Evaluating high-sensitivity Troponin T thresholds and their association with cardiovascular mortality in hemodialysis patients

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However, its increased sensitivity can reduce specificity, particularly in patients with chronic kidney disease (CKD). This study aims to evaluate the baseline hs-TnT levels in patients with chronic kidney disease (CKD) on hemodialysis and identify variables associated with worse cardiovascular outcomes. Methods A prospective cohort study was conducted with CKD patients undergoing hemodialysis at a reference center in Brazil, followed for 24 months. Baseline hs-TnT levels were measured before and after dialysis, and data on comorbidities and cardiovascular events were collected. Statistical analysis identified associations between hs-TnT levels and mortality. Results Of 136 enrolled patients, 88 completed the study, with a mean age of 67 years, predominantly male.. TElevated pre-dialysis hs-TnT levels were observed in most patients, particularly those with a history of coronary artery disease (CAD) and diabetes. Elevated pre-dialysis troponin levels were significantly associated with increased risk of cardiovascular mortality within 24 months. A cutoff of 83.3 ng/L for hs-TnT demonstrated a sensitivity of 75%, specificity of 80.9%, and accuracy of 79.5% for predicting mortality within two years. After adjusting for confounders, patients with pre-dialysis hs-TnT levels ≥ 83.3 ng/L had a 10-fold increased risk of cardiovascular death and a 6-fold increased risk of all-cause mortality. Conclusions Baseline hs-TnT levels ≥ 83.3 ng/L in CKD patients on hemodialysis predict increased cardiovascular and overall mortality. Establishing baseline hs-TnT levels in CKD patients undergoing hemodialysis is essential for early detection of ACS and the identification of high-risk individuals. troponin dialysis chronic kidney disease cardiovascular mortality Figures Figure 1 Figure 2 INTRODUCTION Cardiac troponin (c-TnT) levels represent the gold standard in diagnosing suspected acute coronary syndrome (ACS), especially with the advent of high-sensitivity assays (hs-TnT), which offer earlier detection of elevated levels [ 1 , 2 ]. While the increased sensitivity of these tests improves early detection,it concurrently diminishes their specificity, raising concerns about the interpretation of elevated levels in patients without ACS. Elevated troponin levels are critical for diagnosing myocardial necrosis, but there is growing interest in whether increased values in asymptomatic patients can predict adverse outcomes and signify an elevated risk for major cardiovascular events (MACEs) [ 3 , 4 ]. The threshold for hs-TnT was established using the standard deviation from a healthy population, which fails to account for comorbidities that could alter troponin levels. In individuals with chronic kidney disease (CKD) on dialysis, heart failure (HF), anemia, sepsis, or other comorbidities, these cutoff points may be higher than the normal range, potentially delaying ACS diagnosis in these populations [ 5 , 6 ]. Studies have shown that troponin levels are elevated in 5–51% of pre-dialysis CKD patients, even without anginal symptoms, and elevated troponin levels are independently associated with poorer outcomes [ 7 , 9 – 12 ]. Determining the baseline hs-TnT levels for patients in dialysis centers could help in the early detection of ACS, thereby reducing the risk of adverse cardiovascular outcomes, including cardiovascular mortality. This study aims to evaluate baseline hs-TnT levels in CKD patients at a reference center and identify variables that, alongside troponin levels, are linked to worse cardiovascular outcomes. METHODS Participants This study received approval from the institutional review board (IRB number 63792622.6.0000.5330), and CKD patients undergoing hemodialysis were prospectively enrolled at a reference center in Southern Brazil (Hospital Moinhos de Vento, Porto Alegre). Participants were followed for 24 months, from October 2020 to October 2022. All procedures adhered to ethical standards and the 1964 Helsinki Declaration. Informed consent was obtained from all participants. Inclusion criteria were patients aged over 18 years with CKD undergoing intermittent hemodialysis for at least three months. Exclusion criteria encompassed patients with a recent acute myocardial infarction (within the previous 30 days), those undergoing peritoneal dialysis, those expected to undergo kidney transplantation within 24 months, or those with missing data. Laboratory assessment Blood samples were collected immediately before and after hemodialysis. hs-TnT levels were measured using a high-sensitivity assay (Elecsys, Roche Diagnostics) with a detection range from 3 ng/L to 14 ng/L. Troponin levels were assessed at months 1, 2, 3, 12, and 24. Other routine biochemistry tests were conducted at the local dialysis unit laboratory. Data extraction and outcomes Data collection was carried out by the researchers involved through research and review of medical records, added to the values ​​of laboratory tests collected. Data were collected from the medical records of the participants at the time of inclusion in the study and in the subsequent months, focusing on previous pathologies, cardiovascular risk factors and symptoms such as typical chest pain during hemodialysis or chest pain leading to emergency care during 24 months of the study. Other variables such as age, sex, hemoglobin value, glycated hemoglobin, iron and ferritin level, parathyroid hormone, calcium, phosphorus were also evaluated. The mean, median and standard deviation of baseline, pre and post-dialysis troponin were determined. Comorbidities and troponin levels were correlated with the occurrence of MACEs, cardiovascular mortality, and other causes of death. Statistical analysis Data were presented as frequencies, percentages, means with standard deviations, or medians with interquartile ranges. The Wilcoxon test was used for comparing pre- and post-dialysis troponin levels, while the Mann-Whitney test was applied for comparing median troponin levels and outcomes. The Student's t-test and chi-square or Fisher's exact tests were used for group comparisons. The Spearman correlation test assessed the relationship between numerical variables and troponin levels. A Receiver Operating Characteristic (ROC) curve was used to determine the optimal cutoff for pre-dialysis troponin levels to predict death within 24 months. A Poisson regression model was employed to control for confounding factors. All parameters at a significance level of p-value less than 0.10 in the univariate analysis were included in a multivariate model. The parameters included in the multivariate analysis were considered statistically significant if the overall p-value was less than 0.05 [ 13 ]. Models were adjusted for all relevant variables, and a p-value of < 0.05 was considered significant. RESULTS Study subjects Of the initial 136 patients, 88 completed the study. The cohort had a mean age of 67.1 ± 15.2 years, with 62.5% male. Comorbidities were prevalent: 95% had hypertension, 50% diabetes, and 36% had a history of ischemic heart disease. Only 43% achieved target hemoglobin levels consistent with end-stage CKD on hemodialysis. Hemodialysis was the most common dialysis modality (79.5%). Baseline characteristics are summarized in