Role of the Serum Creatinine to Albumin Ratio in the Evaluation short- and long-term all-cause mortality of Patients with Aortic Valve Replacement: A Retrospective Cohort Study

Role of the Serum Creatinine to Albumin Ratio in the Evaluation short- and long-term all-cause mortality of Patients with Aortic Valve Replacement: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Role of the Serum Creatinine to Albumin Ratio in the Evaluation short- and long-term all-cause mortality of Patients with Aortic Valve Replacement: A Retrospective Cohort Study Qingwei Ni, Ruihao Jiang, Yuanzhen Lin, Weicheng Ni, Zhijie Mao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6313927/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: The identification of novel biomarkers has significantly enhanced prognostic capabilities in the context of cardiovascular diseases. Among these emerging markers, the Serum creatinine-to-albumin ratio (CAR) has garnered increasing attention as a potential prognostic indicator across a variety of clinical settings. To our knowledge, the association between short- and long-term all-cause mortality in patients with aortic valve replacement (AVR) and the CAR has not been investigated. This study discusses the role of CAR in the evaluation of patients with AVR. Methods: We performed a retrospective analysis of 700 patients who underwent AVR and whose data were extracted from the MIMIC-IV database. The main purpose is to evaluate all-cause mortality in different periods. We extracted demographic baseline data, vital signs, laboratory tests, and other relevant information from the MIMIC-IV database. Machine learning techniques were employed to select features based on the 28-Day all-cause mortality outcome of the patients. The X-tile software was used to determine the optimal threshold for the CAR. Cox regression analyses were used to investigate the relationship between the CAR and all-cause mortality. Additionally, ROC curve analysis was conducted to evaluate the predictive performance of different indicators for the outcome. Additionally, subgroup analyses were conducted. Results: Our analysis of 700 patients from the MIMIC-IV database who underwent aortic valve replacement revealed that the CAR is a significant predictor of 1-year all-cause mortality. The CAR ideal threshold, determined by X-tile software, was 0.43. LASSO regression, identified CAR as one of the important features in mortality prediction models. Restricted cubic spline analysis demonstrated a significant nonlinear association between the CAR and both 28-Day, 90-Day and 1-year mortality. Cox regression analysis confirmed a dose-dependent increase in all the periods mortality risk with the higher CAR groups. Kaplan-Meier survival analysis showed the lowest survival probability in the higher CAR group. ROC curves indicated that the CAR had a higher AUC for the prediction of 1-year mortality (AUC 0.655) than the other indicators did. These results suggest that the CAR is a robust and independent predictor of mortality in critically ill patients with AVR. Conclusions: Our findings suggest that the CAR holds significant promise as a prognostic marker for 1-year mortality in patients undergoing AVR. It can serve as a tool for risk stratification and prognostic assessment in AVR patients. Serum creatinine-to-albumin ratio (CAR) Mortality Aortic valve replacement (AVR) Prognosis MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Aortic valve disease, encompassing structural and functional abnormalities such as aortic stenosis (AS) and aortic regurgitation (AR), represents a major global health burden. Degenerative-calcific AS constitutes the most prevalent valvular heart disease in developed populations, according to the Euro Heart Survey on Valvular Disease[ 1 ]. Aortic valve disease significantly impacts the quality of life and prognosis of affected individuals, with epidemiological data revealing a 5-year survival rate below 50% for untreated severe cases[ 2 ]. The disease trajectory is frequently complicated by progressive heart failure, malignant arrhythmias, and systemic embolic events, underscoring the critical need for timely intervention. While aortic valve replacement (AVR) – whether surgical or transcatheter (TAVR) – has revolutionized management by restoring valvular function and improving survival[ 3 , 4 ], significant heterogeneity persists in postoperative outcomes. AVR restores the normal function of the aortic valve by replacing the diseased valve, with studies showing that survival rates and quality of life are significantly improved after aortic valve replacement[ 5 ]. However, the incidence of postoperative complications cannot be ignored, including prosthesis-related complications, thromboembolism, and persistent myocardial dysfunction[ 6 ], highlighting an unmet need for precision risk stratification tools. In this case, simple and accessible biomarkers have great potential for improving the prognosis of diseases. The quest for prognostic biomarkers has intensified with emerging evidence linking systemic inflammation and metabolic dysregulation to valvulopathy progression. In recent years, several new biomarkers such as the neutrophil/lymphocyte ratio (NLR), red blood cell distribution width/albumin ratio (RAR), CRP-albumin ratio and triglyceride glucose index (Tyg) have been proved to be closely related to the prognosis of cardiovascular diseases[ 7 – 10 ], reflecting the pathophysiological interplay between chronic inflammation, endothelial dysfunction, and adverse remodeling. Similarly, using biomarkers to evaluate the prognosis of patients undergoing aortic valve replacement is a quick and feasible method. Aortic valve disease is intricately linked to chronic inflammation[ 11 ]. Concurrently, the resultant hemodynamic alterations further activate systemic immune-inflammatory cascades[ 12 ]. In the inflammatory state, the release of inflammatory mediators and cytokines has an impact on physiological functions and causes changes in blood biochemical results[ 13 ]. Among these biomarkers, the creatinine-to-albumin ratio (CAR) has attracted increasing attention[ 14 ]. CAR, a composite marker synergistically capturing renal function and nutritional-metabolic status, has been shown to be a promising predictor of adverse prognosis of many diseases. It can be easily calculated from routine laboratory tests, making it a convenient and cost-effective tool for clinical use. Studies have demonstrated that an elevated CAR is associated with worse prognosis in various cardiovascular conditions[ 15 – 17 ], highlighting its potential value in risk stratification and management of patients with aortic valve disease. Mechanistically, elevated serum creatinine signifies impaired glomerular filtration, while hypoalbuminemia mirrors both inflammatory catabolism and malnutrition – two hallmarks of the cardiovascular-kidney-metabolic (CKM) syndrome continuum. The concept of CKM syndrome further emphasizes the interplay between metabolic factors, cardiovascular, and renal diseases[ 18 ]. CKM is characterized by the interaction of metabolic risk factors, chronic kidney disease, and the cardiovascular system, leading to multi-organ dysfunction and increased incidence of adverse cardiovascular outcomes. The relationship between CAR and CKM is of particular interest as both involve the interplay between renal function and metabolic status, suggesting that CAR could be a valuable biomarker in the context of CKM for predicting prognosis and guiding treatment strategies. Despite the potential value of CAR in predicting prognosis, the relationship between CAR and short- and long-term all-cause mortality in patients who have undergone AVR remains unclear. To address this gap, we utilized the Medical Information Mart for Intensive Care IV (version 2.2) database to collect data on the hospitalization status of AVR patients admitted between 2008 and 2019, employing machine learning and multiple regression methods. We hypothesize that CAR elevation prior to AVR independently predicts mortality through synergistic pathways involving renal dysfunction, metabolic derangement, and subclinical inflammation. The study aimed to explore the association between short- and long-term all-cause mortality and CAR in these patients, providing more refined prognostic information for clinical decision-making. Methods 1. Study population. This retrospective study investigated health-related data obtained from the MIMIC-IV database (version 2.2), which is a common and large database that was developed and managed by the MIT Computational Physiology Laboratory. This database is comprised of extensive and high-quality medical records of patients who were admitted to the intensive critical care units of the Beth Israel Deaconess Medical Center[ 19 ]. We identified patients aged 18 years or older who underwent aortic valve replacement (AVR) from the MIMIC-IV database. The selection of patients was based on international classification of diseases (ICD) diagnostic codes for AVR, including a comprehensive set of codes such as 3521, 02RF08Z, 02RF38Z, 3522, 3505, 02RF0JZ, 02RF0KZ, X2RF332, 02QF0ZZ, 3506, 02RF3JZ, 02RF3KZ, 02RF37Z, X2RF432, and X2RF032. We meticulously excluded patients with missing values, incomplete demographic data, or those with an ICU length of stay of less than 24 hours to ensure the accuracy and reliability of our analysis. Finally, a total of 700 patients were enrolled in this study and grouped into two groups according to the optimal cut-off value of CAR (Fig. 1). To protect patient privacy, all personal data were de-identified, and patient identification was substituted with random numbers. Ethical approval and informed consent were waived. The researchers initiating this study have obtained permission to use the database after successfully passing the qualification tests required for database access. 2. Data collection. The software PostgreSQL (version 13.7.2) and Navicat Premium (version 15) were employed to extract information using Structured Query Language (SQL), ensuring efficient and accurate retrieval of patient records. The primary exposure variable was the CAR, which was calculated as the ratio of serum creatinine to serum albumin levels, both measured at ICU admission to establish a baseline for our analysis. Additionally, a comprehensive dataset was curated, including patient demographics, comorbidities, laboratory values, and clinical parameters (Table 1 ). The potential variables were categorized into four main groups: (1) demographics variables, such as age and gender; (2) comorbidities, including atrial fibrillation, sepsis, heart failure, diabetes, and renal disease; (3) laboratory indicators, comprising red blood cell (RBC) count, white blood cell (WBC) count, hemoglobin levels, platelet count, serum sodium, and serum creatinine; and (4) severity of illness scores at admission, such as the Acute Physiology Score III (APS III), the Simplified Acute Physiology Score II (SAPS-II), and the Sepsis-related Organ Failure Assessment score (SOFA)[ 20 , 21 ]. Table 1 Covariates extracted in detail from the MIMIC-IV database. Items Composition Demographic variables Age, Gender, Race, Weight, Height, BMI Vital Signs HR, SBP, DBP, MAP, RR, SpO2, Temperature Clinical Treatments Glucocorticoids, Immunosuppressant, Furosemide, CCB, ACEI/ARB, Betablocker, Digoxin, Amiodarone, Spirolactone, MV, CRRT, TAVR Comorbidities AKI, CKD, Sepsis, HF, AF, Hypertension, Diabetes, Pneumonia, COPD, Stroke Laboratory variables Neutrophil counts, Lymphocyte counts, RBC, WBC, RDW, Plt, Hb, HCT, Cr, Alb, BUN, TB, AST, ALT, Glucose, APPT, PT, INR, K, Na, AG, Lac Clinical Outcomes LOS ICU, LOS hospital, ICU mortality, In-hospital mortality,14-day mortality, 21-day mortality, 28-day mortality, 90-day mortality, 1-year mortality MIMIC-IV, the Medical Information Mart for Intensive Care database; BMI, body mass index; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; RR, respiratory rate; SpO2, oxygen saturation; CCB, calcium channel blocker; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker; MV, mechanical ventilation; CRRT, continuous renal replacement therapy; TAVR, transcatheter aortic valve replacement; AKI, acute kidney injury; CKD, chronic kidney disease; HF, heart failure; AF, atrial fibrillation; COPD, chronic obstructive pulmonary disease; RBC, red blood cell; WBC, white blood cell; RDW, erythrocyte distribution width; Plt, platelet; Hb, hemoglobin; HCT, hematocrit; Cr, creatinine; Alb, albumin; BUN, blood urea nitrogen; TB, total bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; APPT, activated partial thromboplastin time; PT, prothrombin time; INR, international normalized ratio; K, serum potassium; Na, serum sodium; AG, anion gap; Lac, lactate; CAR, serum creatinine to albumin ratio; LOS ICU, length of ICU stay; LOS hospital, length of hospital stay. 3. Endpoint events. The primary endpoint of the study was all-cause mortality within 14-day, 28-day, 90-day, and 1-year of admission after hospital admission. In addition, we also evaluated the length of stay in ICU, the total length of stay and the treatment measures during this period. 