Coronary SyndromeNCT06561815
Body Composition Estimated by Bioelectrical Impedance Analysis in Patients with Acute Coronary Syndrome.
An observational study of Coronary Syndrome and Obesity, run by Hospital de Clinicas José de San Martín at 1 site in Argentina.
Trial summary
This study addresses the critical issue of obesity and its impact on patients with acute coronary syndrome (ACS). While obesity is a known risk factor for cardiovascular diseases, emerging evidence suggests that obese patients with coronary artery disease may have better survival outcomes-a phenomenon known as the… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Coronary Syndrome, Obesity
- How many people
- 100 participants
- Where
- 1 siteArgentina
- Study dates
- 11 Feb 2023to 30 Nov 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: All patients hospitalized in the Cardiovascular Care Unit with a diagnosis of acute coronary syndrome.
Inclusion Criteria: All patients hospitalized in the Cardiovascular Care Unit with a diagnosis of acute coronary syndrome.
Exclusion Criteria: Decline to participate.
Trial locations and contacts
1 site in 1 country.
Argentina1 site, 1 recruiting
- Recruiting
Hospital de Clínicas "José de San Martín"
Buenos Aires, Buenos Aires, C1120AAG
Diego Costa, MD, MSc, +54 9 1161984472, diegosta@gmail.com
Ricardo Pérez de la Hoz, MD, MSc, PhD, +54 9 11 44193525, rphoz2010@gmail.com
About this study
This study addresses the critical issue of obesity and its impact on patients with acute coronary syndrome (ACS). While obesity is a known risk factor for cardiovascular diseases, emerging evidence suggests that obese patients with coronary artery disease may have better survival outcomes-a phenomenon known as the \"obesity paradox.\"
Our research aims to explore this paradox by examining the body composition of ACS patients using bioimpedance analysis (BIA). We will evaluate parameters such as lean mass, body fat, and fluid volume to assess their relationship with clinical outcomes, including mortality and the incidence of heart and kidney failure.
By focusing on body composition rather than just BMI, this study seeks to provide a more accurate understanding of how these factors influence patient outcomes. Conducted at a major hospital in Argentina, the study will contribute valuable insights into the role of body composition in the prognosis of ACS, potentially informing more personalized treatment strategies.
Read the detailed description
- Introduction It is indisputable that obesity is a global epidemic. According to the latest data from the World Health Organization (WHO), the prevalence of obesity has tripled since 1975. In 2016, there were 650 million obese people worldwide, representing 13% of those over 18 years of age. Its definition is simple: a body mass index greater than 30. Moreover, it is a preventable condition. In developing countries, its prevalence is steadily increasing. According to data from the Permanent Household Survey conducted in Argentina, the prevalence of obesity in 2013 was 20.8%; this represented an increase of 15.6% compared to the 2009 edition (prevalence of 18.0%) and 42.5% compared to the 2005 edition (prevalence of 14.6%). The obesity indicator was higher among men (22.9%) than among women (18.8%) and higher among older people (with a maximum of 29.6% in the 50 to 64 age group) compared to younger individuals (7.7%). The health importance of obesity is mainly due to its relationship with cardiovascular disease. In a classic study analyzing 457,785 men and 588,369 women published in 1999, it was found that the total mortality risk due to obesity was 2.58 and 2.0 times higher, respectively. The relative risk of cardiovascular death in obese men was 2.9, with a 95% confidence interval (CI95%) of 2.37 to 3.56. Obesity increases the metabolism of free fatty acids, reduces insulin sensitivity, increases sympathetic activity, promotes inflammation, and causes a state of higher coagulability: all of these factors can contribute to the development of coronary disease. Despite the well-known predisposition to cardiovascular disease caused by obesity, once established, its relationship with body weight is more complex. In 2002, a study analyzing the impact of obesity in patients undergoing percutaneous coronary intervention (PCI) was published. Gruber et al. analyzed data from 9,633 patients and observed that obese individuals were generally younger but had more cardiovascular risk factors (hypertension, diabetes, hypercholesterolemia, and smoking). Although angiographic success rates were similar in all groups, the presence of normal BMI was associated with higher cardiovascular mortality. For long-term death, BMI presented in multivariable analysis an odds ratio (OR) of 0.94, with a CI95% of 0.94 - 0.98, showing to be a protective factor. This effect was named the "obesity paradox," which could be formulated as follows: obese individuals have a higher risk of coronary disease, but obese coronary patients have a lower risk of mortality. This was confirmed in a more recent study in patients with ST-elevation acute coronary syndrome (STEMI), where after analyzing data from 50,149 patients, hospital mortality rates of 7.7% were observed for individuals with normal weight and 4.3%, 4.4%, and 6.1% for subjects with class I, II, and III obesity, respectively. The first explanations for