Table 1. Troponin analyses The median pre-dialysis hs-TnT level was 64.7 ng/L (IQR: 36.9–91.8). Post-dialysis troponin levels significantly decreased in over 75% of patients (p < 0.01), although no significant association was found between post-dialysis troponin levels and mortality. Patients with a history of coronary artery disease (CAD) exhibited higher pre-dialysis troponin levels (p < 0.05), with a median of 81.7 ng/L (IQR: 60.2–111.7) compared to 55.0 ng/L (IQR: 27.7–77.2) in non-CAD patients. Diabetic patients also had higher troponin levels (76.5 ng/L vs. 46.2 ng/L; p < 0.01). A significant positive correlation between glycated hemoglobin and troponin levels was found during the first year of follow-up (Table 3). Additionally, a significant correlation was noted between ferritin levels in the second year and troponin levels in the first year, suggesting that elevated troponin levels in 2020 were associated with increased ferritin levels in 2021. No significant difference was observed for iron, phosphorus, parathyroid hormone (PTH), or calcium levels. Outcomes During the follow-up, 12% of patients experienced chest pain during hemodialysis or sought emergency care for chest pain. Elevated pre-dialysis troponin levels were significantly associated with mortality within 24 months (p < 0.001) (Table 5, Fig. 1 ). Of the total cohort, 6.8% of patients died from MACEs, while 20.5% died from other causes. The ROC curve identified a cutoff value of 83.3 ng/L for pre-dialysis troponin, with sensitivity of 75%, specificity of 80.9%, positive predictive value of 53.6%, and negative predictive value of 91.7%, resulting in an overall accuracy of 79.5% (Table 6). Figure 2 illustrates the analysis of the troponin cutoff with death within 24 months. Patients with pre-dialysis troponin levels ≥ 83.3 ng/L had a significantly higher risk of mortality within 24 months (p < 0.001). After adjusting for age, sex, glycated hemoglobin, iron levels, and comorbidities such as hypertension, diabetes, heart failure, and ischemic heart disease, individuals with troponin levels ≥ 83.3 ng/L were at a significantly higher risk for cardiovascular death (10 times higher) and death within two years (6 times higher) (Table 7). DISCUSSION Our findings align with previous studies, showing elevated hs-TnT levels in almost all CKD patients on hemodialysis [ 10 – 12 ], with age, glycated hemoglobin, ferritin levels, and a history of CAD contributing to these increases. The predictability of hs-TnT for long-term cardiovascular outcomes has been explored in several studies [ 10 – 12 , 14 – 16 ]. Previously data with non hs-TnT showed conflicting results of the mortality and MACEs in hemodialysis patients [ 14 – 16 ], while new studies with hs-TnT has shown positive results, helping not only in diagnostic confirmation, but also in the detection of patients at higher risk for cardiovascular events [ 17 , 18 ]. Our study supports these findings, highlighting troponin's important role in predicting cardiovascular outcomes in this population. The diagnosis of acute myocardial infarction (AMI) in CKD patients remains challenging, as the 99th percentile for troponin is based on a healthy general population. CKD patients, particularly those on hemodialysis, often have basal troponin levels above this threshold. A previous study of 198 asymptomatic hemodialysis patients found a median hs-TnT level of 61.1 ng/L (IQR: 36.6–102.0) [ 14 ]. Over an average follow-up of 13.5 months, 15.1% of patients developed MACEs, and 10.1% died. Patients in the highest quartile of hs-TnT (≥ 102 ng/L) had an increased risk of long-term mortality (HR 3.34; 95% CI 1.39–8.04, P = 0.005), with hs-TnT serving as an independent predictor of long-term mortality in hemodialysis patients. In our study, a hs-TnT cutoff value of 83.3 ng/L was associated with long-term mortality, with superior results compared to previous findings, such as those by Noppakun et al. [ 14 ], who used a cutoff of 106 ng/L with a sensitivity of 50% and specificity of 78.1%. One potential reason for the observed differences could be the distinct characteristics of the patient populations. The higher burden of comorbidities and a longer follow-up period in our study population could have led to a greater overall risk of mortality and cardiovascular events. Higher ferritin levels were also linked to increased mortality risk [ 19 ], with our study showing that higher troponin levels in 2020 were associated with significantly higher ferritin levels in 2021. However, confounding factors like recent infections and hospitalizations limit the ability to establish a direct causal relationship. This study has several limitations that should be considered when interpreting the results. First, this is an observational study with a relatively small sample size, which may limit the generalizability of the findings to larger and more diverse populations. Additionally, all participants were recruited from a single reference center, which may reduce the external validity of the results, as clinical practices and patient characteristics could vary in different settings. Another limitation is that, while troponin was the primary biomarker used to assess cardiac outcomes, the inclusion of other biomarkers, such as B-type natriuretic peptide (BNP) or C-reactive protein (CRP), could have provided a more comprehensive evaluation of cardiovascular risk and the underlying mechanisms of the patients' conditions. Finally, the study did not account for potential confounders such as acute infections or recent hospitalizations, which could influence troponin levels and other outcomes. Therefore, future studies with larger sample sizes, multiple research centers, and the inclusion of additional biomarkers are needed to validate and expand upon the findings presented. In conclusion, in our sample, a cut off level of hS-TnT greater than 83.3 ng/L was associated with increased cardiovascular and overall mortality. Determining baseline troponin levels in patients with chronic kidney disease on hemodialysis is crucial for early detection of acute coronary syndrome (ACS) and for identifying individuals at high risk for adverse cardiovascular outcomes. This approach not only facilitates a quicker and more accurate diagnosis of ACS but also enables better risk stratification, ultimately improving clinical decision-making and patient management. By establishing personalized troponin thresholds, healthcare providers can optimize care, reducing diagnostic delays and preventing unnecessary interventions in patients with elevated troponin levels. Declarations Ethics approval and consent to participate The Hospital Moinhos de Vento committee of research ethics granted ethical approval for this study (IRB number 63792622.6.0000.5330). Informed consent was obtained from all participants. All procedures adhered to ethical standards and the 1964 Helsinki Declaration. Consent for publication Not applicable. Competing interests None of the authors has any conflict of interest to express. This includes financial or personal relationships that could inappropriately influence his or her actions. Acknowledgement Not applicable. Funding Not applicable. Authors‘ contributions: PW, ML, MK, CDF, RE and DS designed the study; PW, CDF, AS, CMM acquired the data; PW, CDF, LN analyzed the data; PW, GSP, RA and CM drafted the manuscript; ML, MK, CDF, AS, LN, RE and DS critically reviewed and edited the manuscript. All approved the final version of the manuscript. Data availability The data that supports the findings of this study are available from the corresponding author upon reasonable request due to the privacy of the patients’ data. Clinical trial number: Not applicable References Badiou S, Boudet A, Leray-Moragues H, et al. Monthly reference change value of cardiac troponin in hemodialysis patients as a useful tool for long-term cardiovascular management. Clin Biochem. 