4. Statistical analysis. Descriptive statistics were used to summarize the study population and baseline characteristics, providing a clear overview of the demographic and clinical features of our cohort. Continuous variables were presented as the means ± standard deviations or medians (interquartile ranges), and categorical variables are presented as frequencies and percentages, allowing a thorough understanding of the data distribution. After determining the ideal CAR cutoff value based on whether death occurred on day 28 after admission, we divided the study patient population into two groups: low CAR and high CAR. The choice of the optimal cutoff point that maximized the risk ratio is presented in Fig. 2, as well as the relationship of CAR ≥ 0.43 and the distribution of CAR. The association between CAR and mortality was assessed using Kaplan-Meier survival analysis and Cox proportional hazards regression models, both of which are well-established methods for analyzing time-to-event data. To further explore the predictive ability of CAR and other clinical parameters, we employed machine learning algorithms. LASSO regression was chosen for its ability to handle complex relationships and feature interactions within the data. ROC curves were constructed to evaluate the predictive performance of the CAR and other clinical parameters for mortality, providing a visual representation of the models' discriminative power. Subgroup analyses were conducted to examine the potential impact of CAR on various parameters. A two-sided P < 0.05 was regarded as statistically significant. All the statistical analysis were performed by the R software (version 4.4.1), SPSS 27.0 (IBM SPSS Statistics, Armonk, NY, USA), and Python (version 3.12). Results 1. Baseline demographic and clinical characteristics. In this study, a total of 700 patients with AVR and were treated in the ICU were ultimately included (see Fig. 1). Among them, 265 patients (37.86%) were women, while 435 patients (62.14%) were men. Utilizing the X-tile software, the optimal cutoff value for the CAR was determined based on whether the patient had died by day 28 following ICU admission. The analysis revealed that a CAR value of 0.43 was the most appropriate threshold (see Fig. 2). Consequently, the study population was divided into two groups: those with a low CAR (< 0.43) and those with a high CAR (≥ 0.43). Patients in the high CAR group exhibited worse scores in several critical illness severity indices, including SOFA, SAPS II, APS III, and Charlson comorbidity index. Additionally, patients with comorbidities such as AF, AKI, CKD, and heart failure were more likely to be categorized in the high CAR group. Laboratory findings showed that patients in the high CAR group had elevated levels of neutrophil count, RDW, serum glucose, anion gap, serum potassium, creatinine, blood lactate acid, blood urea nitrogen, troponin, and NT-proBNP. Further investigation revealed that patients in the high CAR group faced a significantly higher risk of adverse outcomes. They were less likely to receive diuretics and ACEI/ARBs, and more inclined to undergo CRRT. These patients also experienced longer durations of hospital stay and ICU stay. Moreover, the high CAR group had significantly higher mortality rates at 14 days (2.82% vs. 7.69%, P = 0.005), 21 days (3.58% vs. 10.65%, P < 0.001), 28 days (3.95% vs. 11.83%, P < 0.001), 90 days (7.53% vs. 20.71%, P < 0.001), and 1 year (12.43% vs. 33.14%, P < 0.001). For detailed results, refer to Table 2 . Table 2 Baseline characteristics of the study population. Characteristics N Creatinine to Albumin Ratio(CAR) P value Low group < 0.43 High group ≥ 0.43 N Clinical parameters 700 531 169 Age, years 71.06 ± 14.59 71.31 ± 14.07 70.27 ± 16.12 0.416 Sex, n (%) < 0.001 Female 265 (37.86) 230 (43.31) 35 (20.71) Male 435 (62.14) 301 (56.69) 134 (79.29) Ethnicity, n (%) 0.239 White 533 (76.14) 410 (77.21) 123 (72.78) Black and Other 167 (23.86) 121 (22.79) 46 (27.22) Weight, kg 84.28 ± 20.51 82.92 ± 19.33 88.55 ± 23.40 0.002 Height, cm 169.48 ± 10.47 168.53 ± 10.47 172.44 ± 9.93 < 0.001 BMI, kg/m2 29.31 ± 6.57 29.18 ± 6.39 29.72 ± 7.09 0.354 Vital signs Heart rate, beats/min 84.04 ± 15.07 83.46 ± 14.40 85.85 ± 16.93 0.073 SBP, mmHg 114.10 ± 21.61 113.93 ± 21.00 114.64 ± 23.51 0.707 DBP, mmHg 58.26 ± 14.13 58.42 ± 13.90 57.76 ± 14.87 0.601 MBP, mmHg 71.90 ± 13.95 72.01 ± 13.47 71.54 ± 15.41 0.704 Respiratory rate, beats/min 16.24 ± 5.63 15.97 ± 5.49 17.09 ± 5.98 0.024 SPO2, % 97.87 ± 4.00 98.09 ± 3.29 97.17 ± 5.64 0.009 Temperature, ℃ 36.60 ± 1.97 36.70 ± 0.72 36.29 ± 3.78 0.020 Laboratory parameters WBC count, 10 9 /L 13.08 ± 6.78 12.80 ± 6.26 13.97 ± 8.16 0.051 Neutrophil count, 10 9 /L 11.05 ± 6.27 10.54 ± 5.04 12.65 ± 8.94 < 0.001 Lymphocytes, 10 9 /L 1.98 ± 1.56 2.03 ± 1.58 1.85 ± 1.47 0.194 Hemoglobin, g/dl 9.48 ± 2.04 9.47 ± 2.02 9.54 ± 2.09 0.703 Platelet count, 10 9 /L 165.28 ± 80.69 165.61 ± 81.26 164.24 ± 79.13 0.848 Hematocrit, % 28.72 ± 5.98 28.60 ± 5.90 29.13 ± 6.25 0.312 RDW, % 14.75 ± 1.91 14.59 ± 1.92 15.23 ± 1.79 < 0.001 APPT, seconds 43.89 ± 28.26 40.74 ± 23.96 53.79 ± 37.18 < 0.001 Anion gap, mmol/L 13.04 ± 3.64 16.34 ± 7.05 17.14 ± 5.97 < 0.001 Serum calcium, mmol/L 8.44 ± 0.77 1.17 ± 0.14 1.12 ± 0.13 < 0.001 Serum chloride, mmol/L 106.85 ± 6.16 107.35 ± 5.71 105.27 ± 7.19 < 0.001 Serum glucose, mg/dl 134.08 ± 50.41 129.85 ± 44.19 147.38 ± 64.65 < 0.001 Serum sodium, mmol/L 138.92 ± 4.06 138.94 ± 3.83 138.83 ± 4.71 0.748 Serum potassium, mmol/L 4.32 ± 0.64 4.22 ± 0.57 4.60 ± 0.76 < 0.001 Blood lactate acid, mmol/L 2.41 ± 1.53 2.37 ± 1.41 2.53 ± 1.86 0.255 Albumin, g/dl 3.19 ± 0.54 3.28 ± 0.51 2.88 ± 0.55 < 0.001 Bilirubin, mg/dl 1.04 ± 1.10 1.05 ± 1.05 1.03 ± 1.25 0.799 BUN, mg/dl 23.97 ± 16.53 18.86 ± 8.63 40.00 ± 23.68 < 0.001 SCr, mg/dl 1.32 ± 1.46 0.89 ± 0.25 2.69 ± 2.49 < 0.001 CK, U/L 582.64 ± 1714.02 539.45 ± 1737.04 718.36 ± 1637.17 0.238 CK-MB, U/L 26.29 ± 57.72 24.73 ± 55.41 31.20 ± 64.40 0.205 Troponin, ng/mL 0.84 ± 1.77 0.67 ± 1.50 1.38 ± 2.35 < 0.001 NT-proBNP, ng/L 8398.72 ± 9642.36 6880.07 ± 8469.15 13170.33 ± 11417.93 < 0.001 CAR 0.44 ± 0.53 0.27 ± 0.08 0.95 ± 0.90 < 0.001 Scoring systems SOFA 6.17 ± 3.27 5.62 ± 3.10 7.91 ± 3.19 < 0.001 SAPS II 40.42 ± 12.01 39.07 ± 12.11 44.67 ± 10.63 < 0.001 APS III 44.98 ± 18.23 42.21 ± 18.11 53.68 ± 15.74 < 0.001 Charlson 5.36 ± 2.52 5.03 ± 2.35 6.42 ± 2.77 < 0.001 GCS 13.62 ± 3.27 13.40 ± 3.55 14.31 ± 2.02 0.002 Oasis 32.71 ± 7.72 32.10 ± 7.79 34.62 ± 7.17 < 0.001 Comorbidities, n (%) AF 270 (38.57) 141 (15.33) 270 (29.35) < 0.001 AKI 605 (86.43) 451 (84.93) 154 (91.12) 0.041 COPD 44 (6.29) 31 (5.84) 13 (7.69) 0.387 CKD 163 (23.29) 80 (15.07) 83 (49.11) < 0.001 Diabetes 189 (27.00) 134 (25.24) 55 (32.54) 0.062 Heart failure 371 (53.00) 258 (48.59) 113 (66.86) < 0.001 Hypertension 322 (46.00) 289 (54.43) 33 (19.53) < 0.001 Malignancy 111 (15.86) 81 (15.25) 30 (17.75) 0.439 Sepsis 455 (65.00) 338 (63.65) 117 (69.23) 0.186 Stroke 84 (12.00) 57 (10.73) 27 (15.98) 0.068 Pneumonia 140 (20.00) 96 (18.08) 44 (26.04) 0.024 Medicine, n (%) Glucocorticoid 100 (14.29) 79 (14.88) 21 (12.43) 0.428 Diuretic 649 (92.71) 503 (94.73) 146 (86.39) < 0.001 CCB 141 (20.14) 108 (20.34) 33 (19.53) 0.819 ACEI/ARB 268 (38.29) 220 (41.43) 48 (28.40) 0.002 Beta-blocker 628 (89.71) 481 (90.58) 147 (86.98) 0.179 Amiodarone 289 (41.29) 220 (41.43) 69 (40.83) 0.891 TAVR, n (%) 114 (16.29) 86 (16.20) 28 (16.57) 0.909 Ventilation, n (%) 674 (96.29) 513 (96.61) 161 (95.27) 0.421 CRRT, n (%) 64 (9.14) 28 (5.27) 36 (21.30) < 0.001 Length of Hospital stay, days 15.13 ± 11.56 14.09 ± 10.54 18.39 ± 13.83 < 0.001 Length of ICU stay, days 6.34 ± 8.31 5.80 ± 7.36 8.04 ± 10.61 < 0.001 Mortality, n (%) 14-Day 28 (4.00) 15 (2.82) 13 (7.69) 0.005 21-Day 37 (5.29) 19 (3.58) 18 (10.65) < 0.001 28-Day 41 (5.86) 21 (3.95) 20 (11.83) < 0.001 90-Day 75 (10.71) 40 (7.53) 35 (20.71) < 0.001 1-year 122 (17.43) 66 (12.43) 56 (33.14) < 0.001 Abbreviations: SBP: systolic blood pressure, DBP: diastolic blood pressure, MBP: mean blood pressure, SPO2: percutaneous oxygen saturation, WBC: White blood cell, RDW: red cell distribution width, APPT: activated partial thromboplastin time, AF: atrial fibrillation, COPD: chronic obstructive pulmonary disease, SCr: serum creatine, BUN blood urea nitrogen, SAPSII: simplified acute physiology score II, SOFA: Sequential Organ Failure Assessment, APS III: acute physiology score II, CCB: calcium channel blocker, ACEI/ARB: angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, TAVR: transcatheter aortic valve replacement. Data were presented as the mean士SD and n (%). 2. Feature selection with machine learning. In this study, we utilized machine learning techniques, specifically LASSO regression, to identify key predictors of 28-day all-cause mortality. We chose LASSO due to its superior ability to handle complex data structures and provide robust predictive performance. Our analysis revealed that the CAR was a significant predictor of 28-day all-cause mortality (see Figure 3). In the LASSO regression analysis, the CAR maintained its prominence as a pivotal predictor of mortality. The Lasso model, known for its ability to perform variable selection by penalizing less important predictors, further confirmed the CAR's significance. The bar chart representation of the Lasso regression coefficients (Figure 3) visually reinforced the CAR's status as the most influential predictor. The CAR's coefficient was notably higher compared to other variables, indicating a strong and independent association with the risk of 28-day all-cause mortality. Additional details of the LASSO regression model are provided in the supplementary materials. 3. RCS analysis. Our analysis explored the association between the CAR and short-, medium- and long-term mortality risk using restricted cubic spline (RCS) curves and logistic regression. The findings revealed a significant and non-linear relationship between the CAR and both 28-day, 90-day and 1-year all-cause mortality. The RCS curves depicted in Figure 4 illustrate the non-linear association between the CAR and mortality risk. For 28-day and 90-day mortality, the hazard ratio (HR) increased incrementally with higher CAR values. Similarly, the RCS curve for 1-year mortality demonstrated a similar trend, with a statistically significant non-linear increase in the HR with rising CAR values. This consistent pattern across both time points strengthens the evidence that CAR is a potential predictor of mortality risk in critically ill patients. 4. Univariate and multivariate Cox regression models of CAR with mortality in patients with AVR. Univariate and multivariate Cox regression analyses were conducted to explore the potential relationship between the CAR and mortality in patients with AVR. An elevated CAR (≥0.43) was significantly associated with an increased risk of mortality at various time points in unadjusted Model 1: 14-day (HR = 2.81, 95% CI: 1.34-5.90, P for trend = 0.006), 28-day (HR = 3.11, 95% CI: 1.66-5.83, P for trend < 0.001), 90-day (HR = 2.97, 95% CI: 1.89-4.68, P for trend < 0.001), and 1-year (HR = 3.03, 95% CI: 2.12-4.33, P for trend < 0.001). In multivariate Model 2, the group of patients with a CAR ≥ 0.43 continued to exhibit a higher risk of mortality after adjusting for confounding factors such as age, gender, race, weight, and height: 14-day (HR = 3.94, 95% CI: 1.53-10.17, P for trend = 0.002), 28-day (HR = 3.30, 95% CI: 1.55-7.02, P for trend < 0.001), 90-day (HR = 2.28, 95% CI: 1.33-3.92, P for trend < 0.001), and 1-year (HR = 2.49, 95% CI: 1.63-3.81, P for trend < 0.001). A subsequent multivariate analysis in Model 3, which included additional potential confounders, also demonstrated an independent association between a higher CAR and increased mortality risk across these time intervals. Detailed results are presented in Table 3. Table 3 Univariate and multivariate Cox regression models of CAR with mortality in patients with AVR. Outcome Model1 Model2 Model3 HR (95%CI) P HR (95%CI) P HR (95%CI) P 14-day CAR < 0.43 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) CAR ≥ 0.43 2.81 (1.34 ~ 5.90) 0.006 3.94 (1.53 ~ 10.17) 0.005 3.62 (1.34 ~ 9.80) 0.011 P for Trend 0.001 0.001 0.002 28-day CAR < 0.43 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) CAR ≥ 0.43 3.11 (1.66 ~ 5.83) < 0.001 3.30 (1.55 ~ 7.02) 0.002 2.97 (1.35 ~ 6.51) 0.007 P for Trend < 0.001 < 0.001 < 0.001 90-day CAR < 0.43 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) CAR ≥ 0.43 2.97 (1.89 ~ 4.68) < 0.001 2.28 (1.33 ~ 3.92) 0.003 2.18 (1.24 ~ 3.82) 0.007 P for Trend < 0.001 < 0.001 < 0.001 1-year CAR < 0.43 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) CAR ≥ 0.43 3.03 (2.12 ~ 4.33) < 0.001 2.49 (1.63 ~ 3.81) < .001 2.33 (1.51 ~ 3.60) < 0.001 P for Trend < 0.001 < 0.001 < 0.001 HR: Hazard Ratio, CI: Confidence Interval Model1: Unadjusted Model2: Adjusted for age, gender, race, weight, height. Model3: Adjusted for age, gender, race, weight, height, WBC, RBC, RDW, chloride, glucose, Ph, heart rate, respiratory rate, diabetes, pneumonia, stroke, COPD, ACEI/ARB, SIRS, GCS. 5. Kaplan-Meier curve and ROC curve analysis. Patients with CAR ≥ 0.43 had higher mortality at 14-day, 28-day, 90-day, and 1-year intervals compared to those with CAR < 0.43, as demonstrated by the K-M survival curves (2.83% vs. 7.69%, P = 0.004; 3.58% vs. 10.65%, P < 0.001; 3.77% vs. 11.24%, P < 0.001; 7.53% vs. 20.71%, P < 0.001; 12.43% vs. 33.14%, P < 0.001). The results are presented in Figure 5. These findings underscore the strong association between CAR levels and patient survival rates over both short and long-term periods. Moreover, they highlight the potential of CAR as a clinical decision-support tool, particularly in predicting patient mortality risk. ROC curves were generated for predicting all-cause death in patients at 14-day, 28-day, 90-day, and 1-year following admission using eight measures: serum creatinine to albumin ratio (CAR), creatinine, albumin, Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Systemic Inflammatory Response Syndrome (SIRS), Troponin T, NT-proBNP (Figure 6). For specific details regarding Figure 6, please refer to Table 4. Our results revealed that, in terms of AUC values, CAR was superior to creatinine, albumin, troponin, SOFA, OASIS, SIRS score, and SOFA score at 28-day, 90-day, and 1-year intervals. The detailed results are presented in Table 4. Consequently, our findings highlight the significant predictive benefits of the CAR. Table 4 Information of ROC curves. Variables AUC (%) 95%CI (%) Threshold Sensitivity Specificity 14-Day SOFA 61.37 52.04–70.69 5.5 78.57 46.28 OASIS 66.84 56.41–77.27 39.5 42.86 82.14 SIRS 59.27 48.86–69.68 2.5 71.43 43.3 CAR 61.43 50.11–72.75 0.43 53.57 76.04 Creatinine 55.32 43.41–67.22 1.15 50 65.62 Albumin 67.32 56.71–77.93 2.95 60.71 68.3 Troponin T 61.6 49.99–73.20 0.43 57.14 68.01 NT-proBNP 62.57 52.81–72.33 5635 71.43 55.36 28-Day SOFA 57.67 49.43–65.90 5.5 71.79 46.29 OASIS 62.09 52.64–71.54 40.5 33.33 85.78 SIRS 57.06 48.33–65.79 2.5 69.23 43.42 CAR 64.56 55.16–73.96 0.43 53.85 76.55 Creatinine 60.52 50.58–70.47 1.45 38.46 82.45 Albumin 61.44 51.97–70.90 2.85 46.15 75.34 Troponin T 60.18 50.47–69.89 0.43 56.41 68.38 NT-proBNP 66.11 58.38–73.83 5635 76.92 56.13 90-Day SOFA 60.39 54.18–66.60 5.5 72 47.36 OASIS 62.96 56.12–69.81 36.5 48 72.32 SIRS 51.97 45.37–58.57 2.5 61.33 43.2 CAR 65.24 58.30-72.17 0.42 50.67 77.12 Creatinine 61.5 54.30-68.69 1.15 53.33 67.2 Albumin 62.51 55.67–69.34 2.95 50.67 69.28 Troponin T 58.64 51.48–65.81 0.43 50.67 69.12 NT-proBNP 66.88 60.94–72.83 4981.5 80 54.08 1-Year SOFA 57.55 51.99–63.11 5.5 65.57 47.58 OASIS 58.46 52.57–64.34 36.5 45.08 73.36 SIRS 51.3 45.72–56.87 1.5 14.75 91.52 CAR 65.45 59.70–71.20 0.42 50 79.24 Creatinine 62.86 56.91–68.81 1.35 43.44 81.66 Albumin 59.12 53.53–64.71 2.85 36.89 76.47 Troponin T 57.1 51.16–63.03 0.43 46.72 69.9 NT-proBNP 62.9 57.51–68.29 4981.5 69.67 54.67 ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval; CAR, serum creatinine to albumin ratio; Cr, creatinine; Alb, albumin; SOFA, Sequential Organ Failure Assessment; OASIS, Oxford Acute Severity of Illness Score; SIRS, Systemic Inflammatory Response Syndrome; Troponin T, cardiac troponin T; NT-proBNP, N-terminal pro-B-type natriuretic peptide. 