Abstract
Background
Cardiovascular diseases are the leading cause of death among patients on hemodialysis, with approximately 40% of the cardiovascular deaths linked to acute coronary syndrome. We aimed to investigate the incidence and risk factors of acute coronary syndrome in patients undergoing hemodialysis.
Methods
Patients undergoing hemodialysis were prospectively enrolled from January 2018. Data regarding hospitalization due to acute coronary syndrome were collected at 3-month intervals through December 31, 2021. Cox regression model was used to estimate the association between baseline factors and incident acute coronary syndrome during follow-up.
Results
Patients’ mean age was 66 years, 48% were men, and 16% had a history of coronary artery disease at enrolment. Over a median follow-up of 1,187 days, 85 patients were hospitalized due to acute coronary syndrome. Left main or triple vessel disease was identified in 67 patients. Risk factors associated with incident acute coronary syndrome included aging, male sex, smoking, low diastolic blood pressure, and baseline comorbidities, in addition to dialysis factors including low urea clearance, central venous catheter use, and history of dialysis access dysfunction. After multivariate analysis, age, diabetes, hyperlipidemia, smoking, and frequent interventions for vascular access remained significant risk factors.
Conclusions
A high acute coronary syndrome incidence was observed in our cohort, with traditional risk factors playing a consistent role with that in the general population. A history of frequent dialysis access dysfunction was also associated with incident acute coronary syndrome.
Keywords: Acute coronary syndrome, Central vein access, Hemodialysis, Vascular access dysfunction
Abbreviations
ACS, Acute coronary syndrome
AVF, Arteriovenous fistula
BMI, Body mass index
BP, Blood pressure
CAD, Coronary artery disease
CCS, Chronic coronary syndrome
CI, Confidence interval
ESRD, End stage renal disease
HR, Hazard ratio
IQR, Interquartile range
MI, Myocardial infarction
PAD, Peripheral artery disease
PTA, Percutaneous transluminal angioplasty
SD, Standard deviation
US, United State
INTRODUCTION
Cardiovascular diseases are the leading cause of death among patients on maintenance hemodialysis, and approximately 40% of the cardiovascular deaths are attributable to acute coronary syndrome (ACS).1 Despite the decline in myocardial infarction (MI) incidence in the general population,2 ACS incidence in patients undergoing hemodialysis remains remarkably high.3-5 Traditional coronary risk factors, such as diabetes mellitus, are prevalent in patients undergoing hemodialysis; however, they cannot fully explain the high incidence of ACS.6 In addition, the contribution of traditional risk factors may differ from that of the general population.7,8
Despite the growing number of patients on dialysis, there is a lack of prospective studies in this population describing the risk factors for ACS.9,10 Most available data describing risk factors for ACS are derived from cross-sectional studies or retrospective analyses of administrative databases, which are inherently prone to recall bias or reverse causality.3-5,11 The role of traditional risk factors is inconsistent across these studies.5,12 In addition, dialysis and vascular access-related factors are rarely addressed in published studies.13,14 Vascular access is a unique source of morbidity and mortality in patients on hemodialysis.14,15 Nonetheless, there is a lack of data concerning the contribution of dialysis or vascular access-related factors to ACS risk in patients on maintenance hemodialysis.
The Hsinchu V.A. study, is a prospective cohort study that investigated the impact of clinical factors on cardiovascular and vascular access events in patients undergoing hemodialysis. Detailed clinical, dialysis, and vascular access information was collected at baseline and updated regularly. Cardiovascular events, including incident chronic coronary syndrome (CCS) and ACS, were prospectively documented during the follow-up period. In this study, we performed a secondary analysis of the Hsinchu V.A. study data to explore the incidence and risk factors of ACS among patients on maintenance hemodialysis.
MATERIALS AND METHODS
Study design and participants
The prospective cohort Hsinchu V.A. study was initially designed to investigate the impact of blood pressure variability and frailty on dialysis access and cardiovascular events in patients receiving maintenance hemodialysis (ClinicalTrials.gov identifier: NCT04692636).16,17 In total, 1,136 patients receiving maintenance hemodialysis as of January 1, 2018, were recruited to the original cohort from 12 hemodialysis centres in Hsinchu district, Taiwan. The Hsinchu district has a population of one million and comprises one city and 13 townships. Four of the 12 hemodialysis centres were hospital-based, while eight were dialysis clinics. The inclusion criteria were: (1) age between 18 and 90 years, (2) maintenance dialysis for more than 6 months, and (3) no hospitalization in the preceding 3 months. This study is a retrospective analysis of data from the Hsinchu V.A. study and focused on the incidence and risk factors of ACS in patients on hemodialysis. Data on the history of vascular access dysfunction 1 year before enrolment were retrospectively collected from medical and dialysis records. This study was approved by the Institutional Review Board of the National Taiwan University Hospital, Hsinchu Branch, and was conducted per the Declaration of Helsinki and the Council for International Organizations of Medical Sciences International Ethical Guidelines. All participants provided written informed consent before enrolment in the study.
