Clinical features and prognostic factors of Cardiorenal anemia syndrome in China: a retrospective single-center study

Clinical features and prognostic factors of Cardiorenal anemia syndrome in China: a retrospective single-center study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical features and prognostic factors of Cardiorenal anemia syndrome in China: a retrospective single-center study Mengyue Zhu, Min Liu, Chunlei Lu, Dafeng He, Jiao Li, Xia Xu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4274074/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Dec, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 11 You are reading this latest preprint version Abstract Background There is little research on cardiorenal anemia syndrome (CRAS) in China. This study was to describe the characteristics of patients with CRAS and to explore predictive risk factors of all-cause mortality. Methods A total of 81795 patients were hospitalized from August 2012 to August 2021 in the nephrology department and cardiology department, of which 820 patients with CRAS met the inclusion criteria and were recruited into this study. The 820 patients with CRAS were divided into three groups based on New York Heart Association (NYHA) functional class: a NYHA Class II group (n = 124), a NYHA Class III group (n = 492), and a NYHA Class IV group (n = 204). Demographics and laboratory tests were collected and risk factors of death events were analyzed. The primary endpoint of the study was death. Results 820 patients were included, with a median age of 65.00 (51.00–75.00) years and 61.2% were men. The median follow-up was 27.0 (13.0–51.0) months. 416 (50.7%) patients died during follow-up. Age, smoking history, cerebral infarction, NYHA functional class, albumin, serum creatinine (SCr), left ventricular end-diastolic diameter (LVEDD), and left ventricular ejection fraction (LVEF) remained independent predictors in patients with CRAS ( P < 0.05) after adjusting to the potential confounders. Conclusions Heart failure and renal dysfunction are a fatal combination and are associated with poor prognosis in patients with CRAS. CRAS Heart failure Chronic kidney disease Anemia Prognosis factors Figures Figure 1 Figure 2 Figure 3 Introduction Cardiorenal anemia syndrome (CRAS) is a term that refers to a complex group of disorders, including heart failure (HF), chronic kidney disease (CKD) combined with anemia [ 1 – 5 ], which has been widely used in recent years after understanding the importance and association between those mentioned above three. Cardiovascular disease (CVD), including HF, is one of the most common comorbidities in patients with CKD and the leading cause of death in patients with CKD [ 6 ]. Approximately 25–50% of patients with HF have CKD [ 1 ], and the risk of death is increased by 56% compared with those without CKD [ 7 ]. At the same time, the risk of anemia in patients with CKD and CVD significantly increased, and some studies have found that anemia is also an independent risk factor for the progression of CVD and CKD, considerably increasing the risk of hospitalization and death in patients with HF, promoting the progression of kidney disease, and increasing the risk of renal replacement therapy [ 8 ]. There is a triangular relationship between the three, a vicious circle in which any of the three can cause the other or be caused by the other. CRAS is attributed to numerous factors, but the exact pathophysiologic mechanisms remain uncertain. Almost all previous studies have been conducted to investigate CRAS in patients with HF, without analyzing the clinical features and prognosis of CRAS alone. There is currently limited data on clinical features of the CRAS in China. Therefore, this study is aimed to investigate the characteristics and risk factors of survival in patients with CRAS at a single center in China. Methods Study cohort 81795 patients were admitted to the Department of Nephrology and Cardiovascular Medicine of the Northern Jiangsu People's Hospital between August 2012 and August 2021, of which 820 patients met our inclusion criteria for CRAS. CRAS was defined as a syndrome in which HF, CKD, and anemia coexist, referring to other literature [ 1 – 5 ]. The exclusion criteria were the following: 1) patients age 85 years; 2) patients diagnosed with congenital heart disease; 3) patients with malignant tumors, hematologic disease, severe hyperthyroidism, or severe infections; 4) history of blood transfusion in the past 4 months; 5) patients with acute heart failure and acute kidney injury; and 6) incomplete clinical data or lack of follow-up data (Figure. 1). Data on demographics, comorbidities, vital signs, and laboratory tests were collected using electronic medical records at baseline and during the follow-up. The following echocardiography parameters were collected: left ventricular end-diastolic diameter (LVEDD) and left ventricular ejection fraction (LVEF). Two clinicians independently graded the patients’ NYHA functional class based on their HF symptoms. The study complied with the Declaration of Helsinki and was approved by the institutional Ethics Research Committee of Northern Jiangsu People's Hospital (No. 2022ky310). Definitions The primary endpoint of the study was death. Heart failure was defined by reference to the European Society of Cardiology (ESC) criteria as judged by patients’ symptoms, signs, and ancillary tests [ 9 ]. Chronic kidney disease was defined as estimated glomerular filtrate (eGFR) 177 mmol/L for at least 3 months. The eGFR was determined using the CKD-EPI equation [ 10 ]. According to the World Health Organization, anemia is defined as a concentration of hemoglobin < 130 g/L in men and < 120 g/L in women [ 11 ]. Anemia is classified as mild, moderate, severe, and very severe anemia based on the expert consensus on anemia in China. Mild anemia is a hemoglobin lower than normal and greater than or equal to 90 g/L. Moderate anemia is a hemoglobin greater than or equal to 60 and less than 90 g/L. The amount of hemoglobin in severe anemia is greater than or equal to 30 and less than 60 g/L. The amount of hemoglobin in very severe anemia is less than 30 g/L. Systolic blood pressure (SP) refers to systolic blood pressure at admission. Statistical analyses Quantitative data were expressed as mean ± standard deviation or median with interquartile range, depending on whether the data were normally distributed, and differences were compared by t-test or Kruskal-Wallis rank sum test. Categorical data were expressed as numbers (%), and differences were assessed by χ 2 test or Fisher’s exact test. Patient survival was analyzed by the Kaplan-Meier curve and log-rank test. Univariable and multivariable Cox regression analyses were performed to identify predictors of death. Multivariable Cox regression analyses were adjusted for potential confounders. Variables were selected based on clinical relevance. Results are expressed as hazard ratio (HR) with 95% confidence interval (95% CI). Receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to compare the values of different parameters in the prognosis of CRAS patients. A 2-sided P -value < 0.05 was used to determine statistical significance. Empower ( www.empowerstats.com ; X&Y Solutions Inc, Boston, MA) and R ( http://www.R-project.org ) were used for all statistical analyses. Results Patient characteristics A total of 820 patients diagnosed with CRAS were included in the study. Patients were categorized according to the NYHA functional class on admission into three groups: one with NYHA Class II, one with NYHA Class III, and one with NYHA Class IV. There were 124, 492, and 204 patients with NYHA Class II, NYHA Class III, and NYHA Class IV, respectively. We found that there were significant differences in gender, age, diabetes mellitus, atrial fibrillation, edema, dialysis, SCr, N-terminal pro-brain natriuretic peptide (NT-proBNP), patient survival months, and mortality between the three groups ( P < 0.05). Baseline characteristics of patients with CRAS are shown in Table 1 . There were 41 patients with CKD stage 3, 109 with CKD stage 4, and 670 with CKD stage 5. There were 366 patients with mild anemia, 429 with moderate anemia, and 25 with severe anemia. The median age of the patients was 65.00 (51.00–75.00) years, and 61.2% were men. In the whole sample, the median SCr was 601.6 (378.0-818.0) umol/L, the median hemoglobin was 88.0 (75.0-101.0) g/L, and the median NT-proBNP was 31200.0 (11800.0-83000.0) pg/mL. The rates of statin use, renal replacement therapy, and correction of anemia were 29.3%, 47.1%, and 69.4%, respectively. Median follow-up was 27.0 (13.0–51.0) months, 86.5% of patients had at least six months of follow-up, and 416 (50.7%) patients died. The mortality rate was observed to increase with worsening of heart failure, with mortality rates of 44.4%, 47.4%, and 62.8% in NYHA functional class II, III, and IV, respectively. Table 1 Baseline characteristics of patients with CRAS Characteristic Total (N = 820) NYHA Class II (N = 124) NYHA Class III (N = 492) NYHA Class IV (N = 204) P -value Male, n(%) 502 (61.2) 85 (68.6) 307 (62.4) 110 (53.9) 0.022 Age, years 65.0 (51.0–75.0) 61.0 (45.0–72.0) 64.0 (51.0–74.0) 69.0 (51.0–76.0) 0.001 Hypertension, n(%) 725 (88.4) 103 (83.1) 437 (88.8) 185 (90.7) 0.102 SP, mmHg 150.0 (135.0-170.0) 150.0 (136.0-171.3) 150.0 (134.0-169.0) 152.0 (138.0-175.3) 0.305 Smoking, n(%) 157 (19.2) 18 (14.5) 98 (19.9) 41 (20.1) 0.363 Diabetes mellitus, n(%) 386 (47.1) 44 (35.5) 207 (42.1) 104 (51) 0.016 Myocardial infarction, n(%) 38 (4.6) 5 (4.0) 23 (4.7) 10 (4.9) 0.934 Cerebral infarction, n(%) 140 (17.1) 21 (17.0) 80 (16.3) 39 (19.1) 0.659 Atrial fibrillation, n(%) 108 (13.2) 6 (4.8) 66 (13.4) 36 (17.7) 0.004 Edema, n(%) 501 (61.1) 63 (50.8) 291 (59.2) 147 (72.1) < 0.001 Dialysis 386 (47.1) 60 (48.4) 248 (50.4) 78 (38.2) 0.013 White blood cells, *10^9/L 6.4 (4.5–8.4) 6.0 (4.9-8.0) 6.4 (5.0-8.5) 6.7 (5.0-8.6) 0.076 Hemoglobin, g/L 88.0 (75.0-101.0) 87.5 (75.0-103.0) 88.0 (76.0-102.0) 89.0 (74.8–100.0) 0.929 Albumin ,g/L 35.9 (31.7–39.9) 36.1 (31.6–40.7) 36.6 (32.2–40.0) 34.7 (31.4–40.0) 0.155 SCr, umol/L 601.6 (378.0-818.0) 634.0 (396.3-889.3) 614.5 (402.9-821.3) 529.0 (328.0-763.2) 0.003 eGFR, ml/min/1.73m2 7.1 (5.0-12.2) 6.7 (4.8–11.1) 7.0 (5.0-11.5) 7.9 (5.1–13.8) 0.075 NT-proBNP 31200.0 (11800.0-83000.0) 38100.0 (15000.0-96977.0) 28950.0 (9862.5-78025.0) 33016.8 (13475.0-84207.5) 0.012 cTnT, ng/mL 0.1 (0.0-0.3) 0.1 (0.0-0.3) 0.1 (0.0-0.2) 0.1 (0.0-0.4) 0.413 LVEF, % 55.0 (45.0–60.0) 54.5 (46.0–60.0) 55.0 (46.0–60.0) 53.0 (42.8–60.0) 0.055 LVEDD, mm 54.0 (50.0–58.0) 55.0 (50.0–59.0) 53.0 (49.8–58.0) 54.0 (49.0–59.0) 0.458 Survival, months 27.0 (13.0–51.0) 39.5 (17.0-77.3) 27.50 (14.0–49.0) 18.00 (9.00–44.0) < 0.001 Death, n(%) 416 (50.7) 55 (44.4) 233 (47.4) 128 (62.8) < 0.001 Values are median (interquartile range) or n (%). CRAS, cardiorenal anemia syndrome; NYHA, New York Heart Association functional class; SP, systolic blood pressure; SCr, serum creatinine; eGFR, estimated glomerular filtration rate; NT-proBNP, N-terminal pro-brain natriuretic peptide; cTnT, serum cardiac troponin T; LVEF, left ventricular ejection fraction; LVEDD, left ventricular end-diastolic diameter. Predictors of death In univariable Cox regression analyses, age, SP, smoking history, myocardial infarction, cerebral infarction, atrial fibrillation, dialysis, NYHA functional class, SCr, and LVEDD were observed to show a significant association with the risk of death. After adjusting the gender, age, smoking history, SP, myocardial infarction, cerebral infarction, atrial fibrillation, NYHA functional class, dialysis, SCr, LVEDD, and LVEF, multivariable Cox regression analyses showed that age, smoking history, cerebral infarction, NYHA functional class, albumin, SCr, LVEDD, and LVEF were close-dependent related to the risk of death ( P < 0.05) (Table 2 ). The Kaplan-Meier survival curves of patients with CRAS in three groups were shown in Fig. 2 , which showed that the higher NYHA functional class, the worse the prognosis of patients with CRAS ( P < 0.001). Table 2 Predictors of death (univariable and multivariable analyses) Variable Unadjusted HR (95%CI) P -value Adjusted HR (95%CI) P -value Gender 1.25 (1.03, 1.52) 0.024 1.23 (0.99, 1.52) 0.063 Age, years 1.04 (1.03, 1.05) < 0.001 1.04 (1.03, 1.05) < 0.001 Hypertension 1.07 (0.79, 1.45) 0.658 1.02 (0.75, 1.40) 0.887 SP, per 10 mmHg 0.95 (0.91, 0.98) 0.004 0.98 (0.94, 1.02) 0.330 Smoking 1.37 (1.08, 1.76) 0.011 1.38 (1.06, 1.80) 0.018 Diabetes mellitus 1.21 (0.99, 1.46) 0.058 1.01 (0.83, 1.24) 0.904 Myocardial infarction 1.80 (1.19, 2.72) 0.005 1.17 (0.76, 1.78) 0.479 Cerebral infarction 1.79 (1.41, 2.26) < 0.001 1.36 (1.07, 1.74) 0.012 Atrial fibrillation 1.46 (1.12, 1.90) 0.005 0.86 (0.65, 1.15) 0.303 Dialysis 0.62 (0.51, 0.76) < 0.001 0.83 (0.66, 1.04) 0.105 NYHA, per I class 1.46 (1.25, 1.70) < 0.001 1.28 (1.10, 1.51) 0.002 Hemoglobin, per 10 g/L 1.02 (0.96, 1.07) 0.586 0.97 (0.91, 1.03) 0.266 Albumin, g/L 0.99 (0.97, 1.00) 0.146 0.98 (0.96, 0.99) 0.005 SCr, per 88.4 umol/L 0.93 (0.91, 0.96) < 0.001 1.04 (1.01, 1.07) 0.007 NT-proBNP, per 100 pg/ml 1.00 (1.00, 1.00) 0.708 1.00 (1.00, 1.00) 0.073 cTnT, per 0.1 ng/mL 1.01 (0.99, 1.03) 0.421 1.00 (0.98, 1.02) 0.719 LVEDD, per 10 mm 0.78 (0.67, 0.90) < 0.001 0.82 (0.68, 0.97) 0.023 LVEF, per 10% 0.92 (0.84, 1.01) 0.087 0.81 (0.72, 0.91) < 0.001 HR, hazard ratio; CI, confidence interval; SP, systolic blood pressure; NYHA, New York Heart Association functional class; SCr, serum creatinine; NT-proBNP, N-terminal pro-brain natriuretic peptide; cTnT, serum cardiac troponin T; LVEDD, left ventricular end-diastolic diameter; LVEF, left ventricular ejection fraction. Multivariable analyses adjusted gender, age, smoking history, SP, myocardial infarction, cerebral infarction, atrial fibrillation, NYHA functional class, dialysis, SCr, LVEDD, and LVEF. Comparison of clinical characteristics and laboratory parameters in predicting prognosis in CRAS patients Receiver operating characteristic curve (ROC) analysis was used to compare the values of different parameters in the prognosis of CRAS patients (Figure. 3). The age ( P < 0.001), SCr ( P < 0.001), LVEDD ( P < 0.001), and NYHA functional class ( P = 0.001) all had diagnostic values with statistical significance for prognosis of patients with CRAS. Among these values, the area under the curve (AUC) value of age was the largest (AUC = 0.709) (Table 3 ). Table 3 Comparsion of eight influencing factors in patients with CRAS Test AUC 95%CI P -value Smoking (Yes/NO) 0.503 0.464–0.543 0.870 LVEF, % 0.522 0.482–0.561 0.279 Albumin, g/L 0.526 0.487–0.566 0.193 Cerebral infarction (Yes/NO) 0.546 0.507–0.586 0.022 NYHA, class 0.565 0.526–0.604 0.001 LVEDD, mm 0.568 0.529–0.607 < 0.001 SCr, umol/L 0.590 0.551–0.629 < 0.001 Age, years 0.709 0.674–0.744 < 0.001 AUC, area under the curve; CI, confidence interval; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association functional class; LVEDD, left ventricular end-diastolic diameter; SCr, serum creatinine; LVEDD, left ventricular end-diastolic diameter. Discussion There is a strong link between heart disease and kidney disease, and the two influence each other, which has long been a concern by the medical community [ 12 , 13 ]. The first mention of cardiorenal syndrome (CRS) was in 1913, when Thomas Lewis proposed the presence of a close relationship between the heart and the kidney [ 14 ]. Since then, much progress has been made in the pathogenesis, classification, and treatment of cardiorenal syndrome. In 2008, under the initiative of Ronco et al. [ 15 ], the Acute Disease Quality Initiative (ADQI) working group proposed the first consensus on the definition and classification of CRS. CRS often coexists with anemia, which leads to reciprocal and progressive cardiac and renal deterioration. The triad of heart failure, chronic kidney disease, and anemia is termed cardiorenal anemia syndrome (CRAS). CRAS is considerably prevalent, and the management of patients with CRAS remains a challenging undertaking worldwide because of the complexity and heterogeneity of this syndrome and the lack of evidence-based clinical guidelines. At present, CRAS is common in patients with HF and is an independent predictor of all-cause mortality [ 3 ], but few studies have reported the clinical features of CRAS and its prognostic factors. Therefore, this study focused on describing the baseline characteristics of patients with CRAS and the influencing factors of their prognosis. We compared the baseline characteristics of the patients with CRAS according to their NYHA functional class and found that there were significant differences in gender, age, diabetes mellitus, atrial fibrillation, edema, dialysis, SCr, NT-proBNP, patient survival months, and mortality between the three groups ( P < 0.05). Although there was no relationship between gender and prognosis after multivariate adjustment, there was a significant difference in gender between the three groups, which is an interesting phenomenon. Marta et al. found sex-related differences in patients with cardiorenal syndrome. More advanced kidney disease, anemia, and an increased prevalence of preserved ejection fraction are more common in women with CRS, whereas heart failure with reduced ejection fraction was more frequently observed in men [ 16 ]. It has been found that CRAS is associated with increasing age and diabetes mellitus [ 3 ], which is similar to our research, and we found that with the increase of NYHA functional class, the proportion of CRAS patients with diabetes mellitus and age increased. Diabetes mellitus is also a major risk factor for the development of coronary artery disease and HF [ 17 , 18 ], and diabetes mellitus is the most common cause of CKD in Western countries and China [ 19 ]. Moreover, as the NYHA functional class increased, the proportion of edema, atrial fibrillation, and NT-proBNP values were higher, while the LVEF values were lower, indicating that CRAS patients had more severe HF or other cardiovascular diseases. NT-proBNP is associated with HF NYHA functional class, LVEF, and ventricular pressure, thereby contributing to prognosis and risk classification in patients with HF [ 20 ]. Moreover, NT-proBNP is associated with renal insufficiency and is more sensitive in predicting cardiovascular and all-cause mortality in patients with CKD [ 21 ]. The development of CRAS is multifactorial, and some risk factors have been recognized. Advanced age, diabetes mellitus, ischaemic etiology, NYHA functional class, and LVEF are independently associated with CRAS in patients with HF [ 2 , 5 ]. Most studies have explored the effect of the presence or absence of CRAS on prognosis in patients with HF, and