2016 Oct;49(15):1195-1198. Archan S, Fleisher LA. From creatine kinase-MB to troponin: the adoption of a new standard. Anesthesiology. 2010 Apr;112(4):1005-12. Reichlin T, Hochholzer W, Bassetti S, et al. Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med. 2009 Aug 27;361(9):858-67. Twerenbold R, Jaffe A, Reichlin T, Reiter M, Mueller C. High-sensitive troponin T measurements: what do we gain and what are the challenges? Eur Heart J. 2012 Mar;33(5):579-86. Martins CS. Troponina: Estrutura, Fisiopatologia e Importância Clínica para Além da Isquemia Miocárdica. Arq Med 2009; 23(6):221-240. Spies C, Haude V, Fitzner R, et al. Serum cardiac troponin T as a prognostic marker in early sepsis. Chest 1998;113:1055–63. Artunc F, Mueller C, Breidthard T, et al. Sensitive troponins-which suits better for hemodialysis patients? Associated factors and prediction of mortality. PLoS One. 2012;7:e7610. van der Linden N, Cornelis T, Kimenai DM, et al. Origin of Cardiac Troponin T Elevations in Chronic Kidney Disease. Circulation. 2017 Sep 12;136(11):1073-1075. Deegan PB, Lafferty ME, Blumsohn A, et al. Prognostic value of troponin T in hemodialysis patients is independent of comorbidity. Kidney Int. 2001 Dec;60(6):2399-405 Wongcharoen W, Chombandit T, Phrommintikul A, Noppakun K. Variability of high-sensitivity cardiac troponin T and I in asymptomatic patients receiving hemodialysis. Sci Rep. 2021 Aug 30;11(1):17334. Artunc F, Mueller C, Breidthardt T, et al. Sensitive troponins--which suits better for hemodialysis patients? Associated factors and prediction of mortality. PLoS One. 2012;7(10):e47610. Mbagaya W, Luvai A, Lopez B. Biological variation of cardiac troponin in stable haemodialysis patients. Ann Clin Biochem. 2015 Sep;52(Pt 5):562-8. Bursac Z, Gauss CH, Williams DK, et al (2008) Purposeful selection of variables in logistic regression. Source Code Biol Med 3:17. Noppakun K, Ratnachina K, Osataphan N, Phrommintikul A, Wongcharoen W. Prognostic values of high sensitivity cardiac troponin T and I for long-term mortality in hemodialysis patients. Hickman PE, McGill D, Potter JM, et al. Multiple biomarkers including cardiac troponins T and I measured by high-sensitivity assays, as predictors of long-term mortality in patients with chronic renal failure who underwent dialysis. Snaedal S, Bárány P, Lund SH, et al. High-sensitivity troponins in dialysis patients: variation and prognostic value. Clin Kidney J. 2020 Dec 12;14(7):1789-1797. Reichlin T, Hochholzer W, Bassetti S, et al. Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med. 2009;361(9):858-67. Twerenbold R, Jaffe A, Reichlin T, Reiter M, Mueller C. High-sensitive troponin T measurements: what do we gain and what are the challenges? Eur Heart J. 2012;33(5):579-86. Karaboyas A, Morgenstern H, Pisoni RL, et al. Association between serum ferritin and mortality: findings from the USA, Japan and European Dialysis Outcomes and Practice Patterns Study. Nephrol Dial Transplant. 2018 Dec 1;33(12):2234-2244. Tables Table 1. Characteristics of the study sample at baseline Variables n=88 Age (y) 67.1 ± 15,2 Gender Male 55 (62.5) Etiology of CKD Diabetes Mellitus 17 (19.3) Hypertension 30 (34.1) Glomerulonephritis 14 (15.9) Hereditary disease 6 (6.8) Other 17 (19.3) Unknown 4 (4.5) Comorbidities Diabetes 42 (19.3) Hypertension 83 (94.3) HF 49 (55.7) CAD 32 (36.4) Diabetes 42 (19.3) History of transplant 5 (5.7) Smoking 13 (14.8) Cancer 16 (18.2) Dialysis modality HD 70 (79.5) HDF 18 (20.4) Table 2. Laboratory Exams Variables 2020 (n=88) 2021 (n=64) 2022 (n=53) Hemoglobin 11.2±1,8 11.2±1,8 10.8±1,7 Hemoglobin ranges ≤ 8 3 (3.4) 2 (3.1) 1 (1.9) 8.1 - 9.0 8 (9.1) 2 (3.1) 8 (15.1) 9.1 – 10 11 (12.5) 12 (18.8) 9 (17.0) 10.1 – 12 38 (43.2) 29 (45.3) 24 (45.3) > 12 28 (31.8) 19 (29.7) 11 (20.8) Iron 72.1±32.6 69,3±24.6 75.7±27.0 Transferrin saturation 35.4±13.4 34.9±13.2 39.8±12.7 Ferritin (P25-P75) 480 (270-924) 511 (211-1043) 490 (228-1009) Ferritin ranges ≤ 200 14 (15.9) 15 (23.4) 12 (22.6) 201 – 300 9 (10.2) 8 (12.5) 3 (5.7) 301 – 600 24 (27.3) 15 (23.4) 14 (26.4) 601 - 1.000 20 (22.7) 9 (14.1) 11 (20.8) > 1000 21 (23.9) 17 (26.6) 13 (24.5) Glycated hemoglobin 5.72±1.33 - 5.57±1.19 Ionic calcium - - 1.25±0.13 Phosphorus - - 5.57±1.87 Pre Dialysis Troponin (P25-P75) 64.7 (36.9-91.8) 59 (31.3-84.5) 58 (35.5-90.8) Post Dialysis Troponin (P25-P75) 60.3 (33.8-83.0) 51 (30.0-69.0) - Troponin variation (P25-P75) -4.5 (-9.0-(-0.7) -9.0 (-17-(-3) - Note: Data presented as number (frequency), mean ± standard deviation or median IIQ - p25-p75 Table 3. Association of pre-dialysis troponin levels in 2020 with exams using the Spearman correlation coefficient Variables Pre-dialysis troponin levels 2020 p Spearman correlation coefficient Hemoglobin 2020 -0.112 0.299 Iron 2020 -0.135 0.209 Transferrin saturation 2020 -0.132 0.221 Ferritin 2020 0.088 0.416 Glycated Hemoglobin 2020 0.319 0.002 Hemoglobin 2021 -0.132 0.300 Iron 2021 0.180 0.154 Transferrin saturation 2021 0.224 0.075 Ferritin 2021 0.267 0.033 Hemoglobin 2022 0.235 0.091 Iron 2022 -0.126 0.370 Transferrin saturation 2022 -0.008 0.953 Ferritin 2022 0.146 0.296 Glycated Hemoglobin 2022 0.328 0.016 PTH 2 years -0.035 0.803 Ionic calcium 2 years -0.224 0.108 Phosphorus 2 years 0.074 0.598 Table 4. Outcomes Variables n (%) Chest pain on dialysis or emergency room visit – n(%) 2020 (n=88) 9 (10.2) 2021 (n=63) 9 (14.3) 2022 (n=61) 0 (0.0) Cardiovascular death within 2 years – n(%) Yes 6 (6.8) Death within 2 years – n(%) Yes 18 (20.5) Note: Data presented as number (frequency) Table 5. Pre-dialysis troponin levels and variation after dialysis according to outcomes Variables Pre-dialysis troponin levels in 2020 Troponin variation (post-pre dialysis) in 2020 Median (P25 – P75) P Median (P25 – P75) P Chest pain on dialysis or emergency room visit in 2020 0.158 0.173 No 63 (33.3 – 92.3) -3.7 (-9 – (0) Yes 78 (59.5 – 103.7) -6.3 (-11.5 – (-5) Chest pain on dialysis or emergency room visit in 2021 0.492 0.630 No 59 (32.3 – 79.1) -4 (-8 – (0) Yes 68,7 (34.3 – 81.1) -5.7 (-9.3 – (-0.33) Cardiovascular death within 2 years 0.169 0.753 No 64 (35.9 – 87.1) -4.8 (-9.1 – (-0.5) Yes 102.8 (40.8 – 213.7) -3.6 (-8.1 – (-0.4) Death within 2 years <0.001 0.244 No 60.7 (30.3 – 77.3) -4.3 (-8 – (-0.2) Yes 102.8 (76.1 – 162.9) -6.7 (-12.1 – (-3) Table 6. Classification of pre-dialysis troponin levels using the ROC curve according to outcomes Variables Pre-dialysis Troponin 2021 <83.3 (n=60) ≥ 83.3 (n=28) P Chest pain on dialysis or emergency room visit in 2020 1.000 Yes 6 (10.0) 3 (10.7) Chest pain on dialysis or emergency room visit in 2021 1.000 Yes 7 (14.0) 2 (15.4) Cardiovascular death within 2 years Yes 2 (3.3) 4 (14.3) 0.078 Death within 2 years Yes 5 (7.3) 15 (53.6) <0.001 Table 7. Multivariate Poisson Regression Model to evaluate the effect of troponin levels ≥83.3 pre-dialysis in 2020 on cardiovascular and general death Variables Relative Risk* (IC 95%) p Cardiovascular death within 2y 9.78 (2.83 – 33.9) <0.001 Death within 2y 6.00 (2.05 – 17.5) 0.001 * adjusted for age, gender, glycated hemoglobin in 2020, iron, hypertension, diabetes, heart failure and coronary heart disease Additional Declarations No competing interests reported. 