%. Majority (222; 55%) were males, and patients’ age ranged from 17 years to 86 years with a median (inter-quartile range) of 56 (43.25–65) years. Overall, 46 (11.4%) were receiving quadruple therapy. ACEIs/ARBs, beta-blockers and MRA were given to 212(52.5%), 314 (77.7%), and238 (58.9%) patients, respectively. SGLT2Is were prescribed to only 109 (27%) patients. Age older than 65 years (AOR = 4.34; 95% CI = 1.59, 11.89), history of previous hospitalization (AOR = 2.50; 95% CI = 1.21, 5.15) and taking < 5 medications (AOR = 9.6; 95% CI = 2.79, 33.07) were associated with GDMT underutilization. Conclusion There is a large gap in GDMT implementation, with majority of the patients having either underutilization or under-dosing, particularly those older than 65 years, with history of previous hospitalization and taking < 5 medications. Thus, efforts should be directed to design customized guidelines along with institution of sensitization and training programs while also considering multidisciplinary care. Guideline directed medical therapy drug utilization drug optimization heart failure Ethiopia Figures Figure 1 Background Heart failure (HF) is a heterogeneous clinical syndrome that is caused by functional and/or structural cardiac abnormality resulting in symptomatic left ventricle (LV) dysfunction ( 1 ). Similarly, the 2022 AHA/ACC/HFSA guideline addresses HF as a complex clinical syndrome with symptoms and signs that result from any structural or functional impairment of ventricular filling or ejection of blood ( 2 ). It is a chronic, progressing and ultimately debilitating clinical entity that is caused by ventricular pump dysfunction, or by overload of volume (preload) or pressure (afterload) ( 3 ). Worldwide, HF is a serious public health problem, affecting more than 64.3 million people ( 4 – 6 ). Heart failure with reduced ejection fraction (HFrEF) is a distinct form of cardiac failure characterized by left ventricular ejection fraction (LVEF) ≤ 40% on echocardiography ( 2 , 7 ). This specific cardiac phenotype accounts for about half of all the reported cases of HF, and its prevalence is projected to rise chiefly as a result of major therapeutic advances and a growing ageing population ( 8 ). Again, with the changing trends of cardiovascular diseases as a function of several maladaptive lifestyle behaviors ( 9 ), the magnitude of HFrEF can be extrapolated to be worrisome in developing countries such as Ethiopia, where HF with reduced ejection fraction accounted for 31.5% of the cardiovascular diseases ( 10 ). Generally, contemporary treatment guidelines for patients with HFrEF recommend a quadruple-therapy approach, consisting of an angiotensin-converting enzyme (ACE) inhibitor or angiotensin II type I receptor blocker (ARB) if ACEIs are not tolerated, a betablocker (BB) and a mineralocorticoid/aldosterone receptor antagonist (MRA), and a sodium–glucose co-transporter 2 (SGLT2) inhibitor. If patients have chronic symptomatic HFrEF with NYHA class II or III symptoms and they tolerate an ACEi or ARB, they should be switched to an ARNi ( 2 , 11 ). This combination, which interacts with multiple neurohormonal pathways, has been consistently shown to reduce mortality and improve survival in multiple landmark trials, and is thus strongly recommended by for patients with HFrEF in contemporary guidelines ( 2 , 3 ). Despite the extensively documented cardiovascular benefits of guideline directed medical treatment (GDMT), suboptimal pharmacotherapy remains an extensive problem in any HFrEF population in the absence of contraindication. However, underutilization of such life-prolonging treatments at trial-proven doses, with subsequent unacceptably poor outcomes have been reported ( 12 ). Such substandard clinical practice is likely to be rampant in underprivileged countries, such as Ethiopia in which majority of clinicians were noticed to have low level of adherence to the latest guidelines ( 13 ). Typically, optimization of GDMT is performed during regular clinical visits although subsequent delay in optimization oftentimes is observed due to relatively infrequent visits and other challenges such as laboratory, blood pressure monitoring, and concern of side effects. Missed opportunities and in fact, significant gap, of such kind have been practically implicated to result in poor prognosis of patients with HF in Ethiopian setting ( 14 ). Apart from this, there is scarcity of data in this regard in Ethiopia, particularly after the issuance of the 2022 AHA/ACC/HFSA guideline. Thus, keeping the aforementioned evidences in view, this study is designed with intention of assessing the patterns of utilization and drug optimization and associated factors of guideline directed medical therapy in heart failure with reduced ejection fraction patients attending cardiac centers at selected hospitals, Addis Ababa, Ethiopia. Methods and Materials Study setting, design and period A facility-based, cross-sectional study was conducted in Addis Ababa, Ethiopia, specifically at three cardiac centers: Tikur Anbessa Specialized Hospital (TASH), Yekatit 12 Hospital Medical College (YHMC), and Addis Cardiac Center (ACC). Addis Ababa, the capital city of Ethiopia, comprises eleven sub-cities and 117 woredas with thirteen governmental and around 40 private hospitals, serving a population of approximately 5,703,628 as of February 2024 ( 15 ). Specialized cardiology care is available in both public and private facilities, with this study focusing on selected hospitals with cardiac services. Source population and study population The study included all heart failure patients with reduced EF attending cardiac clinics in Addis Ababa, Ethiopia as the source population. The study population, on the other hand, consisted of randomly chosen heart failure patients with a baseline left ventricular ejection fraction of ≤ 40% who were visiting cardiac clinics in selected health facilities during the study period and met the eligibility criteria. Inclusion criteria encompassed adult patients (age ≥ 18 years) diagnosed with heart failure clinically, confirmed by echocardiography to have a baseline ejection fraction ≤ 40%, and those under regular follow-up for a minimum of 2 months at the time of data collection. Exclusion criteria involved pregnant patients and those with documented bilateral renal artery stenosis. Sample size determination and sampling technique The sample size for this study is calculated using a single population proportion formula. Taking the proportion of patients receiving GDMT (that is, MRAs) to be 49% from the earlier study reported in Ethiopia ( 16 ), the sample size is calculated as: \(n=\frac{{z}^{2}p(1-p)}{{e}^{2}}\) , where n = the required sample size; p = the proportion of patients receiving MRAs = 0.49; Z α\2 = the critical value at 95% confidence level = 1.96, and e = margin of error = 5%. Accordingly, the minimum required sample size became \(384\) , which was increased to 422 with addition of 10% for contingency. In Addis Ababa, two public hospitals, TASH and YHMC, were purposefully selected due to patient load and gaps in clinical care. The private facility, ACC, was chosen for its high volume of heart failure (HF) patients. A total of 2655 adult patients with HF and LVEF less than 40% were included (1240 at TASH, 670 at YHMC, and 745 at ACC) in the sampling frame. The required sample size of 422 was achieved through proportional stratified sampling, with 197 from TASH, 107 from YHMC, and 118 from ACC. Patients were stratified by facility and selected using systematic random sampling (k = 6) for their medical records. Study variables Utilization of guideline-directed medical therapy was the dependent variable in this study. The independent variables were socio-demographics data, which included age, sex, residence, educational status, employment status, type of facility, use of community-based health insurance. Additionally, the behavioral and clinical variables such as frequency of follow up, comorbidity, type of underlying cardiac condition, previous hospitalization, duration of the illness, NYHA functional class, pill burden and laboratory variables including serum creatinine, potassium level, and LVEF were among the independent variables. Data collection tools and procedures Data were collected from eligible patients’ medical records using a structured checklist. The data collection format consisted of background information, clinical variables, and laboratory parameters), and it was adapted from related literatures ( 16 – 19 ). Data were gathered from logbook records and respective medical records of patients. In events where pertinent patients’ details were missing, respective patients were contacted virtually via registered address or physically during follow up visits. Two professional healthcare workers were recruited and trained on data collection procedures. Data quality control and assurance To ensure data quality, a pre-test was carried out on 22 patients, and those patients were excluded from the final analysis. The principal investigator provided regular supervision to data collectors to ensure data completeness, clarity, and accuracy. Any unclear or incomplete data was excluded from the analysis. Data processing and analysis Data entry, coding, and cleaning were done in Microsoft Excel 2016, then exported to SPSS 26 for statistical analysis. Patient characteristics were analyzed using descriptive statistics. Binary logistic regression assessed variable associations. Those variables with p-value of ≤ 0.25 in bivariable logistic regression were selected for multivariable logistic regression analysis to compute adjusted odds ratio at 95% confidence interval. Hosmer-Lemeshow goodness-of-fit test was run to test the fitness of the model, declaring data fitness if p value > 0.05. Variables with p ≤ 0.05 showed significant associations with the outcome. Operational definitions Utilization was described as the prescription of 2022 AHA/ACC/HFSA recommended cardiac medications (ACEI/ARBs/ARNI, BBs, MRAs, and SGLT2 inhibitors) in accordance with the guideline. Underutilization, on the other hand, referred to instances where these recommended medications were not appropriately used. Optimal or target dose indicated patients who were either on the target dose or had been titrated to the highest tolerated dose. Suboptimal dose referred to cases where patients had not been titrated to the target dose. Intermediate dose denoted a dosage ranging from 50–99% of the recommended dose, while a low dose was considered to be less than 50% of the recommended dose. The specific target doses for each medication class were derived from the 2022 AHA/ACC/HFSA guidelines ( 2 ). Results Demographic characteristics of the study population A total of 404 cardiac patients with HFrEF were included, making a response rate of 95.7%. Majority (222; 55%) were males, with male-to-female ration of 1.2 to 1. Patients’ age ranged from 17 years to 86 years with a median of 56 years and interquartile range of 43.25–65. Approximately three-fourths (296;73.3%) were younger than 65 years (Table 1). Table 1. Socio-demographic characteristics of HFrEF patients attending the selected hospitals of Addis Ababa from September 1st to October 31st, 2023 Majority of the patients (318, 78.7%) were urban dwellers. A little more than a quarter (n = 116) of the patients had attended up to secondary school while 106 (26.2%) had obtained a