Data collection
We obtained information on demographics, comorbidities, medications, dialysis-related data, and laboratory data from the medical records of participating dialysis centres. These data were collected at baseline and updated every 3 months by trained study coordinators. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared; patients were divided into the following categories: underweight (< 18.5 kg/m2), normal weight (18.5-24 kg/m2), overweight (24-27 kg/m2), and obesity (> 27 kg/m2).16,17 Per the dialysis unit routine, pre-dialysis blood pressure (BP) was measured in a seated position using automated oscillometric devices. BP values were extracted every 90 days from the computerized database of dialysis centres. Comorbidities including coronary artery disease (CAD) (defined as a history of MI, positive non-invasive or invasive test results, or disease requiring interventions), stroke, diabetes, and history of vascular access dysfunction were collected from the patient’s dialysis records and ascertained by the patient’s primary nephrologist as needed. Dialysis factors, including hemodialysis duration, hemodialysis frequency, urea clearance, albumin, haemoglobin, calcium, phosphate, and vascular access type (fistula, graft, central vein catheter), were updated every 3 months.
Outcomes
Events and corresponding dates were recorded by the facility coordinators on interval summary questionnaires collected every 3 months for all-cause death, cardiovascular death (attributed to acute MI, atherosclerotic heart disease, cardiac arrest of unknown cause), all-cause hospitalization, hospitalization due to adverse cardiovascular events (including MI, stroke, amputation, intervention, or surgery for vascular diseases), and imaging studies for vascular diseases (vascular duplex ultrasound, computed tomography, magnetic resonance imaging, and angiography). The events were confirmed by principal investigators after a review of medical records and discussions with physicians of dialysis centres, as needed. Patients were censored at the time of death, kidney transplant, peritoneal dialysis, or transfer to a non-study centre. Hospitalization due to MI was the primary outcome of interest in this continuous analysis. ACS diagnosis was based on the principal discharge codes of ST-elevation MI (ICD9 codes 410) and non-ST elevation MI (ICD9 Code 411) on discharge notes. The diagnosis was reviewed by the principal investigator based on the presence of two of the three following criteria: prolonged (20 minutes) ischemic chest pain, the elevation of cardiac biomarkers (creatinine kinase-MB or relative index) more than two times the upper limit of normal, and electrocardiographic changes (ST/T-wave changes or new Q waves).20
Statistical analysis
The baseline characteristics of the study participants are presented as mean [standard deviation (SD)] for normally distributed continuous variables, median [interquartile range (IQR)] for non-normally distributed continuous variables, and proportions for categorical variables. MI events over time were plotted using the Kaplan-Meier method and compared using the log-rank test. For each approach, data were censored at death, kidney transplantation, transfer to peritoneal dialysis, or loss to follow-up. The risk factors for ACS events were evaluated using Cox proportional hazards regression models. The assumption of proportionality was confirmed graphically using a log-log plot and was deemed acceptable for the selected risk factors. Baseline factors in univariate analysis with a p value < 0.05 were included in a multivariate adjustment, including age groups, gender, diastolic blood pressure, smoking, history of diabetes mellitus, hyperlipidemia, coronary artery disease, peripheral arterial disease, stroke, access types, prior percutaneous transluminal angioplasty (PTA) frequency in one year, Kt/V, use of antiplatelet, β-blocker, and statin. Two sensitivity tests were performed to validate the association between access type and previous interventions for ACS incidence. The first group excluded patients who had changed access types during the follow-up period. The second excluded patients who used arteriovenous fistula (AVF) or arteriovenous graft for at least 1 year before enrolment. All p values were two-tailed, and p < 0.05 was considered statistically significant. All analyses were performed using SPSS version 20.0 for Windows (IBM Corp., Armonk, NY).
