Risk factors and prognostic modeling in Cardiorenal Syndrome Type 2: a retrospective study of multicenter

Risk factors and prognostic modeling in Cardiorenal Syndrome Type 2: a retrospective study of multicenter | 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 Risk factors and prognostic modeling in Cardiorenal Syndrome Type 2: a retrospective study of multicenter Bin Wang, Xie Zheng, Qinghui Fu, Xiaoqian Luo, Sijun Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5006638/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cardiorenal syndrome (CRS) represents a complex interaction between heart failure and renal insufficiency, leading to progressive disease. Type 2 CRS is characterized by the development of renal dysfunction secondary to chronic cardiac disease. The prevalence of Type 2 CRS is substantial, af fecting up to 45-63% of patients admitted for chronic heart failure. Despite its high morbidity and mortality, there is a lack of robust diagnostic tools and prognostic models to guide clinical management. Methods: This multicenter retrospective study included patients diagnosed with CRS type 2 based on the 2019 American Heart Association definition. Data were collected from electronic medical records of three hospitals between January 2021 and December 2023. Advanced statistical methods, including receiver operating characteristic (ROC) curve analysis, univariate Kaplan-Meier (KM) analysis, and multivariate Cox proportional hazards regression, were utilized to develop a nomogram for predicting patient prognosis. Results: The study included 519 patients with CRS-2. Independent predictors of adverse outcomes included elevated serum creatinine and blood urea nitrogen (BUN) levels, decreased platelet count, elevated B-type natriuretic peptide (BNP), and decreased oxygen partial pressure (PaO2). These findings suggest that close monitoring of these markers is essential in clinical practice to identify patients at high risk of adverse events early on. Conclusion: Our study provides evidence that serum creatinine, BUN, platelet count, BNP, and PaO2 are independent predictors of adverse outcomes in patients with Type 2 CRS. These findings have important implications for clinical practice and emphasize the need for a comprehensive approach to the management of this challenging condition. Figures Figure 1 Figure 2 Figure 3 Introduction Cardiovascular disease (CVD) remains a significant global health burden, with renal dysfunction often coexisting and exacerbating the clinical course of heart disease[ 1 ]. Heart failure patients often have abnormal kidney function, and common indicators of kidney function include glomerular filtration rate, creatinine, blood urea nitrogen and uric acid[ 2 ]. This interaction between heart failure and renal insufficiency leading to progressive progression of the disease is defined as cardiorenal syndrome (CRS)[ 3 , 4 ]. Type 2 Cardiorenal Syndrome (CRS) is a complex clinical scenario characterized by the development of renal dysfunction secondary to chronic cardiac disease[ 5 ]. The prevalence of Type 2 CRS is substantial, with studies suggesting that up to 45–63% of patients admitted for chronic heart failure (HF) exhibit concurrent renal dysfunction[ 6 ]. This comorbidity not only prolongs hospital stays and drives up healthcare costs but also significantly increases the risk of rehospitalization and mortality[ 7 , 8 ]. The pathophysiology of CRS-2 involves a myriad of mechanisms including neurohormonal activation, abnormal endothelial activation, and the release of pro-inflammatory cytokines[ 9 – 11 ]. Until recently, there are limited researches on the risk factors and prediction methods for the prognosis of CRS-2 in China. Given the high morbidity and mortality associated with CRS-2, there is a critical need for improved diagnostic tools and prognostic models to guide clinical management and patient care. This study aimed to evaluate the clinical outcomes and identify predictive factors for adverse events in CRS-2 patients by conducting a retrospective analysis of a large cohort of patients diagnosed with CRS-2 at three hospitals. We hypothesized that by utilizing advanced statistical methods including receiver operating characteristic (ROC) curve analysis, univariate Kaplan-Meier (KM) analysis, and multivariate Cox proportional hazards regression, we could develop a robust nomogram for predicting patient prognosis in CRS-2. This nomogram would serve as a valuable tool for clinicians to stratify patients by risk and tailor therapeutic strategies accordingly. Methods Data collection This is a multicenter retrospective study. The study protocol was approved by the institutional review board of the People’s hospital of Anji, and all procedures were conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The patients with a diagnosis of CRS-2 were retrospectively identified from the electronic medical records of three hospitals between January 2021 and December 2023. Inclusion criteria for this study: patients diagnosed with CRS type 2 based on the the 2019 American Heart association definition [ 12 ]. Patients were required to have documented evidence of cardiac dysfunction (heart failure, myocardial infarction) and concurrent or subsequent renal injury or failure. Patients who met the following criteria were excluded: (1) younger than 18 years old; (2) neoplastic disease; (3) pregnancy or severe immune system disorders; (4) end-stage chronic disease or cancer; (5) Emergency operation; (6) chronic liver dysfunction; (7) Patients who had incomplete information in their clinical data; (8) criteria for s CRS-2 not met within 24 hours after admission to the hospital; (9) patients who lost follow-up. Clinical data including demographic information, medical history, laboratory test results, and treatment details were abstracted from electronic health records. The outcome of interest was all-cause mortality, which was defined as death from any cause during the follow-up period. Statistical Analysis Statistical analyses to identify risk factors were performed using SPSS 19.0 for Windows (SPSS, Chicago, IL). Categorical variables were grouped based on clinical findings, and decisions on the groups were made before modelling. Continuous variables were compared using the Students’t test or the Mann-Whitney U test for variables that did not conform to the normal distribution. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off points for continuous variables in predicting the outcome. Univariate survival analysis was conducted using Kaplan-Meier methods to assess the association between individual variables and the primary outcome. Multivariate analysis was then performed using Cox proportional hazards regression to identify independent predictors of all-cause mortality. A nomogram was constructed based on the multivariate model, and its predictive accuracy was evaluated using the concordance index (C-index) and calibration curves. Internal validation of the nomogram was carried out using bootstrapping techniques with 1000 resamples. Results Baseline Characteristics Between January 2021 and December 2023, 921 patients with a primary diagnosis of Type 2 Cardiorenal Syndrome were admitted to our hospitals. According to inclusion and exclusion criteria, the study included 519 patients with Type 2 CRS, of whom 57 (10.9%) died during the follow-up period (180 days). The median age of the cohort was 61 years, and 55.7% were male. Table 1 summarizes the baseline characteristics of the entire cohort. Table 1 Baseline characteristics Characteristics overall Age, median (IQR) 61 (51, 68) Gender Male 289(55.7%) Female 230(44.3%) Heart rate, median (IQR) 80 (69, 93) BMI, median (IQR) 20.55 (18.52, 23.44) type.of.heart.failure, n (%) Both 397 (76.6%) Left 113 (21.8%) Right 8 (1.5%) NYHA.cardiac.function.classification, n (%) IV 212 (40.9%) III 206 (59.1%) Left ventricular end-diastolic diameter, median (IQR) 54 (47, 62) Myocardial infarcion n (%) Yes 43 (8.3%) No 476 (91.7%) Congestive heart failure, n (%) Yes 486 (93.6%) No 33 (6.4%) Peripheral vascular disease, n (%) No 492 (94.8%) Yes 27 (5.2%) Cerebrovascular disease, n (%) No 485 (93.4%) Yes 34 (6.6%) COPD, n (%) No 465 (89.6%) Yes 54 (10.4%) Peptic ucler n (%) No 507 (97.7%) Yes 12 (2.3%) Diabetes, n (%) No 369 (71.1%) Yes 150 (28.9%) Comparison Between Survivors and Non-Survivors In this study, statistically significant differences were observed in serum creatinine, blood urea nitrogen (BUN), Glomerular filtration rate, platelet count, Serum potassium, glutamyltranspeptidase, B-type natriuretic peptide (BNP), and Oxygen partial pressure between survivors and non-survivors (all p < 0.05). In contrast, no significant differences were found in Left ventricular end-diastolic diameter, uric acid, White blood cell, activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen, Hypersensitive troponin, Creatine kinase isoenzyme, albumin, Total bilirubin and lactate (all p > 0.05). The comparison between survivors and non-survivors is shown in Table 2 . Table 2 Comparison of parameter among the survival and non-survival Characteristics Non-survival group Survival group P value n 57 462 Left ventricular end-diastolic diameter (mm), mean ± sd 56.929 ± 14.594 54.771 ± 11.582 0.199 serum creatinine (µmol/L), mean ± sd 402.78 ± 201.3 178.08 ± 60.505 < 0.001 Urea nitrogen (mmol/L), mean ± sd 22.015 ± 8.3221 14.558 ± 6.0946 < 0.001 Uric acid (µmol/L), mean ± sd 601.25 ± 219.76 608.68 ± 175.62 0.770 Glomerular filtration rate (ml/min), mean ± sd 15.05 ± 11.842 31.256 ± 11.046 < 0.001 White blood cell (10 9 /L), mean ± sd 7.9051 ± 4.373 7.8944 ± 4.2441 0.986 Platelet (10 9 /L), mean ± sd 161.47 ± 71.696 143.03 ± 65.81 0.049 APTT (s), mean ± sd 35.575 ± 6.9434 36.577 ± 8.219 