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	<front>
		<journal-meta>
			<journal-id journal-id-type="publisher-id">ABC HF</journal-id>
			<journal-title-group>
				<journal-title>ABC: Heart Failure &amp; Cardiomyopathy</journal-title>
				<abbrev-journal-title abbrev-type="publisher">ABC Heart Fail Cardiomyop</abbrev-journal-title>
			</journal-title-group>
			<issn pub-type="epub">2764-3107</issn>
			<publisher>
				<publisher-name>Sociedade Brasileira de Cardiologia - SBC</publisher-name>
			</publisher>
		</journal-meta>
		<article-meta>
			<article-id pub-id-type="publisher-id">abchf.20220050</article-id>
			<article-id pub-id-type="doi">10.36660/abchf.20220050</article-id>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>Original Article</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>Association of Age with Optimal Medical Therapy in Patients with Chronic Heart Failure</article-title>
			</title-group>
			<contrib-group>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-5899-2186</contrib-id>
					<name>
						<surname>Koga</surname>
						<given-names>Vitoria A. A.</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-6441-1667</contrib-id>
					<name>
						<surname>Dall’Asta</surname>
						<given-names>Luiza</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-2030-2615</contrib-id>
					<name>
						<surname>Jacyntho</surname>
						<given-names>Thiago L. P.</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-0568-1971</contrib-id>
					<name>
						<surname>De-Marchi</surname>
						<given-names>Leonardo C.</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-5859-3169</contrib-id>
					<name>
						<surname>Mulinari</surname>
						<given-names>Rodrigo P.</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-8502-8373</contrib-id>
					<name>
						<surname>Ladeira</surname>
						<given-names>Bruna A.</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-3003-2500</contrib-id>
					<name>
						<surname>Nemeth</surname>
						<given-names>Maria E. R. F.</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-5538-7455</contrib-id>
					<name>
						<surname>Silvestre</surname>
						<given-names>Odilson M.</given-names>
					</name>
					<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-1761-8662</contrib-id>
					<name>
						<surname>Bonatto</surname>
						<given-names>Marcely Gimenes</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-4741-322X</contrib-id>
					<name>
						<surname>Moura</surname>
						<given-names>Lidia Ana Zytynski</given-names>
					</name>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-0095-9049</contrib-id>
					<name>
						<surname>Fernandes-Silva</surname>
						<given-names>Miguel Morita</given-names>
					</name>
					<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
					<xref ref-type="corresp" rid="c1"/>
				</contrib>
				<aff id="aff1">
					<label>1</label>
					<institution content-type="orgname">Pontifícia Universidade Católica do Paraná</institution>
					<addr-line>
						<named-content content-type="city">Curitiba</named-content>
						<named-content content-type="state">PR</named-content>
					</addr-line>
					<country country="BR">Brazil</country>
					<institution content-type="original">Pontifícia Universidade Católica do Paraná, Curitiba, PR – Brazil</institution>
				</aff>
				<aff id="aff2">
					<label>2</label>
					<institution content-type="orgname">Universidade Federal do Acre</institution>
					<addr-line>
						<named-content content-type="city">Rio Branco</named-content>
						<named-content content-type="state">AC</named-content>
					</addr-line>
					<country country="BR">Brazil</country>
					<institution content-type="original">Universidade Federal do Acre, Rio Branco, AC – Brazil</institution>
				</aff>
				<aff id="aff3">
					<label>3</label>
					<institution content-type="orgname">Universidade Federal do Paraná</institution>
					<addr-line>
						<named-content content-type="city">Curitiba</named-content>
						<named-content content-type="state">PR</named-content>
					</addr-line>
					<country country="BR">Brazil</country>
					<institution content-type="original">Universidade Federal do Paraná, Curitiba, PR – Brazil</institution>
				</aff>
			</contrib-group>
			<author-notes>
				<corresp id="c1"><bold>Mailing Address: Miguel Morita Fernandes-Silva</bold> • Departamento de Clínica Médica – Universidade Federal do Paraná – Rua General Carneiro, 181, 10º andar. Postal Code <postal-code>80060-900</postal-code>, Curitiba, PR – Brazil E-mail: <email>miguelmorita@ufpr.br</email>
				</corresp>
				<fn fn-type="conflict">
					<p><bold>Potential Conflict of Interest</bold></p>
					<p>No potential conflict of interest relevant to this article was reported.</p>
				</fn>
				<fn fn-type="con">
					<p><bold>Author Contributions</bold></p>
					<p>Conception and design of the research: Fernandes-Silva MM; Acquisition of data: Koga VAA, Dall’Asta L, Jacyntho TLP, De-Marchi LC, Mulinari RP, Ladeira BA, Nemeth MERF; Analysis and interpretation of the data: Koga VAA, Fernandes-Silva MM; Statistical analysis: Dall’Asta L, Fernandes-Silva MM; Writing of the manuscript: Koga VAA, Dall’Asta L, Jacyntho TLP, De-Marchi LC, Mulinari RP; Critical revision of the manuscript for intellectual content: Silvestre OM, Bonatto MG, Moura LAZ, Fernandes-Silva MM.</p>
				</fn>
			</author-notes>
			<pub-date date-type="pub" publication-format="electronic">
				<day>29</day>
				<month>06</month>
				<year>2022</year>
			</pub-date>
			<pub-date date-type="collection" publication-format="electronic">
				<season>Apr-Jun</season>
				<year>2022</year>
			</pub-date>
			<volume>2</volume>
			<issue>2</issue>
			<fpage>138</fpage>
			<lpage>145</lpage>
			<history>
				<date date-type="received">
					<day>17</day>
					<month>11</month>
					<year>2021</year>
				</date>
				<date date-type="rev-recd">
					<day>16</day>
					<month>12</month>
					<year>2021</year>
				</date>
				<date date-type="accepted">
					<day>11</day>
					<month>03</month>
					<year>2022</year>
				</date>
			</history>
			<permissions>
				<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/" xml:lang="en">
					<license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License</license-p>
				</license>
			</permissions>
			<abstract>
				<title>Abstract</title>
				<sec>
					<title>Background:</title>
					<p>The adherence to guideline-directed medical therapy in patients with heart failure (HF) remains suboptimal.</p>
				</sec>
				<sec>
					<title>Objectives:</title>
					<p>We evaluated the association between age and adherence to guideline-directed medical therapy in patients with chronic HF and explored whether polypharmacy and comorbidities might explain this association.</p>
				</sec>
				<sec>
					<title>Methods:</title>
					<p>We performed a cross-sectional study of 374 patients with chronic HF and left ventricle ejection fraction &lt; 50% (23 to 89 years old, 33% women) between 2018 and 2019. GDMT was defined as using HF-related disease-modifying medications at the target dose according to guidelines. Patients were classified in 3 age groups (23 to 57, 58 to 67, and 68 to 89 years old).</p>
				</sec>
				<sec>
					<title>Results:</title>
					<p>Older patients were less likely to receive optimal therapy (33% versus 24% versus 15%, p &lt; 0.001 for each age category, respectively). After adjusting for potential confounders, the chances of receiving medical therapy at optimal dose significantly reduced for each age-decade increase (OR 0.66 [95% confidence interval 0.48 – 0.92], p = 0.013). The proportion of this association that was explained by polypharmacy (0% [0% – 3.5%]) or comorbidities (7% [0% – 41%]) was negligible.</p>
				</sec>
				<sec>
					<title>Conclusion:</title>
					<p>We found that age was inversely associated with optimal drug therapy for HF, and polypharmacy or comorbidities do not appear to explain this.</p>
				</sec>
			</abstract>
			<kwd-group xml:lang="en">
				<title>Keywords:</title>
				<kwd>Heart Failure</kwd>
				<kwd>Drug Therapy</kwd>
				<kwd>Polypharmacy</kwd>
				<kwd>Aging</kwd>
			</kwd-group>
			<counts>
				<fig-count count="4"/>
				<table-count count="6"/>
				<equation-count count="0"/>
				<ref-count count="30"/>
				<page-count count="8"/>
			</counts>
		</article-meta>
	</front>
	<body>
		<sec sec-type="intro">
			<title>Introduction</title>
			<p>Heart failure (HF) affects 26 million people worldwide and is increasing in prevalence.<sup><xref ref-type="bibr" rid="B1">1</xref></sup> The expenditures are notable and will raise considerably in an aging population. HF has high mortality and morbidity, and treatment with different class of drugs can improve survival of these patients, as demonstrated in clinical trials.<sup><xref ref-type="bibr" rid="B2">2</xref>–<xref ref-type="bibr" rid="B10">10</xref></sup> Therapy using these drugs at target doses similar to those used in trials are paramount to modify the natural course of the disease, and they have been recommended by HF-related guidelines, which has been denominated guideline-directed medical therapy (GDMT).<sup><xref ref-type="bibr" rid="B11">11</xref></sup></p>
			<p>Despite the substantial evidence accumulated in the last 3 decades, the adherence to GDMT remains low. A previous study showed that only 1% of eligible patients with HF simultaneously received the target doses of angiotensin converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB)/angiotensin II receptor-neprilysin inhibitor (ARNI), beta-blocker, and mineralocorticoid receptor antagonist (MRA) therapy recommended by guidelines.<sup><xref ref-type="bibr" rid="B12">12</xref></sup> Failure to achieve the target dose has been associated with worse survival.<sup><xref ref-type="bibr" rid="B13">13</xref></sup> The reasons for low compliance to GDMT are complex and likely multifactorial. A few studies have explored these reasons and suggested that drug optimization appear to be lower among older patients with HF, when compared to younger ones, but other factors may also play a role in the low treatment compliance, such as low income and health illiteracy.<sup><xref ref-type="bibr" rid="B14">14</xref>–<xref ref-type="bibr" rid="B16">16</xref></sup> However, there is a lack of studies evaluating rates of compliance to GDMT in low- and middle-income countries.</p>
			<p>We, therefore, aimed to analyze the relation between age and GDMT in patients with HF treated in an institution from a middle-income country. We also explored whether the number of prescribed medications (polypharmacy) and number of comorbidities can help explain this association, as they might contribute to poor adherence to treatment.</p>
		</sec>
		<sec sec-type="methods">
			<title>Methods</title>
			<sec>
				<title>Study population</title>
				<p>This is a cross-sectional observational study approved by the local institutional ethics committee under protocol number 3.227.412. We included consecutive patients over 18 years old referred to the Heart Failure Outpatient Clinic of the <italic>Santa Casa de Misericórdia de Curitiba</italic>, a tertiary university center dedicated to specialized care of patients with HF from Brazil’s Unified Public Health System (SUS), from May 2018 to February 2019 in Curitiba, Paraná, Brazil. All of them were first diagnosed with HF and received medical treatment in primary care centers from SUS. They should be referred to the specialized center if they have been hospitalized for HF, or if they were considered refractory to medical treatment. Inclusion criteria were previous diagnosis of HF and left ventricle ejection fraction (LVEF) below 50%, measured by an echocardiogram performed within the previous 12 months. The patients were approchead during their routine consultation, and all the data were collected during the visit and from medical records. All patients provided written informed consent. Those who refused to participate in the study or had insufficient information, such as missing data on echocardiogram or laboratory exams, were excluded.</p>