college diploma or more. More than one-third 142 (35.1%) were employed whereas a hundred and eight (26.7%) were unemployed. Further, 162 (40.1%) of the patients were enrolled to the on-site community-based health insurance (CBHI) system (Table 1). Behavioral and clinical characteristics of the study population Most of the patients (370;91.6%) claimed not to have ever smoked a cigarette, with only thirty-four (8.6%) having a history of smoking. Likewise, only forty-nine (12.1%) described to consume alcoholic beverages. Besides, majority (55%; n = 222) of the patients had normal BMI measurements (18.5–24.9) while a hundred and nineteen (29.5%) were obese, with BMI of 25 to 29.9 (Table 2). Furthermore, majority (207; 51.2%) of the patients had history of hospitalization for heart failure in the preceding one year. Majority (233;57.7%) were described to have HFrEF for more than two years, and 166 (41.1%) of the studied subjects were reported to have a regular clinical follow up at the cardiac outpatient departments (OPDs) every 2.1 to 3 months. More than one-third (152;37.6%) of the patients were in NYHA class II. The most frequently mentioned underlying cardiac abnormalities were ischemic heart disease (IHD) and dilated cardiomyopathy (DCMP), which were documented in 192 (47.5%) and 104 (25.7%) of the patients, respectively. About half (195;48.3%) of the patients had normal to high estimated GFR (≥ 90) (Table 2). Table 2. Behavioral and clinical profile of HFrEF patients attending selected hospitals of Addis Ababa from September 1st to October 31st, 2023 Majority of the patients (290;71.8%) had at least one coexisting medical condition. Of these, about two-thirds (183;63.1%) had two or more comorbid conditions (Table 2). The most frequently identified comorbidities were hypertension (n = 159;54.8%) and diabetes mellitus (n = 135;46.6%) (Fig. 1 ). Figure 1 . Frequency of comorbidities among HFrEF patients attending selected hospitals of Addis Ababa from September 1st to October 31st, 2023 Medication-related profile of the study population Out of the total, 212 patients (52.5%) were on ACEIs or ARBs, with beta-blockers prescribed to 314 patients (77.7%). Additionally, 109 patients (27%) were taking SGLT2 inhibitors, and 238 patients (58.9%) were on MRAs. Only 29 patients (7.2%) were using ARNI. In total, 46 patients (11.4%) were on quadruple therapy, including one medication from each class. There was no statistically significant difference in the implementation of GMDT across the facilities studied (Table 3). Only 35 patients (8.7%) received optimal ACEi/ARB doses, with just 3 (0.7%) on target beta-blocker doses. About 26% were on optimal SGLT2i doses, while 50.7% received optimal MRA doses. Cough affected 8.9% of patients, angioedema 1%, and 18.8% had elevated serum potassium levels (Table 3). Table 3. Pharmacological characteristics of HFrEF patients attending the selected hospitals of Addis Ababa from September 1st to October 31st, 2023 Overall, only thirty-five (8.7%) of the patients were receiving optimal dose of ACEIs or ARBs, with 70 (17.3%) taking intermediate dose. Very few (3; 0.7%) were taking optimal dose of BB whereas more than one-fourth (106; 26.2%) were on optimal dose SGLT2i. Moreover, while half (205; 50.7%) were receiving optimal dose of MRAs, only 33 (8.2%) were taking intermediate dose of these drugs (Table 4). Table 4. Type and dose of GDMT used inpatients attending the selected hospitals of Addis Ababa from September 1st to October 31st, 2023 (n = 404) Factors associated with the use of GDMT Multiple regression analysis showed that the odds of not receiving proper GMDT was significantly higher among patients who were 65 years or older compared to those who were younger than 65 years (AOR = 4.34; 95% CI = 1.59, 11.89). Furthermore, the likelihood of GDMT underutilization was higher among those who had any history of hospitalization in the preceding one year in comparison to those who had no any (AOR = 2.50; 95% CI = 1.21,5.15). Finally, patients taking four or less medications showed higher likelihood of not being on the guideline recommended quadruple therapy as compared with those taking at least five regular medications (AOR = 9.6; 95% CI = 2.79,33.07) (Table 5) Table 5. Factors associated with GDMT use among patients attending selected hospitals of Addis Ababa, Ethiopia, 2023 Discussion This study aimed to assess the patterns of utilization and drug optimization and associated factors of guideline directed medical therapy in heart failure with reduced ejection fraction patients attending selected hospitals in Addis Ababa, Ethiopia. The results demonstrated that only one-tenth (11.4%) patients were on appropriate GDMT (quadruple therapy), with certain factors such as age older than 65 years, history of previous hospitalization and lesser number of medications being associated with GDMT underutilization. In this study, only one-tenth (11.4%) of the studied patients were taking all classes of GDMT. This is low compared to the CHAMP-HF study, where 22.1% of the patients were simultaneously prescribed some dose of ACEI/ARB/ARNI, beta-blocker, and MRA therapy among patients eligible for all classes of medication ( 20 ). In particular, 52.5% of HFrEF patients were receiving either ACEIs or ARBs. This finding is in agreement with the Egyptian study that documented that 51.4% of cardiac patients were on the same group of medications ( 21 ). However, the present proportion of patients using RAS inhibitors was lower than that of the multinational study from the ASIAN-HF registry and the Korean study, which documented that RAS inhibitors were utilized among 77% and 75.3% of the cardiac patients, respectively ( 19 , 22 ). The relatively lower utilization of ACEIs or ARBs in the current study as compared with the studies done in developed nations may explained by clinical inertia due to lack of frequent renal function and electrolyte monitoring as to the standard, which might be partly due to financial reasons.. The current finding differs from previous research in the United States, Taiwan, and Italy, where RAS inhibitors were used in 81.4%, 73%, and 62% of HFrEF cases, respectively ( 20 , 23 , 24 ). In Italy, 68.2% of cardiac patients received RAS inhibitors ( 25 ). This percentage is lower than the 74.7% reported in Ethiopia ( 18 ). Moreover, 77.7% of patients in the present study were on beta-blockers, similar to the ASIAN-HF registry's multinational study (79%) ( 19 ). This is comparable to Demissie et al.'s findings (79%) ( 26 ). However, it exceeds rates in the CHAMP-HF study, Southwest Ethiopia, and Korea (67%, 67%, and 54.9%, respectively) ( 17 , 20 , 22 ). In contrast, it falls below the US and Italy rates (93.4–94.4%) for beta-blocker usage.( 23 , 25 ). In this study, 27% of the studied patients were on SGLT2 inhibitors. This is comparatively higher than the previous Ethiopian and Spanish studies, where only 5% and 6.7% of patients with HFrEF were on SGLT2 inhibitors, respectively ( 26 , 27 ). This can be due to the better availability of the medications in urban areas. On the contrary, the present finding was much lower than the pattern observed in the nation-wide German study, in which more than half (54.8%) of cardiac patients were on SGLT2 inhibitors ( 28 ). Furthermore, this study found 58.9% on MRAs, similar to Ethiopia (58.7%) and Egypt (54.9%) ( 26 , 29 ). On the contrary, lower rates were seen in southwest Ethiopia (32.5%) and Italy (17.4%), while Oman had higher usage (77%) ( 17 , 25 , 30 ). Generally, the underutilization of SGLT2 inhibitors in the current study is most likely due to low availability of these drugs in the market and financial constraints. In aggregate, this study showed that most of the patients were not in quadruple therapy, with no apparent justification for not initiating the medications recorded upon review of the health record or discussion with the patient. Such low level of GDMT implementation in our setup might be due to lack of compelling local guidelines, perceived fear of intolerance, absence of frequent laboratoy monitoring for drug adverse effects, lack of access to prescribed medications (especially SGLT2is) and differences in the practice of multidisciplinary care, with suboptimal medical practitioners’ expertise and high clinical inertia in resource-poor countries. It could be also pointed out resource-poor settings such as ours do not widely use technology to enhance adherence to GDMT, as opposed to developed ones ( 31 ). Apart from this, the proportion (11.4%) of patients on appropriate GDMT (quadruple therapy) obtained in this study is low given the recent evidence, such as the STRONG-HF trial, that demonstrated that rapid drug implementation and up-titration in the absence of absolute contraindications is superior to the traditional and more gradual step-by-step approach in which valuable time is wasted to up-titration ( 32 – 34 ). In this study, age older than or equal to 65 years was negatively associated with use of GDMT among the studied patients. This is in agreement with the previous Egyptian and Ethiopian studies, which independently found that younger age was associated with better GDMT utilization ( 18 , 29 , 35 ). Among others, this can be explained by the probability that older patients are likely to be dependent on others to take their medications appropriately and they are also more likely to have coexisting medical conditions such as renal dysfunction and orthostatic hypotension, which may prohibit their eligibility for morbidity- and mortality-reducing therapies, such as ACEi, ARB, ARNI, BBs or MRA. Patients with history of hospitalization in the preceding one year were more likely to have underutilized the novel GDMT compared to those with no previous hospitalization. This is consistent with the report of Niriayo et al., who found that previous hospitalization for heart failure was associated with poor GDMT utilization( 18 ). This is, in fact, can be an indicator of the fact that lack of adherence to the evidence-based quadruple therapy (GDMT) results in increased morbidity among cardiac patients, which would likely result in frequent hospitalization for inpatient care ( 26 , 36 , 37 ). Patients on polypharmacy were more likely to utilize GDMT than those on lesser number of medications. Although there is limited previous literature regarding this finding, this apparent association can be justified by the fact that the those patients who are on polypharmacy are probably more sicker more counseled by health care providers and their family members, that might increase their utilization of GDMT. Moreover, patients with multi-morbidity (and as a consequence, on polypharmacy) are likely to be subjected to subspecialist consultations, which may in turn lead to a meticulous clinical decision regarding GMDT therapy. This study is among the leading studies to be done in Ethiopian setting, particularly after the endorsement of the latest guideline (2022 AHA/ACC/HFSA). However, its cross-sectional design limits causal inferences and alternative interpretations. The study did not include exhaustive list of physician and pharmacy related variables that would influence the GDMT utilization and optimization. Conclusions This study demonstrated a major gap in use and dose titration of GDMT, as these drugs were either underutilized or under-dosed in most of the HFrEF patients despite their proven clinical benefits. Moreover, despite