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19:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5897880/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5897880/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75000777,"identity":"43755891-d325-4bcb-bceb-e8b3aef45fde","added_by":"auto","created_at":"2025-01-29 09:44:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePre-dialysis troponin levels in 2020 according to death within 24 months\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5897880/v1/f5b31c0c127634877be46a3b.jpg"},{"id":75000776,"identity":"c51e1cc9-2166-4fe0-932b-409d114c97d6","added_by":"auto","created_at":"2025-01-29 09:44:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve for pre-dialysis troponin levels in 2020 according to death within 24 months\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe area under the curve was 0.80 (95% CI: 0.68-0.92). The cutoff point determined by the curve was 83.3 (sensitivity of 75%, specificity of 80.9%, positive predictive value of 53.6%, negative predictive value of 91.7% and accuracy of 79.5%).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5897880/v1/66b59e2223bc7bc3aeb04cd2.jpg"},{"id":96803201,"identity":"9597bb6f-fb15-419f-9678-881cdc5a932f","added_by":"auto","created_at":"2025-11-26 08:54:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1332313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5897880/v1/fe1fb3d8-bf48-48c0-92ae-18f402a21a06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating high-sensitivity Troponin T thresholds and their association with cardiovascular mortality in hemodialysis patients","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCardiac troponin (c-TnT) levels represent the gold standard in diagnosing suspected acute coronary syndrome (ACS), especially with the advent of high-sensitivity assays (hs-TnT), which offer earlier detection of elevated levels [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the increased sensitivity of these tests improves early detection,it concurrently diminishes their specificity, raising concerns about the interpretation of elevated levels in patients without ACS. Elevated troponin levels are critical for diagnosing myocardial necrosis, but there is growing interest in whether increased values in asymptomatic patients can predict adverse outcomes and signify an elevated risk for major cardiovascular events (MACEs) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe threshold for hs-TnT was established using the standard deviation from a healthy population, which fails to account for comorbidities that could alter troponin levels. In individuals with chronic kidney disease (CKD) on dialysis, heart failure (HF), anemia, sepsis, or other comorbidities, these cutoff points may be higher than the normal range, potentially delaying ACS diagnosis in these populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies have shown that troponin levels are elevated in 5\u0026ndash;51% of pre-dialysis CKD patients, even without anginal symptoms, and elevated troponin levels are independently associated with poorer outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDetermining the baseline hs-TnT levels for patients in dialysis centers could help in the early detection of ACS, thereby reducing the risk of adverse cardiovascular outcomes, including cardiovascular mortality. This study aims to evaluate baseline hs-TnT levels in CKD patients at a reference center and identify variables that, alongside troponin levels, are linked to worse cardiovascular outcomes.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e This study received approval from the institutional review board (IRB number 63792622.6.0000.5330), and CKD patients undergoing hemodialysis were prospectively enrolled at a reference center in Southern Brazil (Hospital Moinhos de Vento, Porto Alegre). Participants were followed for 24 months, from October 2020 to October 2022. All procedures adhered to ethical standards and the 1964 Helsinki Declaration. Informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eInclusion criteria were patients aged over 18 years with CKD undergoing intermittent hemodialysis for at least three months. Exclusion criteria encompassed patients with a recent acute myocardial infarction (within the previous 30 days), those undergoing peritoneal dialysis, those expected to undergo kidney transplantation within 24 months, or those with missing data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaboratory assessment\u003c/h3\u003e\n\u003cp\u003eBlood samples were collected immediately before and after hemodialysis. hs-TnT levels were measured using a high-sensitivity assay (Elecsys, Roche Diagnostics) with a detection range from 3 ng/L to 14 ng/L. Troponin levels were assessed at months 1, 2, 3, 12, and 24. Other routine biochemistry tests were conducted at the local dialysis unit laboratory.\u003c/p\u003e\n\u003ch3\u003eData extraction and outcomes\u003c/h3\u003e\n\u003cp\u003eData collection was carried out by the researchers involved through research and review of medical records, added to the values ​​of laboratory tests collected. Data were collected from the medical records of the participants at the time of inclusion in the study and in the subsequent months, focusing on previous pathologies, cardiovascular risk factors and symptoms such as typical chest pain during hemodialysis or chest pain leading to emergency care during 24 months of the study. Other variables such as age, sex, hemoglobin value, glycated hemoglobin, iron and ferritin level, parathyroid hormone, calcium, phosphorus were also evaluated.\u003c/p\u003e \u003cp\u003eThe mean, median and standard deviation of baseline, pre and post-dialysis troponin were determined. Comorbidities and troponin levels were correlated with the occurrence of MACEs, cardiovascular mortality, and other causes of death.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were presented as frequencies, percentages, means with standard deviations, or medians with interquartile ranges. The Wilcoxon test was used for comparing pre- and post-dialysis troponin levels, while the Mann-Whitney test was applied for comparing median troponin levels and outcomes. The Student's t-test and chi-square or Fisher's exact tests were used for group comparisons. The Spearman correlation test assessed the relationship between numerical variables and troponin levels. A Receiver Operating Characteristic (ROC) curve was used to determine the optimal cutoff for pre-dialysis troponin levels to predict death within 24 months. A Poisson regression model was employed to control for confounding factors.