the obesity paradox revolved around a higher risk of bleeding due to increased anticoagulation and the possible presence of other non-cardiovascular diseases in low-weight patients. Later, attention was given to adiponectin, a mediator predominantly produced by adipose tissue, although it can also be synthesized in cardiac myocytes in response to angiotensin II. In rodents, adiponectin increases fatty acid oxidation in muscle, improving insulin sensitivity. Despite being produced in adipocytes, blood levels of adiponectin are inversely proportional to BMI, and hypoadiponectinemia in obese individuals is related to insulin resistance and higher levels of plasma C-reactive protein. Adiponectin levels below 4.0 mg/dl can double the risk of coronary disease. Beyond possible explanations for the obesity paradox, it has been found that BMI does not always reflect fat mass, and body composition may not be represented by this simple index. In a study where both BMI and waist circumference were measured in patients with ACS, a trend was found towards a higher number of events in patients with lower BMI but higher waist circumference. In another study, waist circumference showed a higher correlation with myocardial infarction size than BMI. There is limited evidence on the role of body composition in patients with ACS. In a retrospective study in China, the Clínica Universidad de Navarra formula was used to estimate body fat (BF) in patients with impaired renal function, where sex is considered 0 in men and 1 in women: BF = -44.988 + (0.503 × age) + (10.689 × sex) + (3.172 × BMI) - (0.026 × BMI^2) + (0.181 × BMI × sex) - (0.02 × BMI × age) - (0.005 × BMI^2 × sex) + (0.00021 × BMI^2× age). They found that in multivariable analysis, increased body fat and decreased lean mass were associated with a higher risk of death. They also found higher mortality in patients with higher BMI, but without a significant increase with intermediate BMI values. Another study, conducted in the United States, analyzed fat mass based on estimation by body folds in 570 patients with stable coronary disease and found that both reduced fat mass and lean mass were associated with higher mortality. Adiposity and lean mass can be evaluated non-invasively, cheaply, and quickly using bioimpedance analysis (BIA). Total body composition estimation through total body bioimpedance can be carried out using the equation V = ρ x S^2/R, where V is the conduction volume representing total body water or fat-free mass, ρ is the specific resistivity of the conductor, S is height, and R is total body resistance, measured with four surface electrodes placed on one wrist and one ankle. In Latin America, at least two BIA validation studies have been conducted to estimate body composition. In the study by Fjeld et al. in Peru, the correlation coefficient for cross-validation of formulas for calculating body composition was 0.96. In Argentina, in 2008, Rodríguez et al. conducted a study aimed at comparing body composition estimated by two simple anthropometric methods, BIA, and dual-energy X-ray absorptiometry (DXA) and studying the correlations between them in a pediatric population. They found good correlation between simple anthropometric methods (waist circumference) and bioimpedance and DXA, but the results were not interchangeable, even between BIA and DXA. In cross-validation studies of formulas to estimate body composition from bioimpedance, high correlation coefficients and small standard errors of estimation were found, with acceptable precision. Due to this, it is considered that, despite their limitations, they can be extrapolated to different populations. However, it is important to note that although the estimates are validated and highly accurate for evaluating populations, they are less reliable for determining body composition in individual subjects. Very few studies have measured body composition using BIA in patients with stable coronary disease. Puri et al. estimated the percentage of BF using this method in 477 individuals divided into three groups: normal, with established coronary disease, and at high risk of coronary disease. BF was higher in the at-risk group for coronary disease (30.7%), compared to 25.4% in patients with established coronary disease and 23.9% in controls. However, most strikingly, despite a high correlation between BF percentage and BMI (r = 0.8), 34% of men and 44% of women with normal BMI had increased BF. This suggests that BMI significantly underestimates obesity in certain patients. In a cross-sectional study conducted on 161 patients undergoing coronary angiography for stable coronary disease, characteristics were compared between those with coronary lesions and those without. BMI and body composition estimated by BIA were taken into account. There were no differences in total weight and BMI, but BF was higher among those with coronary lesions, while lean mass was higher among those without lesions. Only one study has used BIA in patients with ACS. It was found that visceral fat is a better risk indicator for the no-reflow phenomenon after PCI in STEMI patients compared to BMI and total BF. Given the limited experience with BIA in patients with ACS and the effects that body composition may have on clinical outcomes and as a confounding factor with BMI, this study proposes to investigate this topic in greater depth.