guideline recommendations, most of the cardiac patients were receiving suboptimal dose, highlighting the rates of target dose achievement in clinical practice is still gruesome. Additionally, this study revealed that patients having certain characteristics such as age older than 65 years, history of previous hospitalization and lesser number of medications were more prone for GDMT underutilization. Recommendation The findings from this study highlight that health care professionals should exert some effort to adhere to the latest available guideline to utilize GDMT and up-titrate to target dose or maximum tolerable dose for better clinical outcomes, with due emphasis on those having certain characteristics such as age older than 65 years, history of previous hospitalization and lesser number of medications. Local health policy makers should design customized guidelines while also considering sensitization and training program. A multidisciplinary care with the inclusion of clinical pharmacists should be considered. Further researches with better designs should be carried out to validate the current findings. Abbreviations ACC Addis Cardiac Center ACE Angiotensin-Converting Enzyme ACEIs Angiotensin-converting enzyme inhibitors AHA American Heart Association ARB Angiotensin II Receptor Blocker ARNIs Angiotensin receptor neprilysin inhibitors CHAMP-HF Change the Management of Patients with Heart Failure ESC European Society of Cardiology GDMT Guideline-directed medical therapy GFR Glomerular Filtration Rate HFrEF Heart Failure with Reduced Ejection Fraction LVEF Left Ventricular Ejection Fraction MRAs Mineralocorticoid Receptor Antagonists NYHA High New York Heart Association RAS Renin-Angiotensin System SGLT2is Sodium–glucose co-transporter 2 inhibitors SPSS Statistical Package for Social Sciences TASH Tikur Anbessa Specialized Hospital Y12HMC Yekatit 12 Hospital Medical College Declarations Acknowledgements We would like to thank the Department of Internal Medicine of Yekatit 12 Hospital Medical College for facilitating the undertaking of this study as well as the data collectors for their time and cooperation. Funding The authors received no specific funding for this work. Availability of data and materials All the data that support the findings of this study are available from the corresponding author upon reasonable request. Authors’ contributions MA conceived and designed the study, involved in the proposal development, data analysis, interpretation, and manuscript writing. All other of the authors contributed to data analysis and interpretation, drafting and revising the manuscript. All gave final approval of the final version to be submitted, and agreed to be accountable for all aspects of the work. Competing interests The authors certify that no actual or potential conflicts of interest with regard to this work exist. Consent for publication Not applicable. Ethics approval and consent to participate Ethical clearance was obtained from Ethical Review Committee of Yekatit 12 Hospital Medical College. 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Utilization and Optimization of Beta-Blockers on Heart Failure Patients with Reduced Ejection Fraction (HFrEF) at Cardiac Clinic of Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia: A Cross-Sectional Study. Res Rep Clin Cardiol. 2023;14(May):21–33. Escobar C, Palacios B, Varela L, Gutiérrez M, Duong M, Chen H, et al. Prevalence, Characteristics, Management and Outcomes of Patients with Heart Failure with Preserved, Mildly Reduced, and Reduced Ejection Fraction in Spain. J Clin Med. 2022;11(17):1–18. Kerwagen F, Riemer U, Wachter R, von Haehling S, Abdin A, Böhm M et al. Impact of the COVID-19 pandemic on implementation of novel guideline-directed medical therapies for heart failure in Germany: a nationwide retrospective analysis. Lancet Reg Heal - Eur [Internet]. 2023;35:100778. https://doi.org/10.1016/j.lanepe.2023.100778 . El Hadidi S, Samir Bazan N, Byrne S, Darweesh E, Bermingham M. Heart Failure Prescribing Quality at Discharge from a Critical Care Unit in Egypt: The Impact of Multidisciplinary Care. Pharmacy. 2020;8(3):159. Al-Aghbari S, Al-Maqbali JS, Alawi AMA, Za’abi M, Al, Al-Zakwani I. Guideline-directed medical therapy in heart failure patients with reduced ejection fraction in Oman: utilization, reasons behind non-prescribing, and dose optimization. Pharm Pract (Granada). 2022;20(2):1–8. Romero E, Yala S, Sellers-Porter C, Lynch G, Mwathi V, Hellier Y, et al. Remote monitoring titration clinic to implement guideline-directed therapy for heart failure patients with reduced ejection fraction: a pilot quality-improvement intervention. Front Cardiovasc Med. 2023;10(June):1–8. Greene SJ, Bauersachs J, Brugts JJ, Ezekowitz JA, Filippatos G, Gustafsson F, et al. Management of Worsening Heart Failure With Reduced Ejection Fraction: JACC Focus Seminar 3/3. J Am Coll Cardiol. 2023;82:559–71. Mebazaa A, Davison B, Chioncel O, Cohen-Solal A, Diaz R, Filippatos G et al. Safety, tolerability and efficacy of up-titration of guideline-directed medical therapies for acute heart failure (STRONG-HF): a multinational, open-label, randomised, trial. Lancet [Internet]. 2022;400(10367):1938–52. https://doi.org/10.1007/s10741-023-10325-2 . Arrigo M, Biegus J, Asakage A, Mebazaa A, Davison B, Edwards C, et al. Safety, tolerability and efficacy of up-titration of guideline-directed medical therapies for acute heart failure in elderly patients: A sub-analysis of the STRONG-HF randomized clinical trial. Eur J Heart Fail. 2023;25(7):1145–55. Gelaye AT, Seid MA, Baffa LD. Angiotensin-Converting Enzyme Inhibitor Dose Optimization and Its Associated Factors at Felege Hiwot Comprehensive Specialized Hospital, Bahir Dar, Ethiopia. Vasc Health Risk Manag. 2022;18(July):481–93. Inamdar AA, Inamdar AC. Heart failure: Diagnosis, management and utilization. J Clin Med. 2016;5(7). McCullough PA, Mehta HS, Barker CM, Van Houten J, Mollenkopf S, Gunnarsson C, et al. Mortality and guideline-directed medical therapy in real-world heart failure patients with reduced ejection fraction. Clin Cardiol. 2021;44(9):1192–8. Tables Table 1 . Socio-demographic characteristics of HFrEF patients attending the selected hospitals of Addis Ababa from September 1 st to October 31 st , 2023 Variable Quadruple therapy Total (Frequency) Percentage (%) No Yes Sex Male 255 41 222 55.0 Female 103 5 182 45.0 Age group <65 years 190 32 296 73.3 ≥65 years 168 14 108 26.7 Residence Urban 283 35 318 78.7 Rural 75 11 86 21.3 Educational status No formal education 84 5 89 22.0 Primary education 78 15 93 23.0 Secondary education 102 14 116 28.7 Tertiary and above 94 12 106 26.2 Occupation Unemployed 96 12 108 26.7 Employed 118 24 142 35.1 Self-employed 89 10 99 24.5 Retired 46 0 46 11.4 Other 9 0 9 2.2 Enrolled to CBHI Yes 145 17 162 40.1 No 213 29 242 59.9 CBHI, Community-based health insurance Table 2 . Behavioral and clinical profile of HFrEF patients attending selected hospitals of Addis Ababa from September 1 st to October 31 st , 2023 Variable Quadruple therapy Frequency (Total) Percent (%) No Yes Cigarette smoking Yes 31 4 34 8.4 No 327 43 370 91.6 Alcohol consumption Yes 41 8 49 12.1 No 317 38 355 87.9 Body mass index 2 years 204 24 233 57.7 Frequency of follow up Every 1 to 2 months 147 17 164 40.6 Every 2.1 to 3 months 150 16 166 41.1 Every 3.1 to 6 months 61 13 74 18.3 NYHA functional class I 66 11 77 19.1 II 134 18 152 37.6 III 89 6 95 23.5 IV 69 11 80 19.8 Underlying cardiac pathology Ischemic heart disease 166 26 192 47.5 Cardiomyopathy 50 6 104 25.7 Hypertensive heart disease 91 13 56 13.9 Valvular heart disease 36 1 37 9.2 Congenital heart disease 15 0 15 3.9 Glomerular filtration rate ≥90 176 19 195 48.3 60–89 120 19 139 34.4 30–59 44 8 52 12.9 15–29 16 0 16 4.0 <15 2 0 2 0.5 Presence of comorbidity* Yes 255 35 290 71.8 No 103 11 114 28.2 Number of comorbidities (n=290) € <2 95 12 107 36.9 ≥2 160 23 183 63.1 EF: Ejection fraction; GFR: Glomerular filtration rate; NYHA: New York Heart Association *comorbidities included were diabetes mellitus, hypertension, dyslipidemia, chronic lung disease, chronic kidney disease, stroke, as depicted in Figure 1. Table 3 . Pharmacological characteristics of HFrEF patients attending the selected hospitals of Addis Ababa from September 1 st to October 31 st , 2023 Variable Frequency Percentage (%) Medications used ACEIs/ARBs 212 52.5 Beta-blockers 314 77.7 SGLT 109 27.0 MRA 238 58.9 ARNI 29 7.2 Quadruple therapy 46 11.4 Side effects/contraindication Cough 36 8.9 Angioedema 4 1.0 Not documented 364 90.1 Pill burden <5 medications 120 29.7 ≥5 medications 284 70.3 Potassium level Hyperkalemia 76 18.8 Normokalemia 319 79.0 Hypokalemia 9 2.2 Quadruple therapy £ No(%) Yes(%) Tikur Anbessa Specialized Hospital (n=190) 163(85.8) 27(14.2) 47.0 Yekatit 12 Hospital Medical College (n=104) 95(91.3) 9(8.7) 25.7 Addis Cardiac Center (n=110) 100(90.9) 10(9.1) 27.2 £ Chi square test was 2.85 with p-value of 0.241 ARB, Angiotensin-receptor blockers; ARNI, Angiotensin receptor neprilysin inhibitors Table 4. Type and dose of GDMT used inpatients attending the selected hospitals of Addis Ababa from September 1 st to October 31 st , 2023 (n=404) Variable Medications ACEI/ARB BB SGLT2i MRA* Number of patients on optimal dose (%) 35(8.7) 3(0.7) 106(26.2) 205(50.7) Number of patients on 50 to <99% of the target dose (%) 70(17.3) 44(10.9) 3(0.7) 33(8.2) Number of patients on 1-49% of the target dose (%) 107(26.5) 267(66.1) 0(0) 0(0) Number of patients not receiving any dose (%) 192(47.5) 90(22.3) 295(73.0) 166(41.1) *14 of the 16 patients with GFR of 15 to 29 were on MRAs. Table 5 . Factors associated with GDMT use among patients attending selected hospitals of Addis Ababa, Ethiopia, 2023 Variable Quadruple therapy COR (95%CI) AOR (95%CI) No (%) Yes (%) Age group <65 years 255(86.1) 41(13.9) 1 1 ≥65 years 103(97.2) 5(2.8) 3.31(1.27,8.62) 4.34(1.59,11.89)** Sex Male 190(85.6) 32(14.4) 1 1 Female 168(92.3) 14(7.7) 2.02(1.04,3.92) 1.91(0.92,3.95) Educational status No formal education 84(94.4) 5(5.6) 2.14(0.72,6.34) 1.76(0.56,5.54) Primary education 78(83.4) 15(16.6) 0.66(0.29,1.50) 0.57(0.23,1.40) Secondary education 102(87.9) 14(12.1) 0.93(0.41,2.11) 3.09(1.00,9.55) Tertiary and above 94(88.7) 12(11.3) 1 1 Body mass index <18.5 25(92.6) 2(7.4) 1 1 18.5–24.9 198(89.2) 24(10.8) 0.66(0.15,2.96) 0.49(0.10,2.40) 25–29.9 100(84.0) 19(16.0) 0.42(0.09,1.93) 0.34(0.07,1.71) ≥30 35(97.2) 1(2.8) 2.80(0.24,32.62) 1.99(0.16,24.70) Alcohol consumption Yes 41(83.7) 8(16.3) 1 1 No 317(89.3) 38(10.7) 1.63(0.71,3.73) 1.30(0.50,3.40) Previous hospitalization Yes 191(92.3) 16(7.7) 2.14(1.13,4.07) 2.50(1.21,5.15)* No 167(84.8) 30(15.2) 1 1 Pill burden <5 medications 117(97.5) 3(2l5) 6.96(2.12,22.90) 9.60(2.79,33.07)*** ≥5 medications 241(84.8) 43(15.2) 1 1 Only factors which showed p-value of <0.25 in binary regression are shown here. *P-value< 0.05; **P-value < 0.01; ***P-value < 0.001 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-4348655","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321300827,"identity":"a466b9da-85eb-4e31-bb5f-14ec7ac175e8","order_by":0,"name":"Michael Adamseged","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABMElEQVRIiWNgGAWjYDACZgY2IGnBwHAAwmecz97Y+ADI4OHDr0UCoWVjz+HDBiAtbLjtQdPScCMtTQImjg3otjM/e/CjQkKe7/jp5A8fftnINvacMav8mmMnw8bA/PDRDUwtZofZzA17zkgYzjyTu01yZl+acTt7j9lt2W3JQIexGRvnYNPCwybB2ybBuOFA7jZm3p7DiSBbbktuYwZq4WGTxqFF8u8/CfsN599u/vy3539iw40cs2LJbfV4tUjzNkgkbriRu0Ga4ceBRJD3GT9uO4xHC5uZtMwxieSZN95uk+xtSDYGBbI047bjPGzMOPxy/vAzyTc1NrZ953M3f/jxx04WFJUff26rtudnb374GIsWVMDYBqGZecAkIeVg8Aeq9QdRqkfBKBgFo2CEAAACNG3EhBTFvwAAAABJRU5ErkJggg==","orcid":"","institution":"Yekatit 