\u003c/p\u003e \u003cp\u003eAll parameters at a significance level of p-value less than 0.10 in the univariate analysis were included in a multivariate model. The parameters included in the multivariate analysis were considered statistically significant if the overall p-value was less than 0.05 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Models were adjusted for all relevant variables, and a p-value of \u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects\u003c/h2\u003e \u003cp\u003eOf the initial 136 patients, 88 completed the study. The cohort had a mean age of 67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2 years, with 62.5% male. Comorbidities were prevalent: 95% had hypertension, 50% diabetes, and 36% had a history of ischemic heart disease. Only 43% achieved target hemoglobin levels consistent with end-stage CKD on hemodialysis. Hemodialysis was the most common dialysis modality (79.5%). Baseline characteristics are summarized in Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTroponin analyses\u003c/h3\u003e\n\u003cp\u003eThe median pre-dialysis hs-TnT level was 64.7 ng/L (IQR: 36.9\u0026ndash;91.8). Post-dialysis troponin levels significantly decreased in over 75% of patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), although no significant association was found between post-dialysis troponin levels and mortality.\u003c/p\u003e \u003cp\u003ePatients with a history of coronary artery disease (CAD) exhibited higher pre-dialysis troponin levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a median of 81.7 ng/L (IQR: 60.2\u0026ndash;111.7) compared to 55.0 ng/L (IQR: 27.7\u0026ndash;77.2) in non-CAD patients. Diabetic patients also had higher troponin levels (76.5 ng/L vs. 46.2 ng/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A significant positive correlation between glycated hemoglobin and troponin levels was found during the first year of follow-up (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eAdditionally, a significant correlation was noted between ferritin levels in the second year and troponin levels in the first year, suggesting that elevated troponin levels in 2020 were associated with increased ferritin levels in 2021. No significant difference was observed for iron, phosphorus, parathyroid hormone (PTH), or calcium levels.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eDuring the follow-up, 12% of patients experienced chest pain during hemodialysis or sought emergency care for chest pain. Elevated pre-dialysis troponin levels were significantly associated with mortality within 24 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;5, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the total cohort, 6.8% of patients died from MACEs, while 20.5% died from other causes. The ROC curve identified a cutoff value of 83.3 ng/L for pre-dialysis troponin, with sensitivity of 75%, specificity of 80.9%, positive predictive value of 53.6%, and negative predictive value of 91.7%, resulting in an overall accuracy of 79.5% (Table\u0026nbsp;6). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the analysis of the troponin cutoff with death within 24 months.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePatients with pre-dialysis troponin levels\u0026thinsp;\u0026ge;\u0026thinsp;83.3 ng/L had a significantly higher risk of mortality within 24 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for age, sex, glycated hemoglobin, iron levels, and comorbidities such as hypertension, diabetes, heart failure, and ischemic heart disease, individuals with troponin levels\u0026thinsp;\u0026ge;\u0026thinsp;83.3 ng/L were at a significantly higher risk for cardiovascular death (10 times higher) and death within two years (6 times higher) (Table\u0026nbsp;7).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings align with previous studies, showing elevated hs-TnT levels in almost all CKD patients on hemodialysis [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], with age, glycated hemoglobin, ferritin levels, and a history of CAD contributing to these increases.\u003c/p\u003e \u003cp\u003eThe predictability of hs-TnT for long-term cardiovascular outcomes has been explored in several studies [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previously data with non hs-TnT showed conflicting results of the mortality and MACEs in hemodialysis patients [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], while new studies with hs-TnT has shown positive results, helping not only in diagnostic confirmation, but also in the detection of patients at higher risk for cardiovascular events [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our study supports these findings, highlighting troponin's important role in predicting cardiovascular outcomes in this population.\u003c/p\u003e \u003cp\u003eThe diagnosis of acute myocardial infarction (AMI) in CKD patients remains challenging, as the 99th percentile for troponin is based on a healthy general population. CKD patients, particularly those on hemodialysis, often have basal troponin levels above this threshold. A previous study of 198 asymptomatic hemodialysis patients found a median hs-TnT level of 61.1 ng/L (IQR: 36.6\u0026ndash;102.0) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Over an average follow-up of 13.5 months, 15.1% of patients developed MACEs, and 10.1% died. Patients in the highest quartile of hs-TnT (\u0026ge;\u0026thinsp;102 ng/L) had an increased risk of long-term mortality (HR 3.34; 95% CI 1.39\u0026ndash;8.04, P\u0026thinsp;=\u0026thinsp;0.005), with hs-TnT serving as an independent predictor of long-term mortality in hemodialysis patients.\u003c/p\u003e \u003cp\u003eIn our study, a hs-TnT cutoff value of 83.3 ng/L was associated with long-term mortality, with superior results compared to previous findings, such as those by Noppakun et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], who used a cutoff of 106 ng/L with a sensitivity of 50% and specificity of 78.1%. One potential reason for the observed differences could be the distinct characteristics of the patient populations. The higher burden of comorbidities and a longer follow-up period in our study population could have led to a greater overall risk of mortality and cardiovascular events.\u003c/p\u003e \u003cp\u003eHigher ferritin levels were also linked to increased mortality risk [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], with our study showing that higher troponin levels in 2020 were associated with significantly higher ferritin levels in 2021. However, confounding factors like recent infections and hospitalizations limit the ability to establish a direct causal relationship.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the results. First, this is an observational study with a relatively small sample size, which may limit the generalizability of the findings to larger and more diverse populations. Additionally, all participants were recruited from a single reference center, which may reduce the external validity of the results, as clinical practices and patient characteristics could vary in different settings. Another limitation is that, while troponin was the primary biomarker used to assess cardiac outcomes, the inclusion of other biomarkers, such as B-type natriuretic peptide (BNP) or C-reactive protein (CRP), could have provided a more comprehensive evaluation of cardiovascular risk and the underlying mechanisms of the patients' conditions. Finally, the study did not account for potential confounders such as acute infections or recent hospitalizations, which could influence troponin levels and other outcomes. Therefore, future studies with larger sample sizes, multiple research centers, and the inclusion of additional biomarkers are needed to validate and expand upon the findings presented.