			</sec>
			<sec>
				<title>Exposure</title>
				<p>Patients’ ages were defined according to birth date as registered in medical records and evaluated as a continuous variable. The patients were also classified into 3 groups according to age tertiles: the first tertile from 23 to 57 years old, the second from 58 to 67 years old, and the third from 68 to 89 years old.</p>
			</sec>
			<sec>
				<title>Outcome</title>
				<p>The outcome was the proportion of patients under GDMT, i.e. using optimal medical treatment as recommended by the 2018 Brazilian Heart Failure Guidelines (<italic>Diretriz Brasileira de Insuficiência Cardíaca Crônica e Aguda</italic>).<sup><xref ref-type="bibr" rid="B17">17</xref></sup> Patients were considered under GDMT if they were using the following drugs at the target dose according to European Society of Cardiology guidelines (Supplemental Table 1): 1) a HF-specific beta-blocker (carvedilol, metoprolol succinate or bisoprolol); 2) either an ACEI, ARB or ARNI; and 3) a MRA if symptomatic (New York Heart Association [NYHA] class II to IV).</p>
			</sec>
			<sec>
				<title>Other covariates</title>
				<p>Sex, etiology of HF, NYHA functional class, and creatinine blood levels were obtained from medical records. Blood pressure was measured during the patient’s visit as recommended by international guidelines.<sup><xref ref-type="bibr" rid="B11">11</xref>,<xref ref-type="bibr" rid="B17">17</xref></sup> Height was measured in orthostatic position using a calibrated anthropometer, and weight was systematically measured on a calibrated scale. Body mass index (BMI) was calculated as weight (kilograms) divided by height (meters) squared.</p>
				<p>Time from the diagnosis of HF was the period in months between the diagnosis of HF and the date the patient was included in the study. The moment of diagnosis was estimated during patient interview based on either the first hospitalization due to HF or when they started to present typical symptoms of HF and were told they had HF, whichever happened first.</p>
				<p>We estimated the severity of the disease by calculating the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score, which is a score that predicts the 1- and 3-year mortality in patients with HF.<sup><xref ref-type="bibr" rid="B18">18</xref>,<xref ref-type="bibr" rid="B19">19</xref></sup> This score combines 13 independent clinical variables such as LVEF, age, systolic blood pressure (SBP), BMI, creatinine levels, NYHA class, sex, current smoking status, diabetes, chronic obstructive pulmonary disease (COPD), first diagnosis of HF &gt; 18 months, beta-blocker, and ACEI/ARB (Supplemental Table 2).</p>
			</sec>
			<sec>
				<title>Polypharmacy and comorbidities</title>
				<p>We defined polypharmacy as the use of medications belonging to pharmacologic classes other than those in the GDMT definition (i.e. ACEI, ARB, ARM, ARNI, or HF-specific beta-blocker).<sup><xref ref-type="bibr" rid="B11">11</xref>,<xref ref-type="bibr" rid="B17">17</xref></sup> They include medications related to HF, such as ivabradine, digoxin, loop diuretic, thiazide, hydralazine, and nitrate, as well as to comorbidities, such as statins, antiarrhythmics, anticoagulants, aspirin, clopidogrel, and others. For instance, if the patient was taking enalapril, carvedilol, digoxin, furosemide, statin, and aspirin, polypharmacy should be counted as four. As they are part of GDMT definition, enalapril and carvedilol did not count toward polyphamarcy.</p>
				<p>The number of comorbities were defined according to the presence of hypertension, diabetes, coronary artery disease, chronic kidney disease, and COPD.</p>
			</sec>
			<sec>
				<title>Statistical analyses</title>
				<p>Continuous variables were evaluated for the Gaussian distribution of the data and were compared among the 3 age tertiles using ANOVA or Kruskal-Wallis test accordingly. Categorical variables were compared among groups using chi-squared test. To evaluate the independent association between age and GDMT, we performed multivariate logistic regression analysis with age as a continuous variable adjusted for sex, BMI, etiology of HF, LVEF, SBP, heart rate, NYHA functional class III/IV, MAGGIC score, and creatinine blood levels. Finally, we added polypharmacy and comorbidities to the model as potential mediators for the association between age and GMDT based on the previous hypothesis that elderly patients might take a greater number of medications and/or have more comorbidities, which might lead to less treatment optimization for HF due to drug side effects and lack of compliance. Structural equation models were built to assess the direct and indirect effects of age and to estimate the percentage of the total effect that is mediated by polypharmacy. All analyses were performed using Stata version 15 (Stata Corp, College Station, TX, USA).</p>
			</sec>
		</sec>
		<sec sec-type="results">
			<title>Results</title>
			<sec>
				<title>Study population</title>
				<p>We evaluated 504 patients with HF from May 2018 to February 2019. Those with LVEF ≥ 50% (n = 123) or missing data (n = 7) were excluded, resulting in 374 patients for the present analysis. The mean age of the patients was 61 ± 12 (range 23 to 89) years old; 21 (6%) patients were octogenarians, and 33% were women. <xref ref-type="table" rid="t1">Table 1</xref> displays the patients’ characteristics according to age tertiles. Older patients had lower BMI, were more likely to present the ischemic and Chagas etiologies of HF, and had higher creatinine blood-levels and MAGGIC score, as compared to younger ones. LVEF, SBP, heart rate, functional class, and duration of HF were similar across age tertiles (<xref ref-type="table" rid="t1">Table 1</xref>). The proportion of patients using sodium-glucose cotransporter-2 inhibitors was very small (1 [0.8%], 1 [0.8%], and 2 [1.7%], p value = 0.48, in the three age tertiles, respectively).</p>
				<table-wrap id="t1">
					<label>Table 1</label>
					<caption>
						<title>Patient characteristics according to age tertile</title>
					</caption>
					<table frame="hsides" rules="rows">
						<colgroup width="20%">
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead style="border-top: thin solid; border-bottom: thin solid; border-color: #000000">
							<tr>
								<th align="left" rowspan="2" valign="middle">Age tertiles</th>
								<th align="center" valign="middle">First tertile</th>
								<th align="center" valign="middle">Second tertile</th>
								<th align="center" valign="middle">Third tertile</th>
								<th align="center" rowspan="2" valign="middle">p value</th>
							</tr>
							<tr>
								<th align="center" valign="middle">23 a 57 y n=130</th>
								<th align="center" valign="middle">58 a 67 y n=128</th>
								<th align="center" valign="middle">68 a 89 y n=116</th>
							</tr>
						</thead>
						<tbody style="border-bottom: thin solid; border-color: #000000">
							<tr>
								<td align="left" valign="top">Female, n (%)</td>
								<td align="center" valign="top">39 (30)</td>
								<td align="center" valign="top">42 (32.8)</td>
								<td align="center" valign="top">42 (36.2)</td>
								<td align="center" valign="top">0.59</td>
							</tr>
							<tr>
								<td align="left" valign="top">BMI, kg/m²</td>
								<td align="center" valign="top">29.6 ± 6.6</td>
								<td align="center" valign="top">27.9 ± 5.2</td>
								<td align="center" valign="top">26.5 ± 4.8</td>
								<td align="center" valign="top">&lt; 0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">Etiology of HF, n(%)</td>
								<td align="center" valign="top"/>
								<td align="center" valign="top"/>
								<td align="center" valign="top"/>
								<td align="center" valign="top">&lt; 0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">Ischemic</td>
								<td align="center" valign="top">41 (31.5)</td>
								<td align="center" valign="top">53 (41.4)</td>
								<td align="center" valign="top">59 (50.9)</td>
								<td align="center" valign="top"/>
							</tr>
							<tr>
								<td align="left" valign="top">Chagasic</td>
								<td align="center" valign="top">5 (3.8)</td>
								<td align="center" valign="top">9 (7.0)</td>
								<td align="center" valign="top">15 (12.9)</td>
								<td align="center" valign="top"/>
							</tr>
							<tr>
								<td align="left" valign="top">Other</td>
								<td align="center" valign="top">84 (64.6)</td>
								<td align="center" valign="top">66 (51.6)</td>
								<td align="center" valign="top">42 (36.2)</td>
								<td align="center" valign="top"/>
							</tr>
							<tr>
								<td align="left" valign="top">Ejection fraction, %</td>
								<td align="center" valign="top">33.4 ± 8.5</td>
								<td align="center" valign="top">33.1 ± 8.9</td>
								<td align="center" valign="top">32.1 ± 7.9</td>
								<td align="center" valign="top">&lt; 0.42</td>
							</tr>
							<tr>
								<td align="left" valign="top">SBP, mmHg</td>
								<td align="center" valign="top">112.1 ± 19.4</td>
								<td align="center" valign="top">109.1 ± 18.8</td>
								<td align="center" valign="top">110.2 ± 21.3</td>
								<td align="center" valign="top">0.48</td>
							</tr>
							<tr>
								<td align="left" valign="top">Heart rate, bpm</td>
								<td align="center" valign="top">73.4 ± 13.7</td>
								<td align="center" valign="top">71.9 ± 14.1</td>
								<td align="center" valign="top">71.5 ± 13.1</td>
								<td align="center" valign="top">0.53</td>
							</tr>
							<tr>
								<td align="left" valign="top">NYHA 3 or 4, n(%)</td>
								<td align="center" valign="top">35 (26.9)</td>
								<td align="center" valign="top">34 (26.6)</td>
								<td align="center" valign="top">32 (27.6)</td>
								<td align="center" valign="top">0.98</td>
							</tr>
							<tr>
								<td align="left" valign="top">Hypertension (%)</td>
								<td align="center" valign="top">77 (59.2)</td>
								<td align="center" valign="top">87 (67.9)</td>
								<td align="center" valign="top">88 (75.9)</td>
								<td align="center" valign="top">0.005</td>
							</tr>
							<tr>
								<td align="left" valign="top">Diabetes (%)</td>
								<td align="center" valign="top">31(23.8)</td>
								<td align="center" valign="top">37 (28.9)</td>
								<td align="center" valign="top">47 (40.5)</td>
								<td align="center" valign="top">0.005</td>
							</tr>
							<tr>
								<td align="left" valign="top">Coronary artery disease (%)</td>
								<td align="center" valign="top">48 (36.9)</td>
								<td align="center" valign="top">62 (48.4)</td>
								<td align="center" valign="top">71 (61.2)</td>
								<td align="center" valign="top">&lt; 0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">Chronic kidney disease</td>
								<td align="center" valign="top">9 (6.9)</td>
								<td align="center" valign="top">16 (12.5)</td>
								<td align="center" valign="top">27 (23.3)</td>
								<td align="center" valign="top">&lt; 0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">COPD</td>
								<td align="center" valign="top">3 (2.3)</td>
								<td align="center" valign="top">8 (6.2)</td>
								<td align="center" valign="top">10 (8.6)</td>
								<td align="center" valign="top">0.031</td>
							</tr>
							<tr>
								<td align="left" valign="top">2 or more comorbities</td>
								<td align="center" valign="top">49 (37.7)</td>
								<td align="center" valign="top">67 (52.3)</td>
								<td align="center" valign="top">80 (68.9)</td>
								<td align="center" valign="top">&lt; 0.001</td>
							</tr>
							<tr>
								<td align="left" colspan="5" valign="top"><bold>Target dose according to GDMT, n(%)</bold></td>
							</tr>
							<tr>
								<td align="left" valign="top">ACEI/ARB or ARNI</td>
								<td align="center" valign="top">71(54.6)</td>
								<td align="center" valign="top">60 (46.9)</td>
								<td align="center" valign="top">45(38.3)</td>
								<td align="center" valign="top">0.013</td>