6. Subgroup analyses for the CAR on clinical outcomes in patients with AVR. Figure 7 illustrates the presence of the relationship between the 14-day, 28-day, 90-day, and 1-year CAR and all-cause mortality in different subgroups of patients with AVR. When stratified analyses were performed for age, gender, AKI, CKD, diabetes, sepsis, stroke, and heart failure, the forest plots showed no significant interaction between CAR and most subgroups. Discussion This retrospective cohort study of 700 AVR patients establishes the CAR as a robust predictor of all-cause mortality across both short-term (14-28 days) and extended follow-up periods (90 days to 1 year), and the predictive ability of CAR is applicable to both surgery and catheter replacement. Utilizing machine learning algorithms and multivariable analyses, we identified a CAR cutoff of 0.43 as the optimal threshold for risk stratification, and it showed good prediction performance among various variables. Patients with high CAR (≥0.43) showed significantly increased mortality [14-day: HR 3.62 (1.34 ~ 9.80), P =0.011; 1-year HR 2.33 (1.51 ~ 3.60), P <0.001] and poor clinical outcomes, including prolonged ICU stay and increased demand for continuous renal replacement therapy. The prognostic superiority of CAR over isolated creatinine or albumin measurements—suggests its unique capacity to encapsulate the synergistic interplay between renal dysfunction, nutrition metabolization and inflammation. Aortic valve replacement is the main method to treat severe aortic valve disease. Despite the continuous improvement of surgical techniques, the prognosis of postoperative patients still faces challenges, including heart failure, renal insufficiency and cardiovascular events[22]. This persistent clinical challenge underscores the critical need for identifying reliable biomarkers to optimize risk stratification and prognostic prediction in AVR patients. Among potential biomarkers, serum creatinine has emerged as a clinically significant indicator with dual prognostic implications. As a fundamental marker of renal function, creatinine's predictive value extends to cardiovascular outcomes through multiple mechanisms[23]. In aortic stenosis patients, longitudinal creatinine fluctuations demonstrate strong correlation with adverse clinical outcomes[24, 25]. Particularly in aortic valve implantation populations, elevated baseline creatinine levels significantly increase the risk of postoperative acute kidney injury (AKI), necessitating careful perioperative monitoring[26, 27]. In addition, the prognostic utility of creatinine extends beyond renal outcomes to encompass survival prediction. A study demonstrated that integrating creatinine-derived estimated glomerular filtration rate (eGFR) with quality-of-life assessments using the Kansas City Cardiomyopathy Questionnaire significantly enhances risk stratification accuracy in TAVR patients[28]. This synergy between biochemical markers and clinical assessments highlights the importance of multidimensional evaluation frameworks. The clinical implications become particularly pronounced in patients with chronic kidney disease (CKD), where concurrent aortic stenosis creates a pathological synergy that substantially worsens prognosis[29]. This interdependence between renal and cardiovascular systems necessitates comprehensive management strategies that address both organ systems simultaneously. Emerging evidence further suggests that dynamic creatinine interactions with other biomarkers may refine prognostic models. The uric acid/creatinine ratio has shown significant association with major adverse cardiovascular and cerebrovascular events (MACCE), revealing the prognostic relevance of metabolic homeostasis[30]. Similarly, the hemoglobin-to-creatinine ratio demonstrates predictive capacity for midterm all-cause mortality and heart failure hospitalization in severe aortic stenosis patients undergoing TAVR[31]. These composite indices underscore the evolving understanding of creatinine's role in multidimensional risk assessment. Serum albumin, the most abundant plasma protein synthesized exclusively by the liver, serves as a multifunctional regulator maintaining colloidal osmotic pressure and participating in antioxidant, anti-inflammatory, and metabolic processes[32]. Emerging evidence positions hypoalbuminemia as a significant prognostic determinant across cardiovascular pathologies, with particular clinical relevance in aortic stenosis management[33]. The prognostic power of albumin manifests through three principal dimensions in transcatheter aortic valve replacement populations. First, as a metabolic integrator, serum albumin concentration reflects the interplay of nutritional status, hepatic synthesis capacity, and systemic inflammation[34]. In the long term, the free fatty acids transport function of albumin may be involved in the development of hepatic lipid accumulation and dysregulated glucose metabolism in obesity[35]. Second, preoperative hypoalbuminemia (<3.5 g/dL) demonstrates strong correlation with postoperative morbidity, showing increased risk for 30-day composite endpoints encompassing infection, acute kidney injury, and mortality[36]. Third, longitudinal studies reveal albumin's predictive capacity extends beyond immediate perioperative risks to long-term survival outcomes[37]. This prognostic utility is enhanced through composite biomarker strategies. The albumin-bilirubin score, originally developed for hepatic function assessment, has demonstrated remarkable adaptability in severe aortic stenosis cohorts[38]. High albumin-bilirubin score (> -2.25) was found as an independent risk factor associated with 30-day and 1-year mortality and total primary outcomes, suggesting shared pathophysiological pathways between cardiometabolic and nutrient metabolism. Furthermore, the C-reactive protein to albumin ratio, as a novel inflammatory- nutritional index, is an independent predictor of long-term mortality in patients undergoing TAVR due to symptomatic aortic stenosis[39]. There are multisystem consequences of poor cardiovascular-kidney-metabolic (CKM) health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality[40]. The serum creatinine-to-albumin ratio has emerged as a novel composite biomarker with enhanced prognostic utility in aortic valve replacement, synergistically integrating renal metabolic and systemic inflammatory/nutritional information. And it is expected to be a new comprehensive biomarker for the assessment of Cardiovascular-Kidney-Metabolic Syndrome (CKMS) in this population. This dual-parameter approach is biologically grounded in their potential interaction within CKMS: creatinine elevation reflects declining glomerular filtration rate and nitrogenous waste accumulation[41], while hypoalbuminemia indicates impaired nutrient metabolism, chronic inflammation, and oxidative stress[42]—collectively marking multiorgan dysregulation in CKMS progression. Mechanistically, elevated CAR may deteriorate outcomes through three interconnected pathways: (1) Metabolic derangement, characterized by concurrent impaired creatinine clearance and reduced albumin synthesis, disrupts amino acid homeostasis and induces glucose metabolism dysregulation[43], ultimately exacerbating myocardial energy crisis; (2) Inflammatory amplification, as diminished albumin's antioxidant capacity weakens cytokine neutralization, while renal dysfunction delays inflammatory mediator clearance, fostering a pro-inflammatory milieu[44, 45]; (3) Volume overload, driven by albumin-depleted oncotic pressure reduction and creatinine-associated sodium retention, synergistically worsening congestive heart failure[46]. We believe that elevated CAR may reflect, to some extent, the pathophysiological synergies of CKM dysfunction. As demonstrated in our study, the high CAR group exhibited significant hyperglycinemia (147.38±64.65 mg/dL versus 129.85±44.19 mg/dL) and urolithiasis (BUN 40.00±23.68 versus 18.86±8.63 mg/dL) suggesting concomitant glucose metabolism and renal clearance defects. At the same time, underlying chronic inflammation may lead to increased albumin consumption and decreased albumin synthesis[47]. This dual process amplifies CAR elevation, establishing a self-perpetuating inflammatory-metabolic loop. In addition, significantly elevated NT-proBNP levels in the high CAR cohort represent the compensated heart failure stage. These interrelated pathways are expected to position CAR as a dynamic link between CKM syndrome progression and adverse clinical outcomes. This study establishes the prognostic stratification capacity of the creatinine-to-albumin ratio across acute and chronic postoperative phases, revealing three critical insights. First, CAR exhibits a temporal predictive gradient, demonstrating superior discrimination for long-term mortality compared to short-term outcomes, highlighting distinct therapeutic windows: immediate albumin optimization protocols combined with continuous renal replacement therapy may mitigate 30-day risks, whereas sustained renoprotective strategies and nutritional interventions maybe essential for improving 12-month survival. Second, machine learning validation through least absolute shrinkage and selection operator (LASSO) regression identified CAR ≥ 0.43 as an independent prognostic cutoff, with this threshold robustly stratifying survival outcomes across validation cohorts. Third, CAR maintains consistent prognostic accuracy across age strata and comorbidity profiles, underscoring its clinical utility in resource-constrained settings, and CAR is an easy-to-access, validated composite indicator. Moreover, the translational potential of CAR lies in its dual clinical utility: (1) As a preoperative screening tool, CAR ≥0.43 may prompt intensified nutritional optimization and renal protection protocols; (2) Postoperative CAR trends could guide dynamic risk re-stratification, potentially triggering earlier CRRT initiation, nutritional support or anti-inflammatory therapies. Future implementation research can explore the changes of CAR to guide patients with different renal function or albumin levels to give corresponding treatment. Despite these advances, this study has several limitations that warrant consideration. First, the retrospective single-center design inherently carries risks of residual confounding, and although we adjusted for potential confounders using multivariate analysis, complete elimination of confounding factors remains challenging. Second, the absence of detailed surgical parameters (including valve type, operation time, and surgical approach) in the MIMIC-IV database precluded analysis of potential interactions between CAR and surgical techniques. Third, static measurement of CAR at a single timepoint fails to capture dynamic perioperative fluctuations; serial monitoring in future studies may better elucidate the relationship between temporal CAR trajectories and clinical outcomes. Furthermore, given the known ethnic and regional variations in creatinine-albumin metabolism, multicenter prospective studies are required to validate the generalizability of CAR. Finally, clinical trials investigating CAR-guided perioperative albumin supplementation strategies and optimal timing for CRRT initiation could facilitate translation of these findings into clinical practice. Conclusion In conclusion, for patients with AVR, the CAR has been identified as a potential independent predictor of all-cause mortality, both in the short term and long term. This finding holds significant clinical relevance, as it provides healthcare providers with a valuable tool for early assessment of disease severity and identification of patients who may have a less favorable prognosis. By leveraging the predictive power of CAR, clinicians can tailor interventions more effectively, potentially improving patient outcomes. To further substantiate the prognostic value of CAR in AVR patients, there is a need for large-scale, multicenter, prospective studies. Abbreviations Abbreviation Full Term AVR Aortic Valve Replacement CAR Serum Creatinine to Albumin Ratio RDW Red Cell Distribution Width NLR Neutrophil/Lymphocyte Ratio RAR Red Blood Cell Distribution Width/Albumin Ratio Tyg Triglyceride Glucose Index AS Aortic Stenosis AR Aortic Regurgitation TAVR Transcatheter Aortic Valve Replacement ICU Intensive Care Unit CBC Complete Blood Count APS III Acute Physiology Score III SAPS II Simplified Acute Physiology Score II SOFA Sepsis-related Organ Failure Assessment OASIS Oxford Acute Severity of Illness Score SIRS Systemic Inflammatory Response Syndrome ROC Receiver Operating Characteristic AUC Area Under the Curve OR Odds Ratio HR Hazard Ratio RCS Restricted Cubic Spline NYHA New York Heart Association BNP B-type Natriuretic Peptide NT-proBNP N-terminal Pro B-type Natriuretic Peptide COPD Chronic Obstructive Pulmonary Disease ARB Angiotensin Receptor Blockers ACEI Angiotensin-Converting Enzyme Inhibitors CCBs Calcium Channel Blockers Declarations Ethical Approval and Consent to Participate Ethical Approval: The MIMIC-IV database was established with approval from the Massachusetts Institute of Technology (Cambridge, MA) and Beth Israel Deaconess Medical Center (Boston, MA). The data within the database are anonymized to protect patient privacy. Our study, being a retrospective analysis using this publicly available anonymized dataset, does not require additional ethical approval. We have adhered to the ethical standards proposed by the Helsinki Declaration of 1964. Consent to Participate: The original data collection for the MIMIC-IV database involved patient consent. For our study, as it utilizes anonymized health records and does not pose any direct impact on patients, no further written informed consent from participants is required. The requirement for written informed consent from participants or their legal guardians/next of kin has been waived by the ethics committee/institutional review board. Consent for Publication This manuscript does not contain any form of personal data; hence no publication consent is required. Availability of Data and Materials The datasets generated and/or analyzed during the current study utilizing the MIMIC-IV database (Version 2.2) are publicly available. All information regarding the database can be found on the official MIMIC-IV database website [https://mimic.mit.edu/]. As the data used in this study are de-identified and the research is based on a retrospective analysis of the publicly available MIMIC-IV dataset, no additional data beyond what is accessible on the MIMIC-IV website will be shared. For further inquiries or specific data requests, please contact the corresponding author. Competing Interests The authors declare that they have no competing interests. Funding This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No.LY22H020011 and National Natural Science Foundation of China (82470697). Authors' Contributions Q.N. and R.J. wrote the main manuscript text. Y.L. and Z.G. provided methodological support. W.N. and Q.Z. provided statistical guidance and prepared the tables. L.W. and Z.M. prepared the figures. C.C. and H.Z. reviewed the manuscript; all authors were involved in the study discussions and have read and approved the final manuscript. H.Z. is the corresponding authors. Acknowledgements We express our deepest gratitude to the participants and staff involved in the MIMIC-IV database project, whose dedication and contributions have been invaluable in facilitating this research and advancing our understanding of critical care medicine. References Iung B, Baron G, Butchart EG, Delahaye F, Gohlke-Barwolf C, Levang OW, Tornos P, Vanoverschelde JL, Vermeer F, Boersma E et al : A prospective survey of patients with valvular heart disease in Europe: The Euro Heart Survey on Valvular Heart Disease. Eur Heart J 2003, 24(13):1231-1243. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6313927","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442342471,"identity":"dcba3071-b539-4385-a2d8-dd25d8fd37a0","order_by":0,"name":"Qingwei Ni","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingwei","middleName":"","lastName":"Ni","suffix":""},{"id":442342473,"identity":"1e1b76bd-23d2-4211-898d-f906b3079b1c","order_by":1,"name":"Ruihao Jiang","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruihao","middleName":"","lastName":"Jiang","suffix":""},{"id":442342475,"identity":"479b29e9-c2fd-4ebc-a219-aae1dbf98e1f","order_by":2,"name":"Yuanzhen Lin","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanzhen","middleName":"","lastName":"Lin","suffix":""},{"id":442342477,"identity":"09c5bb36-1d5c-4ccf-b303-a60c2772a784","order_by":3,"name":"Weicheng Ni","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weicheng","middleName":"","lastName":"Ni","suffix":""},{"id":442342479,"identity":"65af051b-7cd2-4556-95d0-d44cbec36d30","order_by":4,"name":"Zhijie Mao","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhijie","middleName":"","lastName":"Mao","suffix":""},{"id":442342482,"identity":"0db84425-94a8-4de1-b3bc-ecc00c4b9248","order_by":5,"name":"Qian Zhou","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhou","suffix":""},{"id":442342484,"identity":"32e8ad48-4d22-4e37-b20c-a5dd6c319a06","order_by":6,"name":"Liangguo Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liangguo","middleName":"","lastName":"Wang","suffix":""},{"id":442342486,"identity":"8c4ba1af-497a-4bf8-a7b9-12e3402377bb","order_by":7,"name":"Zhan Gao","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhan","middleName":"","lastName":"Gao","suffix":""},{"id":442342489,"identity":"83652812-6c5a-4ff2-a76d-61503ec6ca6a","order_by":8,"name":"Changxi Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changxi","middleName":"","lastName":"Chen","suffix":""},{"id":442342490,"identity":"c4c095cf-6450-4419-896e-a8ef284bc200","order_by":9,"name":"Hao Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACgwMMbAwfKmx4+NkbSNDCOONMmoxkzwEStDBzthy2MbjhQKyWG+nPHjM2nOdhuMHA+OFjDhFazG4kpBsX7rjNwzi7gVly5jbitByTnnnmNg+zzAE2Zl7itCS2SfO2neNhk0ggUov9jWQ2oJYDPDxEa7E884xNcsaZZB4JnoPNxPnF4Hj6M4kPFXb29sebD374SIwWBoEEGIuxgRj1QMB/gEiFo2AUjIJRMHIBALdUOotVf69SAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-03-26 15:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6313927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6313927/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81934174,"identity":"426dfb5b-2afb-4915-aae2-c2b9a0efa6e8","added_by":"auto","created_at":"2025-05-05 05:43:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart for participants from the MIMIC-IV (v 2.2).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.Flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/c9e8b4367ff77b2eed02813f.png"},{"id":81934176,"identity":"6a80ccac-fad2-43f5-ba91-80a89bac3926","added_by":"auto","created_at":"2025-05-05 05:43:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":452497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimal cutoff point for CAR (A). The distribution of CAR ≥ 0.43, and KM curve (B).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.OptimalcutoffpointforCARA.ThedistributionofCAR0.43andKMcurveB..png","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/fcbbe38c9999458819a1a95c.png"},{"id":81934187,"identity":"71f4cd39-d172-4af4-b6f2-57214be8c4b7","added_by":"auto","created_at":"2025-05-05 05:43:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":918093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature selection using LASSO regression.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.FeatureselectionusingLASSOregression..png","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/ab328900c9477cce93ada7a3.png"},{"id":81934181,"identity":"e73b981a-f825-4bcb-a18a-0b141c63c5a2","added_by":"auto","created_at":"2025-05-05 05:43:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":307369,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of 28-day, 90-day, 1-year mortality.\u003c/p\u003e","description":"","filename":"Figure4.RCSanalysisof28day90day1yearmortality..png","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/4b5168e403f78a818d866f81.png"},{"id":81936087,"identity":"ed21c550-6822-4655-9eaf-1d3b8f1af592","added_by":"auto","created_at":"2025-05-05 06:03:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":191731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival analysis curves for all-cause mortality in patients with AVR at 14-day (A), 28-day (B), 90-day (C), and 1-year (D) of hospital admission.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/de1e5956d3b5e74211b89c69.png"},{"id":81934183,"identity":"e307702d-e318-48b0-b017-80bbd2d2adce","added_by":"auto","created_at":"2025-05-05 05:43:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1253093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves for predicting all-cause mortality in patients with AVR at 14-day (A), 28-day (B), 90-day (C), and 1-year (D) after admission.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.ROCcurvesforpredictingallcausemortalityinpatientswithAVRat14dayA28dayB90dayCand1yearDafteradmission..png","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/7be15f08d52fd69c55d81e2c.png"},{"id":81934931,"identity":"bb5ea9c6-2d9a-4326-bb32-7c5f00da0413","added_by":"auto","created_at":"2025-05-05 06:00:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":760618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of subgroup analysis of the relationship between all-cause mortality and CAR in patients with AVR admitted 14-day (A), 28-day (B), 90-day (C), and 1-year (D).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.ForestplotsofsubgroupanalysisoftherelationshipbetweenallcausemortalityandCARinpatientswithAVRadmitted14dayA28dayB90dayCand1yearD.png","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/f345aecc6180149de18cea59.png"},{"id":83932203,"identity":"506149b2-5e0d-4ee8-adcf-0273bdb56ff2","added_by":"auto","created_at":"2025-06-04 15:38:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5347513,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/ae7d6772-4531-494a-b19c-50bdabe3c486.pdf"},{"id":81936083,"identity":"3a27670c-6041-4afd-be6d-4f1926d39dfa","added_by":"auto","created_at":"2025-05-05 06:03:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19038,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1..pdf","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/3e171c8731e328ad02c49688.pdf"},{"id":81936051,"identity":"c4202540-f89d-4aee-8658-efba6f01ad3b","added_by":"auto","created_at":"2025-05-05 06:02:16","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13361,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial2..pdf","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/07a91e895489336c7b236efc.pdf"},{"id":81936023,"identity":"a95610fe-c5d4-42b5-b71e-e94f3ecd9926","added_by":"auto","created_at":"2025-05-05 06:02:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19765,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial3.AnalysisoffeatureimportanceandmodelperformancebyLassoregression..pdf","url":"https://assets-eu.researchsquare.com/files/rs-6313927/v1/cf2df9daf6a16da7eefd7b63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Role of the Serum Creatinine to Albumin Ratio in the Evaluation short- and long-term all-cause mortality of Patients with Aortic Valve Replacement: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAortic valve disease, encompassing structural and functional abnormalities such as aortic stenosis (AS) and aortic regurgitation (AR), represents a major global health burden. Degenerative-calcific AS constitutes the most prevalent valvular heart disease in developed populations, according to the Euro Heart Survey on Valvular Disease[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Aortic valve disease significantly impacts the quality of life and prognosis of affected individuals, with epidemiological data revealing a 5-year survival rate below 50% for untreated severe cases[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. The disease trajectory is frequently complicated by progressive heart failure, malignant arrhythmias, and systemic embolic events, underscoring the critical need for timely intervention. While aortic valve replacement (AVR) \u0026ndash; whether surgical or transcatheter (TAVR) \u0026ndash; has revolutionized management by restoring valvular function and improving survival[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e], significant heterogeneity persists in postoperative outcomes. AVR restores the normal function of the aortic valve by replacing the diseased valve, with studies showing that survival rates and quality of life are significantly improved after aortic valve replacement[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the incidence of postoperative complications cannot be ignored, including prosthesis-related complications, thromboembolism, and persistent myocardial dysfunction[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e], highlighting an unmet need for precision risk stratification tools. In this case, simple and accessible biomarkers have great potential for improving the prognosis of diseases.\u003c/p\u003e\n\u003cp\u003eThe quest for prognostic biomarkers has intensified with emerging evidence linking systemic inflammation and metabolic dysregulation to valvulopathy progression. In recent years, several new biomarkers such as the neutrophil/lymphocyte ratio (NLR), red blood cell distribution width/albumin ratio (RAR), CRP-albumin ratio and triglyceride glucose index (Tyg) have been proved to be closely related to the prognosis of cardiovascular diseases[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], reflecting the pathophysiological interplay between chronic inflammation, endothelial dysfunction, and adverse remodeling. Similarly, using biomarkers to evaluate the prognosis of patients undergoing aortic valve replacement is a quick and feasible method. Aortic valve disease is intricately linked to chronic inflammation[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Concurrently, the resultant hemodynamic alterations further activate systemic immune-inflammatory cascades[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the inflammatory state, the release of inflammatory mediators and cytokines has an impact on physiological functions and causes changes in blood biochemical results[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Among these biomarkers, the creatinine-to-albumin ratio (CAR) has attracted increasing attention[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. CAR, a composite marker synergistically capturing renal function and nutritional-metabolic status, has been shown to be a promising predictor of adverse prognosis of many diseases. It can be easily calculated from routine laboratory tests, making it a convenient and cost-effective tool for clinical use. Studies have demonstrated that an elevated CAR is associated with worse prognosis in various cardiovascular conditions[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], highlighting its potential value in risk stratification and management of patients with aortic valve disease. Mechanistically, elevated serum creatinine signifies impaired glomerular filtration, while hypoalbuminemia mirrors both inflammatory catabolism and malnutrition \u0026ndash; two hallmarks of the cardiovascular-kidney-metabolic (CKM) syndrome continuum. The concept of CKM syndrome further emphasizes the interplay between metabolic factors, cardiovascular, and renal diseases[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. CKM is characterized by the interaction of metabolic risk factors, chronic kidney disease, and the cardiovascular system, leading to multi-organ dysfunction and increased incidence of adverse cardiovascular outcomes. The relationship between CAR and CKM is of particular interest as both involve the interplay between renal function and metabolic status, suggesting that CAR could be a valuable biomarker in the context of CKM for predicting prognosis and guiding treatment strategies.\u003c/p\u003e\n\u003cp\u003eDespite the potential value of CAR in predicting prognosis, the relationship between CAR and short- and long-term all-cause mortality in patients who have undergone AVR remains unclear. To address this gap, we utilized the Medical Information Mart for Intensive Care IV (version 2.2) database to collect data on the hospitalization status of AVR patients admitted between 2008 and 2019, employing machine learning and multiple regression methods. We hypothesize that CAR elevation prior to AVR independently predicts mortality through synergistic pathways involving renal dysfunction, metabolic derangement, and subclinical inflammation. The study aimed to explore the association between short- and long-term all-cause mortality and CAR in these patients, providing more refined prognostic information for clinical decision-making.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1. Study population.\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study investigated health-related data obtained from the MIMIC-IV database (version 2.2), which is a common and large database that was developed and managed by the MIT Computational Physiology Laboratory. This database is comprised of extensive and high-quality medical records of patients who were admitted to the intensive critical care units of the Beth Israel Deaconess Medical Center[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. We identified patients aged 18 years or older who underwent aortic valve replacement (AVR) from the MIMIC-IV database. The selection of patients was based on international classification of diseases (ICD) diagnostic codes for AVR, including a comprehensive set of codes such as 3521, 02RF08Z, 02RF38Z, 3522, 3505, 02RF0JZ, 02RF0KZ, X2RF332, 02QF0ZZ, 3506, 02RF3JZ, 02RF3KZ, 02RF37Z, X2RF432, and X2RF032. We meticulously excluded patients with missing values, incomplete demographic data, or those with an ICU length of stay of less than 24 hours to ensure the accuracy and reliability of our analysis. Finally, a total of 700 patients were enrolled in this study and grouped into two groups according to the optimal cut-off value of CAR (Fig. 1). To protect patient privacy, all personal data were de-identified, and patient identification was substituted with random numbers. Ethical approval and informed consent were waived. The researchers initiating this study have obtained permission to use the database after successfully passing the qualification tests required for database access.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e2. Data collection.