\u003c/p\u003e \u003cp\u003eIn conclusion, in our sample, a cut off level of hS-TnT greater than 83.3 ng/L was associated with increased cardiovascular and overall mortality. Determining baseline troponin levels in patients with chronic kidney disease on hemodialysis is crucial for early detection of acute coronary syndrome (ACS) and for identifying individuals at high risk for adverse cardiovascular outcomes. This approach not only facilitates a quicker and more accurate diagnosis of ACS but also enables better risk stratification, ultimately improving clinical decision-making and patient management. By establishing personalized troponin thresholds, healthcare providers can optimize care, reducing diagnostic delays and preventing unnecessary interventions in patients with elevated troponin levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Hospital Moinhos de Vento committee of research ethics granted ethical approval for this study (IRB number 63792622.6.0000.5330). Informed consent was obtained from all participants. All procedures adhered to ethical standards and the 1964 Helsinki Declaration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors has any conflict of interest to express. This includes financial or personal relationships that could inappropriately influence his or her actions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026lsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePW, ML, MK, CDF, RE and DS designed the study; PW, CDF, AS, CMM acquired the data; PW, CDF, LN analyzed the data; PW, GSP, RA and CM drafted the manuscript; ML, MK, CDF, AS, LN, RE and DS critically reviewed and edited the manuscript. All approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available from the corresponding author upon reasonable request due to the privacy of the patients\u0026rsquo; data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBadiou S, Boudet A, Leray-Moragues H, et al. Monthly reference change value of cardiac troponin in hemodialysis patients as a useful tool for long-term cardiovascular management. Clin Biochem. 2016 Oct;49(15):1195-1198.\u003c/li\u003e\n\u003cli\u003eArchan S, Fleisher LA. From creatine kinase-MB to troponin: the adoption of a new standard. Anesthesiology. 2010 Apr;112(4):1005-12.\u003c/li\u003e\n\u003cli\u003eReichlin T, Hochholzer W, Bassetti S, et al. Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med. 2009 Aug 27;361(9):858-67.\u003c/li\u003e\n\u003cli\u003eTwerenbold R, Jaffe A, Reichlin T, Reiter M, Mueller C. High-sensitive troponin T measurements: what do we gain and what are the challenges? Eur Heart J. 2012 Mar;33(5):579-86.\u003c/li\u003e\n\u003cli\u003eMartins CS. Troponina: Estrutura, Fisiopatologia e Import\u0026acirc;ncia Cl\u0026iacute;nica para Al\u0026eacute;m da Isquemia Mioc\u0026aacute;rdica. Arq Med 2009; 23(6):221-240.\u003c/li\u003e\n\u003cli\u003eSpies C, Haude V, Fitzner R, et al. Serum cardiac troponin T as a prognostic marker in early sepsis. Chest 1998;113:1055\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eArtunc F, Mueller C, Breidthard T, et al. Sensitive troponins-which suits better for hemodialysis patients? Associated factors and prediction of mortality. PLoS One. 2012;7:e7610.\u003c/li\u003e\n\u003cli\u003evan der Linden N, Cornelis T, Kimenai DM, et al. Origin of Cardiac Troponin T Elevations in Chronic Kidney Disease. Circulation. 2017 Sep 12;136(11):1073-1075.\u003c/li\u003e\n\u003cli\u003eDeegan PB, Lafferty ME, Blumsohn A, et al. Prognostic value of troponin T in hemodialysis patients is independent of comorbidity. Kidney Int. 2001 Dec;60(6):2399-405\u003c/li\u003e\n\u003cli\u003eWongcharoen W, Chombandit T, Phrommintikul A, Noppakun K. Variability of high-sensitivity cardiac troponin T and I in asymptomatic patients receiving hemodialysis. Sci Rep. 2021 Aug 30;11(1):17334.\u003c/li\u003e\n\u003cli\u003eArtunc F, Mueller C, Breidthardt T, et al. Sensitive troponins--which suits better for hemodialysis patients? Associated factors and prediction of mortality. PLoS One. 2012;7(10):e47610. \u003c/li\u003e\n\u003cli\u003eMbagaya W, Luvai A, Lopez B. Biological variation of cardiac troponin in stable haemodialysis patients. Ann Clin Biochem. 2015 Sep;52(Pt 5):562-8. \u003c/li\u003e\n\u003cli\u003eBursac Z, Gauss CH, Williams DK, et al (2008) Purposeful selection of variables in logistic regression. Source Code Biol Med 3:17.\u003c/li\u003e\n\u003cli\u003eNoppakun K, Ratnachina K, Osataphan N, Phrommintikul A, Wongcharoen W. Prognostic values of high sensitivity cardiac troponin T and I for long-term mortality in hemodialysis patients.\u003c/li\u003e\n\u003cli\u003eHickman PE, McGill D, Potter JM, et al. Multiple biomarkers including cardiac troponins T and I measured by high-sensitivity assays, as predictors of long-term mortality in patients with chronic renal failure who underwent dialysis. \u003c/li\u003e\n\u003cli\u003eSnaedal S, B\u0026aacute;r\u0026aacute;ny P, Lund SH, et al. High-sensitivity troponins in dialysis patients: variation and prognostic value. Clin Kidney J. 2020 Dec 12;14(7):1789-1797.\u003c/li\u003e\n\u003cli\u003eReichlin T, Hochholzer W, Bassetti S, et al. Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med. 2009;361(9):858-67.\u003c/li\u003e\n\u003cli\u003eTwerenbold R, Jaffe A, Reichlin T, Reiter M, Mueller C. High-sensitive troponin T measurements: what do we gain and what are the challenges? Eur Heart J. 2012;33(5):579-86.\u003c/li\u003e\n\u003cli\u003eKaraboyas A, Morgenstern H, Pisoni RL, et al. Association between serum ferritin and mortality: findings from the USA, Japan and European Dialysis Outcomes and Practice Patterns Study. Nephrol Dial Transplant. 2018 Dec 1;33(12):2234-2244. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of the study sample at baseline\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"467\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en=88\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e67.1 \u0026plusmn; 15,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e55 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eEtiology of CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e17 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e30 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eGlomerulonephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e14 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eHereditary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e6 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e17 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e4 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e42 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e83 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e49 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e32 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e42 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eHistory of transplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e5 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e13 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e16 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eDialysis modality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e70 (79.