							</tr>
							<tr>
								<td align="left" valign="top">BB</td>
								<td align="center" valign="top">80 (61.5)</td>
								<td align="center" valign="top">71 (55.5)</td>
								<td align="center" valign="top">57 (49.6)</td>
								<td align="center" valign="top">0.06</td>
							</tr>
							<tr>
								<td align="left" valign="top">MRA<xref ref-type="table-fn" rid="TFN2">*</xref>
								</td>
								<td align="center" valign="top">64 (80.0)</td>
								<td align="center" valign="top">63 (80.8)</td>
								<td align="center" valign="top">69 (75.0)</td>
								<td align="center" valign="top">0.44</td>
							</tr>
							<tr>
								<td align="left" valign="top">GDMT</td>
								<td align="center" valign="top">43 (33.1)</td>
								<td align="center" valign="top">31 (24.2)</td>
								<td align="center" valign="top">17 (14.7)</td>
								<td align="center" valign="top">&lt; 0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">MAGGIC score, points</td>
								<td align="center" valign="top">12.9 ± 5.5</td>
								<td align="center" valign="top">17.3 ± 6</td>
								<td align="center" valign="top">24.2 ± 5.3</td>
								<td align="center" valign="top">&lt; 0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">EGFR, mL/min per 1.73m²<xref ref-type="table-fn" rid="TFN3">**</xref>
								</td>
								<td align="center" valign="top">81.7±25.3</td>
								<td align="center" valign="top">72.6±21.2</td>
								<td align="center" valign="top">54.9±24.4</td>
								<td align="center" valign="top">&lt; 0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">Onset of HF, years</td>
								<td align="center" valign="top">3.3 [1.4-6.0]</td>
								<td align="center" valign="top">4.2 [1.4-7.7]</td>
								<td align="center" valign="top">2.6 [1.2-6.0]</td>
								<td align="center" valign="top">0.35</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN1">
							<p>ACEI: angiotensin-converting enzyme inhibitors; ARB: angiotensin II receptor blockers; ARNI: angiotensin receptor-neprilysin inhibitors; BB: beta-blocker; BMI: body mass index; COPD: chronic obstructive pulmonary disease; Cr: creatinine; EGFR: estimated glomerular filtration rate; HF: heart failure; GDMT: guideline-directed medical treatment; MRA: mineralocorticoid receptor antagonist; NYHA: New York Heart Association; SBP: systolic blood pressure.</p>
						</fn>
						<fn id="TFN2">
							<label>*</label>
							<p>Only for symptomatic patients</p>
						</fn>
						<fn id="TFN3">
							<label>**</label>
							<p>EGFR was estimated by the CKD-EPI formula.<sup><xref ref-type="bibr" rid="B30">30</xref></sup></p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</sec>
			<sec>
				<title>Age and guideline-directed medical therapy</title>
				<p>Older patients were less likely to receive optimal medical therapy according to GDMT. For each age decade increase, the chance of receiving optimal medical therapy significantly reduced (OR 0.67 [95% confidence interval 0.56 – 0.82], <xref ref-type="table" rid="t2">Table 2</xref>). This association remained significant after adusting for potential confounders, such as sex, BMI, etiology of HF, LVEF, SBP, heart rate, NYHA functional class III/IV, MAGGIC score, and creatinine blood levels (OR 0.66 [95% confidence interval 0.48 – 0.92], <xref ref-type="table" rid="t2">Table 2</xref>, <xref ref-type="fig" rid="f1">Figures 1</xref> and <xref ref-type="fig" rid="f2">2</xref>). There was no interaction between age and GDMT association and sex (p for interaction = 0.51).</p>
				<table-wrap id="t2">
					<label>Table 2</label>
					<caption>
						<title>Association between age and guideline-directed medical therapy after accounting for potential confounders</title>
					</caption>
					<table frame="hsides" rules="rows">
						<colgroup width="20%">
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead style="border-top: thin solid; border-bottom: thin solid; border-color: #000000">
							<tr>
								<th align="left" valign="middle"/>
								<th align="center" valign="middle">N</th>
								<th align="center" valign="middle">OR</th>
								<th align="center" valign="middle">95% CI</th>
								<th align="center" valign="middle">p value</th>
							</tr>
						</thead>
						<tbody style="border-bottom: thin solid; border-color: #000000">
							<tr>
								<td align="left" colspan="5" valign="top"><bold>Age, for each 10-year increase</bold></td>
							</tr>
							<tr>
								<td align="left" valign="top">Unajusted</td>
								<td align="center" valign="top">374</td>
								<td align="center" valign="top">0.67</td>
								<td align="center" valign="top">(0.56-0.82)</td>
								<td align="center" valign="top">&lt;0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">Adjusted for model 1</td>
								<td align="center" valign="top">358</td>
								<td align="center" valign="top">0.71</td>
								<td align="center" valign="top">(0.57-0.88)</td>
								<td align="center" valign="top">0.02</td>
							</tr>
							<tr>
								<td align="left" valign="top">Adjusted for model 2</td>
								<td align="center" valign="top">334</td>
								<td align="center" valign="top">0.66</td>
								<td align="center" valign="top">(0.48-0.92)</td>
								<td align="center" valign="top">0.013</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN4">
							<p>Model 1: Adjusted for sex, body mass index, etiology of heart failure, left ventricle ejection fraction, systolic blood pressure, heart rate, New York Heart Association functional class III/IV, and creatinine blood levels. Model 2: Adjusted for Model 1 + MAGGIC score. CI: confidence interval; OR: odds ratio.</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
				<fig id="f1">
					<label>Figure 1</label>
					<caption>
						<title>Proportion of patients at target dose of each heart failure medication as recommended by guidelines according to age tertiles. ACEI: angiotensin-converting enzyme inhibitors; ARB: angiotensin II receptor blockers; ARNI: angiotensin receptor-neprilysin inhibitors; GDMT: guideline-directed medical treatment; MRA: mineralocorticoid receptor antagonist.</title>
					</caption>
					<graphic xlink:href="2764-3107-abchf-002-02-0138-gf01.tif"/>
				</fig>
				<fig id="f2">
					<label>Figure 2</label>
					<caption>
						<title>Association between medical treatment of heart failure according to guidelines and age after adjusting for potential confounders GDMT: guideline-directed medical treatment. *Adjusted for sex, body mass index, etiology of heart failure, left ventricle ejection fraction, systolic blood pressure, heart rate, New York Heart Association functional class III/IV, MAGGIC score, and creatinine blood levels</title>
					</caption>
					<graphic xlink:href="2764-3107-abchf-002-02-0138-gf02.tif"/>
				</fig>
			</sec>
			<sec>
				<title>Polypharmacy and comorbidities</title>
				<p>Older patients were more likely to use bisoprolol instead of carvedilol as HF-specific beta-blockers (<xref ref-type="table" rid="t3">Table 3</xref>). They were also more likely to use loop diuretics and statins (<xref ref-type="table" rid="t3">Table 3</xref>). Use of ACEI, ARB, MRA, digoxin, thiazide, hydralazine, nitrate, antiarrhythmic, anticoagulant, aspirin, and clopidogrel were similar between the age tertiles (<xref ref-type="table" rid="t3">Table 3</xref>). The proportion of the association between age and GDMT mediated by polypharmacy was 0% (0% – 3.5%). We also analyzed the number of comorbidities (hypertension, diabetes, coronary artery disease, chronic kidney disease, and COPD) to evaluate whether a high proportion of comorbidities might explain the inverse association between age and GDMT. We found that the number of comorbities mediated only 7% (0% – 41%) of this association.</p>
				<table-wrap id="t3">
					<label>Table 3</label>
					<caption>
						<title>Association between use of medications and age categories</title>
					</caption>
					<table frame="hsides" rules="rows">
						<colgroup width="20%">
							<col/>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead style="border-top: thin solid; border-bottom: thin solid; border-color: #000000">
							<tr>
								<th align="left" rowspan="2" valign="middle"/>
								<th align="center" valign="middle">First tertile</th>
								<th align="center" valign="middle">Second tertile</th>
								<th align="center" valign="middle">Terceiro tercil</th>
								<th align="center" rowspan="2" valign="middle">p value</th>
							</tr>
							<tr>
								<th align="center" valign="middle">23 to 57 y n=130</th>
								<th align="center" valign="middle">58 to 67 y n=128</th>
								<th align="center" valign="middle">68 to 89 y n=116</th>
							</tr>
						</thead>
						<tbody style="border-bottom: thin solid; border-color: #000000">
							<tr>
								<td align="left" colspan="5" valign="top"><bold>Disease-modifying medications</bold></td>
							</tr>
							<tr>
								<td align="left" valign="top">Carvedilol, n(%)</td>
								<td align="center" valign="top">98 (75.4)</td>
								<td align="center" valign="top">86 (67.2)</td>
								<td align="center" valign="top">66 (56.9)</td>
								<td align="center" valign="top">0.002</td>
							</tr>
							<tr>
								<td align="left" valign="top">Metoprolol succinate, n(%)</td>
								<td align="center" valign="top">11 (8.5)</td>
								<td align="center" valign="top">11 (8.6)</td>
								<td align="center" valign="top">12 (10.3)</td>
								<td align="center" valign="top">0.61</td>
							</tr>
							<tr>
								<td align="left" valign="top">Bisoprolol, n(%)</td>
								<td align="center" valign="top">18 (13.8)</td>
								<td align="center" valign="top">27 (21.1)</td>
								<td align="center" valign="top">35 (30.2)</td>
								<td align="center" valign="top">0.002</td>
							</tr>
							<tr>
								<td align="left" valign="top">ACEI, n(%)</td>
								<td align="center" valign="top">67 (51.5)</td>
								<td align="center" valign="top">55 (43)</td>
								<td align="center" valign="top">48 (41.4)</td>
								<td align="center" valign="top">0.11</td>
							</tr>
							<tr>
								<td align="left" valign="top">ARB, n(%)</td>
								<td align="center" valign="top">34 (26.2)</td>
								<td align="center" valign="top">47 (36.7)</td>
								<td align="center" valign="top">30 (25.9)</td>
								<td align="center" valign="top">0.98</td>
							</tr>
							<tr>
								<td align="left" valign="top">ARNI, n(%)</td>
								<td align="center" valign="top">24 (18.5)</td>
								<td align="center" valign="top">22 (17.2)</td>
								<td align="center" valign="top">27 (23.3)</td>
								<td align="center" valign="top">0.36</td>
							</tr>
							<tr>
								<td align="left" valign="top">MRA, n(%)</td>
								<td align="center" valign="top">103 (79.2)</td>
								<td align="center" valign="top">98 (76.6)</td>
								<td align="center" valign="top">88 (75.9)</td>
								<td align="center" valign="top">0.53</td>
							</tr>
							<tr>
								<td align="left" colspan="5" valign="top"><bold>Other HF-related medications</bold></td>
							</tr>
							<tr>
								<td align="left" valign="top">Ivabradine, n(%)</td>
								<td align="center" valign="top">16 (12.3)</td>
								<td align="center" valign="top">3 (2.3)</td>
								<td align="center" valign="top">4 (3.4)</td>
								<td align="center" valign="top">0.003</td>
							</tr>
							<tr>
								<td align="left" valign="top">Digoxin, n(%)</td>
								<td align="center" valign="top">34 (26.2)</td>
								<td align="center" valign="top">24 (18.8)</td>
								<td align="center" valign="top">24 (20.7)</td>
								<td align="center" valign="top">0.29</td>
							</tr>
							<tr>
								<td align="left" valign="top">Loop diuretic, n(%)</td>
								<td align="center" valign="top">88 (67.7)</td>
								<td align="center" valign="top">88 (68.8)</td>
								<td align="center" valign="top">90 (77.6)</td>
								<td align="center" valign="top">0.09</td>
							</tr>
							<tr>