12 Hospital Medical College","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Adamseged","suffix":""},{"id":321300831,"identity":"1d952454-decd-46c1-8198-3f60c6a3df25","order_by":1,"name":"Mekoya Mengistu","email":"","orcid":"","institution":"Yekatit 12 Hospital Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mekoya","middleName":"","lastName":"Mengistu","suffix":""},{"id":321300833,"identity":"a071b5a1-f57b-43db-944d-5e8b45f04385","order_by":2,"name":"Gashaw Solela","email":"","orcid":"","institution":"Yekatit 12 Hospital Medical College","correspondingAuthor":false,"prefix":"","firstName":"Gashaw","middleName":"","lastName":"Solela","suffix":""},{"id":321300834,"identity":"91985fb3-8c3c-4037-b716-3d15954d055f","order_by":3,"name":"Abel Andargie Berhane","email":"","orcid":"","institution":"Yekatit 12 Hospital Medical College","correspondingAuthor":false,"prefix":"","firstName":"Abel","middleName":"Andargie","lastName":"Berhane","suffix":""},{"id":321300836,"identity":"1ff3ad83-dc9d-4d6a-a23c-3b6ca7c4dabd","order_by":4,"name":"Getachew W/Yohannes","email":"","orcid":"","institution":"Yekatit 12 Hospital Medical College","correspondingAuthor":false,"prefix":"","firstName":"Getachew","middleName":"","lastName":"W/Yohannes","suffix":""}],"badges":[],"createdAt":"2024-04-30 11:21:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4348655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4348655/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61001811,"identity":"f906f312-f273-4a41-839d-559cc7fd7973","added_by":"auto","created_at":"2024-07-24 13:15:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35571,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of comorbidities among HFrEF patients attending selected hospitals of Addis Ababa from September 1\u003csup\u003est\u003c/sup\u003e to October 31\u003csup\u003est\u003c/sup\u003e, 2023\u003c/p\u003e\n\u003cp\u003e*Chronic lung diseases observed were chronic obstructive pulmonary disorder and post tuberculosis fibrosis, and chronic bronchitis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4348655/v1/9a530754f3acc4e4aa4de664.png"},{"id":63468921,"identity":"fe6a7e89-6126-4e60-8a06-c436e64dabc1","added_by":"auto","created_at":"2024-08-28 12:52:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1019306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4348655/v1/c4d3a537-2346-4ec5-89ed-339dd82c184a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patterns of utilization and optimization of guideline-directed medical therapy and associated factors among heart failure patients with reduced ejection fraction in selected hospitals of Addis Ababa, Ethiopia: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eHeart failure (HF) is a heterogeneous clinical syndrome that is caused by functional and/or structural cardiac abnormality resulting in symptomatic left ventricle (LV) dysfunction (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Similarly, the 2022 AHA/ACC/HFSA guideline addresses HF as a complex clinical syndrome with symptoms and signs that result from any structural or functional impairment of ventricular filling or ejection of blood (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It is a chronic, progressing and ultimately debilitating clinical entity that is caused by ventricular pump dysfunction, or by overload of volume (preload) or pressure (afterload) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Worldwide, HF is a serious public health problem, affecting more than 64.3\u0026nbsp;million people (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHeart failure with reduced ejection fraction (HFrEF) is a distinct form of cardiac failure characterized by left ventricular ejection fraction (LVEF)\u0026thinsp;\u0026le;\u0026thinsp;40% on echocardiography (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This specific cardiac phenotype accounts for about half of all the reported cases of HF, and its prevalence is projected to rise chiefly as a result of major therapeutic advances and a growing ageing population (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Again, with the changing trends of cardiovascular diseases as a function of several maladaptive lifestyle behaviors (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), the magnitude of HFrEF can be extrapolated to be worrisome in developing countries such as Ethiopia, where HF with reduced ejection fraction accounted for 31.5% of the cardiovascular diseases (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Generally, contemporary treatment guidelines for patients with HFrEF recommend a quadruple-therapy approach, consisting of an angiotensin-converting enzyme (ACE) inhibitor or angiotensin II type I receptor blocker (ARB) if ACEIs are not tolerated, a betablocker (BB) and a mineralocorticoid/aldosterone receptor antagonist (MRA), and a sodium\u0026ndash;glucose co-transporter 2 (SGLT2) inhibitor. If patients have chronic symptomatic HFrEF with NYHA class II or III symptoms and they tolerate an ACEi or ARB, they should be switched to an ARNi (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This combination, which interacts with multiple neurohormonal pathways, has been consistently shown to reduce mortality and improve survival in multiple landmark trials, and is thus strongly recommended by for patients with HFrEF in contemporary guidelines (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Despite the extensively documented cardiovascular benefits of guideline directed medical treatment (GDMT), suboptimal pharmacotherapy remains an extensive problem in any HFrEF population in the absence of contraindication. However, underutilization of such life-prolonging treatments at trial-proven doses, with subsequent unacceptably poor outcomes have been reported (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Such substandard clinical practice is likely to be rampant in underprivileged countries, such as Ethiopia in which majority of clinicians were noticed to have low level of adherence to the latest guidelines (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Typically, optimization of GDMT is performed during regular clinical visits although subsequent delay in optimization oftentimes is observed due to relatively infrequent visits and other challenges such as laboratory, blood pressure monitoring, and concern of side effects. Missed opportunities and in fact, significant gap, of such kind have been practically implicated to result in poor prognosis of patients with HF in Ethiopian setting (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Apart from this, there is scarcity of data in this regard in Ethiopia, particularly after the issuance of the 2022 AHA/ACC/HFSA guideline. Thus, keeping the aforementioned evidences in view, this study is designed with intention of assessing the patterns of utilization and drug optimization and associated factors of guideline directed medical therapy in heart failure with reduced ejection fraction patients attending cardiac centers at selected hospitals, Addis Ababa, Ethiopia.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting, design and period\u003c/h2\u003e \u003cp\u003eA facility-based, cross-sectional study was conducted in Addis Ababa, Ethiopia, specifically at three cardiac centers: Tikur Anbessa Specialized Hospital (TASH), Yekatit 12 Hospital Medical College (YHMC), and Addis Cardiac Center (ACC). Addis Ababa, the capital city of Ethiopia, comprises eleven sub-cities and 117 woredas with thirteen governmental and around 40 private hospitals, serving a population of approximately 5,703,628 as of February 2024 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Specialized cardiology care is available in both public and private facilities, with this study focusing on selected hospitals with cardiac services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSource population and study population\u003c/h2\u003e \u003cp\u003eThe study included all heart failure patients with reduced EF attending cardiac clinics in Addis Ababa, Ethiopia as the source population. The study population, on the other hand, consisted of randomly chosen heart failure patients with a baseline left ventricular ejection fraction of \u0026le;\u0026thinsp;40% who were visiting cardiac clinics in selected health facilities during the study period and met the eligibility criteria. Inclusion criteria encompassed adult patients (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years) diagnosed with heart failure clinically, confirmed by echocardiography to have a baseline ejection fraction\u0026thinsp;\u0026le;\u0026thinsp;40%, and those under regular follow-up for a minimum of 2 months at the time of data collection. Exclusion criteria involved pregnant patients and those with documented bilateral renal artery stenosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample size determination and sampling technique\u003c/h2\u003e \u003cp\u003eThe sample size for this study is calculated using a single population proportion formula. Taking the proportion of patients receiving GDMT (that is, MRAs) to be 49% from the earlier study reported in Ethiopia (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), the sample size is calculated as: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n=\\frac{{z}^{2}p(1-p)}{{e}^{2}}\\)\u003c/span\u003e\u003c/span\u003e, where n\u0026thinsp;=\u0026thinsp;the required sample size; p\u0026thinsp;=\u0026thinsp;the proportion of patients receiving MRAs =\u0026thinsp;0.49; Z\u003csub\u003eα\\2\u003c/sub\u003e = the critical value at 95% confidence level\u0026thinsp;=\u0026thinsp;1.96, and e\u0026thinsp;=\u0026thinsp;margin of error\u0026thinsp;=\u0026thinsp;5%. Accordingly, the minimum required sample size became \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(384\\)\u003c/span\u003e\u003c/span\u003e, which was increased to 422 with addition of 10% for contingency.\u003c/p\u003e \u003cp\u003eIn Addis Ababa, two public hospitals, TASH and YHMC, were purposefully selected due to patient load and gaps in clinical care. The private facility, ACC, was chosen for its high volume of heart failure (HF) patients. A total of 2655 adult patients with HF and LVEF less than 40% were included (1240 at TASH, 670 at YHMC, and 745 at ACC) in the sampling frame. The required sample size of 422 was achieved through proportional stratified sampling, with 197 from TASH, 107 from YHMC, and 118 from ACC. Patients were stratified by facility and selected using systematic random sampling (k\u0026thinsp;=\u0026thinsp;6) for their medical records.