- Objectives 2.1 General To describe the body composition of patients with ACS estimated through BIA and evaluate its association with clinical outcomes. 2.2 Specific Describe the body composition of patients with ACS in terms of lean mass, body fat, and fluid volume estimated through BIA. Analyze the relationship between body composition parameters with age, sex, and BMI. Determine if there is an association between a higher percentage of body fat and mortality during hospitalization and at one year. Determine if there is an association between a lower percentage of lean mass and mortality during hospitalization and at one year. Determine if a lower percentage of body water is associated with a higher incidence of renal failure during hospitalization. Determine if a higher percentage of body water is associated with a higher incidence of heart failure during hospitalization.
- Hypotheses There is a difference in the percentage of body fat between those who die and those who survive during hospitalization and at one year. The percentage of lean mass is lower among those who die and those who survive during hospitalization and at one year. Patients with lower body water estimated by BIA have a higher risk of acute renal failure (ARF) during hospitalization. Patients with higher body water estimated by BIA have a higher risk of heart failure during hospitalization.
- Methodology 4.1 Study Design This is a prospective cohort study of patients with ACS. 4.2 Population The study population will include all adult patients with ACS. 4.3 Data Collection Patient data will be collected from the electronic medical record. BIA will be performed within the first 72 hours of admission. 4.4 Data Analysis Statistical analysis will be conducted using R software. Categorical variables will be compared using the chi-square test or Fisher\'s exact test as appropriate. Continuous variables will be analyzed using t-tests or Mann-Whitney U tests for independent samples, depending on their distribution. Multivariable analysis will be conducted using logistic regression.
- Ethical Considerations The study protocol will be submitted to the Research Ethics Committee of the Hospital for approval before implementation. All participants will sign an informed consent form before inclusion in the study.
- Conditions
- Coronary Syndrome, Obesity
- Keywords
- Obesity Paradox, Acute Coronary Syndrome, Body Composition
- Medical subject headings
- Angina, Unstable, Obesity, Acute Coronary Syndrome
What is being measured
The primary outcome is the main question the study is designed to answer.
cardiovascular death
Death attributed to cardiovascular conditions.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
11 Feb 202330 Nov 2025
- First posted20 Aug 2024
- Study start11 Feb 2023
- Primary completion30 Aug 2025 (estimated)
- Study completion30 Nov 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Coronary SyndromeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hospital de Clinicas José de San Martín
- Sponsor type
- University, hospital or other
- Collaborators
- University of Buenos Aires
- Responsible party
- Principal investigator, Diego Costa, Hospital de Clinicas José de San Martín
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06561815
- Sponsor's protocol ID
- EX-2021-02228037-UBA-DMEA#FMED
- Status last verified
- Aug 2024
- First submitted
- 13 Aug 2024
- Last updated
- 20 Aug 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Body Composition Estimated by Bioelectrical Impedance Analysis in Patients with Acute Coronary Syndrome.
- ClinicalTrials.gov ID
- NCT06561815
- Acronym / sponsor ID
- EX-2021-02228037-UBA-DMEA#FMED
- Conditions
- Coronary Syndrome, Obesity
- Keywords
- Obesity Paradox, Acute Coronary Syndrome, Body Composition
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Hospital de Clinicas José de San MartínUniversity, hospital or other
- Collaborators
- University of Buenos Aires
- Study start
- 11 Feb 2023 (actual)
- Primary completion
- 30 Aug 2025 (estimated)
- Study completion
- 30 Nov 2025 (estimated)
- First posted
- 20 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study addresses the critical issue of obesity and its impact on patients with acute coronary syndrome (ACS). While obesity is a known risk factor for cardiovascular diseases, emerging evidence suggests that obese patients with coronary artery disease may have better survival outcomes-a phenomenon known as the \"obesity paradox.\"
Our research aims to explore this paradox by examining the body composition of ACS patients using bioimpedance analysis (BIA). We will evaluate parameters such as lean mass, body fat, and fluid volume to assess their relationship with clinical outcomes, including mortality and the incidence of heart and kidney failure.
By focusing on body composition rather than just BMI, this study seeks to provide a more accurate understanding of how these factors influence patient outcomes. Conducted at a major hospital in Argentina, the study will contribute valuable insights into the role of body composition in the prognosis of ACS, potentially informing more personalized treatment strategies.