few studies have described the prognostic factors of CRAS itself, whether it is HF, renal insufficiency, anemia, or others. Therefore, this article found that age, smoking history, cerebral infarction, NYHA functional class, albumin, SCr, LVEDD, and LVEF were closely related to the risk of death in patients with CRAS ( P < 0.05). In addition, We further compared survival of patients with CRAS in three groups by the Kaplan-Meier curve, which showed that the higher NYHA functional class, the worse the prognosis of patients with CRAS. All these indicated that HF plays an important role in the prognosis factors of CRAS. Next, we used ROC analysis to compare the values of different parameters in the prognosis of CRAS patients, and we found that the AUC value of age was the largest (AUC = 0.709). Undoubtedly, advanced age is a prognostic risk factor for heart failure and chronic kidney disease, and Domenico et al. also found that age was independently related to CRAS ( P < 0.001) [ 5 ]. Likewise, cigarette smoking has been identified as an independent risk factor for cardiovascular events and incident CKD [ 22 , 23 ], which is similar to our conclusions, and we found that smoking history is an independent risk factor for the prognosis of CRAS patients. We also found cerebral infarction, albumin, and SCr were closely related to the risk of death in patients with CRAS. Cerebrovascular disease and cardiovascular disease not only have similarities in the occurrence and development of diseases, such as cerebral infarction and myocardial infarction, but also have a high overlap rate in the affected population. Albumin has anti-inflammatory, antioxidant, and antithrombotic properties [ 24 ]. Hypoalbuminemia is a strong predictor of increased all-cause and cardiovascular mortality based on several cohort studies and meta-analyses [ 16 , 25 – 27 ]. Pedro et al. explored the survival rates of HF patients with CRAS in Tanzania and found that renal dysfunction was a predictor of mortality [ 4 ], which is consistent with our conclusions. Patients with CKD and HF have an increased risk of anemia. The presence of anemia increases in parallel with the severity of HF, the CKD stage, and the patients' age, while the treatment of anemia leads to an improvement in cardiac and renal function as well as fewer hospitalizations for HF [ 28 ]. Here, we found that anemia in CRAS patients was not related to prognosis, either because these patients were treated aggressively or because of sample size. Conclusion CRAS is a comprehensive term encompassing the intricate relationship between simultaneous cardiac, renal impairments and anemia, wherein the deterioration of one organ initiates, perpetuates, or accelerates the decline of the other. The pathologic triangle formed by HF, CKD, and anemia carries high morbidity and mortality rates and decreases quality of life. Our study showed that 50.7% of patients with CRAS died during follow-up. Moreover, the mortality rate of patients is closely related to age, smoking history, cerebral infarction, NYHA functional class, albumin, SCr, LVEDD, and LVEF. Although the pathophysiologic mechanisms underlying CRAS remain uncertain, we can identify and treat the potentially reversible causes early based on prognostic influencing factors, which may improve outcomes in these CRAS patients. Our study still had some limitations. The retrospective study reflected patient data and findings only from a single center in China. Moreover, Hb concentrations on admission were measured, which may not accurately reflect Hb levels after discharge or changes in Hb levels over time. We also did not attempt to identify the cause or assess the treatment or maintenance of anemia during follow-up. Declarations Acknowledgments Not applicable Funding This work was supported by the Scientific Research Project of Jiangsu Provincial Healthcare Commission (Grant number Z2022068) and Hospital Research Projects (Grant number SBQN22006 and FCJS202340). Availability of data and materials Availability of data and material datasets collected and analyzed in the current study are available from the corresponding author on reasonable request. Ethical approval and Informed consent This study was approved by the ethics committee of Northern Jiangsu People's Hospital (No. 2022ky310) and was carried out following the principle of the Helsinki Declaration. This is a retrospective study using de-identified data, and our institutional review board did not require consent from the patient. Competing Interest All the authors declared no competing interests. Author Contribution RW, DH and CL contributed to the conception of the study. ML, JL and XX screened the data, and MZ analyzed and interpreted the data. MZ,YC, CZ, JX and GZ contributed to the follow-up. MZ finished the manuscript. HM, GB, and CL supervised and edited the manuscript. All authors contributed to the work and approved the submitted version for publication. Data Availability Data is provided within the manuscript or supplementary information files References Al-Jarallah M, Rajan R, Al-Zakwani I, et al. Incidence and impact of cardiorenal anaemia syndrome on all-cause mortality in acute heart failure patients stratified by left ventricular ejection fraction in the Middle East. ESC Heart Fail. 2019;6(1):103-10. Kim CJ, Choi IJ, Park HJ, et al. Impact of Cardiorenal Anemia Syndrome on Short- and Long-Term Clinical Outcomes in Patients Hospitalized with Heart Failure. Cardiorenal Med. 2016;6(4):269-78. Lu KJ, Kearney LG, Hare DL, et al. Cardiorenal anemia syndrome as a prognosticator for death in heart failure. Am J Cardiol. 2013;111(8):1187-91. 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Akirov A, Masri-Iraqi H, Atamna A, et al. Low Albumin Levels Are Associated with Mortality Risk in Hospitalized Patients. Am J Med. 2017;130(12):1465.e11-1465.e19. Owen WF Jr, Lew NL, Liu Y, et al. The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis. N Engl J Med. 1993;329(14):1001-6. Wu CY, Hu HY, Huang N, et al. Albumin levels and cause-specific mortality in community-dwelling older adults. Prev Med. 2018;112:145-51. Silverberg DS, Wexler D, Blum M, et al. The use of subcutaneous erythropoietin and intravenous iron for the treatment of the anemia of severe, resistant congestive heart failure improves cardiac and renal function and functional cardiac class, and markedly reduces hospitalizations. J Am Coll Cardiol. 2000;35(7):1737-44. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Dec, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 09 Sep, 2024 Reviews received at journal 05 Sep, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviews received at journal 24 Aug, 2024 Reviewers agreed at journal 24 Aug, 2024 Reviewers invited by journal 11 Aug, 2024 Editor assigned by journal 09 Aug, 2024 Editor invited by journal 22 Apr, 2024 Submission checks completed at journal 18 Apr, 2024 First submitted to journal 16 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4274074","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293946010,"identity":"b43fbc3c-cd00-4f6d-ac88-6fccca1830cf","order_by":0,"name":"Mengyue Zhu","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Mengyue","middleName":"","lastName":"Zhu","suffix":""},{"id":293946011,"identity":"f955b6fc-df25-45d9-a5de-abb0581dc047","order_by":1,"name":"Min Liu","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Group Suqian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Liu","suffix":""},{"id":293946012,"identity":"eb94a752-c271-4dd7-9f61-84ab29e0d54b","order_by":2,"name":"Chunlei Lu","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chunlei","middleName":"","lastName":"Lu","suffix":""},{"id":293946013,"identity":"37ceb764-408d-4e40-8a5f-1bdbcccd46eb","order_by":3,"name":"Dafeng He","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Dafeng","middleName":"","lastName":"He","suffix":""},{"id":293946014,"identity":"6c6e3486-d0d2-43ee-82d1-890b7c453623","order_by":4,"name":"Jiao Li","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Li","suffix":""},{"id":293946015,"identity":"37c567af-30a0-4cd2-9c15-ae6599337d2c","order_by":5,"name":"Xia Xu","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Xu","suffix":""},{"id":293946016,"identity":"c1627ac7-f39f-42f3-99e7-552f08855b46","order_by":6,"name":"Ying Cui","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Cui","suffix":""},{"id":293946025,"identity":"1fa11ccf-cc42-47d5-a7d3-59fef22254b2","order_by":7,"name":"Chuanyan Zhao","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chuanyan","middleName":"","lastName":"Zhao","suffix":""},{"id":293946026,"identity":"862dbd04-5e07-493d-99d3-5ed675f99da5","order_by":8,"name":"Jun Xu","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Xu","suffix":""},{"id":293946027,"identity":"712fcf16-3bf2-4f04-87e9-9425b56fe46c","order_by":9,"name":"Gang Zhou","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Zhou","suffix":""},{"id":293946028,"identity":"094ba1d3-8e39-4374-bddd-b0aaac1d0a28","order_by":10,"name":"Hongbin Mou","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hongbin","middleName":"","lastName":"Mou","suffix":""},{"id":293946029,"identity":"692e65d8-7b22-4ad3-ae41-181a04e8ff11","order_by":11,"name":"Guangyu Bi","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Guangyu","middleName":"","lastName":"Bi","suffix":""},{"id":293946030,"identity":"6f8a69c3-503c-476a-8c8a-048e267d6281","order_by":12,"name":"Changhua Liu","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Changhua","middleName":"","lastName":"Liu","suffix":""},{"id":293946034,"identity":"e75777ac-974f-4e68-ac4c-14ddd535ded7","order_by":13,"name":"Rong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYJACCSDmkWdmPvggocKGeC1yhu1tyQYPzqQRr8WY4cwZNcmHbYcIKzc43nvwNk/NncTGGTlsFQlsBxj427sT8Gs5cy7ZmufYs8R2idxjNxJ47jBInDm7Aa8Wsxs5ZtI8bIeBtuSl3UiQeMZgIJFLQMv9N0At/w4nNgD1FiQYHCZCyw0eM2netsMg75sxJCQQocX+TI6x5dy+w+BAlkg4kMZD0C+S7WcMb7z5dhgclR9//rOR42/vxa8FA/CQpnwUjIJRMApGAVYAABsPTmOg5qIBAAAAAElFTkSuQmCC","orcid":"","institution":"Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-04-16 07:33:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4274074/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4274074/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-024-04452-3","type":"published","date":"2024-12-31T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55380904,"identity":"0286189f-f52d-44e3-b989-e17ae33be2a0","added_by":"auto","created_at":"2024-04-26 13:48:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":299872,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this study's patient selection and exclusion process.