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables\u003c/h2\u003e \u003cp\u003e Utilization of guideline-directed medical therapy was the dependent variable in this study. The independent variables were socio-demographics data, which included age, sex, residence, educational status, employment status, type of facility, use of community-based health insurance. Additionally, the behavioral and clinical variables such as frequency of follow up, comorbidity, type of underlying cardiac condition, previous hospitalization, duration of the illness, NYHA functional class, pill burden and laboratory variables including serum creatinine, potassium level, and LVEF were among the independent variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData collection tools and procedures\u003c/h2\u003e \u003cp\u003eData were collected from eligible patients\u0026rsquo; medical records using a structured checklist. The data collection format consisted of background information, clinical variables, and laboratory parameters), and it was adapted from related literatures (\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Data were gathered from logbook records and respective medical records of patients. In events where pertinent patients\u0026rsquo; details were missing, respective patients were contacted virtually via registered address or physically during follow up visits. Two professional healthcare workers were recruited and trained on data collection procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData quality control and assurance\u003c/h2\u003e \u003cp\u003eTo ensure data quality, a pre-test was carried out on 22 patients, and those patients were excluded from the final analysis. The principal investigator provided regular supervision to data collectors to ensure data completeness, clarity, and accuracy. Any unclear or incomplete data was excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData processing and analysis\u003c/h2\u003e \u003cp\u003eData entry, coding, and cleaning were done in Microsoft Excel 2016, then exported to SPSS 26 for statistical analysis. Patient characteristics were analyzed using descriptive statistics. Binary logistic regression assessed variable associations. Those variables with p-value of \u0026le;\u0026thinsp;0.25 in bivariable logistic regression were selected for multivariable logistic regression analysis to compute adjusted odds ratio at 95% confidence interval. Hosmer-Lemeshow goodness-of-fit test was run to test the fitness of the model, declaring data fitness if p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Variables with p\u0026thinsp;\u0026le;\u0026thinsp;0.05 showed significant associations with the outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOperational definitions\u003c/h2\u003e \u003cp\u003e Utilization was described as the prescription of 2022 AHA/ACC/HFSA recommended cardiac medications (ACEI/ARBs/ARNI, BBs, MRAs, and SGLT2 inhibitors) in accordance with the guideline. Underutilization, on the other hand, referred to instances where these recommended medications were not appropriately used. Optimal or target dose indicated patients who were either on the target dose or had been titrated to the highest tolerated dose. Suboptimal dose referred to cases where patients had not been titrated to the target dose. Intermediate dose denoted a dosage ranging from 50\u0026ndash;99% of the recommended dose, while a low dose was considered to be less than 50% of the recommended dose. The specific target doses for each medication class were derived from the 2022 AHA/ACC/HFSA guidelines (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDemographic characteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 404 cardiac patients with HFrEF were included, making a response rate of 95.7%. Majority (222; 55%) were males, with male-to-female ration of 1.2 to 1. Patients\u0026rsquo; age ranged from 17 years to 86 years with a median of 56 years and interquartile range of 43.25\u0026ndash;65. Approximately three-fourths (296;73.3%) were younger than 65 years (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;1.\u003c/b\u003e Socio-demographic characteristics of HFrEF patients attending the selected hospitals of Addis Ababa from September 1st to October 31st, 2023\u003c/p\u003e \u003cp\u003eMajority of the patients (318, 78.7%) were urban dwellers. A little more than a quarter (n\u0026thinsp;=\u0026thinsp;116) of the patients had attended up to secondary school while 106 (26.2%) had obtained a college diploma or more. More than one-third 142 (35.1%) were employed whereas a hundred and eight (26.7%) were unemployed. Further, 162 (40.1%) of the patients were enrolled to the on-site community-based health insurance (CBHI) system (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBehavioral and clinical characteristics of the study population\u003c/h2\u003e \u003cp\u003eMost of the patients (370;91.6%) claimed not to have ever smoked a cigarette, with only thirty-four (8.6%) having a history of smoking. Likewise, only forty-nine (12.1%) described to consume alcoholic beverages. Besides, majority (55%; n\u0026thinsp;=\u0026thinsp;222) of the patients had normal BMI measurements (18.5\u0026ndash;24.9) while a hundred and nineteen (29.5%) were obese, with BMI of 25 to 29.9 (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eFurthermore, majority (207; 51.2%) of the patients had history of hospitalization for heart failure in the preceding one year. Majority (233;57.7%) were described to have HFrEF for more than two years, and 166 (41.1%) of the studied subjects were reported to have a regular clinical follow up at the cardiac outpatient departments (OPDs) every 2.1 to 3 months. More than one-third (152;37.6%) of the patients were in NYHA class II. The most frequently mentioned underlying cardiac abnormalities were ischemic heart disease (IHD) and dilated cardiomyopathy (DCMP), which were documented in 192 (47.5%) and 104 (25.7%) of the patients, respectively. About half (195;48.3%) of the patients had normal to high estimated GFR (\u0026ge;\u0026thinsp;90) (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;2.\u003c/b\u003e Behavioral and clinical profile of HFrEF patients attending selected hospitals of Addis Ababa from September 1st to October 31st, 2023\u003c/p\u003e \u003cp\u003eMajority of the patients (290;71.8%) had at least one coexisting medical condition. Of these, about two-thirds (183;63.1%) had two or more comorbid conditions (Table\u0026nbsp;2). The most frequently identified comorbidities were hypertension (n\u0026thinsp;=\u0026thinsp;159;54.8%) and diabetes mellitus (n\u0026thinsp;=\u0026thinsp;135;46.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Frequency of comorbidities among HFrEF patients attending selected hospitals of Addis Ababa from September 1st to October 31st, 2023\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMedication-related profile of the study population\u003c/h2\u003e \u003cp\u003eOut of the total, 212 patients (52.5%) were on ACEIs or ARBs, with beta-blockers prescribed to 314 patients (77.7%). Additionally, 109 patients (27%) were taking SGLT2 inhibitors, and 238 patients (58.9%) were on MRAs. Only 29 patients (7.2%) were using ARNI. In total, 46 patients (11.4%) were on quadruple therapy, including one medication from each class. There was no statistically significant difference in the implementation of GMDT across the facilities studied (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eOnly 35 patients (8.7%) received optimal ACEi/ARB doses, with just 3 (0.7%) on target beta-blocker doses. About 26% were on optimal SGLT2i doses, while 50.7% received optimal MRA doses. Cough affected 8.9% of patients, angioedema 1%, and 18.8% had elevated serum potassium levels (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;3.\u003c/b\u003e Pharmacological characteristics of HFrEF patients attending the selected hospitals of Addis Ababa from September 1st to October 31st, 2023\u003c/p\u003e \u003cp\u003eOverall, only thirty-five (8.7%) of the patients were receiving optimal dose of ACEIs or ARBs, with 70 (17.3%) taking intermediate dose. Very few (3; 0.7%) were taking optimal dose of BB whereas more than one-fourth (106; 26.2%) were on optimal dose SGLT2i. Moreover, while half (205; 50.7%) were receiving optimal dose of MRAs, only 33 (8.2%) were taking intermediate dose of these drugs (Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;4.\u003c/b\u003e Type and dose of GDMT used inpatients attending the selected hospitals of Addis Ababa from September 1st to October 31st, 2023 (n\u0026thinsp;=\u0026thinsp;404)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with the use of GDMT\u003c/h2\u003e \u003cp\u003eMultiple regression analysis showed that the odds of not receiving proper GMDT was significantly higher among patients who were 65 years or older compared to those who were younger than 65 years (AOR\u0026thinsp;=\u0026thinsp;4.34; 95% CI\u0026thinsp;=\u0026thinsp;1.59, 11.89). Furthermore, the likelihood of GDMT underutilization was higher among those who had any history of hospitalization in the preceding one year in comparison to those who had no any (AOR\u0026thinsp;=\u0026thinsp;2.50; 95% CI\u0026thinsp;=\u0026thinsp;1.21,5.15). Finally, patients taking four or less medications showed higher likelihood of not being on the guideline recommended quadruple therapy as compared with those taking at least five regular medications (AOR\u0026thinsp;=\u0026thinsp;9.6; 95% CI\u0026thinsp;=\u0026thinsp;2.79,33.07) (Table\u0026nbsp;5)\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;5.\u003c/b\u003e Factors associated with GDMT use among patients attending selected hospitals of Addis Ababa, Ethiopia, 2023\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e This study aimed to assess the patterns of utilization and drug optimization and associated factors of guideline directed medical therapy in heart failure with reduced ejection fraction patients attending selected hospitals in Addis Ababa, Ethiopia. The results demonstrated that only one-tenth (11.4%) patients were on appropriate GDMT (quadruple therapy), with certain factors such as age older than 65 years, history of previous hospitalization and lesser number of medications being associated with GDMT underutilization.\u003c/p\u003e \u003cp\u003eIn this study, only one-tenth (11.4%) of the studied patients were taking all classes of GDMT. This is low compared to the CHAMP-HF study, where 22.1% of the patients were simultaneously prescribed some dose of ACEI/ARB/ARNI, beta-blocker, and MRA therapy among patients eligible for all classes of medication (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn particular, 52.5% of HFrEF patients were receiving either ACEIs or ARBs. This finding is in agreement with the Egyptian study that documented that 51.4% of cardiac patients were on the same group of medications (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, the present proportion of patients using RAS inhibitors was lower than that of the multinational study from the ASIAN-HF registry and the Korean study, which documented that RAS inhibitors were utilized among 77% and 75.3% of the cardiac patients, respectively (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The relatively lower utilization of ACEIs or ARBs in the current study as compared with the studies done in developed nations may explained by clinical inertia due to lack of frequent renal function and electrolyte monitoring as to the standard, which might be partly due to financial reasons..