Detailed description
- Introduction It is indisputable that obesity is a global epidemic. According to the latest data from the World Health Organization (WHO), the prevalence of obesity has tripled since 1975. In 2016, there were 650 million obese people worldwide, representing 13% of those over 18 years of age. Its definition is simple: a body mass index greater than 30. Moreover, it is a preventable condition. In developing countries, its prevalence is steadily increasing. According to data from the Permanent Household Survey conducted in Argentina, the prevalence of obesity in 2013 was 20.8%; this represented an increase of 15.6% compared to the 2009 edition (prevalence of 18.0%) and 42.5% compared to the 2005 edition (prevalence of 14.6%). The obesity indicator was higher among men (22.9%) than among women (18.8%) and higher among older people (with a maximum of 29.6% in the 50 to 64 age group) compared to younger individuals (7.7%). The health importance of obesity is mainly due to its relationship with cardiovascular disease. In a classic study analyzing 457,785 men and 588,369 women published in 1999, it was found that the total mortality risk due to obesity was 2.58 and 2.0 times higher, respectively. The relative risk of cardiovascular death in obese men was 2.9, with a 95% confidence interval (CI95%) of 2.37 to 3.56. Obesity increases the metabolism of free fatty acids, reduces insulin sensitivity, increases sympathetic activity, promotes inflammation, and causes a state of higher coagulability: all of these factors can contribute to the development of coronary disease. Despite the well-known predisposition to cardiovascular disease caused by obesity, once established, its relationship with body weight is more complex. In 2002, a study analyzing the impact of obesity in patients undergoing percutaneous coronary intervention (PCI) was published. Gruber et al. analyzed data from 9,633 patients and observed that obese individuals were generally younger but had more cardiovascular risk factors (hypertension, diabetes, hypercholesterolemia, and smoking). Although angiographic success rates were similar in all groups, the presence of normal BMI was associated with higher cardiovascular mortality. For long-term death, BMI presented in multivariable analysis an odds ratio (OR) of 0.94, with a CI95% of 0.94 - 0.98, showing to be a protective factor. This effect was named the "obesity paradox," which could be formulated as follows: obese individuals have a higher risk of coronary disease, but obese coronary patients have a lower risk of mortality. This was confirmed in a more recent study in patients with ST-elevation acute coronary syndrome (STEMI), where after analyzing data from 50,149 patients, hospital mortality rates of 7.7% were observed for individuals with normal weight and 4.3%, 4.4%, and 6.1% for subjects with class I, II, and III obesity, respectively. The first explanations for the obesity paradox revolved around a higher risk of bleeding due to increased anticoagulation and the possible presence of other non-cardiovascular diseases in low-weight patients. Later, attention was given to adiponectin, a mediator predominantly produced by adipose tissue, although it can also be synthesized in cardiac myocytes in response to angiotensin II. In rodents, adiponectin increases fatty acid oxidation in muscle, improving insulin sensitivity. Despite being produced in adipocytes, blood levels of adiponectin are inversely proportional to BMI, and hypoadiponectinemia in obese individuals is related to insulin resistance and higher levels of plasma C-reactive protein. Adiponectin levels below 4.0 mg/dl can double the risk of coronary disease. Beyond possible explanations for the obesity paradox, it has been found that BMI does not always reflect fat mass, and body composition may not be represented by this simple index. In a study where both BMI and waist circumference were measured in patients with ACS, a trend was found towards a higher number of events in patients with lower BMI but higher waist circumference. In another study, waist circumference showed a higher correlation with myocardial infarction size than BMI. There is limited evidence on the role of body composition in patients with ACS. In a retrospective study in China, the Clínica Universidad de Navarra formula was used to estimate body fat (BF) in patients with impaired renal function, where sex is considered 0 in men and 1 in women: BF = -44.988 + (0.503 × age) + (10.689 × sex) + (3.172 × BMI) - (0.026 × BMI^2) + (0.181 × BMI × sex) - (0.02 × BMI × age) - (0.005 × BMI^2 × sex) + (0.00021 × BMI^2× age). They found that in multivariable analysis, increased body fat and decreased lean mass were associated with a higher risk of death. They also found higher mortality in patients with higher BMI, but without a significant increase with intermediate BMI values. Another study, conducted in the United States, analyzed fat mass based on estimation by body folds in 570 patients with stable coronary disease and found that both reduced fat mass and lean mass were associated with higher mortality. Adiposity and lean mass can be evaluated non-invasively, cheaply, and quickly using bioimpedance analysis (BIA). Total body composition estimation through total body bioimpedance can be carried out using the equation V = ρ x S^2/R, where V is the conduction volume representing total body water or fat-free mass, ρ is the specific resistivity of the conductor, S is height, and R is total body resistance, measured with four surface electrodes placed on one wrist and one ankle. In Latin America, at least two BIA validation studies have been conducted to estimate body composition. In the study by Fjeld et al. in Peru, the