\u003c/p\u003e\n\u003cp\u003eNYHA, New York Heart Association functional class; eGFR, estimated glomerular filtration rate; WHO, World Health Organization.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4274074/v1/0e618042e9ee8c1eceb7cc6b.jpeg"},{"id":55380906,"identity":"39d23bfd-deb8-438d-8ffb-6a2e77be23be","added_by":"auto","created_at":"2024-04-26 13:48:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234267,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis for patients with CRAS in three groups.\u003c/p\u003e\n\u003cp\u003eK-M, Kaplan-Meier; CRAS, cardiorenal anemia syndrome; NYHA, New York Heart Association functional class.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4274074/v1/a463b09a514fc9ec04e4690e.jpeg"},{"id":55380905,"identity":"3ec49c43-711b-469d-8c32-5feb3cd15628","added_by":"auto","created_at":"2024-04-26 13:48:18","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":367332,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of eight influencing factors in predicting prognosis in patients with CRAS.\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic curve; CRAS, cardiorenal anemia syndrome; AUC, area under the curve; LVEF, left ventricular ejection fraction; Alb, albumin; NYHA, New York Heart Association functional class; LVEDD, left ventricular end-diastolic diameter; SCr, serum creatinine.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4274074/v1/b24fae4c2c49e7cceed835de.jpeg"},{"id":73093451,"identity":"81790f7e-d35f-4354-949e-1191a907f86e","added_by":"auto","created_at":"2025-01-06 16:19:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546138,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4274074/v1/ba9bdf60-f57f-4cb5-b90c-676e18ee7ff3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical features and prognostic factors of Cardiorenal anemia syndrome in China: a retrospective single-center study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiorenal anemia syndrome (CRAS) is a term that refers to a complex group of disorders, including heart failure (HF), chronic kidney disease (CKD) combined with anemia [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which has been widely used in recent years after understanding the importance and association between those mentioned above three. Cardiovascular disease (CVD), including HF, is one of the most common comorbidities in patients with CKD and the leading cause of death in patients with CKD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Approximately 25\u0026ndash;50% of patients with HF have CKD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and the risk of death is increased by 56% compared with those without CKD [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. At the same time, the risk of anemia in patients with CKD and CVD significantly increased, and some studies have found that anemia is also an independent risk factor for the progression of CVD and CKD, considerably increasing the risk of hospitalization and death in patients with HF, promoting the progression of kidney disease, and increasing the risk of renal replacement therapy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There is a triangular relationship between the three, a vicious circle in which any of the three can cause the other or be caused by the other. CRAS is attributed to numerous factors, but the exact pathophysiologic mechanisms remain uncertain. Almost all previous studies have been conducted to investigate CRAS in patients with HF, without analyzing the clinical features and prognosis of CRAS alone.\u003c/p\u003e \u003cp\u003eThere is currently limited data on clinical features of the CRAS in China. Therefore, this study is aimed to investigate the characteristics and risk factors of survival in patients with CRAS at a single center in China.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy cohort\u003c/b\u003e \u003c/p\u003e \u003cp\u003e81795 patients were admitted to the Department of Nephrology and Cardiovascular Medicine of the Northern Jiangsu People's Hospital between August 2012 and August 2021, of which 820 patients met our inclusion criteria for CRAS. CRAS was defined as a syndrome in which HF, CKD, and anemia coexist, referring to other literature [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The exclusion criteria were the following: 1) patients age\u0026thinsp;\u0026lt;\u0026thinsp;18 years or age\u0026thinsp;\u0026gt;\u0026thinsp;85 years; 2) patients diagnosed with congenital heart disease; 3) patients with malignant tumors, hematologic disease, severe hyperthyroidism, or severe infections; 4) history of blood transfusion in the past 4 months; 5) patients with acute heart failure and acute kidney injury; and 6) incomplete clinical data or lack of follow-up data (Figure. 1).\u003c/p\u003e \u003cp\u003eData on demographics, comorbidities, vital signs, and laboratory tests were collected using electronic medical records at baseline and during the follow-up. The following echocardiography parameters were collected: left ventricular end-diastolic diameter (LVEDD) and left ventricular ejection fraction (LVEF). Two clinicians independently graded the patients\u0026rsquo; NYHA functional class based on their HF symptoms.\u003c/p\u003e \u003cp\u003e The study complied with the Declaration of Helsinki and was approved by the institutional Ethics Research Committee of Northern Jiangsu People's Hospital (No. 2022ky310).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDefinitions\u003c/strong\u003e \u003cp\u003eThe primary endpoint of the study was death. Heart failure was defined by reference to the European Society of Cardiology (ESC) criteria as judged by patients\u0026rsquo; symptoms, signs, and ancillary tests [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Chronic kidney disease was defined as estimated glomerular filtrate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e or serum creatinine levels\u0026thinsp;\u0026gt;\u0026thinsp;177 mmol/L for at least 3 months. The eGFR was determined using the CKD-EPI equation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to the World Health Organization, anemia is defined as a concentration of hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;130 g/L in men and \u0026lt;\u0026thinsp;120 g/L in women [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Anemia is classified as mild, moderate, severe, and very severe anemia based on the expert consensus on anemia in China. Mild anemia is a hemoglobin lower than normal and greater than or equal to 90 g/L. Moderate anemia is a hemoglobin greater than or equal to 60 and less than 90 g/L. The amount of hemoglobin in severe anemia is greater than or equal to 30 and less than 60 g/L. The amount of hemoglobin in very severe anemia is less than 30 g/L. Systolic blood pressure (SP) refers to systolic blood pressure at admission.