\u003c/p\u003e \u003cp\u003eThe current finding differs from previous research in the United States, Taiwan, and Italy, where RAS inhibitors were used in 81.4%, 73%, and 62% of HFrEF cases, respectively (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In Italy, 68.2% of cardiac patients received RAS inhibitors (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This percentage is lower than the 74.7% reported in Ethiopia (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Moreover, 77.7% of patients in the present study were on beta-blockers, similar to the ASIAN-HF registry's multinational study (79%) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This is comparable to Demissie et al.'s findings (79%) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, it exceeds rates in the CHAMP-HF study, Southwest Ethiopia, and Korea (67%, 67%, and 54.9%, respectively) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In contrast, it falls below the US and Italy rates (93.4\u0026ndash;94.4%) for beta-blocker usage.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, 27% of the studied patients were on SGLT2 inhibitors. This is comparatively higher than the previous Ethiopian and Spanish studies, where only 5% and 6.7% of patients with HFrEF were on SGLT2 inhibitors, respectively (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This can be due to the better availability of the medications in urban areas. On the contrary, the present finding was much lower than the pattern observed in the nation-wide German study, in which more than half (54.8%) of cardiac patients were on SGLT2 inhibitors (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Furthermore, this study found 58.9% on MRAs, similar to Ethiopia (58.7%) and Egypt (54.9%) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). On the contrary, lower rates were seen in southwest Ethiopia (32.5%) and Italy (17.4%), while Oman had higher usage (77%) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Generally, the underutilization of SGLT2 inhibitors in the current study is most likely due to low availability of these drugs in the market and financial constraints.\u003c/p\u003e \u003cp\u003eIn aggregate, this study showed that most of the patients were not in quadruple therapy, with no apparent justification for not initiating the medications recorded upon review of the health record or discussion with the patient. Such low level of GDMT implementation in our setup might be due to lack of compelling local guidelines, perceived fear of intolerance, absence of frequent laboratoy monitoring for drug adverse effects, lack of access to prescribed medications (especially SGLT2is) and differences in the practice of multidisciplinary care, with suboptimal medical practitioners\u0026rsquo; expertise and high clinical inertia in resource-poor countries. It could be also pointed out resource-poor settings such as ours do not widely use technology to enhance adherence to GDMT, as opposed to developed ones (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApart from this, the proportion (11.4%) of patients on appropriate GDMT (quadruple therapy) obtained in this study is low given the recent evidence, such as the STRONG-HF trial, that demonstrated that rapid drug implementation and up-titration in the absence of absolute contraindications is superior to the traditional and more gradual step-by-step approach in which valuable time is wasted to up-titration (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, age older than or equal to 65 years was negatively associated with use of GDMT among the studied patients. This is in agreement with the previous Egyptian and Ethiopian studies, which independently found that younger age was associated with better GDMT utilization (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Among others, this can be explained by the probability that older patients are likely to be dependent on others to take their medications appropriately and they are also more likely to have coexisting medical conditions such as renal dysfunction and orthostatic hypotension, which may prohibit their eligibility for morbidity- and mortality-reducing therapies, such as ACEi, ARB, ARNI, BBs or MRA.\u003c/p\u003e \u003cp\u003ePatients with history of hospitalization in the preceding one year were more likely to have underutilized the novel GDMT compared to those with no previous hospitalization. This is consistent with the report of Niriayo et al., who found that previous hospitalization for heart failure was associated with poor GDMT utilization(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). This is, in fact, can be an indicator of the fact that lack of adherence to the evidence-based quadruple therapy (GDMT) results in increased morbidity among cardiac patients, which would likely result in frequent hospitalization for inpatient care (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients on polypharmacy were more likely to utilize GDMT than those on lesser number of medications. Although there is limited previous literature regarding this finding, this apparent association can be justified by the fact that the those patients who are on polypharmacy are probably more sicker more counseled by health care providers and their family members, that might increase their utilization of GDMT. Moreover, patients with multi-morbidity (and as a consequence, on polypharmacy) are likely to be subjected to subspecialist consultations, which may in turn lead to a meticulous clinical decision regarding GMDT therapy.\u003c/p\u003e \u003cp\u003e This study is among the leading studies to be done in Ethiopian setting, particularly after the endorsement of the latest guideline (2022 AHA/ACC/HFSA). However, its cross-sectional design limits causal inferences and alternative interpretations. The study did not include exhaustive list of physician and pharmacy related variables that would influence the GDMT utilization and optimization.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrated a major gap in use and dose titration of GDMT, as these drugs were either underutilized or under-dosed in most of the HFrEF patients despite their proven clinical benefits. Moreover, despite guideline recommendations, most of the cardiac patients were receiving suboptimal dose, highlighting the rates of target dose achievement in clinical practice is still gruesome. Additionally, this study revealed that patients having certain characteristics such as age older than 65 years, history of previous hospitalization and lesser number of medications were more prone for GDMT underutilization.\u003c/p\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eRecommendation\u003c/h2\u003e\n \u003cp\u003eThe findings from this study highlight that health care professionals should exert some effort to adhere to the latest available guideline to utilize GDMT and up-titrate to target dose or maximum tolerable dose for better clinical outcomes, with due emphasis on those having certain characteristics such as age older than 65 years, history of previous hospitalization and lesser number of medications. Local health policy makers should design customized guidelines while also considering sensitization and training program. A multidisciplinary care with the inclusion of clinical pharmacists should be considered. Further researches with better designs should be carried out to validate the current findings.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003cp\u003eACC Addis Cardiac Center\u003c/p\u003e\n \u003cp\u003eACE Angiotensin-Converting Enzyme\u003c/p\u003e\n \u003cp\u003eACEIs Angiotensin-converting enzyme inhibitors\u003c/p\u003e\n \u003cp\u003eAHA American Heart Association\u003c/p\u003e\n \u003cp\u003eARB Angiotensin II Receptor Blocker\u003c/p\u003e\n \u003cp\u003eARNIs Angiotensin receptor neprilysin inhibitors\u003c/p\u003e\n \u003cp\u003eCHAMP-HF Change the Management of Patients with Heart Failure\u003c/p\u003e\n \u003cp\u003eESC European Society of Cardiology\u003c/p\u003e\n \u003cp\u003eGDMT Guideline-directed medical therapy\u003c/p\u003e\n \u003cp\u003eGFR Glomerular Filtration Rate\u003c/p\u003e\n \u003cp\u003eHFrEF Heart Failure with Reduced Ejection Fraction\u003c/p\u003e\n \u003cp\u003eLVEF Left Ventricular Ejection Fraction\u003c/p\u003e\n \u003cp\u003eMRAs Mineralocorticoid Receptor Antagonists\u003c/p\u003e\n \u003cp\u003eNYHA High New York Heart Association\u003c/p\u003e\n \u003cp\u003eRAS Renin-Angiotensin System\u003c/p\u003e\n \u003cp\u003eSGLT2is Sodium\u0026ndash;glucose co-transporter 2 inhibitors\u003c/p\u003e\n \u003cp\u003eSPSS Statistical Package for Social Sciences\u003c/p\u003e\n \u003cp\u003eTASH Tikur Anbessa Specialized Hospital\u003c/p\u003e\n \u003cp\u003eY12HMC Yekatit 12 Hospital Medical College\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Department of Internal Medicine of Yekatit 12 Hospital Medical College for facilitating the undertaking of this study as well as the data collectors for their time and cooperation.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll the data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eMA conceived and designed the study, involved in the proposal development, data analysis, interpretation, and manuscript writing. All other of the authors contributed to data analysis and interpretation, drafting and revising the manuscript. All gave final approval of the final version to be submitted, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;certify that no actual or potential conflicts of interest with regard to this work exist.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthical clearance was obtained from Ethical Review Committee of Yekatit 12 Hospital Medical College. The clearance letter was submitted to each study site before the actual data collection for the study. Letter of cooperation was obtained from the Department of Internal Medicine. The information collected was kept anonymous and confidential.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBozkurt B. Target Dose Versus Maximum Tolerated Dose in Heart Failure: Time to Calibrate and Define Actionable Goals. JACC Hear Fail. 2019;7(4):359\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2022;79:263\u0026ndash;421.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWestphal JG, Bekfani T, Schulze PC. What\u0026rsquo;s new in heart failure therapy 2018?\u0026dagger;. Interact Cardiovasc Thorac Surg. 2018;27(6):921\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2022;1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLippi G, Sanchis-Gomar F. Global epidemiology and future trends of heart failure. AME Med J. 2020;5(Ci):15\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBragazzi NL, Zhong W, Shu J, Abu Much A, Lotan D, Grupper A, et al. Burden of heart failure and underlying causes in 195 countries and territories from 1990 to 2017. Eur J Prev Cardiol. 2021;28(15):1682\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBozkurt B, Coats AJ, Tsutsui H, Abdelhamid M, Adamopoulos S, Albert N, et al. Universal Definition and Classification of Heart Failure: A Report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition o. J Card Fail. 2021;27(4):387\u0026ndash;413.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiller D, Russ M, Greiser KH, Nuding S, Ebelt H, Kluttig A et al. Prevalence of Symptomatic Heart Failure with Reduced and with Normal Ejection Fraction in an Elderly General Population-The CARLA Study. PLoS ONE. 2013;8(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroenewegen A, Rutten FH, Mosterd A, Hoes AW. Epidemiology of heart failure. Eur J Heart Fail. 2020;22(8):1342\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTefera YG, Abegaz TM, Abebe TB, Mekuria AB. The changing trend of cardiovascular disease and its clinical characteristics in Ethiopia: Hospital-based observational study. Vasc Health Risk Manag. 