								<td align="left" valign="top">Thiazide, n(%)</td>
								<td align="center" valign="top">19 (14.6)</td>
								<td align="center" valign="top">15 (11.7)</td>
								<td align="center" valign="top">10 (8.6)</td>
								<td align="center" valign="top">0.15</td>
							</tr>
							<tr>
								<td align="left" valign="top">Hydralazine, n(%)</td>
								<td align="center" valign="top">32 (24.6)</td>
								<td align="center" valign="top">29 (22.7)</td>
								<td align="center" valign="top">21 (18.1)</td>
								<td align="center" valign="top">0.22</td>
							</tr>
							<tr>
								<td align="left" valign="top">Nitrate, n(%)</td>
								<td align="center" valign="top">31 (23.8)</td>
								<td align="center" valign="top">25 (19.5)</td>
								<td align="center" valign="top">23 (19.8)</td>
								<td align="center" valign="top">0.43</td>
							</tr>
							<tr>
								<td align="left" colspan="5" valign="top"><bold>Other medications</bold></td>
							</tr>
							<tr>
								<td align="left" valign="top">Statin, n(%)</td>
								<td align="center" valign="top">75 (57.7)</td>
								<td align="center" valign="top">93 (72.7)</td>
								<td align="center" valign="top">90 (77.6)</td>
								<td align="center" valign="top">&lt;0.001</td>
							</tr>
							<tr>
								<td align="left" valign="top">Antiarrhythmic, n(%)</td>
								<td align="center" valign="top">14 (10.8)</td>
								<td align="center" valign="top">5 (3.9)</td>
								<td align="center" valign="top">9 (7.8)</td>
								<td align="center" valign="top">0.34</td>
							</tr>
							<tr>
								<td align="left" valign="top">Anticoagulant, n(%)</td>
								<td align="center" valign="top">31 (23.8)</td>
								<td align="center" valign="top">27 (21.1)</td>
								<td align="center" valign="top">37 (31.9)</td>
								<td align="center" valign="top">0.16</td>
							</tr>
							<tr>
								<td align="left" valign="top">Aspirin, n(%)</td>
								<td align="center" valign="top">63 (48.5)</td>
								<td align="center" valign="top">79 (61.7)</td>
								<td align="center" valign="top">66 (56.9)</td>
								<td align="center" valign="top">0.17</td>
							</tr>
							<tr>
								<td align="left" valign="top">Clopidogrel, n(%)</td>
								<td align="center" valign="top">10 (7.7)</td>
								<td align="center" valign="top">8 (6.2)</td>
								<td align="center" valign="top">14 (12.1)</td>
								<td align="center" valign="top">0.24</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN5">
							<p>ACEI: angiotensin-converting enzyme inhibitors; ARB: angiotensin II receptor blockers; ARNI: angiotensin receptor-neprilysin inhibitors; HF: heart failure; MRA: mineralocorticoid receptor antagonist.</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</sec>
		</sec>
		<sec sec-type="discussion">
			<title>Discussion</title>
			<p>In this study of patients with chronic HF in a middle-income country, we found that optimal medical therapy for HF was significantly lower in elderly patients, compared to younger ones. This inverse association between age and optimal medical therapy was independent of heart rate, SBP, and disease severity. Also, this does not appear to be explained by polypharmacy or by the number of comorbidities, indicating treatment complexity, among elderly patients. It is noteworthy that the proportion of patients in GDMT was low across all ages. Our results show that there is much room for improvement in therapy for HF in clinical practice, which has the potential to improve survival in these patients.</p>
			<p>Previous studies had suggested that older age can be related to lower rates of reaching target doses for HF-related medications.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> In a study from Japan, it was shown that the presciption rates according to GDMT were significantly lower in patients 80 years old or older.<sup><xref ref-type="bibr" rid="B14">14</xref></sup> Another study, a survey from 36 countries worldwide found an inverse association between age and likelihood of receiving beta-blockers at the target dose in patients with HF with reduced ejection fraction.<sup><xref ref-type="bibr" rid="B15">15</xref>,<xref ref-type="bibr" rid="B20">20</xref></sup> They also found that rates of use of ACEIs and beta-blockers at target dose were quite low (28% and 15%, respectively), and adherence to guidelines varied across different regions around the world, which may result from different cultural and economic aspects.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> Althought they included 5 continents, countries in South America were under-represented in this survey. Our study describes the rates of guideline-based prescriptions of drugs in HF in a Brazilian city, adding that older patients were also less likely to receive optimal medical therapy.<sup><xref ref-type="bibr" rid="B20">20</xref></sup> Conversely, we found higher rates of ACEIs and beta-blockers at target dose than those previous reports, probably reflecting patients treated in a referral center for HF, with access to medications free of cost.</p>
			<p>The reasons for lower prescription according to guidelines in elderly patients are multifactorial. It has been suggested that elderly individuals are prone to hypotension and bradycardia, and these were important reasons for non-prescription of guideline-recommended medications in the QUALIFY survey.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> Nevertheless, we did not find significant differences in SBP and heart rate among age tertiles in our study, and GDMT rates remained lower among elderly patients after adjusting for these parameters.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> Therefore, there might be other factors, such as concern related to adverse effects and treatment inertia, which help explain the lower treatment optimization in this population. There is a well documented “risk-treatment paradox” in HF, where patients with a higher risk of mortality tend to receive less GDMT prescription.<sup><xref ref-type="bibr" rid="B12">12</xref>,<xref ref-type="bibr" rid="B21">21</xref></sup> Elderly patients are usually more complex, with more severe disease and co-morbidities, displaying higher mortality risk. Such complexity results in more unstable conditions that are more difficult to manage, and physicians may feel insecure in optimizing HF-related medications. Moreover, elderly patients may be less likely to report effort dyspnea, and physicians are less prone to optmize treatment when patients report themselves as asymptomatic. Also, physicians may prefer drugs that improve symptoms, with fewer potential adverse effects, instead of prescribing drugs that improve survival in elderly patients.<sup><xref ref-type="bibr" rid="B20">20</xref></sup></p>
			<p>Treatment complexity due to higher prevalence of comorbidities among elderly patients may also be a barrier to optimal medical treatment.<sup><xref ref-type="bibr" rid="B16">16</xref></sup> Cognitive impairment, which is prevalent in elderly patients with HF, has been reported as related with poor medication adherence.<sup><xref ref-type="bibr" rid="B22">22</xref></sup> On the other hand, an analysis from the QUALIFY study suggested that patients with HF and multiple comorbidities, such as coronary artery disease, hypertension, diabetes mellitus, vascular disease, and stroke/transient ischemic attack, were more likely to be at target dose of ACEIs, ARBs, and MRAs, which is expected, as these class of drugs are also indicated for these conditions.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> This suggests that the presence of these comorbidities might actually contribute to optimal medical treatment for HF. Despite the mixed evidence, our results suggest that neither polypharmacy nor number of comorbidities accounted for the association between age and optimal medical treatment in HF.</p>
			<p>Although they are excluded from most trials, elderly patients with HF are likely to benefit from GDMT.<sup><xref ref-type="bibr" rid="B23">23</xref>–<xref ref-type="bibr" rid="B25">25</xref></sup> An observational study showed that GDMT was associated with lower mortality in elderly patients with HF, and this association was consistent amont those 80 years old or older.<sup><xref ref-type="bibr" rid="B24">24</xref></sup> Our study highlights that there is much room to improve survival of patients with HF in clinical practice, particularly elderly patients. Efforts should be made to increase rates of GDMT in clinical practice, improving medical training and reducing medical inertia. For instance, a strategy called “start low go slow” for titration of the drugs and delivery of frequent educational reinforcements may help achieve the target dose for HF drugs in elderly patients.<sup><xref ref-type="bibr" rid="B25">25</xref>,<xref ref-type="bibr" rid="B26">26</xref></sup> Additionally, public policies may help improve comunication and establish goals for GDMT among patients with HF. Dissemination of cardiology guidelines and multidimensional practice-specific performance improvement interventions were associated with an increase in the use of GDMT.<sup><xref ref-type="bibr" rid="B15">15</xref>,<xref ref-type="bibr" rid="B27">27</xref></sup> A multilevel intervention that increases social support by relatives and healthcare providers and integrates different models of care, such as home care, telemedicine, primary care, and HF clinics, can help patients deal with treatment complexity and improve medical treatment.<sup><xref ref-type="bibr" rid="B20">20</xref>,<xref ref-type="bibr" rid="B25">25</xref>,<xref ref-type="bibr" rid="B28">28</xref>,<xref ref-type="bibr" rid="B29">29</xref></sup> Better rates of GDMT help reduce hospitalizations, with a significant economic impact.</p>
			<p>Our study has some limitations that deserve attention. This is a cross-sectional study, which prevents us from establishing a temporal sequence relating patients aging and use of optimal drug doses. Furthermore, this design is subject to survival bias. We also cannot exclude the possibility that the differences and relations observed are due to other unmeasured confounding variables, such as income and education level. In addition, this is a single-center study of patients from SUS, and previous diagnosis of HF and LVEF below 50% may not necessarily reflect practices of others centers. The following specific characteristics of our study popuation should be noted: around 75% in NYHA funcional class I and II, which may reflect symptom improvement after treatment; 20% of patients used ARNI, even though this drug had elevated costs and it was not provided by the government at the time of the study; amost one third used ARB, even though they should be used only in patients who are intolerant to ACEI. Finally, the term “polypharmacy” has been defined in different ways in the literature, most commonly as the use of 5 or more medications, and no standard definition has been stablished.<sup><xref ref-type="bibr" rid="B30">30</xref></sup></p>
		</sec>
		<sec sec-type="conclusions">
			<title>Conclusion</title>
			<p>In this study of patients with HF in a middle-income country, we found that, overall, the rates of medical therapy of HF at the target dose was low. These rates were significantly lower in elderly patients, when compared to younger ones, and this does not appear to be explained by polypharmacy or the higher presence of comorbidities among the elderly.</p>
		</sec>
	</body>
	<back>
		<fn-group>
			<fn fn-type="supported-by" id="fn1">
				<p><bold>Sources of Funding</bold></p>
				<p>There were no external funding sources for this study.</p>
			</fn>
			<fn fn-type="other" id="fn2">
				<p><bold>Study Association</bold></p>
				<p>This study is not associated with any thesis or dissertation work.</p>
			</fn>
		</fn-group>
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	<sub-article article-type="translation" id="S01" xml:lang="pt">