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analyses\u003c/b\u003e \u003c/p\u003e \u003cp\u003eQuantitative data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median with interquartile range, depending on whether the data were normally distributed, and differences were compared by t-test or Kruskal-Wallis rank sum test. Categorical data were expressed as numbers (%), and differences were assessed by χ\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test. Patient survival was analyzed by the Kaplan-Meier curve and log-rank test. Univariable and multivariable Cox regression analyses were performed to identify predictors of death. Multivariable Cox regression analyses were adjusted for potential confounders. Variables were selected based on clinical relevance. Results are expressed as hazard ratio (HR) with 95% confidence interval (95% CI). Receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to compare the values of different parameters in the prognosis of CRAS patients. A 2-sided \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used to determine statistical significance. Empower (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.empowerstats.com\" target=\"_blank\"\u003ewww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; X\u0026amp;Y Solutions Inc, Boston, MA) and R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used for all statistical analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 820 patients diagnosed with CRAS were included in the study. Patients were categorized according to the NYHA functional class on admission into three groups: one with NYHA Class II, one with NYHA Class III, and one with NYHA Class IV. There were 124, 492, and 204 patients with NYHA Class II, NYHA Class III, and NYHA Class IV, respectively. We found that there were significant differences in gender, age, diabetes mellitus, atrial fibrillation, edema, dialysis, SCr, N-terminal pro-brain natriuretic peptide (NT-proBNP), patient survival months, and mortality between the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of patients with CRAS are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. There were 41 patients with CKD stage 3, 109 with CKD stage 4, and 670 with CKD stage 5. There were 366 patients with mild anemia, 429 with moderate anemia, and 25 with severe anemia. The median age of the patients was 65.00 (51.00\u0026ndash;75.00) years, and 61.2% were men. In the whole sample, the median SCr was 601.6 (378.0-818.0) umol/L, the median hemoglobin was 88.0 (75.0-101.0) g/L, and the median NT-proBNP was 31200.0 (11800.0-83000.0) pg/mL. The rates of statin use, renal replacement therapy, and correction of anemia were 29.3%, 47.1%, and 69.4%, respectively. Median follow-up was 27.0 (13.0\u0026ndash;51.0) months, 86.5% of patients had at least six months of follow-up, and 416 (50.7%) patients died. The mortality rate was observed to increase with worsening of heart failure, with mortality rates of 44.4%, 47.4%, and 62.8% in NYHA functional class II, III, and IV, respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of patients with CRAS\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;820)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNYHA Class II\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNYHA Class III\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;492)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNYHA Class IV\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e502 (61.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e85 (68.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e307 (62.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 (53.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65.0 (51.0\u0026ndash;75.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.0 (45.0\u0026ndash;72.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64.0 (51.0\u0026ndash;74.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.0 (51.0\u0026ndash;76.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e725 (88.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e103 (83.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e437 (88.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e185 (90.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSP, mmHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e150.0 (135.0-170.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e150.0 (136.0-171.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e150.0 (134.0-169.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e152.0 (138.0-175.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.305\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e157 (19.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18 (14.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98 (19.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (20.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.363\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes mellitus, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e386 (47.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44 (35.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e207 (42.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104 (51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMyocardial infarction, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38 (4.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5 (4.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23 (4.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.934\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCerebral infarction, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e140 (17.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21 (17.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80 (16.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (19.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.659\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAtrial fibrillation, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e108 (13.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6 (4.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66 (13.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (17.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEdema, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e501 (61.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e63 (50.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e291 (59.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147 (72.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDialysis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e386 (47.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e60 (48.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e248 (50.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78 (38.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite blood cells, *10^9/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.4 (4.5\u0026ndash;8.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.0 (4.9-8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.4 (5.0-8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.7 (5.0-8.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e88.0 (75.0-101.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e87.5 (75.0-103.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e88.0 (76.0-102.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.0 (74.8\u0026ndash;100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.929\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlbumin ,g/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.9 (31.7\u0026ndash;39.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.1 (31.6\u0026ndash;40.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.6 (32.2\u0026ndash;40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.7 (31.4\u0026ndash;40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.155\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCr, umol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e601.6 (378.0-818.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e634.0 (396.3-889.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e614.5 (402.9-821.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e529.0 (328.0-763.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeGFR, ml/min/1.73m2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.1 (5.0-12.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.7 (4.8\u0026ndash;11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.0 (5.0-11.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9 (5.1\u0026ndash;13.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNT-proBNP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31200.0 (11800.0-83000.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38100.0\u003c/p\u003e\n\u003cp\u003e(15000.0-96977.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28950.0\u003c/p\u003e\n\u003cp\u003e(9862.5-78025.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33016.8\u003c/p\u003e\n\u003cp\u003e(13475.0-84207.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecTnT, ng/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1 (0.0-0.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1 (0.0-0.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1 (0.0-0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1 (0.0-0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.413\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLVEF, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.0 (45.0\u0026ndash;60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e54.5 (46.0\u0026ndash;60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.0 (46.0\u0026ndash;60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.0 (42.8\u0026ndash;60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLVEDD, mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e54.0 (50.0\u0026ndash;58.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.0 (50.0\u0026ndash;59.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53.0 (49.8\u0026ndash;58.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.0 (49.0\u0026ndash;59.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.458\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurvival, months\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.0 (13.0\u0026ndash;51.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.5 (17.0-77.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.50 (14.0\u0026ndash;49.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.00 (9.00\u0026ndash;44.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDeath, n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e416 (50.