RESULTS
Baseline characteristics
We enrolled 1,136 participants in this study. Their mean age was 66 (SD, 14), and 592 (48%) were men. The median dialysis duration was 4 years (IQR, 0.7-5.3 years). As presented in Table 1, 54% of the participants had diabetes, 16% had CAD, 9% had cerebrovascular disease, and 9% had peripheral artery disease (PAD). The other baseline characteristics are presented in Table 1.
Table 1. Baseline characteristics of study participants.
| Characteristics | Average | %, SD, IQR |
| Number of patients | 1136 | |
| Age (yr), mean (SD) | 66 | 14 |
| Male gender, n (%) | 592 | 48% |
| Body mass index (kg/m2), mean (SD) | 22.9 | 4.2 |
| Dialysis duration (yr), median (IQR) | 4 | (0.7, 5.3) |
| Current smoking, n (%) | 164 | 14% |
| Diabetes mellitus, n (%) | 608 | 54% |
| Prior coronary artery disease, n (%) | 187 | 16% |
| Prior peripheral artery disease, n (%) | 99 | 9% |
| Prior stroke, n (%) | 100 | 9% |
IQR, interquartile range; SD, standard deviation.
Follow-up and events
Patients were followed-up until December 31, 2021. The median follow-up duration was 1,187 days (IQR, 865-1,460). During the follow-up period, 4 participants received kidney transplants, 3 were transferred to peritoneal dialysis, 139 were transferred to non-study facilities, and 288 patients died. The vascular access type was changed in 52 patients. Newly diagnosed CAD occurred in 212 participants (6.74/100 person-years), and MI occurred in 85 patients (2.70/100 person-years). The incident CCS and ACS events are displayed using Kaplan-Meier plots in Figure 1.
Figure 1.
Incidence of acute coronary syndrome (ACS) and chronic coronary syndrome (CCS) in the prevalent hemodialysis cohort.
Presentation and management
Among the patients with CAD, 114 were diagnosed with CCS using non-invasive tests or coronary angiography, and 85 patients presented with ACS. Regarding the involvement of coronary arteries, approximately 80% of the ACS group patients had left main CAD or triple vessel disease. Among the 85 patients with ACS, 43 underwent percutaneous coronary intervention, 21 underwent bypass surgery, and 19 received medical therapy.
Univariable analysis of factors associated with ACS
The demographic and socioeconomic factors included age > 75 years [hazard ratio (HR), 1.87; 95% confidence interval (CI) 1.15-3.05, p = 0.01], male sex (HR, 1.70; 95% CI, 1.09-2.65, p = 0.02), and current smoking (HR, 1.78; 95% CI, 1.08-2.95, p = 0.02). Among the comorbid diseases, low diastolic BP (HR, 0.83, 95% CI, 0.69-0.99, p = 0.04), diabetes (HR, 2.85, 95% CI, 1.74-4.67, p < 0.01), hyperlipidemia (HR, 2.88, 95% CI, 1.88-4.42, p < 0.01), CAD (HR, 2.71, 95% CI, 1.72-4.26, p < 0.01), PAD (HR, 2.62, 95% CI, 1.52-4.51, p < 0.01), and stroke (HR, 2.28, 95% CI, 1.29-4.05, p < 0.01), were associated with increased ACS risk. Among the dialysis-related factors, urea clearance (HR, 0.34, 95% CI, 0.15-0.76, p = 0.01), dialysis using a central venous catheter (vs. AVF, HR, 2.28, 95% CI, 1.25-4.18, p = 0.01), and ≥ 3 PTA in one year (vs. no PTA, HR, 2.31, 95% CI, 1.36-3.95, p < 0.01) were associated with ACS incidence. In the sensitivity test, after excluding 52 patients with changes in access types, dialysis using a central venous catheter remained a significant predictor of ACS (Table 2). In the sensitivity test, after excluding 349 patients without vascular access ≥ 1 year before enrolment, frequent interventions (PTA ≥ three times in 1 year) remained a significant predictor for ACS (shown in Figure 2).