\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe software PostgreSQL (version 13.7.2) and Navicat Premium (version 15) were employed to extract information using Structured Query Language (SQL), ensuring efficient and accurate retrieval of patient records. The primary exposure variable was the CAR, which was calculated as the ratio of serum creatinine to serum albumin levels, both measured at ICU admission to establish a baseline for our analysis. Additionally, a comprehensive dataset was curated, including patient demographics, comorbidities, laboratory values, and clinical parameters (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The potential variables were categorized into four main groups: (1) demographics variables, such as age and gender; (2) comorbidities, including atrial fibrillation, sepsis, heart failure, diabetes, and renal disease; (3) laboratory indicators, comprising red blood cell (RBC) count, white blood cell (WBC) count, hemoglobin levels, platelet count, serum sodium, and serum creatinine; and (4) severity of illness scores at admission, such as the Acute Physiology Score III (APS III), the Simplified Acute Physiology Score II (SAPS-II), and the Sepsis-related Organ Failure Assessment score (SOFA)[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCovariates extracted in detail from the MIMIC-IV database.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eItems\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eComposition\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDemographic variables\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAge, Gender, Race, Weight, Height, BMI\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eVital Signs\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHR, SBP, DBP, MAP, RR, SpO2, Temperature\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eClinical Treatments\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGlucocorticoids, Immunosuppressant, Furosemide, CCB, ACEI/ARB, Betablocker, Digoxin, Amiodarone, Spirolactone, MV, CRRT, TAVR\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eComorbidities\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAKI, CKD, Sepsis, HF, AF, Hypertension, Diabetes, Pneumonia, COPD, Stroke\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLaboratory variables\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNeutrophil counts, Lymphocyte counts, RBC, WBC, RDW, Plt, Hb, HCT, Cr, Alb, BUN, TB, AST, ALT, Glucose, APPT, PT, INR, K, Na, AG, Lac\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eClinical Outcomes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eLOS ICU, LOS hospital, ICU mortality, In-hospital mortality,14-day mortality, 21-day mortality, 28-day mortality, 90-day mortality, 1-year mortality\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eMIMIC-IV, the Medical Information Mart for Intensive Care database; BMI, body mass index; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; RR, respiratory rate; SpO2, oxygen saturation; CCB, calcium channel blocker; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker; MV, mechanical ventilation; CRRT, continuous renal replacement therapy; TAVR, transcatheter aortic valve replacement; AKI, acute kidney injury; CKD, chronic kidney disease; HF, heart failure; AF, atrial fibrillation; COPD, chronic obstructive pulmonary disease; RBC, red blood cell; WBC, white blood cell; RDW, erythrocyte distribution width; Plt, platelet; Hb, hemoglobin; HCT, hematocrit; Cr, creatinine; Alb, albumin; BUN, blood urea nitrogen; TB, total bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; APPT, activated partial thromboplastin time; PT, prothrombin time; INR, international normalized ratio; K, serum potassium; Na, serum sodium; AG, anion gap; Lac, lactate; CAR, serum creatinine to albumin ratio; LOS ICU, length of ICU stay; LOS hospital, length of hospital stay.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e3. Endpoint events.\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint of the study was all-cause mortality within 14-day, 28-day, 90-day, and 1-year of admission after hospital admission. In addition, we also evaluated the length of stay in ICU, the total length of stay and the treatment measures during this period.\u003c/p\u003e\n\u003ch2\u003e4. Statistical analysis.\u003c/h2\u003e\n\u003cp\u003eDescriptive statistics were used to summarize the study population and baseline characteristics, providing a clear overview of the demographic and clinical features of our cohort. Continuous variables were presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or medians (interquartile ranges), and categorical variables are presented as frequencies and percentages, allowing a thorough understanding of the data distribution. After determining the ideal CAR cutoff value based on whether death occurred on day 28 after admission, we divided the study patient population into two groups: low CAR and high CAR. The choice of the optimal cutoff point that maximized the risk ratio is presented in Fig. 2, as well as the relationship of CAR\u0026thinsp;\u0026ge;\u0026thinsp;0.43 and the distribution of CAR. The association between CAR and mortality was assessed using Kaplan-Meier survival analysis and Cox proportional hazards regression models, both of which are well-established methods for analyzing time-to-event data. To further explore the predictive ability of CAR and other clinical parameters, we employed machine learning algorithms. LASSO regression was chosen for its ability to handle complex relationships and feature interactions within the data. ROC curves were constructed to evaluate the predictive performance of the CAR and other clinical parameters for mortality, providing a visual representation of the models\u0026apos; discriminative power. Subgroup analyses were conducted to examine the potential impact of CAR on various parameters. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant. All the statistical analysis were performed by the R software (version 4.4.1), SPSS 27.0 (IBM SPSS Statistics, Armonk, NY, USA), and Python (version 3.12).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1. Baseline demographic and clinical characteristics.\u003c/strong\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 700 patients with AVR and were treated in the ICU were ultimately included (see Fig. 1). Among them, 265 patients (37.86%) were women, while 435 patients (62.14%) were men. Utilizing the X-tile software, the optimal cutoff value for the CAR was determined based on whether the patient had died by day 28 following ICU admission. The analysis revealed that a CAR value of 0.43 was the most appropriate threshold (see Fig. 2). Consequently, the study population was divided into two groups: those with a low CAR (\u0026lt;\u0026thinsp;0.43) and those with a high CAR (\u0026ge;\u0026thinsp;0.43). Patients in the high CAR group exhibited worse scores in several critical illness severity indices, including SOFA, SAPS II, APS III, and Charlson comorbidity index. Additionally, patients with comorbidities such as AF, AKI, CKD, and heart failure were more likely to be categorized in the high CAR group. Laboratory findings showed that patients in the high CAR group had elevated levels of neutrophil count, RDW, serum glucose, anion gap, serum potassium, creatinine, blood lactate acid, blood urea nitrogen, troponin, and NT-proBNP. Further investigation revealed that patients in the high CAR group faced a significantly higher risk of adverse outcomes. They were less likely to receive diuretics and ACEI/ARBs, and more inclined to undergo CRRT. These patients also experienced longer durations of hospital stay and ICU stay. Moreover, the high CAR group had significantly higher mortality rates at 14 days (2.82% vs. 7.69%, P\u0026thinsp;=\u0026thinsp;0.005), 21 days (3.58% vs. 10.65%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 28 days (3.95% vs. 11.83%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 90 days (7.53% vs. 20.71%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 1 year (12.43% vs. 33.14%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For detailed results, refer to Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of the study population.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003eCharacteristics\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003eN\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003eCreatinine to Albumin Ratio(CAR)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u003cem\u003eP\u003c/em\u003e value\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u003cstrong\u003eLow group\u003c/strong\u003e\u003cbr\u003e\u0026lt;\u0026thinsp;0.43\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u003cstrong\u003eHigh group\u003c/strong\u003e\u003cbr\u003e\u0026ge;\u0026thinsp;0.43\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eN\u003cbr\u003eClinical parameters\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e700\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e531\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e169\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAge, years\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.06\u0026thinsp;\u0026plusmn;\u0026thinsp;14.59\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e70.27\u0026thinsp;\u0026plusmn;\u0026thinsp;16.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.416\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSex, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e265 (37.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e230 (43.31)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e35 (20.71)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e435 (62.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e301 (56.69)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e134 (79.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eEthnicity, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.239\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eWhite\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e533 (76.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e410 (77.21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e123 (72.78)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBlack and Other\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e167 (23.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e121 (22.79)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e46 (27.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eWeight, kg\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e84.28\u0026thinsp;\u0026plusmn;\u0026thinsp;20.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e82.92\u0026thinsp;\u0026plusmn;\u0026thinsp;19.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e88.55\u0026thinsp;\u0026plusmn;\u0026thinsp;23.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHeight, cm\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e169.48\u0026thinsp;\u0026plusmn;\u0026thinsp;10.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e168.53\u0026thinsp;\u0026plusmn;\u0026thinsp;10.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e172.44\u0026thinsp;\u0026plusmn;\u0026thinsp;9.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBMI, kg/m2\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e29.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e29.18\u0026thinsp;\u0026plusmn;\u0026thinsp;6.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e29.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.354\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eVital signs\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHeart rate, beats/min\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e84.04\u0026thinsp;\u0026plusmn;\u0026thinsp;15.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e83.46\u0026thinsp;\u0026plusmn;\u0026thinsp;14.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e85.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.073\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSBP, mmHg\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e114.10\u0026thinsp;\u0026plusmn;\u0026thinsp;21.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e113.93\u0026thinsp;\u0026plusmn;\u0026thinsp;21.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e114.64\u0026thinsp;\u0026plusmn;\u0026thinsp;23.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.707\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDBP, mmHg\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e58.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e58.42\u0026thinsp;\u0026plusmn;\u0026thinsp;13.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e57.76\u0026thinsp;\u0026plusmn;\u0026thinsp;14.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.601\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMBP, mmHg\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.90\u0026thinsp;\u0026plusmn;\u0026thinsp;13.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e72.01\u0026thinsp;\u0026plusmn;\u0026thinsp;13.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.54\u0026thinsp;\u0026plusmn;\u0026thinsp;15.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.704\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRespiratory rate, beats/min\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e16.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e15.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e17.09\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.024\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSPO2, %\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e97.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e98.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e97.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.009\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTemperature, ℃\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e36.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e36.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e36.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.020\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLaboratory parameters\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eWBC count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e13.08\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e12.80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e13.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.051\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNeutrophil count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e11.05\u0026thinsp;\u0026plusmn;\u0026thinsp;6.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e10.54\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e12.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLymphocytes, 10\u003csup\u003e9\u003c/sup\u003e/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.194\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHemoglobin, g/dl\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e9.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.703\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePlatelet count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e165.28\u0026thinsp;\u0026plusmn;\u0026thinsp;80.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e165.61\u0026thinsp;\u0026plusmn;\u0026thinsp;81.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e164.24\u0026thinsp;\u0026plusmn;\u0026thinsp;79.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.848\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHematocrit, %\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e28.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e28.60\u0026thinsp;\u0026plusmn;\u0026thinsp;5.