0.378 PT (s), mean ± sd 1.3977 ± 0.58292 1.4348 ± 0.96335 0.777 Fibrinogen (g/L), mean ± sd 3.5304 ± 1.2398 3.3717 ± 1.1455 0.329 Hypersensitive troponin (ng/ml), mean ± sd 0.37172 ± 0.81508 0.57985 ± 3.1269 0.617 Serum potassium (mmol/L), mean ± sd 4.7989 ± 0.90412 4.3306 ± 0.79531 < 0.001 Creatine kinase isoenzyme (U/L), mean ± sd 26.304 ± 46.05 20.174 ± 15.763 0.323 BNP (pg/mL), mean ± sd 2291.4 ± 1822.9 1593.8 ± 1559.6 0.007 Albumin (g/L), mean ± sd 34.149 ± 6.2067 35.539 ± 4.9732 0.054 Glutamyltranspeptidase (U/L), mean ± sd 41.754 ± 37.911 59.056 ± 61.343 0.003 Total bilirubin (µmol/L), mean ± sd 17.123 ± 20.595 22.511 ± 20.007 0.056 Lactate (mmol/L), mean ± sd 3.1561 ± 3.4634 2.5102 ± 2.0298 0.173 Oxygen partial pressure (mmHg), mean ± sd 94.719 ± 37.575 109.21 ± 38.076 0.007 Receiver Operating Characteristic (ROC) Analysis In Fig. 1, ROC analysis was performed for variables that showed significant differences between survivors and non-survivors. The sensitivity, specificity, cut-off point and Youden index were calculated to assess the predictive value of each indicator comprehensively (Table 3 ). The cut-off values with the Youden index and AUC were as follows: Table 3 The AUC of clinical biomarkers for predicting 180-day mortality Cut-off value sensitivity specificity Youden index Serum creatinine (µmol/L) 260 0.8961 0.7193 0.6154 blood urea nitrogen (mmol/L) 18 0.76407 0.68421 0.4482 Glomerular filtration rate (ml/min) 17 0.89177 0.73684 0.6286 Platelet count (10 9 /L) 174 0.74242 0.47368 0.2161 Serum potassium (mmol/) 5.0 0.80303 0.45614 0.2591 BNP (pg/mL) 2449 0.77273 0.4368 0.2113 Oxygen partial pressure (mmHg) 72 0.84199 0.36842 0.2104 Glutamyltranspeptidase (U/L) 27 0.68615 0.49123 0.1773 Serum creatinine: 260 µmol/L (AUC = 0.849, 95% CI 0.779–0.920, Youden index = 0.6154, p < 0.001); Glomerular filtration rate: 17ml/min: (AUC = 0.845, 95% CI 0.776–0.914, Youden index = 0.6154); BUN: 18 mmol/L (AUC = 0.779, 95% CI 0.713–0.845, Youden index = 0.4482); Platelet count: 174 × 10 9 /L (AUC = 0.578, 95% CI 0.490–0.666, Youden index = 0.2161); BNP: 2449 pg/mL (AUC = 0.613, 95% CI 0.533–0.692, Youden index = 0.2113); Oxygen partial pressure: 72 mmHg (AUC = 0.620, 95% CI 0.537–0.702, Youden index = 0.2104); Serum potassium: 5.0 mmol/L (AUC = 0.647, 95% CI 0.572–0.723, Youden index = 0.2591); Glutamyltranspeptidase: 27U/L (AUC = 0.592, 95% CI 0.515–0.669, Youden index = 0.1773). Univariate and Multivariate Analyses Kaplan-Meier survival analysis was conducted to evaluate the association between the identified biomarkers and overall survival. Figure 2 and Fig. 3A present the univariate analysis results by the cut-off values of serum creatinine, blood urea nitrogen (BUN), glomerular filtration rate, platelet count, B-type natriuretic peptide (BNP), serum potassium, glutamyltranspeptidase and Oxygen partial pressure. Patients with serum creatinine above the cut-off value of 260µmol/L had significantly lower survival rates compared to those below this threshold (log-rank p < 0.001). Patients with BUN above the cut-off value of 18 mmol/L had significantly lower survival rates compared to those below this threshold (log-rank p < 0.001). Patients with glomerular filtration rate under the cut-off value of 17 ml/min had significantly lower survival rates compared to those above this threshold (log-rank p < 0.001). Patients with serum potassium above the cut-off value of 5.0 mmol/L had significantly lower survival rates compared to those above this threshold (log-rank p < 0.001). Patients with platelet counts from 100–174×10 9 /µL had significantly higher survival rates (log-rank p = 0.002). Patients with BNP above the cut-off value of 2449 pg/mL had significantly lower survival rates compared to those below this threshold (log-rank p < 0.001). Patients with oxygen partial pressure below the cut-off value of 72 had significantly lower survival rates compared to those above this threshold (log-rank p < 0.001). Patients with glutamyltranspeptidase 27-50U/L had significantly higher survival rates (log-rank p = 0.03). Cox proportional hazards regression analysis was performed to determine the independent predictors of mortality. Table 4 shows the result of univariate and multivariate analyses. After adjusting for potential confounders, serum creatinine, BUN, platelet count, BNP, and oxygen partial pressure remained independent predictors of mortality. The hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) are provided in Table 4 . Table 4 Univariate analysis and Multivariate stepwise backward Cox regression analysis Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Serum creatinine 519 260µmol/L 89 15.468 (8.669–27.599) < 0.001 10.141 (3.306–31.108) < 0.001 BUN 519 18 mmol/L 148 6.137 (3.509–10.731) 17 ml/min 434 Reference Reference < 17 ml/min 85 12.347 (7.110–21.441) < 0.001 0.927 (0.310–2.767) 0.892 Platelet count 519 < 100× 10 9 /L 146 Reference Reference 100–174× 10 9 /L 247 0.350 (0.191–0.641) 174× 10 9 /L 126 0.534 (0.276–1.035) 0.063 0.763 (0.384–1.517) 0.440 Serum potassium 519 > 5.0 mmol/L 117 Reference Reference < 5.0 mmol/L 402 0.317 (0.188–0.534) 2449 pg/mL 130 Reference Reference < 2449 pg/mL 389 0.402 (0.238–0.678) 50 U/L 173 Reference Reference 27–50 U/L 182 0.526 (0.285–0.972) 0.040 0.893 (0.468–1.704) 0.732 < 27 U/L 164 0.462 (0.239–0.892) 0.021 0.527 (0.268–1.034) 0.063 Oxygen partial pressure 519 72mmHg 424 0.325 (0.190–0.553) < 0.001 0.495 (0.281–0.872) 0.015 Nomogram and Internal Validation A nomogram was constructed based on the multivariate analysis (Fig. 3B). The nomogram includes the five independent predictors of mortality: serum creatinine, BUN, platelet count, BNP, and oxygen saturation. The prediction model for mortality events had good agreement with the predicted risk and the actual occurrence risk in the modeling. Figure 3C shows the prediction model’s calibration curve. Discussion Type 2 Cardiorenal Syndrome (CRS), defined by the simultaneous presence of chronic cardiac and chronic renal dysfunction, represents a significant clinical challenge due to its complexity and poor prognosis. Despite the availability of numerous biomarkers and clinical parameters for evaluating renal function, predicting patient outcomes in 2-type cardiorenal syndrome remains challenging[ 13 – 15 ]. Current renal function indicators, such as serum creatinine[ 16 ] and Interleukin-18[ 17 ], provide important information but do not fully capture the complex interplay between cardiac and renal pathophysiologies, which can significantly influence patient prognosis. To address this gap, it is very important to establish a prognostic evaluation system to evaluate the patient's condition. In this retrospective study, we analyzed the clinical characteristics and outcomes of 519 patients with Type 2 CRS. Our findings revealed that serum creatinine, blood urea nitrogen (BUN), platelet count, B-type natriuretic peptide (BNP), and oxygen partial pressure were independent predictors of adverse outcomes in patients with Type 2 CRS. As a result, we developed a novel nomogram that integrates multiple clinical variables, aiming to provide a more accurate prediction of patient outcomes. These findings have important implications for clinical practice and provide insights into the pathophysiological mechanisms underlying this syndrome. Our analysis of 519 patients with 2-type cardiorenal syndrome revealed that elevated serum creatinine and blood urea nitrogen (BUN) levels were independent predictors of adverse outcomes. This finding is consistent with the underlying pathophysiology of 2-type cardiorenal syndrome, where chronic deterioration of heart function leads to reduced renal perfusion and subsequent renal dysfunction[ 18 ]. As heart failure progresses, the kidneys receive less blood flow, which impairs their ability to filter waste products effectively, resulting in an accumulation of creatinine and BUN[ 19 , 20 ]. The association between worsening heart function and renal impairment has been well documented in the literature, supporting our observation that these biomarkers are strong indicators of poor prognosis[ 21 ]. Our study further underscores the importance of monitoring these markers closely in clinical practice to identify patients who may benefit from early intervention and more aggressive management strategies. In this study, decreased platelet counts as an independent predictor of adverse outcomes. This finding is noteworthy given the limited attention paid to platelet counts in the context of cardiorenal syndrome. A low platelet count, or thrombocytopenia, has been linked to endothelial dysfunction, oxidative stress, and inflammation, all of which are key pathophysiological mechanisms involved in the progression of cardiorenal syndrome[ 22 – 24 ]. Endothelial damage can lead to increased vascular permeability and exacerbate renal hypoperfusion, while oxidative stress and inflammation contribute to further renal injury[ 25 ]. The association between thrombocytopenia and adverse outcomes in cardiorenal syndrome patients suggests that platelet count may serve as a useful clinical marker for identifying those at higher risk of complications[ 26 ]. Our results highlight the need for further investigation into the mechanistic underpinnings of this association and the potential therapeutic implications of targeting platelet-related pathways in the management of 2-type cardiorenal syndrome. BNP is a hormone produced by the ventricles of the heart in response to volume expansion and pressure overload. In the context of 2-type cardiorenal syndrome, BNP levels reflect the severity of cardiac dysfunction and the degree of renal impairment[ 27 ]. Elevated BNP levels indicate increased cardiac strain and can be indicative of heart failure, which