Table 2. Univariate and multivariate Cox regression analysis of predictors for acute coronary syndrome.
| Factors | Unit of increase | Univariate analysis | Multivariate analysis | ||||||
| HR | 95% CI (Upper bond) | 95% CI (Lower bond) | p value | HR | 95% CI (Upper bond) | 95% CI (Lower bond) | p value | ||
| Demographics | |||||||||
| Age | 10 years | 0.99 | 0.85 | 1.15 | 0.89 | ||||
| < 65 | Reference | 1.00 | |||||||
| 65-75 | < 65 | 1.34 | 0.77 | 2.33 | 0.31 | 1.00 | 0.54 | 1.85 | 0.99 |
| > 75 | < 65 | 1.87 | 1.15 | 3.05 | 0.01 | 2.03 | 1.17 | 3.55 | 0.01 |
| Male gender | Female | 1.70 | 1.09 | 2.65 | 0.02 | 1.47 | 0.87 | 2.45 | 0.15 |
| BMI | 10 kg/m2 | 1.17 | 0.71 | 1.94 | 0.54 | ||||
| < 18.5 | 18.5-24 | 1.05 | 0.56 | 1.97 | 0.89 | ||||
| 18.5-24 | Reference | ||||||||
| 24-27 | 18.5-24 | 0.59 | 0.22 | 1.56 | 0.28 | ||||
| > 27 | 18.5-24 | 0.92 | 0.44 | 1.93 | 0.82 | ||||
| Systolic BP | 10 mmHg | 0.99 | 0.91 | 1.09 | 0.87 | ||||
| Diastolic BP | 10 mmHg | 0.83 | 0.69 | 0.99 | 0.04 | 1.08 | 0.87 | 1.34 | 0.48 |
| Smoking | No | 1.78 | 1.08 | 2.95 | 0.02 | 1.76 | 1.04 | 2.98 | 0.04 |
| Comorbid | |||||||||
| Diabetes mellitus | No | 2.85 | 1.74 | 4.67 | < 0.01 | 1.98 | 1.15 | 3.40 | 0.01 |
| Hyperlipidemia | No | 2.88 | 1.88 | 4.42 | < 0.01 | 1.78 | 1.07 | 2.93 | 0.02 |
| CAD | No | 2.71 | 1.72 | 4.26 | < 0.01 | 1.48 | 0.88 | 2.49 | 0.14 |
| PAD | No | 2.62 | 1.52 | 4.51 | < 0.01 | 1.34 | 0.73 | 2.46 | 0.34 |
| Stroke | No | 2.28 | 1.29 | 4.05 | < 0.01 | 1.39 | 0.73 | 2.63 | 0.32 |
| Dialysis factors | |||||||||
| HD duration | 1 year | 0.96 | 0.92 | 1.01 | 0.12 | ||||
| HD frequency < 3/wk | No | 1.65 | 0.52 | 5.23 | 0.39 | ||||
| Access types | |||||||||
| AVF | Reference | 1.00 | 1.00 | ||||||
| AVG | AVF | 1.62 | 0.94 | 2.80 | 0.08 | 1.46 | 0.84 | 2.55 | 0.18 |
| CVC | AVF | 2.28 | 1.25 | 4.18 | 0.01 | 1.90 | 0.88 | 4.09 | 0.10 |
| Prior PTA in 1 yr | |||||||||
| No | Reference | 1.00 | 1.00 | ||||||
| 1-2 | No | 1.83 | 1.08 | 3.13 | 0.03 | 1.93 | 1.12 | 3.31 | 0.02 |
| ≥ 3 | No | 2.31 | 1.36 | 3.95 | 0.002 | 2.20 | 1.27 | 3.81 | 0.005 |
| Biochemistry | |||||||||
| Hemoglobin | 1 g/dL | 1.12 | 0.96 | 1.30 | 0.17 | ||||
| Albumin | 1 g/dL | 1.11 | 2.14 | 0.57 | 0.77 | ||||
| Calcium | 1 mg/dL | 0.96 | 0.74 | 1.23 | 0.72 | ||||
| Phosphate | 1 mg/dL | 1.03 | 0.88 | 1.20 | 0.74 | ||||
| Kt/V (Daugirdes) | 1 | 0.34 | 0.15 | 0.76 | 0.01 | 0.63 | 0.24 | 1.70 | 0.36 |
| Medication | |||||||||
| Antiplatelet | No | 3.23 | 2.11 | 4.94 | < 0.01 | 1.61 | 0.94 | 2.77 | 0.08 |
| β-blocker | No | 2.38 | 1.51 | 3.73 | < 0.01 | 1.95 | 1.17 | 3.23 | 0.01 |
| RAS inhibitor | No | 1.10 | 0.64 | 1.90 | 0.72 | ||||
| Statin | No | 2.38 | 1.48 | 3.82 | < 0.01 | 1.17 | 0.69 | 1.99 | 0.56 |
AVF, arteriovenous fistula; AVG, arteriovenous graft; BMI, body mass index; BP, blood pressure; CAD, coronary artery disease; CI, confidence interval; CVC, central venous catheter; HD, hemodialysis; HR, hazard ratio; Kt/V, urea clearance; PAD, peripheral artery disease; PTA, percutaneous transluminal angioplasty; RAS, renin-angiotensin system.