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.5931%;\"\u003e\n \u003cp\u003eHDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.4069%;\"\u003e\n \u003cp\u003e18 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Laboratory Exams\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"482\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020 (n=88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=64)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e11.2\u0026plusmn;1,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e11.2\u0026plusmn;1,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e10.8\u0026plusmn;1,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eHemoglobin ranges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e\u0026le; 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e3 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e8.1 - 9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e8 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e8 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e9.1 \u0026ndash; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e11 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e12 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e9 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e10.1 \u0026ndash; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e38 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e29 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e24 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e\u0026gt; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e28 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e19 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e11 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e72.1\u0026plusmn;32.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e69,3\u0026plusmn;24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e75.7\u0026plusmn;27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eTransferrin saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e35.4\u0026plusmn;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e34.9\u0026plusmn;13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e39.8\u0026plusmn;12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eFerritin\u003c/p\u003e\n \u003cp\u003e(P25-P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003cp\u003e(270-924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e511\u003c/p\u003e\n \u003cp\u003e(211-1043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003cp\u003e(228-1009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eFerritin ranges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e\u0026le; 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e14 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e15 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e12 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e201 \u0026ndash; 300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e9 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e8 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e3 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e301 \u0026ndash; 600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e24 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e15 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e14 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e601 - 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e20 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e9 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e11 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003e\u0026gt; 1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e21 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e17 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e13 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eGlycated hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e5.72\u0026plusmn;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e5.57\u0026plusmn;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eIonic calcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e1.25\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003ePhosphorus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e5.57\u0026plusmn;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003ePre Dialysis Troponin\u003c/p\u003e\n \u003cp\u003e(P25-P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003cp\u003e(36.9-91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e(31.3-84.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e(35.5-90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003ePost Dialysis Troponin\u003c/p\u003e\n \u003cp\u003e(P25-P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e60.3\u003c/p\u003e\n \u003cp\u003e(33.8-83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003cp\u003e(30.0-69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41.4938%;\"\u003e\n \u003cp\u003eTroponin variation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(P25-P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.805%;\"\u003e\n \u003cp\u003e-4.5\u003c/p\u003e\n \u003cp\u003e(-9.0-(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.4274%;\"\u003e\n \u003cp\u003e-9.0\u003c/p\u003e\n \u003cp\u003e(-17-(-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.52697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2199%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data presented as number (frequency), mean \u0026plusmn; standard deviation or median\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIIQ - p25-p75\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Association of pre-dialysis troponin levels in 2020 with exams using the Spearman correlation coefficient\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"487\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-dialysis troponin levels 2020\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpearman correlation coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHemoglobin 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eIron 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eTransferrin saturation 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eFerritin 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eGlycated Hemoglobin 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.319\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHemoglobin 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eIron 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eTransferrin saturation 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eFerritin 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.267\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHemoglobin 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eIron 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eTransferrin saturation 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eFerritin 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eGlycated Hemoglobin 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.328\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePTH 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eIonic calcium 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e-0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePhosphorus 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"472\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eChest pain on dialysis or emergency room visit \u0026ndash; n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u0026nbsp;2020 (n=88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e9 