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e29.13\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.312\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eRDW, %\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e14.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e14.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e15.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAPPT, seconds\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e43.89\u0026thinsp;\u0026plusmn;\u0026thinsp;28.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e40.74\u0026thinsp;\u0026plusmn;\u0026thinsp;23.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e53.79\u0026thinsp;\u0026plusmn;\u0026thinsp;37.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAnion gap, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e13.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e16.34\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e17.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSerum calcium, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e8.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSerum chloride, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e106.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e107.35\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e105.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSerum glucose, mg/dl\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e134.08\u0026thinsp;\u0026plusmn;\u0026thinsp;50.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e129.85\u0026thinsp;\u0026plusmn;\u0026thinsp;44.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e147.38\u0026thinsp;\u0026plusmn;\u0026thinsp;64.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSerum sodium, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e138.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e138.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e138.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.748\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSerum potassium, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBlood lactate acid, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.255\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAlbumin, g/dl\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBilirubin, mg/dl\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.799\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBUN, mg/dl\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e23.97\u0026thinsp;\u0026plusmn;\u0026thinsp;16.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e18.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e40.00\u0026thinsp;\u0026plusmn;\u0026thinsp;23.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSCr, mg/dl\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCK, U/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e582.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1714.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e539.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1737.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e718.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1637.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.238\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCK-MB, U/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e26.29\u0026thinsp;\u0026plusmn;\u0026thinsp;57.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e24.73\u0026thinsp;\u0026plusmn;\u0026thinsp;55.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e31.20\u0026thinsp;\u0026plusmn;\u0026thinsp;64.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.205\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTroponin, ng/mL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNT-proBNP, ng/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e8398.72\u0026thinsp;\u0026plusmn;\u0026thinsp;9642.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6880.07\u0026thinsp;\u0026plusmn;\u0026thinsp;8469.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e13170.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11417.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eScoring systems\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSOFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSAPS II\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e40.42\u0026thinsp;\u0026plusmn;\u0026thinsp;12.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e39.07\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e44.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.63\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAPS III\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e44.98\u0026thinsp;\u0026plusmn;\u0026thinsp;18.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e42.21\u0026thinsp;\u0026plusmn;\u0026thinsp;18.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e53.68\u0026thinsp;\u0026plusmn;\u0026thinsp;15.74\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCharlson\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGCS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e13.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e13.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e14.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOasis\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e32.71\u0026thinsp;\u0026plusmn;\u0026thinsp;7.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e32.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e34.62\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eComorbidities, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAF\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e270 (38.57)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e141 (15.33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e270 (29.35)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAKI\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e605 (86.43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e451 (84.93)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e154 (91.12)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.041\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCOPD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e44 (6.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e31 (5.84)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e13 (7.69)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.387\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCKD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e163 (23.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e80 (15.07)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e83 (49.11)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDiabetes\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e189 (27.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e134 (25.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e55 (32.54)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.062\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHeart failure\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e371 (53.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e258 (48.59)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e113 (66.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHypertension\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e322 (46.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e289 (54.43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e33 (19.53)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMalignancy\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e111 (15.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e81 (15.25)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e30 (17.75)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.439\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSepsis\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e455 (65.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e338 (63.65)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e117 (69.23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.186\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eStroke\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e84 (12.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e57 (10.73)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e27 (15.98)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.068\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePneumonia\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e140 (20.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e96 (18.08)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e44 (26.04)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.024\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMedicine, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eGlucocorticoid\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e100 (14.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e79 (14.88)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e21 (12.43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.428\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDiuretic\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e649 (92.71)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e503 (94.73)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e146 (86.39)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCCB\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e141 (20.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e108 (20.34)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e33 (19.53)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.819\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eACEI/ARB\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e268 (38.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e220 (41.43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e48 (28.40)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBeta-blocker\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e628 (89.71)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e481 (90.58)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e147 (86.98)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.179\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAmiodarone\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e289 (41.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e220 (41.43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e69 (40.83)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.891\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTAVR, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e114 (16.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e86 (16.20)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e28 (16.57)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.909\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eVentilation, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e674 (96.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e513 (96.61)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e161 (95.27)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.421\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCRRT, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e64 (9.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e28 (5.27)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e36 (21.30)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLength of Hospital stay, days\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e15.13\u0026thinsp;\u0026plusmn;\u0026thinsp;11.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e14.09\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e18.39\u0026thinsp;\u0026plusmn;\u0026thinsp;13.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eLength of ICU stay, days\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.80\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e8.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMortality, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e14-Day\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e28 (4.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e15 (2.82)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e13 (7.69)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e21-Day\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e37 (5.29)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e19 (3.58)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e18 (10.65)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e28-Day\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e41 (5.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e21 (3.95)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e20 (11.83)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e90-Day\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e75 (10.71)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e40 (7.53)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e35 (20.71)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e1-year\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e122 (17.43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e66 (12.43)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e56 (33.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e SBP: systolic blood pressure, DBP: diastolic blood pressure, MBP: mean blood pressure, SPO2: percutaneous oxygen saturation, WBC: White blood cell, RDW: red cell distribution width, APPT: activated partial thromboplastin time, AF: atrial fibrillation, COPD: chronic obstructive pulmonary disease, SCr: serum creatine, BUN blood urea nitrogen, SAPSII: simplified acute physiology score II, SOFA: Sequential Organ Failure Assessment, APS III: acute physiology score II, CCB: calcium channel blocker, ACEI/ARB: angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, TAVR: transcatheter aortic valve replacement. Data were presented as the mean士SD and n (%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. \u0026nbsp; Feature selection with machine learning.