2017;13:143\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma A, Verma S, Bhatt DL, Connelly KA, Swiggum E, Vaduganathan M et al. Optimizing Foundational Therapies in Patients With HFrEF: How Do We Translate These Findings Into Clinical Care? JACC Basic to Transl Sci. 2022;7(5):504\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePacker M, Metra M. Guideline-directed medical therapy for heart failure does not exist: a non-judgmental framework for describing the level of adherence to evidence-based drug treatments for patients with a reduced ejection fraction. Eur J Heart Fail. 2020;22(10):1759\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Spall HGC, Fonarow GC, Mamas MA. Underutilization of Guideline-Directed Medical Therapy in Heart Failure: Can Digital Health Technologies PROMPT Change? J Am Coll Cardiol. 2022;79(22):2214\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussen NM, Workie DL, Biresaw HB. Survival time to complications of congestive heart failure patients at Felege Hiwot comprehensive specialized referral hospital, Bahir Dar, Ethiopia. PLoS One [Internet]. 2022;17(10 October):1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1371/journal.pone.0276440\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0276440\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Population Review. Addis Ababa Population 2024 [Internet]. Demographics, maps Graphics. 2022. p. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://worldpopulationreview.com/world-cities/addis-ababa-population\u003c/span\u003e\u003cspan address=\"https://worldpopulationreview.com/world-cities/addis-ababa-population\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTemesgen R, Guteta S, Abebe S. Assessment of physician adherence to guideline recommended medication in heart failure with reduced ejection fraction at outpatient cardiac clinic; retrospective cross-sectional study at Tikur. Anbessa Specialized Hosp. 2021;60(2):117\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiriayo YL, Asgedom SW, Demoz GT, Gidey K. Treatment optimization of beta-blockers in chronic heart failure therapy. 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Pharm Pract (Granada). 2022;20(2):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomero E, Yala S, Sellers-Porter C, Lynch G, Mwathi V, Hellier Y, et al. Remote monitoring titration clinic to implement guideline-directed therapy for heart failure patients with reduced ejection fraction: a pilot quality-improvement intervention. Front Cardiovasc Med. 2023;10(June):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreene SJ, Bauersachs J, Brugts JJ, Ezekowitz JA, Filippatos G, Gustafsson F, et al. Management of Worsening Heart Failure With Reduced Ejection Fraction: JACC Focus Seminar 3/3. J Am Coll Cardiol. 2023;82:559\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMebazaa A, Davison B, Chioncel O, Cohen-Solal A, Diaz R, Filippatos G et al. Safety, tolerability and efficacy of up-titration of guideline-directed medical therapies for acute heart failure (STRONG-HF): a multinational, open-label, randomised, trial. Lancet [Internet]. 2022;400(10367):1938\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10741-023-10325-2\u003c/span\u003e\u003cspan address=\"10.1007/s10741-023-10325-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArrigo M, Biegus J, Asakage A, Mebazaa A, Davison B, Edwards C, et al. Safety, tolerability and efficacy of up-titration of guideline-directed medical therapies for acute heart failure in elderly patients: A sub-analysis of the STRONG-HF randomized clinical trial. Eur J Heart Fail. 2023;25(7):1145\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGelaye AT, Seid MA, Baffa LD. Angiotensin-Converting Enzyme Inhibitor Dose Optimization and Its Associated Factors at Felege Hiwot Comprehensive Specialized Hospital, Bahir Dar, Ethiopia. Vasc Health Risk Manag. 2022;18(July):481\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInamdar AA, Inamdar AC. Heart failure: Diagnosis, management and utilization. J Clin Med. 2016;5(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCullough PA, Mehta HS, Barker CM, Van Houten J, Mollenkopf S, Gunnarsson C, et al. Mortality and guideline-directed medical therapy in real-world heart failure patients with reduced ejection fraction. Clin Cardiol. 2021;44(9):1192\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSocio-demographic characteristics of HFrEF patients attending the selected hospitals of Addis\u0026nbsp;Ababa\u0026nbsp;from\u0026nbsp;September 1\u003csup\u003est\u003c/sup\u003eto October 31\u003csup\u003est\u003c/sup\u003e, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.74178403755869%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Quadruple therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal (Frequency)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.24324324324324%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.75675675675676%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e73.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e78.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eTertiary and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnrolled to CBHI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.32550860719875%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.553990610328638%\" valign=\"top\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.378716744913927%\" valign=\"top\"\u003e\n \u003cp\u003e59.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCBHI, Community-based health insurance\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eBehavioral and clinical profile of HFrEF patients attending selected hospitals of Addis Ababa from September 1\u003csup\u003est\u003c/sup\u003e to October 31\u003csup\u003est\u003c/sup\u003e, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.213166144200628%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eQuadruple therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFrequency (Total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePercent (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.111111111111114%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.888888888888886%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCigarette smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e91.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e87.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e25\u0026ndash;29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration since diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;2 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency of follow up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eEvery 1 to 2 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eEvery 2.1 to 3 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eEvery 3.1 to 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA functional class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderlying cardiac pathology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eIschemic heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eCardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eHypertensive heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eValvular heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eCongenital heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlomerular filtration rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e60\u0026ndash;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e30\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e15\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresence of comorbidity*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e71.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of comorbidities (n=290)\u003csup\u003e\u0026euro;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e36.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.22884012539185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.009404388714733%\" valign=\"top\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.203761755485893%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.808777429467085%\" valign=\"top\"\u003e\n \u003cp\u003e63.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eEF: Ejection fraction; GFR: Glomerular filtration rate; NYHA: New York Heart Association *comorbidities \u0026nbsp;included were diabetes mellitus, hypertension, dyslipidemia, chronic lung disease, chronic kidney disease, stroke, as depicted in Figure 1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003ePharmacological characteristics of HFrEF patients attending the selected hospitals of Addis Ababa from September 1\u003csup\u003est\u003c/sup\u003e to October 31\u003csup\u003est\u003c/sup\u003e, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedications used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eACEIs/ARBs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eBeta-blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e77.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eSGLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eMRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e58.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eARNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eQuadruple therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSide effects/contraindication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eAngioedema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eNot documented\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e90.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePill burden\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5 medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;5 medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eHyperkalemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eNormokalemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eHypokalemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.526645768025077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eQuadruple therapy\u003csup\u003e\u0026pound;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.45054945054945%\" valign=\"top\"\u003e\n \u003cp\u003eNo(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.54945054945055%\" valign=\"top\"\u003e\n \u003cp\u003eYes(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eTikur Anbessa Specialized Hospital (n=190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.106583072100314%\" valign=\"top\"\u003e\n \u003cp\u003e163(85.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e27(14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eYekatit 12 Hospital Medical College \u0026nbsp;(n=104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.106583072100314%\" valign=\"top\"\u003e\n \u003cp\u003e95(91.