is often associated with renal dysfunction due to reduced renal perfusion[ 28 ]. Furthermore, BNP has direct effects on the kidneys, including diuresis and natriuresis, which help maintain fluid balance and blood pressure. However, in advanced stages of cardiorenal syndrome, the renoprotective effects of BNP may be overwhelmed by other factors, such as renal vasoconstriction and sodium retention, leading to further deterioration of renal function. Thus, BNP serves not only as a marker of cardiac dysfunction but also as an indicator of the severity of renal involvement in 2-type cardiorenal syndrome[ 29 ]. Our findings underscore the importance of BNP as a valuable clinical tool for assessing disease severity and guiding treatment decisions in this complex condition. Our analysis revealed that decreased arterial oxygen partial pressure (PaO2) was an independent predictor of adverse outcomes. This finding underscores the critical role of adequate oxygenation in maintaining organ function, particularly in the context of cardiorenal syndrome. Hypoxia indicates impaired gas exchange and can be a marker of decreased cardiac output[ 30 , 31 ]. Shen et al found that patients with persistent hyperoxia had a higher incidence of kidney injury than those with transient hyperoxia[ 32 ]. In 2-type cardiorenal syndrome, reduced cardiac output leads to decreased systemic perfusion, including renal perfusion, which can exacerbate renal dysfunction. Moreover, hypoxia can directly affect renal tubular function, potentially leading to tubular injury and further compromising renal function[ 33 ]. Additionally, hypoxemia can contribute to systemic inflammation and oxidative stress, which are known to play roles in the pathogenesis of cardiorenal syndrome[ 34 ]. Therefore, PaO2 not only reflects the severity of cardiac and respiratory compromise but also highlights the systemic impact of hypoxemia on multiple organs, including the kidneys. Our findings emphasize the importance of monitoring oxygenation status and addressing any underlying causes of hypoxemia in patients with 2-type cardiorenal syndrome to prevent further organ damage and improve outcomes. The identification of these independent predictors of adverse outcomes has several clinical implications. First, it emphasizes the need for a comprehensive approach to the management of Type 2 CRS, incorporating careful monitoring of renal function, cardiac biomarkers, and oxygenation status. Second, it supports the use of targeted therapies aimed at improving renal function and oxygenation, as well as addressing inflammation and endothelial dysfunction. Finally, it suggests that risk stratification tools incorporating these predictors may be useful in guiding clinical decision-making and resource allocation. This study has several limitations. As a retrospective analysis, it is subject to inherent biases, and the causality between the identified predictors and adverse outcomes cannot be definitively established. Additionally, the study was conducted at only three centers, which may limit the generalizability of the findings. Larger multicenter prospective studies are needed to validate our findings and to further elucidate the mechanisms underlying the association between the identified predictors and adverse outcomes in Type 2 CRS. Conclusion In summary, our study provides evidence that serum creatinine, BUN, platelet count, BNP, and oxygen partial pressure are independent predictors of adverse outcomes in patients with Type 2 CRS. These findings have important implications for clinical practice and emphasize the need for a comprehensive approach to the management of this challenging condition. Further research is warranted to refine risk stratification tools and to develop targeted therapies for patients with Type 2 CRS. Declarations Ethical approval The ethical approval and consent of this study are approved by Clinical Research Ethics Committee of the People’s hospital of Anji. Human Ethics and Consent to Participate All included patients gave their oral and written informed consent. This research involving human data have been performed in accordance with the Declaration of Helsinki. Clinical Trial Number Not applicable Data Availability The datasets generated or analyzed during this study available from the corresponding author on reasonable request. Competing interests All the authors do not have any competing interest to declare. Funding No funding. Authors' contributions Sijun Pan and Bin Wang conceived this study. Sijun Pan designed the study. Qinghui Fu acquired and analyzed the data. Xie Zheng and Xiaoqian Luo contributed analysis tools. Bin Wang wrote the paper. Qinghui Fu were of immense help in the preparation of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable References Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, Falk V, Gonzalez-Juanatey JR, Harjola VP, Jankowska EA et al : 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC . Eur Heart J 2016, 37 (27):2129-2200. Ronco C, Bellasi A, Di Lullo L: Cardiorenal Syndrome: An Overview . Adv Chronic Kidney Dis 2018, 25 (5):382-390. Kumar U, Wettersten N, Garimella PS: Cardiorenal Syndrome: Pathophysiology . Cardiol Clin 2019, 37 (3):251-265. Cruz DN, Bagshaw SM: Heart-kidney interaction: epidemiology of cardiorenal syndromes . Int J Nephrol 2010, 2011 :351291. Ronco C, Haapio M, House AA, Anavekar N, Bellomo R: Cardiorenal syndrome . J Am Coll Cardiol 2008, 52 (19):1527-1539. Mitsas AC, Elzawawi M, Mavrogeni S, Boekels M, Khan A, Eldawy M, Stamatakis I, Kouris D, Daboul B, Gunkel O et al : Heart Failure and Cardiorenal Syndrome: A Narrative Review on Pathophysiology, Diagnostic and Therapeutic Regimens-From a Cardiologist's View . J Clin Med 2022, 11 (23). Ahmed A, Rich MW, Sanders PW, Perry GJ, Bakris GL, Zile MR, Love TE, Aban IB, Shlipak MG: Chronic kidney disease associated mortality in diastolic versus systolic heart failure: a propensity matched study . Am J Cardiol 2007, 99 (3):393-398. Ajibowo AO, Okobi OE, Emore E, Soladoye E, Sike CG, Odoma VA, Bakare IO, Kolawole OA, Afolayan A, Okobi E et al : Cardiorenal Syndrome: A Literature Review . Cureus 2023, 15 (7):e41252. Ronco C, Di Lullo L: Cardiorenal Syndrome in Western Countries: Epidemiology, Diagnosis and Management Approaches . Kidney Dis (Basel) 2017, 2 (4):151-163. Kaze AD, Zhuo M, Kim SC, Patorno E, Paik JM: Association of SGLT2 inhibitors with cardiovascular, kidney, and safety outcomes among patients with diabetic kidney disease: a meta-analysis . Cardiovasc Diabetol 2022, 21 (1):47. de la Espriella R, Bayes-Genis A, Morillas H, Bravo R, Vidal V, Nunez E, Santas E, Minana G, Sanchis J, Facila L et al : Renal function dynamics following co-administration of sacubitril/valsartan and empagliflozin in patients with heart failure and type 2 diabetes . ESC Heart Fail 2020, 7 (6):3792-3800. Rangaswami J, Bhalla V, Blair JEA, Chang TI, Costa S, Lentine KL, Lerma EV, Mezue K, Molitch M, Mullens W et al : Cardiorenal Syndrome: Classification, Pathophysiology, Diagnosis, and Treatment Strategies: A Scientific Statement From the American Heart Association . Circulation 2019, 139 (16):e840-e878. Okura T, Jotoku M, Irita J, Enomoto D, Nagao T, Desilva VR, Yamane S, Pei Z, Kojima S, Hamano Y et al : Association between cystatin C and inflammation in patients with essential hypertension . Clin Exp Nephrol 2010, 14 (6):584-588. Brisco MA, Testani JM: Novel renal biomarkers to assess cardiorenal syndrome . Curr Heart Fail Rep 2014, 11 (4):485-499. Coca SG, Garg AX, Thiessen-Philbrook H, Koyner JL, Patel UD, Krumholz HM, Shlipak MG, Parikh CR, Consortium T-A: Urinary biomarkers of AKI and mortality 3 years after cardiac surgery . J Am Soc Nephrol 2014, 25 (5):1063-1071. Lena A, Anker MS, Springer J: Muscle Wasting and Sarcopenia in Heart Failure-The Current State of Science . Int J Mol Sci 2020, 21 (18). Driver TH, Katz R, Ix JH, Magnani JW, Peralta CA, Parikh CR, Fried L, Newman AB, Kritchevsky SB, Sarnak MJ et al : Urinary kidney injury molecule 1 (KIM-1) and interleukin 18 (IL-18) as risk markers for heart failure in older adults: the Health, Aging, and Body Composition (Health ABC) Study . Am J Kidney Dis 2014, 64 (1):49-56. Gallo G, Lanza O, Savoia C: New Insight in Cardiorenal Syndrome: From Biomarkers to Therapy . Int J Mol Sci 2023, 24 (6). Seckinger D, Ritter O, Patschan D: Risk factors and outcome variables of cardiorenal syndrome type 1 from the nephrologist's perspective . Int Urol Nephrol 2022, 54 (7):1591-1601. She CS, Deng YL, Huang GQ, Cheng C, Zhang FJ: Risk Factors and Outcome Variables of Cardiorenal Syndrome Type 1 in Acute Myocardial Infarction Patients . Int J Gen Med 2022, 15 :1565-1573. Chung EYM, Trinh K, Li J, Hahn SH, Endre ZH, Rogers NM, Alexander SI: Biomarkers in Cardiorenal Syndrome and Potential Insights Into Novel Therapeutics . Front Cardiovasc Med 2022, 9 :868658. Virzi GM, Zhang J, Nalesso F, Ronco C, McCullough PA: The Role of Dendritic and Endothelial Cells in Cardiorenal Syndrome . Cardiorenal Med 2018, 8 (2):92-104. Wang J, Zhang W, Wu L, Mei Y, Cui S, Feng Z, Chen X: New insights into the pathophysiological mechanisms underlying cardiorenal syndrome . Aging (Albany NY) 2020, 12 (12):12422-12431. Virzi GM, Breglia A, Brocca A, de Cal M, Bolin C, Vescovo G, Ronco C: Levels of Proinflammatory Cytokines, Oxidative Stress, and Tissue Damage Markers in Patients with Acute Heart Failure with and without Cardiorenal Syndrome Type 1 . Cardiorenal Med 2018, 8 (4):321-331. Zhang J, Bottiglieri T, McCullough PA: The Central Role of Endothelial Dysfunction in Cardiorenal Syndrome . Cardiorenal Med 2017, 7 (2):104-117. Vondenhoff S, Schunk SJ, Noels H: Increased cardiovascular risk in patients with chronic kidney disease . Herz 2024, 49 (2):95-104. Otaki Y, Watanabe T, Sato N, Shirata T, Kato S, Tamura H, Nishiyama S, Arimoto T, Takahashi H, Shishido T et al : Brain Natriuretic Peptide (BNP) and N-Terminal-proBNP in Cardio-Renal Anemia Syndrome - Difference in Prognostic Ability . Circ Rep 2019, 1 (2):71-77. Okamoto R, Ali Y, Hashizume R, Suzuki N, Ito M: BNP as a Major Player in the Heart-Kidney Connection . Int J Mol Sci 2019, 20 (14). Dutta A, Saha S, Bahl A, Mittal A, Basak T: A comprehensive review of acute cardio-renal syndrome: need for novel biomarkers . Front Pharmacol 2023, 14 :1152055. Agostoni PG, Bussotti M, Nani F, Pontone G, Conca C, Melzi G, Morgagni F, Palermo P, Guazzi M: Oxygen consumption . Cardiologia 1999, 44 (11):987-992. Jiang M, Fan X, Wang Y, Sun X: Effects of hypoxia in cardiac metabolic remodeling and heart failure . Exp Cell Res 2023, 432 (1):113763. Shen Y, Ru W, Cao L, Jiang R, Xu X: Impact of partial pressure of oxygen trajectories on the incidence of acute kidney injury in patients undergoing cardiopulmonary bypass . J Cardiol 2022, 79 (4):545-550. Wang B, Li ZL, Zhang YL, Wen Y, Gao YM, Liu BC: Hypoxia and chronic kidney disease . EBioMedicine 2022, 77 :103942. Oyarce MP, Iturriaga R: Contribution of Oxidative Stress and Inflammation to the Neurogenic Hypertension Induced by Intermittent Hypoxia . Front Physiol 2018, 9 :893. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5006638","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383679450,"identity":"babbab5c-1caf-45d3-8d65-639781230c09","order_by":0,"name":"Bin Wang","email":"","orcid":"","institution":"People’s hsotial of Anji","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":383679451,"identity":"8e567373-a8ad-459e-903f-ede0e47d5344","order_by":1,"name":"Xie Zheng","email":"","orcid":"","institution":"People’s hsotial of Anji","correspondingAuthor":false,"prefix":"","firstName":"Xie","middleName":"","lastName":"Zheng","suffix":""},{"id":383679452,"identity":"5bd7ad8c-f464-4e4c-a51f-16b9e4392567","order_by":2,"name":"Qinghui Fu","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qinghui","middleName":"","lastName":"Fu","suffix":""},{"id":383679453,"identity":"d79196e3-d99b-48bf-be48-9219cceb9880","order_by":3,"name":"Xiaoqian Luo","email":"","orcid":"","institution":"The Second Affiliated Hospital of Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqian","middleName":"","lastName":"Luo","suffix":""},{"id":383679454,"identity":"0be135f4-a308-49be-adfe-ff71d71bae15","order_by":4,"name":"Sijun Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYHACAwaGAhDNfODAhwqitRiAaLbEgzPOkKaFx/gwbwsR6nXbD2/88MPgcB7/7J4PB3gbGOT5xQ7g12J2Jq1YssfgcLHEnbMbDkjuYDCcOTuBgJYDOWYMPAaHExtu5G44YHiGIcHgNiEt59+YMf4Bapl/I+fBgcQ2YrTcyDFjBtmy4UYOw4GDxGl5ViwtY5BebHgjzeBgwxkJIvxyPnnjxzcV1nlyN5Iff/5TYSPPL01ACxQ0w5RJEKUcBOqIM3kUjIJRMApGJgAA50dOJOQAMv8AAAAASUVORK5CYII=","orcid":"","institution":"People’s hsotial of Anji","correspondingAuthor":true,"prefix":"","firstName":"Sijun","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2024-08-31 03:02:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5006638/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5006638/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70948239,"identity":"7a56a536-f2c7-45d6-af2d-a1e1af926b58","added_by":"auto","created_at":"2024-12-09 13:18:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1144016,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve of creatinine, BUN, GFR, platelet count, serum potassium, BNP, glutamyltranspeptidase and oxygen partial pressure on the 180th day in predicting death in heart failure patients with CRS2.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5006638/v1/1fb5b7b94dfdd4112bf29112.png"},{"id":70948241,"identity":"0ee54485-95b2-4e78-9154-4459405eb01f","added_by":"auto","created_at":"2024-12-09 13:18:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":517972,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier plot showing 180-day survival in CRS2 patients grouped by creatinine, BUN, GFR, platelet count, serum potassium, BNP, glutamyltranspeptidase and oxygen partial pressure.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5006638/v1/46571685957c05f2317781c2.png"},{"id":70949328,"identity":"b858c40f-0641-4b9c-b07e-82b557df3e74","added_by":"auto","created_at":"2024-12-09 13:26:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":978721,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The forest plot of univariate analysis results by the cut-off values of serum creatinine, blood urea nitrogen (BUN), glomerular filtration rate, platelet count, B-type natriuretic peptide (BNP), serum potassium, glutamyltranspeptidaseand Oxygen partial pressure. (B) The nomogram model for predicting the 180-day mortality risk in patients with CRS2 (C) Calibration curve of the nomogram for predicting the risk of 180-day mortality in patients with CRS2.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5006638/v1/76842d725769d0b890aa9d36.png"},{"id":73653271,"identity":"83dec627-e1b2-400e-9508-891b263606e5","added_by":"auto","created_at":"2025-01-13 09:54:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4882998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5006638/v1/44c5c8b6-ccc9-4584-834b-3a123cf1d3db.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk factors and prognostic modeling in Cardiorenal Syndrome Type 2: a retrospective study of multicenter","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) remains a significant global health burden, with renal dysfunction often coexisting and exacerbating the clinical course of heart disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Heart failure patients often have abnormal kidney function, and common indicators of kidney function include glomerular filtration rate, creatinine, blood urea nitrogen and uric acid[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This interaction between heart failure and renal insufficiency leading to progressive progression of the disease is defined as cardiorenal syndrome (CRS)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Type 2 Cardiorenal Syndrome (CRS) is a complex clinical scenario characterized by the development of renal dysfunction secondary to chronic cardiac disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The prevalence of Type 2 CRS is substantial, with studies suggesting that up to 45\u0026ndash;63% of patients admitted for chronic heart failure (HF) exhibit concurrent renal dysfunction[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This comorbidity not only prolongs hospital stays and drives up healthcare costs but also significantly increases the risk of rehospitalization and mortality[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The pathophysiology of CRS-2 involves a myriad of mechanisms including neurohormonal activation, abnormal endothelial activation, and the release of pro-inflammatory cytokines[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Until recently, there are limited researches on the risk factors and prediction methods for the prognosis of CRS-2 in China. Given the high morbidity and mortality associated with CRS-2, there is a critical need for improved diagnostic tools and prognostic models to guide clinical management and patient care.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the clinical outcomes and identify predictive factors for adverse events in CRS-2 patients by conducting a retrospective analysis of a large cohort of patients diagnosed with CRS-2 at three hospitals. We hypothesized that by utilizing advanced statistical methods including receiver operating characteristic (ROC) curve analysis, univariate Kaplan-Meier (KM) analysis, and multivariate Cox proportional hazards regression, we could develop a robust nomogram for predicting patient prognosis in CRS-2. This nomogram would serve as a valuable tool for clinicians to stratify patients by risk and tailor therapeutic strategies accordingly.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThis is a multicenter retrospective study. The study protocol was approved by the institutional review board of the People\u0026rsquo;s hospital of Anji, and all procedures were conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The patients with a diagnosis of CRS-2 were retrospectively identified from the electronic medical records of three hospitals between January 2021 and December 2023. Inclusion criteria for this study: patients diagnosed with CRS type 2 based on the the 2019 American Heart association definition [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Patients were required to have documented evidence of cardiac dysfunction (heart failure, myocardial infarction) and concurrent or subsequent renal injury or failure. Patients who met the following criteria were excluded: (1) younger than 18 years old; (2) neoplastic disease; (3) pregnancy or severe immune system disorders; (4) end-stage chronic disease or cancer; (5) Emergency operation; (6) chronic liver dysfunction; (7) Patients who had incomplete information in their clinical data; (8) criteria for s CRS-2 not met within 24 hours after admission to the hospital; (9) patients who lost follow-up.\u003c/p\u003e \u003cp\u003eClinical data including demographic information, medical history, laboratory test results, and treatment details were abstracted from electronic health records. The outcome of interest was all-cause mortality, which was defined as death from any cause during the follow-up period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses to identify risk factors were performed using SPSS 19.0 for Windows (SPSS, Chicago, IL). Categorical variables were grouped based on clinical findings, and decisions on the groups were made before modelling. Continuous variables were compared using the Students\u0026rsquo;t test or the Mann-Whitney U test for variables that did not conform to the normal distribution. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off points for continuous variables in predicting the outcome. Univariate survival analysis was conducted using Kaplan-Meier methods to assess the association between individual variables and the primary outcome. Multivariate analysis was then performed using Cox proportional hazards regression to identify independent predictors of all-cause mortality. A nomogram was constructed based on the multivariate model, and its predictive accuracy was evaluated using the concordance index (C-index) and calibration curves. Internal validation of the nomogram was carried out using bootstrapping techniques with 1000 resamples.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eBetween January 2021 and December 2023, 921 patients with a primary diagnosis of Type 2 Cardiorenal Syndrome were admitted to our hospitals. According to inclusion and exclusion criteria, the study included 519 patients with Type 2 CRS, of whom 57 (10.9%) died during the follow-up period (180 days). The median age of the cohort was 61 years, and 55.7% were male. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of the entire cohort.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (51, 68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e289(55.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230(44.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (69, 93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.55 (18.52, 23.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etype.of.heart.failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e397 (76.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNYHA.cardiac.function.classification, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular end-diastolic diameter, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (47, 62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarcion n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e476 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e486 (93.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e492 (94.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485 (93.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465 (89.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ucler n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507 (97.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e369 (71.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison Between Survivors and Non-Survivors\u003c/h3\u003e\n\u003cp\u003eIn this study, statistically significant differences were observed in serum creatinine, blood urea nitrogen (BUN), Glomerular filtration rate, platelet count, Serum potassium, glutamyltranspeptidase, B-type natriuretic peptide (BNP), and Oxygen partial pressure between survivors and non-survivors (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, no significant differences were found in Left ventricular end-diastolic diameter, uric acid, White blood cell, activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen, Hypersensitive troponin, Creatine kinase isoenzyme, albumin, Total bilirubin and lactate (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The comparison between survivors and non-survivors is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of parameter among the survival and non-survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-survival group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvival group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular end-diastolic diameter (mm), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.929\u0026thinsp;\u0026plusmn;\u0026thinsp;14.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.771\u0026thinsp;\u0026plusmn;\u0026thinsp;11.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eserum creatinine (\u0026micro;mol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402.78\u0026thinsp;\u0026plusmn;\u0026thinsp;201.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178.08\u0026thinsp;\u0026plusmn;\u0026thinsp;60.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen (mmol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.015\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.558\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (\u0026micro;mol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e601.25\u0026thinsp;\u0026plusmn;\u0026thinsp;219.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e608.68\u0026thinsp;\u0026plusmn;\u0026thinsp;175.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular filtration rate (ml/min), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.05\u0026thinsp;\u0026plusmn;\u0026thinsp;11.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.256\u0026thinsp;\u0026plusmn;\u0026thinsp;11.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell (10\u003csup\u003e9\u003c/sup\u003e/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.9051\u0026thinsp;\u0026plusmn;\u0026thinsp;4.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8944\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (10\u003csup\u003e9\u003c/sup\u003e/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.47\u0026thinsp;\u0026plusmn;\u0026thinsp;71.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.03\u0026thinsp;\u0026plusmn;\u0026thinsp;65.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT (s), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.575\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.577\u0026thinsp;\u0026plusmn;\u0026thinsp;8.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (s), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3977\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4348\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen (g/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5304\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3717\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypersensitive troponin (ng/ml), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37172\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57985\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum potassium (mmol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7989\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3306\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase isoenzyme (U/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.304\u0026thinsp;\u0026plusmn;\u0026thinsp;46.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.174\u0026thinsp;\u0026plusmn;\u0026thinsp;15.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/mL), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2291.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1822.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1593.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1559.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.149\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.539\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamyltranspeptidase (U/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.754\u0026thinsp;\u0026plusmn;\u0026thinsp;37.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.056\u0026thinsp;\u0026plusmn;\u0026thinsp;61.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (\u0026micro;mol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.123\u0026thinsp;\u0026plusmn;\u0026thinsp;20.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.511\u0026thinsp;\u0026plusmn;\u0026thinsp;20.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1561\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5102\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen partial pressure (mmHg), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.719\u0026thinsp;\u0026plusmn;\u0026thinsp;37.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.21\u0026thinsp;\u0026plusmn;\u0026thinsp;38.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReceiver Operating Characteristic (ROC) Analysis\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;1, ROC analysis was performed for variables that showed significant differences between survivors and non-survivors. The sensitivity, specificity, cut-off point and Youden index were calculated to assess the predictive value of each indicator comprehensively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The cut-off values with the Youden index and AUC were as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe AUC of clinical biomarkers for predicting 180-day mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003especificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYouden index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eblood urea nitrogen (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular filtration rate (ml/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum potassium (mmol/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen partial pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamyltranspeptidase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e\n \u003cli\u003eSerum creatinine: 260 µmol/L (AUC = 0.849, 95% CI 0.779–0.920, Youden index = 0.6154, p \u0026lt; 0.001);\u003c/li\u003e\n \u003cli\u003eGlomerular filtration rate: 17ml/min: (AUC = 0.845, 95% CI 0.776–0.914, Youden index = 0.6154);\u003c/li\u003e\n \u003cli\u003eBUN: 18 mmol/L (AUC = 0.779, 95% CI 0.713–0.845, Youden index = 0.4482);\u003c/li\u003e\n \u003cli\u003ePlatelet count: 174 × 10\u003csup\u003e9\u003c/sup\u003e/L (AUC = 0.578, 95% CI 0.490–0.666, Youden index = 0.2161);\u003c/li\u003e\n \u003cli\u003eBNP: 2449 pg/mL (AUC = 0.613, 95% CI 0.533–0.692, Youden index = 0.2113);\u003c/li\u003e\n \u003cli\u003eOxygen partial pressure: 72 mmHg (AUC = 0.620, 95% CI 0.537–0.702, Youden index = 0.2104);\u003c/li\u003e\n \u003cli\u003eSerum potassium: 5.0 mmol/L (AUC = 0.647, 95% CI 0.572–0.723, Youden index = 0.2591);\u003c/li\u003e\n \u003cli\u003eGlutamyltranspeptidase: 27U/L (AUC = 0.592, 95% CI 0.515–0.669, Youden index = 0.1773).