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we utilized machine learning techniques, specifically LASSO regression, to identify key predictors of 28-day all-cause mortality. We chose LASSO due to its superior ability to handle complex data structures and provide robust predictive performance. Our analysis revealed that the CAR was a significant predictor of 28-day all-cause mortality (see Figure 3). In the LASSO regression analysis, the CAR maintained its prominence as a pivotal predictor of mortality. The Lasso model, known for its ability to perform variable selection by penalizing less important predictors, further confirmed the CAR\u0026apos;s significance. The bar chart representation of the Lasso regression coefficients (Figure 3) visually reinforced the CAR\u0026apos;s status as the most influential predictor. The CAR\u0026apos;s coefficient was notably higher compared to other variables, indicating a strong and independent association with the risk of 28-day all-cause mortality. Additional details of the LASSO regression model are provided in the supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;RCS analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis explored the association between the CAR and short-, medium- and long-term mortality risk using restricted cubic spline (RCS) curves and logistic regression. The findings revealed a significant and non-linear relationship between the CAR and both 28-day, 90-day and 1-year all-cause mortality. The RCS curves depicted in Figure 4 illustrate the non-linear association between the CAR and mortality risk. For 28-day and 90-day mortality, the hazard ratio (HR) increased incrementally with higher CAR values. Similarly, the RCS curve for 1-year mortality demonstrated a similar trend, with a statistically significant non-linear increase in the HR with rising CAR values. This consistent pattern across both time points strengthens the evidence that CAR is a potential predictor of mortality risk in critically ill patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;Univariate and multivariate Cox regression models of CAR with mortality in patients with AVR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate Cox regression analyses were conducted to explore the potential relationship between the CAR and mortality in patients with AVR. An elevated CAR (\u0026ge;0.43) was significantly associated with an increased risk of mortality at various time points in unadjusted Model 1: 14-day (HR = 2.81, 95% CI: 1.34-5.90, P for trend = 0.006), 28-day (HR = 3.11, 95% CI: 1.66-5.83, P for trend \u0026lt; 0.001), 90-day (HR = 2.97, 95% CI: 1.89-4.68, P for trend \u0026lt; 0.001), and 1-year (HR = 3.03, 95% CI: 2.12-4.33, P for trend \u0026lt; 0.001). In multivariate Model 2, the group of patients with a CAR \u0026ge; 0.43 continued to exhibit a higher risk of mortality after adjusting for confounding factors such as age, gender, race, weight, and height: 14-day (HR = 3.94, 95% CI: 1.53-10.17, P for trend = 0.002), 28-day (HR = 3.30, 95% CI: 1.55-7.02, P for trend \u0026lt; 0.001), 90-day (HR = 2.28, 95% CI: 1.33-3.92, P for trend \u0026lt; 0.001), and 1-year (HR = 2.49, 95% CI: 1.63-3.81, P for trend \u0026lt; 0.001). A subsequent multivariate analysis in Model 3, which included additional potential confounders, also demonstrated an independent association between a higher CAR and increased mortality risk across these time intervals. Detailed results are presented in Table 3.\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate Cox regression models of CAR with mortality in patients with AVR.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003eOutcome\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003eModel1\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003eModel2\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003eModel3\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eHR (95%CI)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u003cem\u003eP\u003c/em\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eHR (95%CI)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u003cem\u003eP\u003c/em\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eHR (95%CI)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u003cem\u003eP\u003c/em\u003e\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e14-day\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u0026thinsp;\u0026lt;\u0026thinsp;0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u0026thinsp;\u0026ge;\u0026thinsp;0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.81 (1.34\u0026thinsp;~\u0026thinsp;5.90)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.006\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.94 (1.53\u0026thinsp;~\u0026thinsp;10.17)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.62 (1.34\u0026thinsp;~\u0026thinsp;9.80)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.011\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eP\u003c/em\u003e for Trend\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003e28-day\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u0026thinsp;\u0026lt;\u0026thinsp;0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u0026thinsp;\u0026ge;\u0026thinsp;0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.11 (1.66\u0026thinsp;~\u0026thinsp;5.83)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.30 (1.55\u0026thinsp;~\u0026thinsp;7.02)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.97 (1.35\u0026thinsp;~\u0026thinsp;6.51)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.007\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eP\u003c/em\u003e for Trend\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003e90-day\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u0026thinsp;\u0026lt;\u0026thinsp;0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u0026thinsp;\u0026ge;\u0026thinsp;0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.97 (1.89\u0026thinsp;~\u0026thinsp;4.68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.28 (1.33\u0026thinsp;~\u0026thinsp;3.92)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.18 (1.24\u0026thinsp;~\u0026thinsp;3.82)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e0.007\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eP\u003c/em\u003e for Trend\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003e1-year\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u0026thinsp;\u0026lt;\u0026thinsp;0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.00 (Reference)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u0026thinsp;\u0026ge;\u0026thinsp;0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.03 (2.12\u0026thinsp;~\u0026thinsp;4.33)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.49 (1.63\u0026thinsp;~\u0026thinsp;3.81)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.33 (1.51\u0026thinsp;~\u0026thinsp;3.60)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cem\u003eP\u003c/em\u003e for Trend\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026lt;\u0026thinsp;0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eHR: Hazard Ratio, CI: Confidence Interval\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eModel1: Unadjusted\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eModel2: Adjusted for age, gender, race, weight, height.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eModel3: Adjusted for age, gender, race, weight, height, WBC, RBC, RDW, chloride, glucose, Ph, heart rate, respiratory rate, diabetes, pneumonia, stroke, COPD, ACEI/ARB, SIRS, GCS.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;Kaplan-Meier curve and ROC curve analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with CAR \u0026ge; 0.43 had higher mortality at 14-day, 28-day, 90-day, and 1-year intervals compared to those with CAR \u0026lt; 0.43, as demonstrated by the K-M survival curves (2.83% vs. 7.69%, P = 0.004; 3.58% vs. 10.65%, P \u0026lt; 0.001; 3.77% vs. 11.24%, P \u0026lt; 0.001; 7.53% vs. 20.71%, P \u0026lt; 0.001; 12.43% vs. 33.14%, P \u0026lt; 0.001). The results are presented in Figure 5. These findings underscore the strong association between CAR levels and patient survival rates over both short and long-term periods. Moreover, they highlight the potential of CAR as a clinical decision-support tool, particularly in predicting patient mortality risk.\u003c/p\u003e\n\u003cp\u003eROC curves were generated for predicting all-cause death in patients at 14-day, 28-day, 90-day, and 1-year following admission using eight measures: serum creatinine to albumin ratio (CAR), creatinine, albumin, Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Systemic Inflammatory Response Syndrome (SIRS), Troponin T, NT-proBNP (Figure 6). For specific details regarding Figure 6, please refer to Table 4. Our results revealed that, in terms of AUC values, CAR was superior to creatinine, albumin, troponin, SOFA, OASIS, SIRS score, and SOFA score at 28-day, 90-day, and 1-year intervals. The detailed results are presented in Table 4. Consequently, our findings highlight the significant predictive benefits of the CAR.\u003c/p\u003e\n\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInformation of ROC curves.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eVariables\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eAUC (%)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e95%CI (%)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eThreshold\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eSensitivity\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eSpecificity\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e14-Day\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSOFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e61.37\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e52.04\u0026ndash;70.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e78.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e46.28\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOASIS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e66.84\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e56.41\u0026ndash;77.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e39.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e42.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e82.14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSIRS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e59.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e48.86\u0026ndash;69.68\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e43.3\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e61.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e50.11\u0026ndash;72.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e53.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e76.04\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCreatinine\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e43.41\u0026ndash;67.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e50\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e65.62\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAlbumin\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e67.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e56.71\u0026ndash;77.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e60.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e68.3\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTroponin T\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e61.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e49.99\u0026ndash;73.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e57.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e68.01\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNT-proBNP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e62.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e52.81\u0026ndash;72.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5635\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.36\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003e28-Day\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSOFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e57.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e49.43\u0026ndash;65.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e71.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e46.29\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOASIS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e62.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e52.64\u0026ndash;71.54\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e40.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e33.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e85.78\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSIRS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e57.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e48.33\u0026ndash;65.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e69.23\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e43.42\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e64.56\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.16\u0026ndash;73.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e53.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e76.55\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCreatinine\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e60.52\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e50.58\u0026ndash;70.47\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e38.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e82.45\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAlbumin\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e61.44\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e51.97\u0026ndash;70.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e46.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e75.34\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTroponin T\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e60.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e50.47\u0026ndash;69.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e56.41\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e68.38\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNT-proBNP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e66.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e58.38\u0026ndash;73.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5635\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e76.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e56.13\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003e90-Day\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSOFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e60.39\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.18\u0026ndash;66.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e72\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e47.36\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOASIS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e62.96\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e56.12\u0026ndash;69.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e36.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e48\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e72.32\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSIRS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e51.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e45.37\u0026ndash;58.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e61.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e43.2\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e65.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e58.30-72.17\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e50.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e77.12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCreatinine\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e61.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.30-68.69\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.15\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e53.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e67.2\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAlbumin\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e62.51\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e55.67\u0026ndash;69.