		<front-stub>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>Artigo Original</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>Associação entre Idade e Tratamento Medicamentoso Otimizado em Pacientes com Insuficiência Cardíaca Crônica</article-title>
			</title-group>
			<contrib-group>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-5899-2186</contrib-id>
					<name>
						<surname>Koga</surname>
						<given-names>Vitoria A. A.</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-6441-1667</contrib-id>
					<name>
						<surname>Dall’Asta</surname>
						<given-names>Luiza</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-2030-2615</contrib-id>
					<name>
						<surname>Jacyntho</surname>
						<given-names>Thiago L. P.</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-0568-1971</contrib-id>
					<name>
						<surname>De-Marchi</surname>
						<given-names>Leonardo C.</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0001-5859-3169</contrib-id>
					<name>
						<surname>Mulinari</surname>
						<given-names>Rodrigo P.</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-8502-8373</contrib-id>
					<name>
						<surname>Ladeira</surname>
						<given-names>Bruna A.</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-3003-2500</contrib-id>
					<name>
						<surname>Nemeth</surname>
						<given-names>Maria E. R. F.</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-5538-7455</contrib-id>
					<name>
						<surname>Silvestre</surname>
						<given-names>Odilson M.</given-names>
					</name>
					<xref ref-type="aff" rid="aff5"><sup>2</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-1761-8662</contrib-id>
					<name>
						<surname>Bonatto</surname>
						<given-names>Marcely Gimenes</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-4741-322X</contrib-id>
					<name>
						<surname>Moura</surname>
						<given-names>Lidia Ana Zytynski</given-names>
					</name>
					<xref ref-type="aff" rid="aff4"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-0095-9049</contrib-id>
					<name>
						<surname>Fernandes-Silva</surname>
						<given-names>Miguel Morita</given-names>
					</name>
					<xref ref-type="aff" rid="aff6"><sup>3</sup></xref>
					<xref ref-type="corresp" rid="c2"/>
				</contrib>
				<aff id="aff4">
					<label>1</label>
					<addr-line>
						<named-content content-type="city">Curitiba</named-content>
						<named-content content-type="state">PR</named-content>
					</addr-line>
					<country country="BR">Brasil</country>
					<institution content-type="original">Pontifícia Universidade Católica do Paraná, Curitiba, PR – Brasil</institution>
				</aff>
				<aff id="aff5">
					<label>2</label>
					<addr-line>
						<named-content content-type="city">Rio Branco</named-content>
						<named-content content-type="state">AC</named-content>
					</addr-line>
					<country country="BR">Brasil</country>
					<institution content-type="original">Universidade Federal do Acre, Rio Branco, AC – Brasil</institution>
				</aff>
				<aff id="aff6">
					<label>3</label>
					<addr-line>
						<named-content content-type="city">Curitiba</named-content>
						<named-content content-type="state">PR</named-content>
					</addr-line>
					<country country="BR">Brasil</country>
					<institution content-type="original">Universidade Federal do Paraná, Curitiba, PR – Brasil</institution>
				</aff>
			</contrib-group>
			<author-notes>
				<corresp id="c2"><bold>Correspondência: Miguel Morita Fernandes-Silva</bold> • Departamento de Clínica Médica – Universidade Federal do Paraná – Rua General Carneiro, 181, 10º andar. CEP <postal-code>80060-900</postal-code>, Curitiba, PR – Brasil E-mail: <email>miguelmorita@ufpr.br</email>
				</corresp>
				<fn fn-type="con">
					<p><bold>Contribuição dos autores</bold></p>
					<p>Concepção e desenho da pesquisa: Fernandes-Silva MM; Obtenção de dados: Koga VAA, Dall’Asta L, Jacyntho TLP, De-Marchi LC, Mulinari RP, Ladeira BA, Nemeth MERF; Análise e interpretação dos dados: Koga VAA, Fernandes-Silva MM; Análise estatística: Dall’Asta L, Fernandes-Silva MM; Redação do manuscrito: Koga VAA, Dall’Asta L, Jacyntho TLP, De-Marchi LC, Mulinari RP; Revisão crítica do manuscrito quanto ao conteúdo intelectual importante: Silvestre OM, Bonatto MG, Moura LAZ, Fernandes-Silva MM.</p>
				</fn>
				<fn fn-type="conflict">
					<p><bold>Potencial conflito de interesse</bold></p>
					<p>Não há conflito com o presente artigo</p>
				</fn>
			</author-notes>
			<abstract>
				<title>Resumo</title>
				<sec>
					<title>Fundamento:</title>
					<p>A adesão à terapia médica orientada por diretrizes em pacientes com insuficiência cardíaca (IC) permanece subótima.</p>
				</sec>
				<sec>
					<title>Objetivos:</title>
					<p>Avaliamos a associação entre idade e adesão à terapia médica orientada por diretrizes em pacientes com IC crônica e exploramos se a polifarmácia e as comorbidades podem explicar essa associação.</p>
				</sec>
				<sec>
					<title>Métodos:</title>
					<p>Realizamos um estudo transversal de 374 pacientes com IC crônica e fração de ejeção do ventrículo esquerdo &lt; 50% (de 23 a 89 anos, 33% mulheres) entre 2018 e 2019. A GDMT foi definida como uso de medicamentos modificadores da doença relacionados à IC na dose-alvo de acordo com as diretrizes. Os pacientes foram classificados em 3 faixas etárias (de 23 a 57 anos, 58 a 67 anos e 68 a 89 anos).</p>
				</sec>
				<sec>
					<title>Resultados:</title>
					<p>Os pacientes mais velhos eram menos propensos a receber a terapia ideal (33% versus 24% versus 15%, p &lt; 0,001 para cada faixa etária, respectivamente). Após o ajuste para possíveis fatores de confusão, as chances de receber terapia médica na dose ideal reduziram significativamente para cada aumento de década de idade (OR 0,66 [intervalo de confiança de 95% 0,48 – 0,92], p = 0,013). A proporção dessa associação explicada por polifarmácia (0% [0% – 3,5%]) ou comorbidades (7% [0% – 41%]) foi insignificante.</p>
				</sec>
				<sec>
					<title>Conclusão:</title>
					<p>Verificamos que a idade foi inversamente associada à terapia medicamentosa ideal para IC e a polifarmácia ou as comorbidades não parecem explicar isso.</p>
				</sec>
			</abstract>
			<kwd-group xml:lang="pt">
				<title>Palavras-chave:</title>
				<kwd>Insuficiência Cardíaca</kwd>
				<kwd>Tratamento Farmacológico</kwd>
				<kwd>Polimedicação</kwd>
				<kwd>Envelhecimento</kwd>
			</kwd-group>
		</front-stub>
		<body>
			<sec sec-type="intro">
				<title>Introdução</title>
				<p>A insuficiência cardíaca (IC) afeta 26 milhões de pessoas ao redor do mundo e sua prevalência está aumentando.<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Os custos são notáveis e aumentarão consideravelmente com o envelhecimento da população. A IC apresenta alta morbimortalidade e o tratamento com diferentes classes de medicamentos pode melhorar a sobrevida desses pacientes, conforme demonstrado em ensaios clínicos.<sup><xref ref-type="bibr" rid="B2">2</xref>–<xref ref-type="bibr" rid="B10">10</xref></sup> A terapia com esses medicamentos nas doses-alvo semelhantes às utilizadas em ensaios são primordiais para modificar o curso natural da doença e tem sido recomendada por diretrizes relacionadas à IC, que foi denominada terapia médica orientada por diretrizes (GDMT, sigla em inglês).<sup><xref ref-type="bibr" rid="B11">11</xref></sup></p>
				<p>Apesar das evidências substanciais acumuladas nas últimas 3 décadas, a adesão à GDMT permanece baixa. Um estudo prévio mostrou que apenas 1% dos pacientes elegíveis com IC receberam simultaneamente as doses-alvo de inibidor da enzima conversora da angiotensina (IECA)/bloqueador do receptor da angiotensina (BRA)/inibidores da neprilisina e antagonistas do receptor de angiotensina II (INRA), betabloqueador e antagonista do receptor mineralocorticoide (ARM) para terapia recomendada pelas diretrizes.<sup><xref ref-type="bibr" rid="B12">12</xref></sup> A falha em atingir a dose-alvo tem sido associada a pior sobrevida.<sup><xref ref-type="bibr" rid="B13">13</xref></sup> As razões para a baixa adesão à GDMT são complexas e provavelmente multifatoriais. Alguns estudos exploraram esses motivos e sugeriram que a otimização de medicamentos parece ser menor entre os pacientes idosos com IC, quando comparados aos mais jovens, mas outros fatores também podem desempenhar um papel na baixa adesão ao tratamento, como renda baixa e analfabetismo em saúde.<sup><xref ref-type="bibr" rid="B14">14</xref>–<xref ref-type="bibr" rid="B16">16</xref></sup> No entanto, faltam estudos que avaliem as taxas de adesão à GDMT em países de baixa e média renda.</p>
				<p>Portanto, visamos analisar a relação entre idade e GDMT em pacientes com IC atendidos em uma instituição de um país de média renda. Também exploramos se o número de medicamentos prescritos (polifarmácia) e o número de comorbidades ajudariam a explicar essa associação, já que podem contribuir para a baixa adesão ao tratamento.</p>
			</sec>
			<sec sec-type="methods">
				<title>Métodos</title>
				<sec>
					<title>População do estudo</title>
					<p>Trata-se de um estudo observacional transversal aprovado pelo comitê de ética institucional local sob protocolo número 3.227.412. Foram incluídos pacientes consecutivos maiores de 18 anos encaminhados ao Ambulatório de Insuficiência Cardíaca da Santa Casa de Misericórdia de Curitiba, centro universitário terciário dedicado ao atendimento especializado de pacientes com IC do Sistema Único de Saúde (SUS), durante o período de maio de 2018 a fevereiro de 2019 em Curitiba, Paraná. Todos foram diagnosticados inicialmente com IC e receberam tratamento médico em unidades básicas de saúde do SUS. Os pacientes devem ser encaminhados ao centro especializado se forem internados por IC ou considerados refratários ao tratamento médico. Os critérios de inclusão foram diagnóstico prévio de IC e fração de ejeção do ventrículo esquerdo (FEVE) inferior a 50%, medida por ecocardiograma realizado nos últimos 12 meses. Os pacientes foram abordados durante sua consulta de rotina e todos os dados foram coletados durante a consulta e nos prontuários. Todos os pacientes forneceram consentimento informado por escrito. Foram excluídos aqueles que se recusaram a participar do estudo ou apresentavam informações insuficientes, como dados ausentes no ecocardiograma ou exames laboratoriais.</p>
				</sec>
				<sec>
					<title>Exposição</title>
					<p>A idade dos pacientes foi definida de acordo com a data de nascimento registrada no prontuário e avaliada como uma variável contínua. Os pacientes também foram classificados em 3 grupos de acordo com os tercis de idade: o primeiro tercil de 23 a 57 anos, o segundo de 58 a 67 anos e terceiro de 68 a 89 anos.</p>
				</sec>
				<sec>
					<title>Desfecho</title>
					<p>O desfecho foi a proporção de pacientes em GDMT, ou seja, em uso de tratamento médico ideal conforme recomendado pela <italic>Diretriz Brasileira de Insuficiência Cardíaca Crônica e Aguda de 2018</italic>.<sup><xref ref-type="bibr" rid="B17">17</xref></sup> Os pacientes foram considerados em GDMT se estivessem em uso dos seguintes medicamentos na dose-alvo de acordo com as diretrizes da Sociedade Europeia de Cardiologia (Tabela Suplementar 1): 1) um betabloqueador específico para IC (carvedilol, succinato de metoprolol ou bisoprolol); 2) um ACEI, ARB ou INRA; e 3) uma ARM se sintomáticos (classe funcional II a IV da New York Heart Association [NYHA]).</p>
				</sec>
				<sec>
					<title>Outras covariáveis</title>
					<p>Foram obtidos dos prontuários médicos: sexo, etiologia da IC, classe funcional da NYHA e níveis sanguíneos de creatinina. A pressão arterial foi aferida durante a visita do paciente conforme recomendado pelas diretrizes internacionais.<sup><xref ref-type="bibr" rid="B11">11</xref>,<xref ref-type="bibr" rid="B17">17</xref></sup> A estatura foi aferida na posição ortostática com um antropômetro calibrado e o peso sistematicamente medido em uma balança calibrada. Foi calculado o índice de massa corporal (IMC) como o peso (quilogramas) dividido pela altura (metros) ao quadrado.</p>