\u003c/li\u003e\n\u003c/ul\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate and Multivariate Analyses\u003c/h3\u003e\n\u003cp\u003eKaplan-Meier survival analysis was conducted to evaluate the association between the identified biomarkers and overall survival. Figure\u0026nbsp;2 and Fig.\u0026nbsp;3A present the univariate analysis results by the cut-off values of serum creatinine, blood urea nitrogen (BUN), glomerular filtration rate, platelet count, B-type natriuretic peptide (BNP), serum potassium, glutamyltranspeptidase and Oxygen partial pressure. Patients with serum creatinine above the cut-off value of 260\u0026micro;mol/L had significantly lower survival rates compared to those below this threshold (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with BUN above the cut-off value of 18 mmol/L had significantly lower survival rates compared to those below this threshold (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with glomerular filtration rate under the cut-off value of 17 ml/min had significantly lower survival rates compared to those above this threshold (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with serum potassium above the cut-off value of 5.0 mmol/L had significantly lower survival rates compared to those above this threshold (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with platelet counts from 100\u0026ndash;174\u0026times;10\u003csup\u003e9\u003c/sup\u003e/\u0026micro;L had significantly higher survival rates (log-rank p\u0026thinsp;=\u0026thinsp;0.002). Patients with BNP above the cut-off value of 2449 pg/mL had significantly lower survival rates compared to those below this threshold (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with oxygen partial pressure below the cut-off value of 72 had significantly lower survival rates compared to those above this threshold (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with glutamyltranspeptidase 27-50U/L had significantly higher survival rates (log-rank p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eCox proportional hazards regression analysis was performed to determine the independent predictors of mortality. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the result of univariate and multivariate analyses. After adjusting for potential confounders, serum creatinine, BUN, platelet count, BNP, and oxygen partial pressure remained independent predictors of mortality. The hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) are provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis and Multivariate stepwise backward Cox regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;260\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;260\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.468 (8.669\u0026ndash;27.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.141 (3.306\u0026ndash;31.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;18 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.137 (3.509\u0026ndash;10.731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.505 (1.337\u0026ndash;4.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular filtration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;17 ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;17 ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.347 (7.110\u0026ndash;21.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.927 (0.310\u0026ndash;2.767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u0026ndash;174\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.350 (0.191\u0026ndash;0.641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.479 (0.256\u0026ndash;0.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;174\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.534 (0.276\u0026ndash;1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.763 (0.384\u0026ndash;1.517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum potassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.0 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5.0 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.317 (0.188\u0026ndash;0.534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.693 (0.396\u0026ndash;1.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2449 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2449 pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402 (0.238\u0026ndash;0.678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.421 (0.245\u0026ndash;0.723)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamyltranspeptidase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u0026ndash;50 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.526 (0.285\u0026ndash;0.972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.893 (0.468\u0026ndash;1.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;27 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.462 (0.239\u0026ndash;0.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.527 (0.268\u0026ndash;1.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen partial pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;72mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;72mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.325 (0.190\u0026ndash;0.553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.495 (0.281\u0026ndash;0.872)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eNomogram and Internal Validation\u003c/h3\u003e\n\u003cp\u003eA nomogram was constructed based on the multivariate analysis (Fig.\u0026nbsp;3B). The nomogram includes the five independent predictors of mortality: serum creatinine, BUN, platelet count, BNP, and oxygen saturation. The prediction model for mortality events had good agreement with the predicted risk and the actual occurrence risk in the modeling. Figure\u0026nbsp;3C shows the prediction model\u0026rsquo;s calibration curve.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eType 2 Cardiorenal Syndrome (CRS), defined by the simultaneous presence of chronic cardiac and chronic renal dysfunction, represents a significant clinical challenge due to its complexity and poor prognosis. Despite the availability of numerous biomarkers and clinical parameters for evaluating renal function, predicting patient outcomes in 2-type cardiorenal syndrome remains challenging[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Current renal function indicators, such as serum creatinine[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and Interleukin-18[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], provide important information but do not fully capture the complex interplay between cardiac and renal pathophysiologies, which can significantly influence patient prognosis. To address this gap, it is very important to establish a prognostic evaluation system to evaluate the patient's condition. In this retrospective study, we analyzed the clinical characteristics and outcomes of 519 patients with Type 2 CRS. Our findings revealed that serum creatinine, blood urea nitrogen (BUN), platelet count, B-type natriuretic peptide (BNP), and oxygen partial pressure were independent predictors of adverse outcomes in patients with Type 2 CRS. As a result, we developed a novel nomogram that integrates multiple clinical variables, aiming to provide a more accurate prediction of patient outcomes. These findings have important implications for clinical practice and provide insights into the pathophysiological mechanisms underlying this syndrome.\u003c/p\u003e \u003cp\u003eOur analysis of 519 patients with 2-type cardiorenal syndrome revealed that elevated serum creatinine and blood urea nitrogen (BUN) levels were independent predictors of adverse outcomes. This finding is consistent with the underlying pathophysiology of 2-type cardiorenal syndrome, where chronic deterioration of heart function leads to reduced renal perfusion and subsequent renal dysfunction[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As heart failure progresses, the kidneys receive less blood flow, which impairs their ability to filter waste products effectively, resulting in an accumulation of creatinine and BUN[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The association between worsening heart function and renal impairment has been well documented in the literature, supporting our observation that these biomarkers are strong indicators of poor prognosis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our study further underscores the importance of monitoring these markers closely in clinical practice to identify patients who may benefit from early intervention and more aggressive management strategies.\u003c/p\u003e \u003cp\u003eIn this study, decreased platelet counts as an independent predictor of adverse outcomes. This finding is noteworthy given the limited attention paid to platelet counts in the context of cardiorenal syndrome. A low platelet count, or thrombocytopenia, has been linked to endothelial dysfunction, oxidative stress, and inflammation, all of which are key pathophysiological mechanisms involved in the progression of cardiorenal syndrome[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Endothelial damage can lead to increased vascular permeability and exacerbate renal hypoperfusion, while oxidative stress and inflammation contribute to further renal injury[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The association between thrombocytopenia and adverse outcomes in cardiorenal syndrome patients suggests that platelet count may serve as a useful clinical marker for identifying those at higher risk of complications[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our results highlight the need for further investigation into the mechanistic underpinnings of this association and the potential therapeutic implications of targeting platelet-related pathways in the management of 2-type cardiorenal syndrome.