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e9(8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.059561128526646%\" valign=\"top\"\u003e\n \u003cp\u003eAddis Cardiac Center (n=110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.106583072100314%\" valign=\"top\"\u003e\n \u003cp\u003e100(90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e10(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.413793103448278%\" valign=\"top\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e\u0026pound;\u003c/sup\u003eChi square test was 2.85 with\u0026nbsp;p-value of 0.241\u003c/p\u003e\n \u003cp\u003eARB, Angiotensin-receptor blockers; ARNI, Angiotensin receptor neprilysin inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eType and dose of GDMT used inpatients attending the selected hospitals of Addis Ababa from September 1\u003csup\u003est\u003c/sup\u003e to October 31\u003csup\u003est\u003c/sup\u003e, 2023 (n=404)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.68652037617555%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.31347962382445%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMedications\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.037383177570092%\" valign=\"top\"\u003e\n \u003cp\u003eACEI/ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.98753894080997%\" valign=\"top\"\u003e\n \u003cp\u003eBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.98753894080997%\" valign=\"top\"\u003e\n \u003cp\u003eSGLT2i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.98753894080997%\" valign=\"top\"\u003e\n \u003cp\u003eMRA*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.68652037617555%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of patients on optimal dose (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.106583072100314%\" valign=\"top\"\u003e\n \u003cp\u003e35(8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e3(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e106(26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e205(50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.68652037617555%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of patients on 50 to \u0026lt;99% of the target dose (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.106583072100314%\" valign=\"top\"\u003e\n \u003cp\u003e70(17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e44(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e3(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e33(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.68652037617555%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of patients on 1-49% of the target dose (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.106583072100314%\" valign=\"top\"\u003e\n \u003cp\u003e107(26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e267(66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.68652037617555%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of patients not receiving any dose (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.106583072100314%\" valign=\"top\"\u003e\n \u003cp\u003e192(47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e90(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e295(73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.068965517241379%\" valign=\"top\"\u003e\n \u003cp\u003e166(41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;*14 of the 16 patients with GFR of 15 to 29 were on MRAs.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eFactors associated with GDMT use among patients attending selected hospitals of Addis Ababa, Ethiopia, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.03448275862069%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eQuadruple therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.464646464646464%\" valign=\"top\"\u003e\n \u003cp\u003eNo (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.535353535353536%\" valign=\"top\"\u003e\n \u003cp\u003eYes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e255(86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e41(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e103(97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e5(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e3.31(1.27,8.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e4.34(1.59,11.89)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e190(85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e32(14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e168(92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e14(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e2.02(1.04,3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1.91(0.92,3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e84(94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e5(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e2.14(0.72,6.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1.76(0.56,5.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e78(83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e15(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e0.66(0.29,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e0.57(0.23,1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e102(87.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e14(12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e0.93(0.41,2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e3.09(1.00,9.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eTertiary and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e94(88.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e12(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e25(92.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e2(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e198(89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e24(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e0.66(0.15,2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e0.49(0.10,2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e25\u0026ndash;29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e100(84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e19(16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e0.42(0.09,1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e0.34(0.07,1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e35(97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e1(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e2.80(0.24,32.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1.99(0.16,24.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e41(83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e8(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e317(89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e38(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e1.63(0.71,3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1.30(0.50,3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e191(92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e16(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e2.14(1.13,4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e2.50(1.21,5.15)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e167(84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e30(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePill burden\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5 medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e117(97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e3(2l5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e6.96(2.12,22.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e9.60(2.79,33.07)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.74921630094044%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;5 medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.420062695924765%\" valign=\"top\"\u003e\n \u003cp\u003e241(84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.614420062695924%\" valign=\"top\"\u003e\n \u003cp\u003e43(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.235109717868337%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.981191222570533%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOnly factors which showed p-value of \u0026lt;0.25 in binary regression are shown here.\u003c/p\u003e\n\u003cp\u003e*P-value\u0026lt; 0.05; **P-value \u0026lt; 0.01; ***P-value \u0026lt; 0.001\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Guideline directed medical therapy, drug utilization, drug optimization, heart failure, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-4348655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4348655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe global burden of heart failure, especially with reduced ejection fraction, is a significant health issue. Current guidelines stress the importance of optimal medication use to maximize patient outcomes. Nevertheless, a notable gap exists in implementing these guidelines worldwide. In Ethiopia, there is limited post-guideline data on the utilization and optimization of medications for patients with heart failure and reduced ejection fraction. This study aims to evaluate the patterns of utilization and drug optimization and associated factors of guideline directed medical therapy among these patients attending cardiac centers at selected public and private hospitals, Addis Ababa, Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA facility-based, cross-sectional study was conducted. Data were collected using a pretested, structured checklist. Data were edited and cleaned via Microsoft Excel 2016 and analyzed using SPSS version 26. Baseline demographic and clinical datawere summarized using descriptive statistics. Multiple logistic regression analysis was run to identify association between dependent and independent variables, by computing odds ratio and 95% confidence interval. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 404 patients were included in this study, with a response rate of 95.7%. Majority (222; 55%) were males, and patients\u0026rsquo; age ranged from 17 years to 86 years with a median (inter-quartile range) of 56 (43.25\u0026ndash;65) years. Overall, 46 (11.4%) were receiving quadruple therapy. ACEIs/ARBs, beta-blockers and MRA were given to 212(52.5%), 314 (77.7%), and238 (58.9%) patients, respectively. SGLT2Is were prescribed to only 109 (27%) patients. Age older than 65 years (AOR\u0026thinsp;=\u0026thinsp;4.34; 95% CI\u0026thinsp;=\u0026thinsp;1.59, 11.89), history of previous hospitalization (AOR\u0026thinsp;=\u0026thinsp;2.50; 95% CI\u0026thinsp;=\u0026thinsp;1.21, 5.15) and taking\u0026thinsp;\u0026lt;\u0026thinsp;5 medications (AOR\u0026thinsp;=\u0026thinsp;9.6; 95% CI\u0026thinsp;=\u0026thinsp;2.79, 33.07) were associated with GDMT underutilization.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is a large gap in GDMT implementation, with majority of the patients having either underutilization or under-dosing, particularly those older than 65 years, with history of previous hospitalization and taking\u0026thinsp;\u0026lt;\u0026thinsp;5 medications. Thus, efforts should be directed to design customized guidelines along with institution of sensitization and training programs while also considering multidisciplinary care.\u003c/p\u003e","manuscriptTitle":"Patterns of utilization and optimization of guideline-directed medical therapy and associated factors among heart failure patients with reduced ejection fraction in selected hospitals of Addis Ababa, Ethiopia: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 13:15:17","doi":"10.21203/rs.3.rs-4348655/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":"f9aa69b8-dab9-4b76-9102-0801cf43204f","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-28T12:44:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 13:15:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4348655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4348655","identity":"rs-4348655","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}