correlation coefficient for cross-validation of formulas for calculating body composition was 0.96. In Argentina, in 2008, Rodríguez et al. conducted a study aimed at comparing body composition estimated by two simple anthropometric methods, BIA, and dual-energy X-ray absorptiometry (DXA) and studying the correlations between them in a pediatric population. They found good correlation between simple anthropometric methods (waist circumference) and bioimpedance and DXA, but the results were not interchangeable, even between BIA and DXA. In cross-validation studies of formulas to estimate body composition from bioimpedance, high correlation coefficients and small standard errors of estimation were found, with acceptable precision. Due to this, it is considered that, despite their limitations, they can be extrapolated to different populations. However, it is important to note that although the estimates are validated and highly accurate for evaluating populations, they are less reliable for determining body composition in individual subjects. Very few studies have measured body composition using BIA in patients with stable coronary disease. Puri et al. estimated the percentage of BF using this method in 477 individuals divided into three groups: normal, with established coronary disease, and at high risk of coronary disease. BF was higher in the at-risk group for coronary disease (30.7%), compared to 25.4% in patients with established coronary disease and 23.9% in controls. However, most strikingly, despite a high correlation between BF percentage and BMI (r = 0.8), 34% of men and 44% of women with normal BMI had increased BF. This suggests that BMI significantly underestimates obesity in certain patients. In a cross-sectional study conducted on 161 patients undergoing coronary angiography for stable coronary disease, characteristics were compared between those with coronary lesions and those without. BMI and body composition estimated by BIA were taken into account. There were no differences in total weight and BMI, but BF was higher among those with coronary lesions, while lean mass was higher among those without lesions. Only one study has used BIA in patients with ACS. It was found that visceral fat is a better risk indicator for the no-reflow phenomenon after PCI in STEMI patients compared to BMI and total BF. Given the limited experience with BIA in patients with ACS and the effects that body composition may have on clinical outcomes and as a confounding factor with BMI, this study proposes to investigate this topic in greater depth.
- Objectives 2.1 General To describe the body composition of patients with ACS estimated through BIA and evaluate its association with clinical outcomes. 2.2 Specific Describe the body composition of patients with ACS in terms of lean mass, body fat, and fluid volume estimated through BIA. Analyze the relationship between body composition parameters with age, sex, and BMI. Determine if there is an association between a higher percentage of body fat and mortality during hospitalization and at one year. Determine if there is an association between a lower percentage of lean mass and mortality during hospitalization and at one year. Determine if a lower percentage of body water is associated with a higher incidence of renal failure during hospitalization. Determine if a higher percentage of body water is associated with a higher incidence of heart failure during hospitalization.
- Hypotheses There is a difference in the percentage of body fat between those who die and those who survive during hospitalization and at one year. The percentage of lean mass is lower among those who die and those who survive during hospitalization and at one year. Patients with lower body water estimated by BIA have a higher risk of acute renal failure (ARF) during hospitalization. Patients with higher body water estimated by BIA have a higher risk of heart failure during hospitalization.
- Methodology 4.1 Study Design This is a prospective cohort study of patients with ACS. 4.2 Population The study population will include all adult patients with ACS. 4.3 Data Collection Patient data will be collected from the electronic medical record. BIA will be performed within the first 72 hours of admission. 4.4 Data Analysis Statistical analysis will be conducted using R software. Categorical variables will be compared using the chi-square test or Fisher\'s exact test as appropriate. Continuous variables will be analyzed using t-tests or Mann-Whitney U tests for independent samples, depending on their distribution. Multivariable analysis will be conducted using logistic regression.
- Ethical Considerations The study protocol will be submitted to the Research Ethics Committee of the Hospital for approval before implementation. All participants will sign an informed consent form before inclusion in the study.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All patients hospitalized in the Cardiovascular Care Unit with a diagnosis of acute coronary syndrome.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
cardiovascular death
Time frame: 1 year
Death attributed to cardiovascular conditions.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Aug 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Coronary Syndrome and Obesity, run by Hospital de Clinicas José de San Martín at 1 site in Argentina.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 20 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Argentina. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Diego Costa, MD, MSc. (+54 9 1161984472, diegosta@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
cardiovascular death (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Aug 2024.