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55 (44.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e233 (47.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128 (62.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eValues are median (interquartile range) or n (%).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eCRAS, cardiorenal anemia syndrome; NYHA, New York Heart Association functional class; SP, systolic blood pressure; SCr, serum creatinine; eGFR, estimated glomerular filtration rate; NT-proBNP, N-terminal pro-brain natriuretic peptide; cTnT, serum cardiac troponin T; LVEF, left ventricular ejection fraction; LVEDD, left ventricular end-diastolic diameter.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of death\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn univariable Cox regression analyses, age, SP, smoking history, myocardial infarction, cerebral infarction, atrial fibrillation, dialysis, NYHA functional class, SCr, and LVEDD were observed to show a significant association with the risk of death. After adjusting the gender, age, smoking history, SP, myocardial infarction, cerebral infarction, atrial fibrillation, NYHA functional class, dialysis, SCr, LVEDD, and LVEF, multivariable Cox regression analyses showed that age, smoking history, cerebral infarction, NYHA functional class, albumin, SCr, LVEDD, and LVEF were close-dependent related to the risk of death (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The Kaplan-Meier survival curves of patients with CRAS in three groups were shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, which showed that the higher NYHA functional class, the worse the prognosis of patients with CRAS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePredictors of death (univariable and multivariable analyses)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnadjusted HR\u003c/p\u003e\n\u003cp\u003e(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAdjusted HR\u003c/p\u003e\n\u003cp\u003e(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.25 (1.03, 1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.23 (0.99, 1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.04 (1.03, 1.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.04 (1.03, 1.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.07 (0.79, 1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.658\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.02 (0.75, 1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.887\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSP, per 10 mmHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95 (0.91, 0.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98 (0.94, 1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.330\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.37 (1.08, 1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.38 (1.06, 1.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.21 (0.99, 1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.01 (0.83, 1.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.904\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMyocardial infarction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.80 (1.19, 2.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.17 (0.76, 1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCerebral infarction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.79 (1.41, 2.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.36 (1.07, 1.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAtrial fibrillation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.46 (1.12, 1.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86 (0.65, 1.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.303\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDialysis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62 (0.51, 0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83 (0.66, 1.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.105\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNYHA, per I class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.46 (1.25, 1.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.28 (1.10, 1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\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 align=\"left\"\u003e\n\u003cp\u003eHemoglobin, per 10 g/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.02 (0.96, 1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.97 (0.91, 1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.266\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlbumin, g/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.99 (0.97, 1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.98 (0.96, 0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCr, per 88.4 umol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.93 (0.91, 0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.04 (1.01, 1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNT-proBNP, per 100 pg/ml\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.708\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.073\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecTnT, per 0.1 ng/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.01 (0.99, 1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00 (0.98, 1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.719\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLVEDD, per 10 mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78 (0.67, 0.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82 (0.68, 0.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLVEF, per 10%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.92 (0.84, 1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81 (0.72, 0.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eHR, hazard ratio; CI, confidence interval; SP, systolic blood pressure; NYHA, New York Heart Association functional class; SCr, serum creatinine; NT-proBNP, N-terminal pro-brain natriuretic peptide; cTnT, serum cardiac troponin T; LVEDD, left ventricular end-diastolic diameter; LVEF, left ventricular ejection fraction.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eMultivariable analyses adjusted gender, age, smoking history, SP, myocardial infarction, cerebral infarction, atrial fibrillation, NYHA functional class, dialysis, SCr, LVEDD, and LVEF.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of clinical characteristics and laboratory parameters in predicting prognosis in CRAS patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic curve (ROC) analysis was used to compare the values of different parameters in the prognosis of CRAS patients (Figure. 3). The age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SCr (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LVEDD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and NYHA functional class (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) all had diagnostic values with statistical significance for prognosis of patients with CRAS. Among these values, the area under the curve (AUC) value of age was the largest (AUC\u0026thinsp;=\u0026thinsp;0.709) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparsion of eight influencing factors in patients with CRAS\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTest\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking (Yes/NO)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.464\u0026ndash;0.543\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.870\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLVEF, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.482\u0026ndash;0.561\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.279\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlbumin, g/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.487\u0026ndash;0.566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.193\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCerebral infarction (Yes/NO)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.546\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.507\u0026ndash;0.586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNYHA, class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.565\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.526\u0026ndash;0.604\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLVEDD, mm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.529\u0026ndash;0.607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCr, umol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.590\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.551\u0026ndash;0.629\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.674\u0026ndash;0.744\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eAUC, area under the curve; CI, confidence interval; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association functional class; LVEDD, left ventricular end-diastolic diameter; SCr, serum creatinine; LVEDD, left ventricular end-diastolic diameter.