					<p>O tempo do diagnóstico de IC foi o período em meses entre o diagnóstico de IC e a data em que o paciente foi incluído no estudo. O momento do diagnóstico foi estimado durante a entrevista do paciente com base na primeira internação por IC ou quando o paciente começou a apresentar sintomas típicos de IC e foi informado que tinha IC, o que ocorresse primeiro.</p>
					<p>Estimamos a gravidade da doença por meio do cálculo do escore de risco do Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC), que é um escore que prediz a mortalidade de 1 e 3 anos em pacientes com IC.<sup><xref ref-type="bibr" rid="B18">18</xref>,<xref ref-type="bibr" rid="B19">19</xref></sup> Esse escore combina 13 variáveis clínicas independentes como FEVE, idade, pressão arterial sistólica (PAS), IMC, níveis de creatinina, classe NYHA, sexo, tabagismo atual, diabetes, doença pulmonar obstrutiva crônica (DPOC), primeiro diagnóstico de IC &gt; 18 meses, betabloqueador e IECA/BRA (Tabela Suplementar 2).</p>
				</sec>
				<sec>
					<title>Polifarmácia e comorbidades</title>
					<p>Definimos a polifarmácia como o uso de medicamentos pertencentes a classes farmacológicas diferentes daquelas na definição da GDMT (ou seja, IECA, BRA, ARM, INRA ou betabloqueador específico para IC).<sup><xref ref-type="bibr" rid="B11">11</xref>,<xref ref-type="bibr" rid="B17">17</xref></sup> Eles incluem medicamentos relacionados à IC, como ivabradina, digoxina, diurético de alça, tiazídico, hidralazina e nitratos, além de comorbidades, como estatinas, antiarrítmicos, anticoagulantes, aspirina, clopidogrel e outros. Por exemplo, se o paciente estava tomando enalapril, carvedilol, digoxina, furosemida, estatina e aspirina, a polifarmácia deve ser contada como 4. Como fazem parte da definição da GDMT, o enalapril e o carvedilol não foram considerados como polifarmácia.</p>
					<p>O número de comorbidades foi definido de acordo com a presença de hipertensão, diabetes, doença arterial coronariana, doença renal crônica e DPOC.</p>
				</sec>
				<sec>
					<title>Análise estatística</title>
					<p>As variáveis contínuas foram avaliadas quanto à distribuição gaussiana dos dados e comparadas entre os 3 tercis de idade usando ANOVA ou teste de Kruskal-Wallis. As variáveis categóricas foram comparadas entre os grupos por meio do teste do qui-quadrado. Para avaliar a associação independente entre idade e GDMT, realizamos análise de regressão logística multivariada com idade como variável contínua ajustada para sexo, IMC, etiologia da IC, FEVE, PAS, frequência cardíaca, classe funcional da NYHA III/IV, escore MAGGIC e níveis sanguíneos de creatinina. Por fim, adicionamos ao modelo a polifarmácia e as comorbidades como potenciais mediadores para a associação entre idade e GMDT com base na hipótese prévia de que pacientes idosos podem tomar maior número de medicamentos e/ou ter mais comorbidades, o que pode levar a uma menor otimização do tratamento para IC devido a efeitos colaterais de medicamentos e falta de adesão. Modelos de equações estruturais foram construídos para avaliar os efeitos diretos e indiretos da idade e estimar a porcentagem do efeito total que é mediado pela polifarmácia. Todas as análises foram realizadas no programa Stata versão 15 (Stata Corp, College Station, Texas, EUA).</p>
				</sec>
			</sec>
			<sec sec-type="results">
				<title>Resultados</title>
				<sec>
					<title>População do estudo</title>
					<p>Avaliamos 504 pacientes com IC entre o período de maio de 2018 a fevereiro de 2019. Foram excluídos aqueles com FEVE ≥ 50% (n = 123) ou dados ausentes (n = 7), resultando em 374 pacientes para a presente análise. A idade média dos pacientes foi de 61 ± 12 (de 23 a 89) anos; 21 (6%) pacientes eram octogenários e 33% eram mulheres. A <xref ref-type="table" rid="t4">Tabela 1</xref> apresenta as características dos pacientes de acordo com os tercis de idade. Pacientes mais velhos apresentavam IMC mais baixo, eram mais propensos a apresentar as etiologias isquêmica e chagásica da IC, e apresentavam níveis sanguíneos de creatinina e escore MAGGIC mais elevados, quando comparados aos mais jovens. FEVE, PAS, frequência cardíaca, classe funcional e duração da IC foram semelhantes entre os tercis de idade (<xref ref-type="table" rid="t4">Tabela 1</xref>). A proporção de pacientes em uso de inibidores do cotransportador-2 sódio-glicose foi muito pequena (1 [0,8%], 1 [0,8%] e 2 [1,7%], valor p = 0,48, nos três tercis de idade, respectivamente).</p>
					<table-wrap id="t4">
						<label>Tabela 1</label>
						<caption>
							<title>Características dos pacientes segundo tercil de idade</title>
						</caption>
						<table frame="hsides" rules="rows">
							<colgroup width="20%">
								<col/>
								<col/>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead style="border-top: thin solid; border-bottom: thin solid; border-color: #000000">
								<tr>
									<th align="left" rowspan="2" valign="middle">Tercis de idade</th>
									<th align="center" valign="middle">Primeiro tercil</th>
									<th align="center" valign="middle">Segundo tercil</th>
									<th align="center" valign="middle">Terceiro tercil</th>
									<th align="center" rowspan="2" valign="middle">Valor p</th>
								</tr>
								<tr>
									<th align="center" valign="middle">23 a 57 anos n=130</th>
									<th align="center" valign="middle">58 a 67 anos n=128</th>
									<th align="center" valign="middle">68 a 89 anos n=116</th>
								</tr>
							</thead>
							<tbody style="border-bottom: thin solid; border-color: #000000">
								<tr>
									<td align="left" valign="top">Sexo feminino, n (%)</td>
									<td align="center" valign="top">39 (30)</td>
									<td align="center" valign="top">42 (32,8)</td>
									<td align="center" valign="top">42 (36,2)</td>
									<td align="center" valign="top">0,59</td>
								</tr>
								<tr>
									<td align="left" valign="top">IMC, kg/m²</td>
									<td align="center" valign="top">29,6 ± 6,6</td>
									<td align="center" valign="top">27,9 ± 5,2</td>
									<td align="center" valign="top">26,5 ± 4,8</td>
									<td align="center" valign="top">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">Etiologia da IC, n(%)</td>
									<td align="center" valign="top"/>
									<td align="center" valign="top"/>
									<td align="center" valign="top"/>
									<td align="center" valign="top">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">Isquêmica</td>
									<td align="center" valign="top">41 (31,5)</td>
									<td align="center" valign="top">53 (41,4)</td>
									<td align="center" valign="top">59 (50,9)</td>
									<td align="center" valign="top"/>
								</tr>
								<tr>
									<td align="left" valign="top">Chagásica</td>
									<td align="center" valign="top">5 (3,8)</td>
									<td align="center" valign="top">9 (7,0)</td>
									<td align="center" valign="top">15 (12,9)</td>
									<td align="center" valign="top"/>
								</tr>
								<tr>
									<td align="left" valign="top">Outra</td>
									<td align="center" valign="top">84 (64,6)</td>
									<td align="center" valign="top">66 (51,6)</td>
									<td align="center" valign="top">42 (36,2)</td>
									<td align="center" valign="top"/>
								</tr>
								<tr>
									<td align="left" valign="top">Fraçao de ejeção, %</td>
									<td align="center" valign="top">33,4 ± 8,5</td>
									<td align="center" valign="top">33,1 ± 8,9</td>
									<td align="center" valign="top">32,1 ± 7,9</td>
									<td align="center" valign="top">&lt; 0,42</td>
								</tr>
								<tr>
									<td align="left" valign="top">PAS, mmHg</td>
									<td align="center" valign="top">112,1 ± 19,4</td>
									<td align="center" valign="top">109,1 ± 18,8</td>
									<td align="center" valign="top">110,2 ± 21,3</td>
									<td align="center" valign="top">0,48</td>
								</tr>
								<tr>
									<td align="left" valign="top">Frequência cardíaca, bpm</td>
									<td align="center" valign="top">73,4 ± 13,7</td>
									<td align="center" valign="top">71,9 ± 14,1</td>
									<td align="center" valign="top">71,5 ± 13,1</td>
									<td align="center" valign="top">0,53</td>
								</tr>
								<tr>
									<td align="left" valign="top">Classe funcional da NYHA 3 ou 4, n(%)</td>
									<td align="center" valign="top">35 (26,9)</td>
									<td align="center" valign="top">34 (26,6)</td>
									<td align="center" valign="top">32 (27,6)</td>
									<td align="center" valign="top">0,98</td>
								</tr>
								<tr>
									<td align="left" valign="top">Hipertensão (%)</td>
									<td align="center" valign="top">77 (59,2)</td>
									<td align="center" valign="top">87 (67,9)</td>
									<td align="center" valign="top">88 (75,9)</td>
									<td align="center" valign="top">0,005</td>
								</tr>
								<tr>
									<td align="left" valign="top">Diabetes (%)</td>
									<td align="center" valign="top">31(23,8)</td>
									<td align="center" valign="top">37 (28,9)</td>
									<td align="center" valign="top">47 (40,5)</td>
									<td align="center" valign="top">0,005</td>
								</tr>
								<tr>
									<td align="left" valign="top">Doença arterial coronariana (%)</td>
									<td align="center" valign="top">48 (36,9)</td>
									<td align="center" valign="top">62 (48,4)</td>
									<td align="center" valign="top">71 (61,2)</td>
									<td align="center" valign="top">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">Doença renal crônica</td>
									<td align="center" valign="top">9 (6,9)</td>
									<td align="center" valign="top">16 (12,5)</td>
									<td align="center" valign="top">27 (23,3)</td>
									<td align="center" valign="top">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">DPOC</td>
									<td align="center" valign="top">3 (2,3)</td>
									<td align="center" valign="top">8 (6,2)</td>
									<td align="center" valign="top">10 (8,6)</td>
									<td align="center" valign="top">0,031</td>
								</tr>
								<tr>
									<td align="left" valign="top">2 ou mais comorbidades</td>
									<td align="center" valign="top">49 (37,7)</td>
									<td align="center" valign="top">67 (52,3)</td>
									<td align="center" valign="top">80 (68,9)</td>
									<td align="center" valign="top">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="left" colspan="5" valign="top"><bold>Dose-alvo de acordo com GDMT, n(%)</bold></td>
								</tr>
								<tr>
									<td align="left" valign="top">IECA/BRA ou INRA</td>
									<td align="center" valign="top">71(54,6)</td>
									<td align="center" valign="top">60 (46,9)</td>
									<td align="center" valign="top">45(38,3)</td>
									<td align="center" valign="top">0,013</td>
								</tr>
								<tr>
									<td align="left" valign="top">BB</td>
									<td align="center" valign="top">80 (61,5)</td>
									<td align="center" valign="top">71 (55,5)</td>
									<td align="center" valign="top">57 (49,6)</td>
									<td align="center" valign="top">0,06</td>
								</tr>
								<tr>
									<td align="left" valign="top">ARM<xref ref-type="table-fn" rid="TFN7">*</xref>
									</td>
									<td align="center" valign="top">64 (80,0)</td>
									<td align="center" valign="top">63 (80,8)</td>
									<td align="center" valign="top">69 (75,0)</td>
									<td align="center" valign="top">0,44</td>
								</tr>
								<tr>
									<td align="left" valign="top">GDMT</td>
									<td align="center" valign="top">43 (33,1)</td>
									<td align="center" valign="top">31 (24,2)</td>
									<td align="center" valign="top">17 (14,7)</td>
									<td align="center" valign="top">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">Escore MAGGIC, pontos</td>
									<td align="center" valign="top">12,9 ± 5,5</td>
									<td align="center" valign="top">17,3 ± 6</td>
									<td align="center" valign="top">24,2 ± 5,3</td>
									<td align="center" valign="top">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">TFGE, mL/min per 1,73 m<sup>2</sup><xref ref-type="table-fn" rid="TFN8">**</xref>