\u003c/p\u003e \u003cp\u003eBNP is a hormone produced by the ventricles of the heart in response to volume expansion and pressure overload. In the context of 2-type cardiorenal syndrome, BNP levels reflect the severity of cardiac dysfunction and the degree of renal impairment[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Elevated BNP levels indicate increased cardiac strain and can be indicative of heart failure, which is often associated with renal dysfunction due to reduced renal perfusion[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, BNP has direct effects on the kidneys, including diuresis and natriuresis, which help maintain fluid balance and blood pressure. However, in advanced stages of cardiorenal syndrome, the renoprotective effects of BNP may be overwhelmed by other factors, such as renal vasoconstriction and sodium retention, leading to further deterioration of renal function. Thus, BNP serves not only as a marker of cardiac dysfunction but also as an indicator of the severity of renal involvement in 2-type cardiorenal syndrome[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our findings underscore the importance of BNP as a valuable clinical tool for assessing disease severity and guiding treatment decisions in this complex condition.\u003c/p\u003e \u003cp\u003eOur analysis revealed that decreased arterial oxygen partial pressure (PaO2) was an independent predictor of adverse outcomes. This finding underscores the critical role of adequate oxygenation in maintaining organ function, particularly in the context of cardiorenal syndrome. Hypoxia indicates impaired gas exchange and can be a marker of decreased cardiac output[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Shen et al found that patients with persistent hyperoxia had a higher incidence of kidney injury than those with transient hyperoxia[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In 2-type cardiorenal syndrome, reduced cardiac output leads to decreased systemic perfusion, including renal perfusion, which can exacerbate renal dysfunction. Moreover, hypoxia can directly affect renal tubular function, potentially leading to tubular injury and further compromising renal function[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, hypoxemia can contribute to systemic inflammation and oxidative stress, which are known to play roles in the pathogenesis of cardiorenal syndrome[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, PaO2 not only reflects the severity of cardiac and respiratory compromise but also highlights the systemic impact of hypoxemia on multiple organs, including the kidneys. Our findings emphasize the importance of monitoring oxygenation status and addressing any underlying causes of hypoxemia in patients with 2-type cardiorenal syndrome to prevent further organ damage and improve outcomes.\u003c/p\u003e \u003cp\u003eThe identification of these independent predictors of adverse outcomes has several clinical implications. First, it emphasizes the need for a comprehensive approach to the management of Type 2 CRS, incorporating careful monitoring of renal function, cardiac biomarkers, and oxygenation status. Second, it supports the use of targeted therapies aimed at improving renal function and oxygenation, as well as addressing inflammation and endothelial dysfunction. Finally, it suggests that risk stratification tools incorporating these predictors may be useful in guiding clinical decision-making and resource allocation.\u003c/p\u003e \u003cp\u003eThis study has several limitations. As a retrospective analysis, it is subject to inherent biases, and the causality between the identified predictors and adverse outcomes cannot be definitively established. Additionally, the study was conducted at only three centers, which may limit the generalizability of the findings. Larger multicenter prospective studies are needed to validate our findings and to further elucidate the mechanisms underlying the association between the identified predictors and adverse outcomes in Type 2 CRS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study provides evidence that serum creatinine, BUN, platelet count, BNP, and oxygen partial pressure are independent predictors of adverse outcomes in patients with Type 2 CRS. These findings have important implications for clinical practice and emphasize the need for a comprehensive approach to the management of this challenging condition. Further research is warranted to refine risk stratification tools and to develop targeted therapies for patients with Type 2 CRS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical approval and consent of this study are approved by Clinical Research Ethics Committee of the People’s hospital of Anji.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll included patients gave their oral and written informed consent. This research involving human data have been performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analyzed during this study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors do not have any competing interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSijun Pan and Bin Wang conceived this study. Sijun Pan designed the study. Qinghui Fu acquired and analyzed the data. Xie Zheng and Xiaoqian Luo contributed analysis tools. Bin Wang wrote the paper. Qinghui Fu were of immense help in the preparation of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePonikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, Falk V, Gonzalez-Juanatey JR, Harjola VP, Jankowska EA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003e2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC\u003c/strong\u003e. \u003cem\u003eEur Heart J \u003c/em\u003e2016, \u003cstrong\u003e37\u003c/strong\u003e(27):2129-2200.\u003c/li\u003e\n\u003cli\u003eRonco C, Bellasi A, Di Lullo L: \u003cstrong\u003eCardiorenal Syndrome: An Overview\u003c/strong\u003e. \u003cem\u003eAdv Chronic Kidney Dis \u003c/em\u003e2018, \u003cstrong\u003e25\u003c/strong\u003e(5):382-390.\u003c/li\u003e\n\u003cli\u003eKumar U, Wettersten N, Garimella PS: \u003cstrong\u003eCardiorenal Syndrome: Pathophysiology\u003c/strong\u003e. \u003cem\u003eCardiol Clin \u003c/em\u003e2019, \u003cstrong\u003e37\u003c/strong\u003e(3):251-265.\u003c/li\u003e\n\u003cli\u003eCruz DN, Bagshaw SM: \u003cstrong\u003eHeart-kidney interaction: epidemiology of cardiorenal syndromes\u003c/strong\u003e. \u003cem\u003eInt J Nephrol \u003c/em\u003e2010, \u003cstrong\u003e2011\u003c/strong\u003e:351291.\u003c/li\u003e\n\u003cli\u003eRonco C, Haapio M, House AA, Anavekar N, Bellomo R: \u003cstrong\u003eCardiorenal syndrome\u003c/strong\u003e. \u003cem\u003eJ Am Coll Cardiol \u003c/em\u003e2008, \u003cstrong\u003e52\u003c/strong\u003e(19):1527-1539.\u003c/li\u003e\n\u003cli\u003eMitsas AC, Elzawawi M, Mavrogeni S, Boekels M, Khan A, Eldawy M, Stamatakis I, Kouris D, Daboul B, Gunkel O\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eHeart Failure and Cardiorenal Syndrome: A Narrative Review on Pathophysiology, Diagnostic and Therapeutic Regimens-From a Cardiologist\u0026apos;s View\u003c/strong\u003e. \u003cem\u003eJ Clin Med \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e(23).\u003c/li\u003e\n\u003cli\u003eAhmed A, Rich MW, Sanders PW, Perry GJ, Bakris GL, Zile MR, Love TE, Aban IB, Shlipak MG: \u003cstrong\u003eChronic kidney disease associated mortality in diastolic versus systolic heart failure: a propensity matched study\u003c/strong\u003e. \u003cem\u003eAm J Cardiol \u003c/em\u003e2007, \u003cstrong\u003e99\u003c/strong\u003e(3):393-398.\u003c/li\u003e\n\u003cli\u003eAjibowo AO, Okobi OE, Emore E, Soladoye E, Sike CG, Odoma VA, Bakare IO, Kolawole OA, Afolayan A, Okobi E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCardiorenal Syndrome: A Literature Review\u003c/strong\u003e. \u003cem\u003eCureus \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e(7):e41252.\u003c/li\u003e\n\u003cli\u003eRonco C, Di Lullo L: \u003cstrong\u003eCardiorenal Syndrome in Western Countries: Epidemiology, Diagnosis and Management Approaches\u003c/strong\u003e. \u003cem\u003eKidney Dis (Basel) \u003c/em\u003e2017, \u003cstrong\u003e2\u003c/strong\u003e(4):151-163.\u003c/li\u003e\n\u003cli\u003eKaze AD, Zhuo M, Kim SC, Patorno E, Paik JM: \u003cstrong\u003eAssociation of SGLT2 inhibitors with cardiovascular, kidney, and safety outcomes among patients with diabetic kidney disease: a meta-analysis\u003c/strong\u003e. \u003cem\u003eCardiovasc Diabetol \u003c/em\u003e2022, \u003cstrong\u003e21\u003c/strong\u003e(1):47.\u003c/li\u003e\n\u003cli\u003ede la Espriella R, Bayes-Genis A, Morillas H, Bravo R, Vidal V, Nunez E, Santas E, Minana G, Sanchis J, Facila L\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRenal function dynamics following co-administration of sacubitril/valsartan and empagliflozin in patients with heart failure and type 2 diabetes\u003c/strong\u003e. \u003cem\u003eESC Heart Fail \u003c/em\u003e2020, \u003cstrong\u003e7\u003c/strong\u003e(6):3792-3800.\u003c/li\u003e\n\u003cli\u003eRangaswami J, Bhalla V, Blair JEA, Chang TI, Costa S, Lentine KL, Lerma EV, Mezue K, Molitch M, Mullens W\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCardiorenal Syndrome: Classification, Pathophysiology, Diagnosis, and Treatment Strategies: A Scientific Statement From the American Heart Association\u003c/strong\u003e. \u003cem\u003eCirculation \u003c/em\u003e2019, \u003cstrong\u003e139\u003c/strong\u003e(16):e840-e878.