Multivariate analysis were adjusted by age groups, gender, diastolic blood pressure, smoking, history of diabetes mellitus, hyperlipidemia, coronary artery disease, peripheral arterial disease, stroke, access types, prior PTA frequency in one year, Kt/V, use of antiplatelet, β-blocker, and statin.
Figure 2.
Association in sensitivity analysis (A) association of access types with risk of incident acute coronary syndrome, after excluding patients who changed access types, adjusted by age, smoking status, diabetes, coronary artery disease, peripheral artery disease, stroke, use of anti-platelets, beta-blockers, and statins (B) association of prior PTA in 1 year, after excluding patients who did not receive dialysis via AVF or AVG for at least 1 year, adjusted by age, smoking status, diabetes, coronary artery disease, peripheral artery disease, stroke, use of anti-platelets, beta-blockers, statins, and access type change. AVF, arteriovenous fistula; AVG, arteriovenous graft; CVC, central vein catheter; HR, hazard ratio. Results were presented by HR with 95% confidence intervals (CIs).
Multivariable analysis
In the multivariable Cox regression analysis, age > 75 years (HR, 2.03, 95% CI, 1.17-3.55, p = 0.01) diabetes mellitus (HR, 1.98, 95% CI, 1.15-3.40, p = 0.01), hyperlipidemia (HR, 1.78, 95% CI, 1.07-2.93, p = 0.02), smoking (HR, 1.76, 95% CI, 1.04-2.98, p = 0.04) and frequent PTA (≥ 3 times) for dialysis access in the previous year (HR, 2.20; 95% CI, 1.27-3.81; p = 0.005) remained significantly associated with increased ACS risk (Table 2). In the sensitivity test, after excluding 52 patients with changes in access types, dialysis using a central venous catheter remained a significant predictor of ACS after multivariate adjustment. In the sensitivity test, after excluding 349 patients without vascular access ≥ 1 year before enrolment, frequent interventions (PTA ≥ three times in 1 year) remained a significant predictor for ACS after multivariate adjustment (shown in Figure 2).
DISCUSSION
Main findings
This prospective study demonstrated an ACS incidence of 2.3/100 person-years in the Taiwanese hemodialysis population, which is much higher than the incidence rate of 0.05/100 person-years in the general population. A high proportion of hemodialysis patients presented with left main CAD or triple-vessel disease. Our analysis revealed that several conventional risk factors, including advanced age > 75 years, hyperlipidemia, diabetes mellitus, and smoking, were risk factors for ACS in patients undergoing hemodialysis. We also discovered that patients with a history of frequent dialysis vascular dysfunction was associated with an increased risk of ACS events.
ACS incidence
Few longitudinal studies have reported ACS incidence in the hemodialysis population, especially after preventive and therapeutic advancements. In a Taiwanese study based on a nationwide claims database, the ACS incidence was reported to be 1.78/100 person-years, higher than the general population’s 0.8/100 person-years.5 The HEMO study reported a 5.2/100 person-year incidence of MI in a United State (US) hemodialysis population.9 Substantial variations in cardiovascular event rates have been reported across geographic regions. For example, the dialysis outcomes and practice patterns study revealed the lowest cardiovascular event rates of 7.5/100 person-years in Japanese patients with PAD, compared to 19.4/100 person-years in North America and 17.4/100 person-years in Europe.10 The lower incidence of MI in this study compared to the US cohort may result from differences in ethnic background and diagnostic criteria.
Dialysis-related factors
Contrary to previous studies on chronic CAD, we did not identify calcium, phosphorous, or calcium-phosphorous products associated with ACS in this study. This is expected since the primary mechanism of ACS is plaque rupture, not vessel narrowing progression. Similar findings have been reported in other studies on ACS in patients with end stage renal disease (ESRD).3 Low creatinine clearance was associated with ACS in the univariate analysis. Low urea clearance is a measure of dialysis adequacy. It may be linked to ACS through uremic retention solutes or fluid overload, which are well-known risks in patients with ESRD.21 The interrelationships among nutrition, inflammation, and cardiovascular diseases have been addressed in many studies.22 Nonetheless, we did not find an association between serum albumin level and ACS. A more comprehensive evaluation of nutritional status and inflammatory markers might be needed to clarify the role of nutrition in ACS in patients undergoing hemodialysis.