									</td>
									<td align="center" valign="top">81,7±25,3</td>
									<td align="center" valign="top">72,6±21,2</td>
									<td align="center" valign="top">54,9±24,4</td>
									<td align="center" valign="top">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">Início de HF, anos</td>
									<td align="center" valign="top">3,3 [1,4-6,0]</td>
									<td align="center" valign="top">4,2 [1,4-7,7]</td>
									<td align="center" valign="top">2,6 [1,2-6,0]</td>
									<td align="center" valign="top">0,35</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN6">
								<p>ARM: antagonista do receptor mineralocorticoide; BB: betabloqueador; BRA: bloqueador do receptor da angiotensina; Cr: creatinina; DPOC: doença pulmonar obstrutiva crônica; GDMT: tratamento médico direcionado por diretrizes; IC: insuficiência cardíaca; IECA: inibidor da enzima conversora da angiotensina; IMC: índice de massa corporal; INRA: inibidores da neprilisina e antagonistas do receptor de angiotensina II; NYHA: New York Heart Association; PAS: pressão arterial sistólica; TFGE: taxa de filtração glomerular estimada.</p>
							</fn>
							<fn id="TFN7">
								<label>*</label>
								<p>Somente para pacientes sintomáticos.</p>
							</fn>
							<fn id="TFN8">
								<label>**</label>
								<p>TFGE foi estimado pela fórmula CKD-EPI.30</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</sec>
				<sec>
					<title>Idade e terapia médica orientada por diretrizes</title>
					<p>Era menos provável que os pacientes mais velhos recebessem a terapia médica ideal de acordo com a GDMT. Para cada aumento de década de idade, a probabilidade de receber terapia médica ideal reduzia significativamente (OR 0,67 [intervalo de confiança de 95% 0,56 – 0,82], <xref ref-type="table" rid="t5">Tabela 2</xref>). Essa associação permaneceu significativa após a análise de possíveis fatores de confusão, como sexo, IMC, etiologia da IC, FEVE, PAS, frequência cardíaca, classe funcional da NYHA III/IV, escore MAGGIC e níveis sanguíneos de creatinina (OR 0,66 [intervalo de confiança de 95% 0,48 – 0,92], <xref ref-type="table" rid="t5">Tabela 2</xref>, <xref ref-type="fig" rid="f3">Figuras 1</xref> e <xref ref-type="fig" rid="f4">2</xref>). Não houve interação entre a associação de idade e GDMT e sexo (p para interação = 0,51).</p>
					<table-wrap id="t5">
						<label>Tabela 2</label>
						<caption>
							<title>Associação entre idade e terapia médica orientada por diretrizes após ajustar para potenciais fatores de confusão</title>
						</caption>
						<table frame="hsides" rules="rows">
							<colgroup width="20%">
								<col/>
								<col/>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead style="border-top: thin solid; border-bottom: thin solid; border-color: #000000">
								<tr>
									<th align="left" valign="middle"/>
									<th align="center" valign="middle">N</th>
									<th align="center" valign="middle">OR</th>
									<th align="center" valign="middle">IC de 95%</th>
									<th align="center" valign="middle">Valor p</th>
								</tr>
							</thead>
							<tbody style="border-bottom: thin solid; border-color: #000000">
								<tr>
									<td align="left" valign="top"><bold>Idade, para cada aumento de 10 anos</bold></td>
									<td align="center" valign="top"/>
									<td align="center" valign="top"/>
									<td align="center" valign="top"/>
									<td align="center" valign="top"/>
								</tr>
								<tr>
									<td align="left" valign="top">Não ajustado</td>
									<td align="center" valign="top">374</td>
									<td align="center" valign="top">0,67</td>
									<td align="center" valign="top">(0,56-0,82)</td>
									<td align="center" valign="top">&lt;0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">Ajustado para modelo 1</td>
									<td align="center" valign="top">358</td>
									<td align="center" valign="top">0,71</td>
									<td align="center" valign="top">(0,57-0,88)</td>
									<td align="center" valign="top">0,02</td>
								</tr>
								<tr>
									<td align="left" valign="top">Ajustado para modelo 2</td>
									<td align="center" valign="top">334</td>
									<td align="center" valign="top">0,66</td>
									<td align="center" valign="top">(0,48-0,92)</td>
									<td align="center" valign="top">0,013</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN9">
								<p>Modelo 1: Ajustado para sexo, índice de massa corporal, etiologia da insuficiência cardíaca, fração de ejeção do ventrículo esquerdo, pressão arterial sistólica, frequência cardíaca, classe funcional da NYHA III/IV e níveis sanguíneos de creatinina. Modelo 2: Ajustado para Modelo 1 + escore MAGGIC. IC: intervalo de confiança; OR: odds ratio.</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
					<fig id="f3">
						<label>Figura 1</label>
						<caption>
							<title>Proporção de pacientes na dose-alvo de cada medicamento para insuficiência cardíaca conforme recomendado pelas diretrizes por tercil de idade. ARM: antagonista do receptor mineralocorticoide; BRA: bloqueador do receptor da angiotensina; GDMT: terapia médica orientada por diretrizes; IECA: inibidor da enzima conversora da angiotensina; INRA: inibidores da neprilisina e antagonistas do receptor de angiotensina II.</title>
						</caption>
						<graphic xlink:href="2764-3107-abchf-002-02-0138-gf01-pt.tif"/>
					</fig>
					<fig id="f4">
						<label>Figura 2</label>
						<caption>
							<title>Associação entre tratamento medicamentoso da insuficiência cardíaca de acordo com as diretrizes e idade após ajuste para potenciais fatores de confusão. GDMT: terapia médica orientada por diretrizes. *Ajustado para sexo, índice de massa corporal, etiologia da insuficiência cardíaca, fração de ejeção do ventrículo esquerdo, pressão arterial sistólica, frequência cardíaca, classe funcional da New York Heart Association III/IV, escore MAGGIC e níveis sanguíneos de creatinina.</title>
						</caption>
						<graphic xlink:href="2764-3107-abchf-002-02-0138-gf02-pt.tif"/>
					</fig>
				</sec>
				<sec>
					<title>Polifarmácia e comorbidades</title>
					<p>Os pacientes mais velhos eram mais propensos a fazerem o uso de bisoprolol em vez de carvedilol como betabloqueadores específicos para IC (<xref ref-type="table" rid="t6">Tabela 3</xref>). Também eram mais propensos a usar diuréticos de alça e estatinas (<xref ref-type="table" rid="t6">Tabela 3</xref>). O uso de IECA, BRA, ARM, digoxina, tiazida, hidralazina, nitrato, antiarrítmico, anticoagulante, aspirina e clopidogrel foram semelhantes entre os tercis de idade (<xref ref-type="table" rid="t6">Tabela 3</xref>). A proporção da associação entre idade e GDMT mediada pela polifarmácia foi de 0% (0% – 3,5%). Também analisamos o número de comorbidades (hipertensão, diabetes, doença arterial coronariana, doença renal crônica e DPOC) para avaliar se uma alta proporção de comorbidades poderia explicar a associação inversa entre idade e GDMT. Constatamos que o número de comorbidades mediava apenas 7% (0% – 41%) dessa associação.</p>
					<table-wrap id="t6">
						<label>Tabela 3</label>
						<caption>
							<title>Associação entre uso de medicamentos e faixa etária</title>
						</caption>
						<table frame="hsides" rules="rows">
							<colgroup width="20%">
								<col/>
								<col/>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead style="border-top: thin solid; border-bottom: thin solid; border-color: #000000">
								<tr>
									<th align="left" rowspan="2" valign="middle"/>
									<th align="center" valign="middle">Primeiro tercil</th>
									<th align="center" valign="middle">Segundo tercil</th>
									<th align="center" valign="middle">Terceiro tercil</th>
									<th align="center" rowspan="2" valign="middle">Valor p</th>
								</tr>
								<tr>
									<th align="center" valign="middle">23 a 57 anos n=130</th>
									<th align="center" valign="middle">58 a 67 anos n=128</th>
									<th align="center" valign="middle">68 a 89 anos n=116</th>
								</tr>
							</thead>
							<tbody style="border-bottom: thin solid; border-color: #000000">
								<tr>
									<td align="left" colspan="5" valign="top"><bold>Medicamentos modificadores da doença</bold></td>
								</tr>
								<tr>
									<td align="left" valign="top">Carvedilol, n(%)</td>
									<td align="center" valign="top">98 (75,4)</td>
									<td align="center" valign="top">86 (67,2)</td>
									<td align="center" valign="top">66 (56,9)</td>
									<td align="center" valign="top">0,002</td>
								</tr>
								<tr>
									<td align="left" valign="top">Succinato de metoprolol, n(%)</td>
									<td align="center" valign="top">11 (8,5)</td>
									<td align="center" valign="top">11 (8,6)</td>
									<td align="center" valign="top">12 (10,3)</td>
									<td align="center" valign="top">0,61</td>
								</tr>
								<tr>
									<td align="left" valign="top">Bisoprolol, n(%)</td>
									<td align="center" valign="top">18 (13,8)</td>
									<td align="center" valign="top">27 (21,1)</td>
									<td align="center" valign="top">35 (30,2)</td>
									<td align="center" valign="top">0,002</td>
								</tr>
								<tr>
									<td align="left" valign="top">IECA, n(%)</td>
									<td align="center" valign="top">67 (51,5)</td>
									<td align="center" valign="top">55 (43)</td>
									<td align="center" valign="top">48 (41,4)</td>
									<td align="center" valign="top">0,11</td>
								</tr>
								<tr>
									<td align="left" valign="top">BRA, n(%)</td>
									<td align="center" valign="top">34 (26,2)</td>
									<td align="center" valign="top">47 (36,7)</td>
									<td align="center" valign="top">30 (25,9)</td>
									<td align="center" valign="top">0,98</td>
								</tr>
								<tr>
									<td align="left" valign="top">INRA, n(%)</td>
									<td align="center" valign="top">24 (18,5)</td>
									<td align="center" valign="top">22 (17,2)</td>
									<td align="center" valign="top">27 (23,3)</td>
									<td align="center" valign="top">0,36</td>
								</tr>
								<tr>
									<td align="left" valign="top">ARM, n(%)</td>
									<td align="center" valign="top">103 (79,2)</td>
									<td align="center" valign="top">98 (76,6)</td>
									<td align="center" valign="top">88 (75,9)</td>
									<td align="center" valign="top">0,53</td>
								</tr>
								<tr>
									<td align="left" colspan="5" valign="top"><bold>Outros medicamentos relacionados à IC</bold></td>
								</tr>
								<tr>
									<td align="left" valign="top">Ivabradina, n(%)</td>
									<td align="center" valign="top">16 (12,3)</td>
									<td align="center" valign="top">3 (2,3)</td>
									<td align="center" valign="top">4 (3,4)</td>
									<td align="center" valign="top">0,003</td>
								</tr>
								<tr>
									<td align="left" valign="top">Digoxina, n(%)</td>
									<td align="center" valign="top">34 (26,2)</td>
									<td align="center" valign="top">24 (18,8)</td>
									<td align="center" valign="top">24 (20,7)</td>
									<td align="center" valign="top">0,29</td>
								</tr>
								<tr>
									<td align="left" valign="top">Diurético de alça, n(%)</td>
									<td align="center" valign="top">88 (67,7)</td>
									<td align="center" valign="top">88 (68,8)</td>
									<td align="center" valign="top">90 (77,6)</td>
									<td align="center" valign="top">0,09</td>
								</tr>
								<tr>
									<td align="left" valign="top">Tiazida, n(%)</td>
									<td align="center" valign="top">19 (14,6)</td>
									<td align="center" valign="top">15 (11,7)</td>
									<td align="center" valign="top">10 (8,6)</td>
									<td align="center" valign="top">0,15</td>
								</tr>
								<tr>
									<td align="left" valign="top">Hidralazina, n(%)</td>
									<td align="center" valign="top">32 (24,6)</td>
									<td align="center" valign="top">29 (22,7)</td>
									<td align="center" valign="top">21 (18,1)</td>