\u003c/li\u003e\n\u003cli\u003eOkura T, Jotoku M, Irita J, Enomoto D, Nagao T, Desilva VR, Yamane S, Pei Z, Kojima S, Hamano Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAssociation between cystatin C and inflammation in patients with essential hypertension\u003c/strong\u003e. \u003cem\u003eClin Exp Nephrol \u003c/em\u003e2010, \u003cstrong\u003e14\u003c/strong\u003e(6):584-588.\u003c/li\u003e\n\u003cli\u003eBrisco MA, Testani JM: \u003cstrong\u003eNovel renal biomarkers to assess cardiorenal syndrome\u003c/strong\u003e. \u003cem\u003eCurr Heart Fail Rep \u003c/em\u003e2014, \u003cstrong\u003e11\u003c/strong\u003e(4):485-499.\u003c/li\u003e\n\u003cli\u003eCoca SG, Garg AX, Thiessen-Philbrook H, Koyner JL, Patel UD, Krumholz HM, Shlipak MG, Parikh CR, Consortium T-A: \u003cstrong\u003eUrinary biomarkers of AKI and mortality 3 years after cardiac surgery\u003c/strong\u003e. \u003cem\u003eJ Am Soc Nephrol \u003c/em\u003e2014, \u003cstrong\u003e25\u003c/strong\u003e(5):1063-1071.\u003c/li\u003e\n\u003cli\u003eLena A, Anker MS, Springer J: \u003cstrong\u003eMuscle Wasting and Sarcopenia in Heart Failure-The Current State of Science\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2020, \u003cstrong\u003e21\u003c/strong\u003e(18).\u003c/li\u003e\n\u003cli\u003eDriver TH, Katz R, Ix JH, Magnani JW, Peralta CA, Parikh CR, Fried L, Newman AB, Kritchevsky SB, Sarnak MJ\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eUrinary kidney injury molecule 1 (KIM-1) and interleukin 18 (IL-18) as risk markers for heart failure in older adults: the Health, Aging, and Body Composition (Health ABC) Study\u003c/strong\u003e. \u003cem\u003eAm J Kidney Dis \u003c/em\u003e2014, \u003cstrong\u003e64\u003c/strong\u003e(1):49-56.\u003c/li\u003e\n\u003cli\u003eGallo G, Lanza O, Savoia C: \u003cstrong\u003eNew Insight in Cardiorenal Syndrome: From Biomarkers to Therapy\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2023, \u003cstrong\u003e24\u003c/strong\u003e(6).\u003c/li\u003e\n\u003cli\u003eSeckinger D, Ritter O, Patschan D: \u003cstrong\u003eRisk factors and outcome variables of cardiorenal syndrome type 1 from the nephrologist\u0026apos;s perspective\u003c/strong\u003e. \u003cem\u003eInt Urol Nephrol \u003c/em\u003e2022, \u003cstrong\u003e54\u003c/strong\u003e(7):1591-1601.\u003c/li\u003e\n\u003cli\u003eShe CS, Deng YL, Huang GQ, Cheng C, Zhang FJ: \u003cstrong\u003eRisk Factors and Outcome Variables of Cardiorenal Syndrome Type 1 in Acute Myocardial Infarction Patients\u003c/strong\u003e. \u003cem\u003eInt J Gen Med \u003c/em\u003e2022, \u003cstrong\u003e15\u003c/strong\u003e:1565-1573.\u003c/li\u003e\n\u003cli\u003eChung EYM, Trinh K, Li J, Hahn SH, Endre ZH, Rogers NM, Alexander SI: \u003cstrong\u003eBiomarkers in Cardiorenal Syndrome and Potential Insights Into Novel Therapeutics\u003c/strong\u003e. \u003cem\u003eFront Cardiovasc Med \u003c/em\u003e2022, \u003cstrong\u003e9\u003c/strong\u003e:868658.\u003c/li\u003e\n\u003cli\u003eVirzi GM, Zhang J, Nalesso F, Ronco C, McCullough PA: \u003cstrong\u003eThe Role of Dendritic and Endothelial Cells in Cardiorenal Syndrome\u003c/strong\u003e. \u003cem\u003eCardiorenal Med \u003c/em\u003e2018, \u003cstrong\u003e8\u003c/strong\u003e(2):92-104.\u003c/li\u003e\n\u003cli\u003eWang J, Zhang W, Wu L, Mei Y, Cui S, Feng Z, Chen X: \u003cstrong\u003eNew insights into the pathophysiological mechanisms underlying cardiorenal syndrome\u003c/strong\u003e. \u003cem\u003eAging (Albany NY) \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e(12):12422-12431.\u003c/li\u003e\n\u003cli\u003eVirzi GM, Breglia A, Brocca A, de Cal M, Bolin C, Vescovo G, Ronco C: \u003cstrong\u003eLevels of Proinflammatory Cytokines, Oxidative Stress, and Tissue Damage Markers in Patients with Acute Heart Failure with and without Cardiorenal Syndrome Type 1\u003c/strong\u003e. \u003cem\u003eCardiorenal Med \u003c/em\u003e2018, \u003cstrong\u003e8\u003c/strong\u003e(4):321-331.\u003c/li\u003e\n\u003cli\u003eZhang J, Bottiglieri T, McCullough PA: \u003cstrong\u003eThe Central Role of Endothelial Dysfunction in Cardiorenal Syndrome\u003c/strong\u003e. \u003cem\u003eCardiorenal Med \u003c/em\u003e2017, \u003cstrong\u003e7\u003c/strong\u003e(2):104-117.\u003c/li\u003e\n\u003cli\u003eVondenhoff S, Schunk SJ, Noels H: \u003cstrong\u003eIncreased cardiovascular risk in patients with chronic kidney disease\u003c/strong\u003e. \u003cem\u003eHerz \u003c/em\u003e2024, \u003cstrong\u003e49\u003c/strong\u003e(2):95-104.\u003c/li\u003e\n\u003cli\u003eOtaki Y, Watanabe T, Sato N, Shirata T, Kato S, Tamura H, Nishiyama S, Arimoto T, Takahashi H, Shishido T\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBrain Natriuretic Peptide (BNP) and N-Terminal-proBNP in Cardio-Renal Anemia Syndrome\u003c/strong\u003e\u003cstrong\u003e - Difference in Prognostic Ability\u003c/strong\u003e. \u003cem\u003eCirc Rep \u003c/em\u003e2019, \u003cstrong\u003e1\u003c/strong\u003e(2):71-77.\u003c/li\u003e\n\u003cli\u003eOkamoto R, Ali Y, Hashizume R, Suzuki N, Ito M: \u003cstrong\u003eBNP as a Major Player in the Heart-Kidney Connection\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2019, \u003cstrong\u003e20\u003c/strong\u003e(14).\u003c/li\u003e\n\u003cli\u003eDutta A, Saha S, Bahl A, Mittal A, Basak T: \u003cstrong\u003eA comprehensive review of acute cardio-renal syndrome: need for novel biomarkers\u003c/strong\u003e. \u003cem\u003eFront Pharmacol \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1152055.\u003c/li\u003e\n\u003cli\u003eAgostoni PG, Bussotti M, Nani F, Pontone G, Conca C, Melzi G, Morgagni F, Palermo P, Guazzi M: \u003cstrong\u003eOxygen consumption\u003c/strong\u003e. \u003cem\u003eCardiologia \u003c/em\u003e1999, \u003cstrong\u003e44\u003c/strong\u003e(11):987-992.\u003c/li\u003e\n\u003cli\u003eJiang M, Fan X, Wang Y, Sun X: \u003cstrong\u003eEffects of hypoxia in cardiac metabolic remodeling and heart failure\u003c/strong\u003e. \u003cem\u003eExp Cell Res \u003c/em\u003e2023, \u003cstrong\u003e432\u003c/strong\u003e(1):113763.\u003c/li\u003e\n\u003cli\u003eShen Y, Ru W, Cao L, Jiang R, Xu X: \u003cstrong\u003eImpact of partial pressure of oxygen trajectories on the incidence of acute kidney injury in patients undergoing cardiopulmonary bypass\u003c/strong\u003e. \u003cem\u003eJ Cardiol \u003c/em\u003e2022, \u003cstrong\u003e79\u003c/strong\u003e(4):545-550.\u003c/li\u003e\n\u003cli\u003eWang B, Li ZL, Zhang YL, Wen Y, Gao YM, Liu BC: \u003cstrong\u003eHypoxia and chronic kidney disease\u003c/strong\u003e. \u003cem\u003eEBioMedicine \u003c/em\u003e2022, \u003cstrong\u003e77\u003c/strong\u003e:103942.\u003c/li\u003e\n\u003cli\u003eOyarce MP, Iturriaga R: \u003cstrong\u003eContribution of Oxidative Stress and Inflammation to the Neurogenic Hypertension Induced by Intermittent Hypoxia\u003c/strong\u003e. \u003cem\u003eFront Physiol \u003c/em\u003e2018, \u003cstrong\u003e9\u003c/strong\u003e:893.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5006638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5006638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cardiorenal syndrome (CRS) represents a complex interaction between heart failure and renal insufficiency, leading to progressive disease. Type 2 CRS is characterized by the development of renal dysfunction secondary to chronic cardiac disease. The prevalence of Type 2 CRS is substantial, af fecting up to 45-63% of patients admitted for chronic heart failure. Despite its high morbidity and mortality, there is a lack of robust diagnostic tools and prognostic models to guide clinical management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This multicenter retrospective study included patients diagnosed with CRS type 2 based on the 2019 American Heart Association definition. Data were collected from electronic medical records of three hospitals between January 2021 and December 2023. Advanced statistical methods, including receiver operating characteristic (ROC) curve analysis, univariate Kaplan-Meier (KM) analysis, and multivariate Cox proportional hazards regression, were utilized to develop a nomogram for predicting patient prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study included 519 patients with CRS-2. Independent predictors of adverse outcomes included elevated serum creatinine and blood urea nitrogen (BUN) levels, decreased platelet count, elevated B-type natriuretic peptide (BNP), and decreased oxygen partial pressure (PaO2). These findings suggest that close monitoring of these markers is essential in clinical practice to identify patients at high risk of adverse events early on.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our study provides evidence that serum creatinine, BUN, platelet count, BNP, and PaO2 are independent predictors of adverse outcomes in patients with Type 2 CRS. These findings have important implications for clinical practice and emphasize the need for a comprehensive approach to the management of this challenging condition.\u003c/p\u003e","manuscriptTitle":"Risk factors and prognostic modeling in Cardiorenal Syndrome Type 2: a retrospective study of multicenter","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 13:17:56","doi":"10.21203/rs.3.rs-5006638/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"486b0aac-707b-496e-98dd-03b1eb975466","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T09:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-09 13:17:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5006638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5006638","identity":"rs-5006638","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}