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is a strong link between heart disease and kidney disease, and the two influence each other, which has long been a concern by the medical community [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The first mention of cardiorenal syndrome (CRS) was in 1913, when Thomas Lewis proposed the presence of a close relationship between the heart and the kidney [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Since then, much progress has been made in the pathogenesis, classification, and treatment of cardiorenal syndrome. In 2008, under the initiative of Ronco et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the Acute Disease Quality Initiative (ADQI) working group proposed the first consensus on the definition and classification of CRS. CRS often coexists with anemia, which leads to reciprocal and progressive cardiac and renal deterioration. The triad of heart failure, chronic kidney disease, and anemia is termed cardiorenal anemia syndrome (CRAS).\u003c/p\u003e \u003cp\u003e CRAS is considerably prevalent, and the management of patients with CRAS remains a challenging undertaking worldwide because of the complexity and heterogeneity of this syndrome and the lack of evidence-based clinical guidelines. At present, CRAS is common in patients with HF and is an independent predictor of all-cause mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], but few studies have reported the clinical features of CRAS and its prognostic factors. Therefore, this study focused on describing the baseline characteristics of patients with CRAS and the influencing factors of their prognosis. We compared the baseline characteristics of the patients with CRAS according to their NYHA functional class and found that there were significant differences in gender, age, diabetes mellitus, atrial fibrillation, edema, dialysis, SCr, NT-proBNP, patient survival months, and mortality between the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although there was no relationship between gender and prognosis after multivariate adjustment, there was a significant difference in gender between the three groups, which is an interesting phenomenon. Marta et al. found sex-related differences in patients with cardiorenal syndrome. More advanced kidney disease, anemia, and an increased prevalence of preserved ejection fraction are more common in women with CRS, whereas heart failure with reduced ejection fraction was more frequently observed in men [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It has been found that CRAS is associated with increasing age and diabetes mellitus [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which is similar to our research, and we found that with the increase of NYHA functional class, the proportion of CRAS patients with diabetes mellitus and age increased. Diabetes mellitus is also a major risk factor for the development of coronary artery disease and HF [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and diabetes mellitus is the most common cause of CKD in Western countries and China [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, as the NYHA functional class increased, the proportion of edema, atrial fibrillation, and NT-proBNP values were higher, while the LVEF values were lower, indicating that CRAS patients had more severe HF or other cardiovascular diseases. NT-proBNP is associated with HF NYHA functional class, LVEF, and ventricular pressure, thereby contributing to prognosis and risk classification in patients with HF [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, NT-proBNP is associated with renal insufficiency and is more sensitive in predicting cardiovascular and all-cause mortality in patients with CKD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe development of CRAS is multifactorial, and some risk factors have been recognized. Advanced age, diabetes mellitus, ischaemic etiology, NYHA functional class, and LVEF are independently associated with CRAS in patients with HF [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Most studies have explored the effect of the presence or absence of CRAS on prognosis in patients with HF, and few studies have described the prognostic factors of CRAS itself, whether it is HF, renal insufficiency, anemia, or others. Therefore, this article found that age, smoking history, cerebral infarction, NYHA functional class, albumin, SCr, LVEDD, and LVEF were closely related to the risk of death in patients with CRAS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, We further compared survival of patients with CRAS in three groups by the Kaplan-Meier curve, which showed that the higher NYHA functional class, the worse the prognosis of patients with CRAS. All these indicated that HF plays an important role in the prognosis factors of CRAS.\u003c/p\u003e \u003cp\u003eNext, we used ROC analysis to compare the values of different parameters in the prognosis of CRAS patients, and we found that the AUC value of age was the largest (AUC\u0026thinsp;=\u0026thinsp;0.709). Undoubtedly, advanced age is a prognostic risk factor for heart failure and chronic kidney disease, and Domenico et al. also found that age was independently related to CRAS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Likewise, cigarette smoking has been identified as an independent risk factor for cardiovascular events and incident CKD [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which is similar to our conclusions, and we found that smoking history is an independent risk factor for the prognosis of CRAS patients. We also found cerebral infarction, albumin, and SCr were closely related to the risk of death in patients with CRAS. Cerebrovascular disease and cardiovascular disease not only have similarities in the occurrence and development of diseases, such as cerebral infarction and myocardial infarction, but also have a high overlap rate in the affected population. Albumin has anti-inflammatory, antioxidant, and antithrombotic properties [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Hypoalbuminemia is a strong predictor of increased all-cause and cardiovascular mortality based on several cohort studies and meta-analyses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Pedro et al. explored the survival rates of HF patients with CRAS in Tanzania and found that renal dysfunction was a predictor of mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which is consistent with our conclusions.\u003c/p\u003e \u003cp\u003ePatients with CKD and HF have an increased risk of anemia. The presence of anemia increases in parallel with the severity of HF, the CKD stage, and the patients' age, while the treatment of anemia leads to an improvement in cardiac and renal function as well as fewer hospitalizations for HF [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Here, we found that anemia in CRAS patients was not related to prognosis, either because these patients were treated aggressively or because of sample size.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCRAS is a comprehensive term encompassing the intricate relationship between simultaneous cardiac, renal impairments and anemia, wherein the deterioration of one organ initiates, perpetuates, or accelerates the decline of the other. The pathologic triangle formed by HF, CKD, and anemia carries high morbidity and mortality rates and decreases quality of life. Our study showed that 50.7% of patients with CRAS died during follow-up. Moreover, the mortality rate of patients is closely related to age, smoking history, cerebral infarction, NYHA functional class, albumin, SCr, LVEDD, and LVEF.\u003c/p\u003e \u003cp\u003eAlthough the pathophysiologic mechanisms underlying CRAS remain uncertain, we can identify and treat the potentially reversible causes early based on prognostic influencing factors, which may improve outcomes in these CRAS patients.\u003c/p\u003e \u003cp\u003eOur study still had some limitations. The retrospective study reflected patient data and findings only from a single center in China. Moreover, Hb concentrations on admission were measured, which may not accurately reflect Hb levels after discharge or changes in Hb levels over time. We also did not attempt to identify the cause or assess the treatment or maintenance of anemia during follow-up.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\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\u003eThis work was supported by the Scientific Research Project of Jiangsu Provincial Healthcare Commission (Grant number Z2022068) and Hospital Research Projects (Grant number SBQN22006 and FCJS202340).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvailability of data and material datasets collected and analyzed in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and Informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of Northern Jiangsu People\u0026apos;s Hospital (No. 2022ky310) and was carried out following the principle of the Helsinki Declaration. This is a retrospective study using de-identified data, and our institutional review board did not require consent from the patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declared no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRW, DH and CL contributed to the conception of the study. ML, JL and XX screened the data, and MZ analyzed and interpreted the data. MZ,YC, CZ, JX and GZ contributed to the follow-up. MZ finished the manuscript. HM, GB, and CL supervised and edited the manuscript. All authors contributed to the work and approved the submitted version for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Jarallah M, Rajan R, Al-Zakwani I, et al. Incidence and impact of cardiorenal anaemia syndrome on all-cause mortality in acute heart failure patients stratified by left ventricular ejection fraction in the Middle East. ESC Heart Fail. 2019;6(1):103-10.