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e2021 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e9 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e2022 (n=61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eCardiovascular death within 2 years \u0026ndash; n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003eDeath within 2 years \u0026ndash; n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e18 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data presented as number (frequency)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Pre-dialysis troponin levels and variation after dialysis according to outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"670\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-dialysis troponin levels in 2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTroponin variation (post-pre dialysis) in 2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (P25 \u0026ndash; P75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (P25 \u0026ndash; P75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eChest pain on dialysis or emergency room visit in 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e63 (33.3 \u0026ndash; 92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-3.7 (-9 \u0026ndash; (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e78 (59.5 \u0026ndash; 103.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-6.3 (-11.5 \u0026ndash; (-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eChest pain on dialysis or emergency room visit in 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e59 (32.3 \u0026ndash; 79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-4 (-8 \u0026ndash; (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e68,7 (34.3 \u0026ndash; 81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-5.7 (-9.3 \u0026ndash; (-0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eCardiovascular death within 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e64 (35.9 \u0026ndash; 87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-4.8 (-9.1 \u0026ndash; (-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e102.8 (40.8 \u0026ndash; 213.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-3.6 (-8.1 \u0026ndash; (-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eDeath within 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e60.7 (30.3 \u0026ndash; 77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-4.3 (-8 \u0026ndash; (-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e102.8 (76.1 \u0026ndash; 162.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e-6.7 (-12.1 \u0026ndash; (-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Classification of pre-dialysis troponin levels using the ROC curve according to outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"472\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 247px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-dialysis Troponin 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;83.3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge; 83.3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eChest pain on dialysis or emergency room visit in 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e6 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eChest pain on dialysis or emergency room visit in 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e7 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eCardiovascular death within 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eDeath within 2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e15 (53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Multivariate Poisson Regression Model to evaluate the effect of troponin levels \u0026ge;83.3 pre-dialysis in 2020 on cardiovascular and general death\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Risk* (IC 95%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCardiovascular death within 2y\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e9.78 (2.83 \u0026ndash; 33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDeath within 2y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e6.00 (2.05 \u0026ndash; 17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* adjusted for age, gender, glycated hemoglobin in 2020, iron, hypertension, diabetes, heart failure and coronary heart disease\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"troponin, dialysis, chronic kidney disease, cardiovascular mortality","lastPublishedDoi":"10.21203/rs.3.rs-5897880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5897880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eCardiac troponin (c-TnT) levels are considered the gold standard for diagnosing acute coronary syndrome (ACS), particularly with the advent of high-sensitivity assays (hs-TnT). However, its increased sensitivity can reduce specificity, particularly in patients with chronic kidney disease (CKD). This study aims to evaluate the baseline hs-TnT levels in patients with chronic kidney disease (CKD) on hemodialysis and identify variables associated with worse cardiovascular outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective cohort study was conducted with CKD patients undergoing hemodialysis at a reference center in Brazil, followed for 24 months. Baseline hs-TnT levels were measured before and after dialysis, and data on comorbidities and cardiovascular events were collected. Statistical analysis identified associations between hs-TnT levels and mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf 136 enrolled patients, 88 completed the study, with a mean age of 67 years, predominantly male.. TElevated pre-dialysis hs-TnT levels were observed in most patients, particularly those with a history of coronary artery disease (CAD) and diabetes. Elevated pre-dialysis troponin levels were significantly associated with increased risk of cardiovascular mortality within 24 months. A cutoff of 83.3 ng/L for hs-TnT demonstrated a sensitivity of 75%, specificity of 80.9%, and accuracy of 79.5% for predicting mortality within two years. After adjusting for confounders, patients with pre-dialysis hs-TnT levels\u0026thinsp;\u0026ge;\u0026thinsp;83.3 ng/L had a 10-fold increased risk of cardiovascular death and a 6-fold increased risk of all-cause mortality.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBaseline hs-TnT levels\u0026thinsp;\u0026ge;\u0026thinsp;83.3 ng/L in CKD patients on hemodialysis predict increased cardiovascular and overall mortality. Establishing baseline hs-TnT levels in CKD patients undergoing hemodialysis is essential for early detection of ACS and the identification of high-risk individuals.\u003c/p\u003e","manuscriptTitle":"Evaluating high-sensitivity Troponin T thresholds and their association with cardiovascular mortality in hemodialysis patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-29 09:44:33","doi":"10.21203/rs.3.rs-5897880/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9af33e2-3bdb-4e42-86e9-993352257417","owner":[],"postedDate":"January 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T08:54:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-29 09:44:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5897880","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5897880","identity":"rs-5897880","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}