									<td align="center" valign="top">0,22</td>
								</tr>
								<tr>
									<td align="left" valign="top">Nitrato, n(%)</td>
									<td align="center" valign="top">31 (23,8)</td>
									<td align="center" valign="top">25 (19,5)</td>
									<td align="center" valign="top">23 (19,8)</td>
									<td align="center" valign="top">0,43</td>
								</tr>
								<tr>
									<td align="left" colspan="5" valign="top"><bold>Outros medicamentos</bold></td>
								</tr>
								<tr>
									<td align="left" valign="top">Estatina, n(%)</td>
									<td align="center" valign="top">75 (57,7)</td>
									<td align="center" valign="top">93 (72,7)</td>
									<td align="center" valign="top">90 (77,6)</td>
									<td align="center" valign="top">&lt;0,001</td>
								</tr>
								<tr>
									<td align="left" valign="top">Antiarrítmico, n(%)</td>
									<td align="center" valign="top">14 (10,8)</td>
									<td align="center" valign="top">5 (3,9)</td>
									<td align="center" valign="top">9 (7,8)</td>
									<td align="center" valign="top">0,34</td>
								</tr>
								<tr>
									<td align="left" valign="top">Anticoagulante, n(%)</td>
									<td align="center" valign="top">31 (23,8)</td>
									<td align="center" valign="top">27 (21,1)</td>
									<td align="center" valign="top">37 (31,9)</td>
									<td align="center" valign="top">0,16</td>
								</tr>
								<tr>
									<td align="left" valign="top">Aspirina, n(%)</td>
									<td align="center" valign="top">63 (48,5)</td>
									<td align="center" valign="top">79 (61,7)</td>
									<td align="center" valign="top">66 (56,9)</td>
									<td align="center" valign="top">0,17</td>
								</tr>
								<tr>
									<td align="left" valign="top">Clopidogrel, n(%)</td>
									<td align="center" valign="top">10 (7,7)</td>
									<td align="center" valign="top">8 (6,2)</td>
									<td align="center" valign="top">14 (12,1)</td>
									<td align="center" valign="top">0,24</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN10">
								<p>ARM: antagonista do receptor mineralocorticoide; BRA: bloqueador do receptor da angiotensina; IECA: inibidor da enzima conversora da angiotensina; IC: insuficiência cardíaca; INRA: inibidores da neprilisina e antagonistas do receptor de angiotensina II.</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</sec>
			</sec>
			<sec sec-type="discussion">
				<title>Discussão</title>
				<p>No presente estudo de pacientes com IC crônica em um país de renda média, constatamos que a terapia medicamentosa ideal para IC foi significativamente menor em pacientes idosos, em comparação com os mais jovens. Essa associação inversa entre idade e terapia medicamentosa ideal foi independente da frequência cardíaca, PAS e gravidade da doença. Além disso, isso não parece ser explicado pela polifarmácia nem pelo número de comorbidades, indicando complexidade do tratamento entre os idosos. É importante ressaltar que a proporção de pacientes em GDMT foi baixa em todas as idades. Nossos resultados indicam que há muito espaço para melhorar a terapia da IC na prática clínica, o que tem potencial para melhorar a sobrevida desses pacientes.</p>
				<p>Estudos anteriores sugeriram que a idade mais avançada pode estar relacionada a menores taxas de casos que atingem as doses-alvo de medicamentos relacionados à IC.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> Em um estudo do Japão, foi demonstrado que as taxas de prescrição de acordo com a GDMT foram significativamente menores em pacientes com 80 ou mais anos de idade.<sup><xref ref-type="bibr" rid="B14">14</xref></sup> Outra pesquisa de 36 países ao redor do mundo constatou uma associação inversa entre a idade e a probabilidade de receber betabloqueadores na dose-alvo em pacientes com IC com fração de ejeção reduzida.<sup><xref ref-type="bibr" rid="B15">15</xref>,<xref ref-type="bibr" rid="B20">20</xref></sup> Também verificou que as taxas de uso de IECA e betabloqueadores na dose-alvo foram bastante baixos (28% e 15%, respectivamente) e a adesão às diretrizes variou em diferentes regiões do mundo, o que pode ser resultado de diferentes aspectos culturais e econômicos.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> Embora tenham incluído 5 continentes, os países da América do Sul foram sub-representados nessa pesquisa. Nosso estudo descreve as taxas de prescrições de medicamentos baseadas em diretrizes para IC em uma cidade brasileira, acrescentando que pacientes mais velhos também eram menos propensos a receber a terapia médica ideal.<sup><xref ref-type="bibr" rid="B20">20</xref></sup> Por outro lado, constatamos taxas mais altas de IECA e betabloqueadores na dose-alvo do que esses relatos anteriores, provavelmente refletindo pacientes atendidos em um centro de referência para IC, com acesso gratuito a medicamentos.</p>
				<p>Os motivos para menor prescrição de acordo com as diretrizes em pacientes idosos são multifatoriais. Tem sido sugerido que os pacientes idosos são propensos à hipotensão e bradicardia e estes foram motivos importantes para a não prescrição de medicamentos recomendados pelas diretrizes na pesquisa QUALIFY.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> No entanto, não constatamos diferenças significativas na PAS e na frequência cardíaca entre os tercis de idade em nosso estudo e as taxas de GDMT permaneceram menores entre os idosos após o ajuste para esses parâmetros.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> Portanto, pode haver outros fatores, por exemplo, a preocupação com os efeitos adversos e a inércia do tratamento, que ajudam a explicar a menor otimização do tratamento nessa população. Existe um “paradoxo risco-tratamento” bem documentado na IC, onde pacientes com maior risco de mortalidade tendem a receber menos prescrição de GDMT.<sup><xref ref-type="bibr" rid="B12">12</xref>,<xref ref-type="bibr" rid="B21">21</xref></sup> Pacientes idosos geralmente são mais complexos, com doença mais grave e comorbidades, apresentando maior risco de mortalidade. Essa complexidade resulta em condições mais instáveis e mais difíceis de manejar e os médicos podem se sentir inseguros em otimizar os medicamentos relacionados à IC. Além disso, os pacientes idosos podem ser menos propensos a relatar dispneia de esforço e os médicos são menos propensos a otimizar o tratamento quando os pacientes se declaram assintomáticos. Além disso, os médicos podem preferir medicamentos que melhorem os sintomas, com menos efeitos adversos potenciais, em vez de prescrever medicamentos que melhorem a sobrevida em pacientes idosos.<sup><xref ref-type="bibr" rid="B20">20</xref></sup></p>
				<p>A complexidade do tratamento devido à maior prevalência de comorbidades entre pacientes idosos também pode ser uma barreira para o tratamento médico ideal.<sup><xref ref-type="bibr" rid="B16">16</xref></sup> O comprometimento cognitivo, que é prevalente em pacientes idosos com IC, tem sido relatado relacionado à baixa adesão à terapia medicamentosa.<sup><xref ref-type="bibr" rid="B22">22</xref></sup> Por outro lado, uma análise do estudo QUALIFY sugeriu que pacientes com IC e múltiplas comorbidades, como doença arterial coronariana, hipertensão, diabetes mellitus, doença vascular e acidente vascular cerebral/ataque isquêmico transitório, eram mais propensos a receber a dose-alvo de IECA, BRA e ARM, o que é esperado, pois essas classes de medicamentos também são indicadas para essas condições.<sup><xref ref-type="bibr" rid="B15">15</xref></sup> Isso sugere que a presença dessas comorbidades pode realmente contribuir para o tratamento medicamentoso ideal para IC. Apesar da evidência mista, nossos resultados sugerem que nem a polifarmácia nem o número de comorbidades foram responsáveis pela associação entre idade e tratamento medicamentoso ideal na IC.</p>
				<p>Embora sejam excluídos da maioria dos estudos, os pacientes idosos com IC provavelmente se beneficiam da GDMT.<sup><xref ref-type="bibr" rid="B23">23</xref>–<xref ref-type="bibr" rid="B25">25</xref></sup> Um estudo observacional mostrou que a GDMT foi associada à menor mortalidade em pacientes idosos com IC e essa associação foi consistente entre os pacientes com 80 anos, ou mais, de idade.<sup><xref ref-type="bibr" rid="B24">24</xref></sup> Nosso estudo destaca que há há muito espaço para melhorar a sobrevida de pacientes com IC na prática clínica, principalmente pacientes idosos. Esforços devem ser feitos para aumentar as taxas de GDMT na prática clínica, melhorando a formação médica e reduzindo a inércia médica. Por exemplo, uma estratégia chamada “começar baixo, ir devagar” para titulação dos medicamentos e fornecimento de reforços educacionais frequentes podem ajudar a atingir a dose alvo de medicamentos para IC em pacientes idosos.<sup><xref ref-type="bibr" rid="B25">25</xref>,<xref ref-type="bibr" rid="B26">26</xref></sup> Adicionalmente, políticas públicas podem ajudar a melhorar a comunicação e estabelecer metas para a GDMT em pacientes com IC. A divulgação de diretrizes de cardiologia e intervenções multidimensionais de melhoria de desempenho específicas da prática foram associadas a um aumento no uso da GDMT.<sup><xref ref-type="bibr" rid="B15">15</xref>,<xref ref-type="bibr" rid="B27">27</xref></sup> Uma intervenção multinível que aumenta o apoio social de familiares e profissionais de saúde e integra diferentes modelos de atenção, como atendimento domiciliar, telemedicina, atenção primária e clínicas de IC, podem ajudar os pacientes a lidar com a complexidade do tratamento e melhorar o tratamento médico.<sup><xref ref-type="bibr" rid="B20">20</xref>,<xref ref-type="bibr" rid="B25">25</xref>,<xref ref-type="bibr" rid="B28">28</xref>,<xref ref-type="bibr" rid="B29">29</xref></sup> Taxas melhores de GDMT ajudam a reduzir as hospitalizações, com impacto econômico significativo.</p>
				<p>Nosso estudo apresenta algumas limitações que merecem atenção. Trata-se de um estudo transversal, o que nos impede de estabelecer uma sequência temporal relacionando o envelhecimento dos pacientes e o uso de doses ótimas de medicamentos. Além disso, este desenho de estudo está sujeito ao viés de sobrevivência. Também não podemos excluir a possibilidade de que as diferenças e relações observadas se devam a outras variáveis de confusão que não foram mensuradas, por exemplo, renda e escolaridade. Além disso, trata-se de um estudo unicêntrico com pacientes do SUS e diagnóstico prévio de IC e FEVE abaixo de 50% podem não necessariamente refletir práticas de outros centros. As seguintes características específicas da nossa população estudada devem ser destacadas: cerca de 75% em classe funcional da NYHA I e II, o que pode refletir melhora dos sintomas após o tratamento; 20% dos pacientes usavam INRA, embora esse medicamento tivesse custos elevados e não fosse fornecido pelo governo no momento do estudo; quase um terço usou BRA, embora devam ser usados apenas em pacientes intolerantes a IECA. Por fim, o termo “polifarmácia” tem sido definido de maneiras diferentes na literatura, mais comumente como o uso de 5 ou mais medicamentos e nenhuma definição padrão tem sido estabelecida.<sup><xref ref-type="bibr" rid="B30">30</xref></sup></p>
			</sec>
			<sec sec-type="conclusions">
				<title>Conclusão</title>
				<p>No presente estudo de pacientes com IC wrificamos que, em geral, as taxas de terapia medicamentosa para IC de dose-alvo foram baixas. Essas taxas foram significativamente mais baixas nos pacientes idosos, quando comparadas aos mais jovens e isso não parece ser explicado pela polifarmácia ou pela maior presença de comorbidades entre os idosos.</p>
			</sec>
		</body>
		<back>
			<fn-group>
				<fn fn-type="supported-by" id="fn3">
					<p><bold>Fontes de financiamento</bold></p>
					<p>O presente estudo não teve fontes de financiamento externas.</p>
				</fn>
				<fn fn-type="other" id="fn4">
					<p><bold>Vinculação acadêmica</bold></p>
					<p>Não há vinculação deste estudo a programas de pós-graduação.</p>
				</fn>
			</fn-group>
		</back>
	</sub-article>
</article>