Dialysis access-related factors
Previous studies have demonstrated that using grafts and catheters, compared to fistulas, is associated with risks of infection, hospitalization, and death.23-25 The association has been reported to be significant in various studies; however, it may be substantially biased by treatment selection. The characteristics of patients that drive the decision of access types may influence patient survival, such as advanced age, poor prognosis, and peripheral vascular diseases. The impact of access selection on the outcomes of patients undergoing hemodialysis remains unclear. The present study examined the relationship between access types and subsequent ACS events at a reasonable timing sequence. We demonstrated the use of permanent catheters, but not grafts, as a significant predictor of ACS. Previous studies have revealed that chronically colonized surface biofilms in synthetic access-related materials may increase the risk of cardiovascular disease in patients on dialysis.26 Data from the USRDS Wave 2 study also demonstrated that using catheters is a significant risk factor for septicaemia, increasing the cardiovascular disease risk.27 Recent studies have also reported that the use of catheters was associated with higher oxidative stress and inflammation parameters than AVF-bearing patients.28,29 The above evidence may explain why using a permanent catheter was associated with incident ACS.
The relationship between a history of vascular access dysfunction and cardiovascular events has been controversial in previous studies. Vascular access shares many risk factors with CAD; however, it is influenced by unique precipitating factors such as anatomical configuration, surgical expertise, cannulation, and haemostasis. A case-control study of 19,422 patients from an insurance database demonstrated that vascular access dysfunction is an early sign of cardiovascular events in patients undergoing hemodialysis.5 Nonetheless, the analysis did not include access type and relevant clinical information, which may have created bias. After excluding patients using catheters and adjusting for access types, demographics, and comorbid vascular diseases, we demonstrated vascular access dysfunction as a relevant predictor of subsequent coronary events, with a dose-response relationship. Evidence from previous studies may explain the relationship between the distinct arterial and venous vasculature, such as endothelial dysfunction, thrombogenic uremic toxins, inflammation, and oxidative stress.30-34
Traditional cardiovascular risk factors
Several factors were significant in the univariate analysis; however, relative paucity of these factors remained significant in the multivariate analysis. As risks in the general population, aging, diabetes, hyperlipidemia, and smoking, remain significant risk factors for ACS after multivariate adjustment. Although 3-month average BP readings were used, pre-dialysis BP levels were not associated with ACS risk. BP has not been consistently associated with ACS in previous studies of ESRD populations.3 Both U-curve and L-curve relationships have been reported, and the nadir may be distinct from that of the general population. It should be noted that pre-dialysis BP was reported in this study, and the impact of BP at various time points might be different. The association between use of antiplatelet, beta-blocker, or statin with subsequent ACS events was an intriguing finding. The use of these medications might be a proxy of cardiovascular disease and the association might be a reverse causality.
Study strengths and limitations
The strengths of this study include the prospective collection of cardiovascular events with the classification confirmed by interviews and a review of medical records. Further, the number of patients and events was relatively large for the comprehensive adjustment of potential confounders. This study had some limitations. First, information on vascular access interventions was retrospectively collected and may have been subject to recall bias. Second, although potential confounding factors were adjusted, unmeasured confounders were possible. Third, the study cohort had a single ethnic background, and the comparability of prevalence statistics and generalizability of the conclusions may be limited. Fourth, we did not analyse changes in the variables over time. Finally, we did not assess inflammatory markers, although the relationship between these markers in patients undergoing hemodialysis remains controversial.
CONCLUSIONS
Patients on maintenance hemodialysis had a remarkably high ACS incidence. Traditional cardiovascular risk factors were correlated with ACS. Moreover, a history of frequent vascular access dysfunction was also associated with incident ACS. We assumed that inflammation as the mediator of dialysis and dialysis access related factors. Future research efforts should focus on elucidation of the ACS risk factors in this population.
DECLARATION OF CONFLICT OF INTEREST
All the authors declare no conflict of interest.
Acknowledgments
This study was funded by grants from the National Taiwan University Hospital, Hsinchu Branch [106-HCH011, 107-HCH025, 108-HCH046, 108-HCH004, 109-HCH001, 109-HCH003, 109-HCH057, 109-HCH068, 110-HCH001, 111-HCH037, and 111-HCH010], and the Ministry of Science and Technology [112-2314-B-002-30, 112-2314-B-002-MY3, 109-2314-B-002-241-MY3 and 111-2314-B-002-288]. The funders had no role in the study design, data collection, analysis, reporting, or decision to submit the manuscript for publication.
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