\u003c/li\u003e\n\u003cli\u003eKim CJ, Choi IJ, Park HJ, et al. Impact of Cardiorenal Anemia Syndrome on Short- and Long-Term Clinical Outcomes in Patients Hospitalized with Heart Failure. Cardiorenal Med. 2016;6(4):269-78.\u003c/li\u003e\n\u003cli\u003eLu KJ, Kearney LG, Hare DL, et al. Cardiorenal anemia syndrome as a prognosticator for death in heart failure. Am J Cardiol. 2013;111(8):1187-91.\u003c/li\u003e\n\u003cli\u003ePallangyo P, Fredrick F, Bhalia S, et al. Cardiorenal Anemia Syndrome and Survival among Heart Failure Patients in Tanzania: A Prospective Cohort Study. BMC Cardiovasc Disord. 2017(1);17:59.\u003c/li\u003e\n\u003cli\u003eScrutinio D, Passantino A, Santoro D, et al. The cardiorenal anaemia syndrome in systolic heart failure: prevalence, clinical correlates, and long-term survival. Eur J Heart Fail. 2011;13(1):61-7.\u003c/li\u003e\n\u003cli\u003eUre\u0026ntilde;a-Torres P, D\u0026apos;Marco L, Raggi P, et al. Valvular heart disease and calcification in CKD: more common than appreciated. Nephrol Dial Transplant. 2020;35(12):2046-53.\u003c/li\u003e\n\u003cli\u003eSmith GL, Lichtman JH, Bracken MB, et al. Renal impairment and outcomes in heart failure: systematic review and meta-analysis. J Am Coll Cardiol. 2006;47(10):1987-96.\u003c/li\u003e\n\u003cli\u003eGroenveld HF, Januzzi JL, Damman K, et al. Anemia and mortality in heart failure patients a systematic review and meta-analysis. J Am Coll Cardiol. 2008;52(10):818-27.\u003c/li\u003e\n\u003cli\u003eMcDonagh TA, Metra M, Adamo M, et al. 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure [published correction appears in Eur Heart J. 2024 Jan 1;45(1):53]. Eur Heart J. 2023;44(37):3627-39.\u003c/li\u003e\n\u003cli\u003eLevey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011 Sep 20;155(6):408]. Ann Intern Med. 2009;150(9):604-12.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Geneva: WHO, 2011\u003c/li\u003e\n\u003cli\u003eLajoie G, Laszik Z, Nadasdy T, et al. The renal-cardiac connection: renal parenchymal alterations in patients with heart disease. Semin Nephrol. 1994;14(5):441-63.\u003c/li\u003e\n\u003cli\u003eMahapatra HS, Lalmalsawma R, Singh NP, et al. Cardiorenal syndrome. Iran J Kidney Dis. 2009;3(2):61-70.\u003c/li\u003e\n\u003cli\u003eLewis T. A Clinical Lecture ON PAROXYSMAL DYSPNOEA IN CARDIORENAL PATIENTS: WITH SPECIAL REFERENCE TO \u0026quot;CARDIAC\u0026quot; AND \u0026quot;URAEMIC\u0026quot; ASTHMA: Delivered at University College Hospital, London,1913 November 12th. Br Med J. 1913;2(2761):1417-20.\u003c/li\u003e\n\u003cli\u003eRonco C, Haapio M, House AA, et al. Cardiorenal syndrome. J Am Coll Cardiol. 2008;52(19):1527-39.\u003c/li\u003e\n\u003cli\u003eCobo Marcos M, de la Espriella R, Gay\u0026aacute;n Ord\u0026aacute;s J, et al. Sex differences in Cardiorenal Syndrome: Insights from CARDIOREN Registry. Curr Heart Fail Rep. 2023;20(3):157-67.\u003c/li\u003e\n\u003cli\u003eCandido R, Srivastava P, Cooper ME, et al. Diabetes mellitus: a cardiovascular disease. Curr Opin Investig Drugs. 2003;4(9):1088-94.\u003c/li\u003e\n\u003cli\u003eJoseph JJ, Deedwania P, Acharya T, et al. Comprehensive Management of Cardiovascular Risk Factors for Adults With Type 2 Diabetes: A Scientific Statement From the American Heart Association. Circulation. 2022;145(9):e722-e759.\u003c/li\u003e\n\u003cli\u003eThomas MC, Cooper ME, Zimmet P. Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease. Nature Reviews Nephrology. 2016;12(2):73-81.\u003c/li\u003e\n\u003cli\u003ePalazzuoli A, Gallotta M, Quatrini I, et al. Natriuretic peptides (BNP and NT-proBNP): measurement and relevance in heart failure. Vasc Health Risk Manag. 2010;6:411-8.\u003c/li\u003e\n\u003cli\u003eFu S, Luo L, Ye P, et al. The ability of NT-proBNP to detect chronic heart failure and predict all-cause mortality is higher in elderly Chinese coronary artery disease patients with chronic kidney disease. Clin Interv Aging. 2013;8:409-17.\u003c/li\u003e\n\u003cli\u003eAmbrose JA, Barua RS. The pathophysiology of cigarette smoking and cardiovascular disease: an update. J Am Coll Cardiol. 2004;43(10):1731-7.\u003c/li\u003e\n\u003cli\u003eXia J, Wang L, Ma Z, et al. Cigarette smoking and chronic kidney disease in the general population: a systematic review and meta-analysis of prospective cohort studies. Nephrol Dial Transplant. 2017;32(3):475-87.\u003c/li\u003e\n\u003cli\u003eManolis AA, Manolis TA, Melita H, et al. Low serum albumin: A neglected predictor in patients with cardiovascular disease. Eur J Intern Med. 2022;102:24-39.\u003c/li\u003e\n\u003cli\u003eAkirov A, Masri-Iraqi H, Atamna A, et al. Low Albumin Levels Are Associated with Mortality Risk in Hospitalized Patients. Am J Med. 2017;130(12):1465.e11-1465.e19.\u003c/li\u003e\n\u003cli\u003eOwen WF Jr, Lew NL, Liu Y, et al. The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis. N Engl J Med. 1993;329(14):1001-6.\u003c/li\u003e\n\u003cli\u003eWu CY, Hu HY, Huang N, et al. Albumin levels and cause-specific mortality in community-dwelling older adults. Prev Med. 2018;112:145-51.\u003c/li\u003e\n\u003cli\u003eSilverberg DS, Wexler D, Blum M, et al. The use of subcutaneous erythropoietin and intravenous iron for the treatment of the anemia of severe, resistant congestive heart failure improves cardiac and renal function and functional cardiac class, and markedly reduces hospitalizations. J Am Coll Cardiol. 2000;35(7):1737-44.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CRAS, Heart failure, Chronic kidney disease, Anemia, Prognosis factors","lastPublishedDoi":"10.21203/rs.3.rs-4274074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4274074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is little research on cardiorenal anemia syndrome (CRAS) in China. This study was to describe the characteristics of patients with CRAS and to explore predictive risk factors of all-cause mortality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 81795 patients were hospitalized from August 2012 to August 2021 in the nephrology department and cardiology department, of which 820 patients with CRAS met the inclusion criteria and were recruited into this study. The 820 patients with CRAS were divided into three groups based on New York Heart Association (NYHA) functional class: a NYHA Class II group (n\u0026thinsp;=\u0026thinsp;124), a NYHA Class III group (n\u0026thinsp;=\u0026thinsp;492), and a NYHA Class IV group (n\u0026thinsp;=\u0026thinsp;204). Demographics and laboratory tests were collected and risk factors of death events were analyzed. The primary endpoint of the study was death.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e820 patients were included, with a median age of 65.00 (51.00\u0026ndash;75.00) years and 61.2% were men. The median follow-up was 27.0 (13.0\u0026ndash;51.0) months. 416 (50.7%) patients died during follow-up. Age, smoking history, cerebral infarction, NYHA functional class, albumin, serum creatinine (SCr), left ventricular end-diastolic diameter (LVEDD), and left ventricular ejection fraction (LVEF) remained independent predictors in patients with CRAS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after adjusting to the potential confounders.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHeart failure and renal dysfunction are a fatal combination and are associated with poor prognosis in patients with CRAS.\u003c/p\u003e","manuscriptTitle":"Clinical features and prognostic factors of Cardiorenal anemia syndrome in China: a retrospective single-center study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 13:48:13","doi":"10.21203/rs.3.rs-4274074/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-09T05:51:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-05T14:15:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326207166768219933460130307668016752230","date":"2024-08-25T18:23:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50379538860809534321595505070128299138","date":"2024-08-25T17:03:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-24T19:48:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257436344009556595676998467799015410225","date":"2024-08-24T19:27:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-11T06:10:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-09T08:24:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-22T08:11:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-18T06:53:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-04-16T07:32:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f287178d-0c8f-4f4f-90c9-ce4e85167a4c","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T16:04:17+00:00","versionOfRecord":{"articleIdentity":"rs-4274074","link":"https://doi.org/10.1186/s12872-024-04452-3","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2024-12-31 15:57:09","publishedOnDateReadable":"December 31st, 2024"},"versionCreatedAt":"2024-04-26 13:48:13","video":"","vorDoi":"10.1186/s12872-024-04452-3","vorDoiUrl":"https://doi.org/10.1186/s12872-024-04452-3","workflowStages":[]},"version":"v1","identity":"rs-4274074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4274074","identity":"rs-4274074","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}