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.95\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e50.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e69.28\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTroponin T\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e58.64\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e51.48\u0026ndash;65.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e50.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e69.12\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNT-proBNP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e66.88\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e60.94\u0026ndash;72.83\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4981.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e80\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.08\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u003cstrong\u003e1-Year\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSOFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e57.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e51.99\u0026ndash;63.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e65.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e47.58\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eOASIS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e58.46\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e52.57\u0026ndash;64.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e36.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e45.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e73.36\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSIRS\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e51.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e45.72\u0026ndash;56.87\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e14.75\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e91.52\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCAR\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e65.45\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e59.70\u0026ndash;71.20\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e50\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e79.24\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCreatinine\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e62.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e56.91\u0026ndash;68.81\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.35\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e43.44\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e81.66\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAlbumin\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e59.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e53.53\u0026ndash;64.71\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e36.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e76.47\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTroponin T\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e57.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e51.16\u0026ndash;63.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e46.72\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e69.9\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNT-proBNP\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e62.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e57.51\u0026ndash;68.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4981.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e69.67\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e54.67\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval; CAR, serum creatinine to albumin ratio; Cr, creatinine; Alb, albumin; SOFA, Sequential Organ Failure Assessment; OASIS, Oxford Acute Severity of Illness Score; SIRS, Systemic Inflammatory Response Syndrome; Troponin T, cardiac troponin T; NT-proBNP, N-terminal pro-B-type natriuretic peptide.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp; \u0026nbsp;Subgroup analyses for the CAR on clinical outcomes in patients with AVR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 7 illustrates the presence of the relationship between the 14-day, 28-day, 90-day, and 1-year CAR and all-cause mortality in different subgroups of patients with AVR. When stratified analyses were performed for age, gender, AKI, CKD, diabetes, sepsis, stroke, and heart failure, the forest plots showed no significant interaction between CAR and most subgroups.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort study of 700 AVR patients establishes the CAR as a robust predictor of all-cause mortality across both short-term (14-28 days) and extended follow-up periods (90 days to 1 year), and the predictive ability of CAR is applicable to both surgery and catheter replacement. Utilizing machine learning algorithms and multivariable analyses, we identified a CAR cutoff of 0.43 as the optimal threshold for risk stratification, and it showed good prediction performance among various variables. Patients with high CAR (\u0026ge;0.43) showed significantly increased mortality [14-day: HR 3.62 (1.34 ~ 9.80), \u003cem\u003eP\u003c/em\u003e=0.011; 1-year HR 2.33 (1.51 ~ 3.60), \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001] and poor clinical outcomes, including prolonged ICU stay and increased demand for continuous renal replacement therapy. The prognostic superiority of CAR over isolated creatinine or albumin measurements\u0026mdash;suggests its unique capacity to encapsulate the synergistic interplay between renal dysfunction,\u0026nbsp;nutrition metabolization and inflammation.\u003c/p\u003e\n\u003cp\u003eAortic valve replacement is the main method to treat severe aortic valve disease. Despite the continuous improvement of surgical techniques, the prognosis of postoperative patients still faces challenges, including heart failure, renal insufficiency and cardiovascular events[22]. This persistent clinical challenge underscores the critical need for identifying reliable biomarkers to optimize risk stratification and prognostic prediction in AVR patients. Among potential biomarkers, serum creatinine has emerged as a clinically significant indicator with dual prognostic implications. As a fundamental marker of renal function, creatinine\u0026apos;s predictive value extends to cardiovascular outcomes through multiple mechanisms[23]. In aortic stenosis patients, longitudinal creatinine fluctuations demonstrate strong correlation with adverse clinical outcomes[24, 25]. Particularly in aortic valve implantation populations, elevated baseline creatinine levels significantly increase the risk of postoperative acute kidney injury (AKI), necessitating careful perioperative monitoring[26, 27]. In addition, the prognostic utility of creatinine extends beyond renal outcomes to encompass survival prediction. A study demonstrated that integrating creatinine-derived estimated glomerular filtration rate (eGFR) with quality-of-life assessments using the Kansas City Cardiomyopathy Questionnaire significantly enhances risk stratification accuracy in TAVR patients[28]. This synergy between biochemical markers and clinical assessments highlights the importance of multidimensional evaluation frameworks. The clinical implications become particularly pronounced in patients with chronic kidney disease (CKD), where concurrent aortic stenosis creates a pathological synergy that substantially worsens prognosis[29]. This interdependence between renal and cardiovascular systems necessitates comprehensive management strategies that address both organ systems simultaneously. Emerging evidence further suggests that dynamic creatinine interactions with other biomarkers may refine prognostic models. The uric acid/creatinine ratio has shown significant association with major adverse cardiovascular and cerebrovascular events (MACCE), revealing the prognostic relevance of metabolic homeostasis[30]. Similarly, the hemoglobin-to-creatinine ratio demonstrates predictive capacity for midterm all-cause mortality and heart failure hospitalization in severe aortic stenosis patients undergoing TAVR[31]. These composite indices underscore the evolving understanding of creatinine\u0026apos;s role in multidimensional risk assessment.\u003c/p\u003e\n\u003cp\u003eSerum albumin, the most abundant plasma protein synthesized exclusively by the liver, serves as a multifunctional regulator maintaining colloidal osmotic pressure and participating in antioxidant, anti-inflammatory, and metabolic processes[32]. Emerging evidence positions hypoalbuminemia as a significant prognostic determinant across cardiovascular pathologies, with particular clinical relevance in aortic stenosis management[33]. The prognostic power of albumin manifests through three principal dimensions in transcatheter aortic valve replacement populations. First, as a metabolic integrator, serum albumin concentration reflects the interplay of nutritional status, hepatic synthesis capacity, and systemic inflammation[34]. In the long term, the free fatty acids transport function of albumin may be involved in the development of hepatic lipid accumulation and dysregulated glucose metabolism in obesity[35]. Second, preoperative hypoalbuminemia (\u0026lt;3.5 g/dL) demonstrates strong correlation with postoperative morbidity, showing increased risk for 30-day composite endpoints encompassing infection, acute kidney injury, and mortality[36]. Third, longitudinal studies reveal albumin\u0026apos;s predictive capacity extends beyond immediate perioperative risks to long-term survival outcomes[37]. This prognostic utility is enhanced through composite biomarker strategies. The albumin-bilirubin score, originally developed for hepatic function assessment, has demonstrated remarkable adaptability in severe aortic stenosis cohorts[38]. High albumin-bilirubin score (\u0026gt; -2.25) was found as an independent risk factor associated with 30-day and 1-year mortality and total primary outcomes, suggesting shared pathophysiological pathways between cardiometabolic and nutrient metabolism. Furthermore, the C-reactive protein to albumin ratio, as a novel inflammatory- nutritional index, is an independent predictor of long-term mortality in patients undergoing TAVR due to symptomatic aortic stenosis[39].\u003c/p\u003e\n\u003cp\u003eThere are multisystem consequences of poor cardiovascular-kidney-metabolic (CKM) health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality[40]. The serum creatinine-to-albumin ratio has emerged as a novel composite biomarker with enhanced prognostic utility in aortic valve replacement, synergistically integrating renal metabolic and systemic inflammatory/nutritional information. And it is expected to be a new comprehensive biomarker for the assessment of Cardiovascular-Kidney-Metabolic Syndrome (CKMS) in this population. This dual-parameter approach is biologically grounded in their potential interaction within CKMS: creatinine elevation reflects declining glomerular filtration rate and nitrogenous waste accumulation[41], while hypoalbuminemia indicates impaired nutrient metabolism, chronic inflammation, and oxidative stress[42]\u0026mdash;collectively marking multiorgan dysregulation in CKMS progression. Mechanistically, elevated CAR may deteriorate outcomes through three interconnected pathways: (1) Metabolic derangement, characterized by concurrent impaired creatinine clearance and reduced albumin synthesis, disrupts amino acid homeostasis and induces glucose metabolism dysregulation[43], ultimately exacerbating myocardial energy crisis; (2) Inflammatory amplification, as diminished albumin\u0026apos;s antioxidant capacity weakens cytokine neutralization, while renal dysfunction delays inflammatory mediator clearance, fostering a pro-inflammatory milieu[44, 45]; (3) Volume overload, driven by albumin-depleted oncotic pressure reduction and creatinine-associated sodium retention, synergistically worsening congestive heart failure[46].\u003c/p\u003e\n\u003cp\u003eWe believe that elevated CAR may reflect, to some extent, the pathophysiological synergies of CKM dysfunction. As demonstrated in our study, the high CAR group exhibited significant hyperglycinemia (147.38\u0026plusmn;64.65 mg/dL versus 129.85\u0026plusmn;44.19 mg/dL) and urolithiasis (BUN 40.00\u0026plusmn;23.68 versus 18.86\u0026plusmn;8.63 mg/dL) suggesting concomitant glucose metabolism and renal clearance defects. At the same time, underlying chronic inflammation may lead to increased albumin consumption and decreased albumin synthesis[47]. This dual process amplifies CAR elevation, establishing a self-perpetuating inflammatory-metabolic loop. In addition, significantly elevated NT-proBNP levels in the high CAR cohort represent the compensated heart failure stage. These interrelated pathways are expected to position CAR as a dynamic link between CKM syndrome progression and adverse clinical outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study establishes the prognostic stratification capacity of the creatinine-to-albumin ratio across acute and chronic postoperative phases, revealing three critical insights. First, CAR exhibits a temporal predictive gradient, demonstrating superior discrimination for long-term mortality compared to short-term outcomes, highlighting distinct therapeutic windows: immediate albumin optimization protocols combined with continuous renal replacement therapy may mitigate 30-day risks, whereas sustained renoprotective strategies and nutritional interventions maybe essential for improving 12-month survival. Second, machine learning validation through least absolute shrinkage and selection operator (LASSO) regression identified CAR \u0026ge; 0.43 as an independent prognostic cutoff, with this threshold robustly stratifying survival outcomes across validation cohorts. Third, CAR maintains consistent prognostic accuracy across age strata and comorbidity profiles, underscoring its clinical utility in resource-constrained settings, and CAR is an easy-to-access, validated composite indicator. Moreover, the translational potential of CAR lies in its dual clinical utility: (1) As a preoperative screening tool, CAR \u0026ge;0.43 may prompt intensified nutritional optimization and renal protection protocols; (2) Postoperative CAR trends could guide dynamic risk re-stratification, potentially triggering earlier CRRT initiation, nutritional support or anti-inflammatory therapies. Future implementation research can explore the changes of CAR to guide patients with different renal function or albumin levels to give corresponding treatment.\u003c/p\u003e\n\u003cp\u003eDespite these advances, this study has several limitations that warrant consideration. First, the retrospective single-center design inherently carries risks of residual confounding, and although we adjusted for potential confounders using multivariate analysis, complete elimination of confounding factors remains challenging. Second, the absence of detailed surgical parameters (including valve type, operation time, and surgical approach) in the MIMIC-IV database precluded analysis of potential interactions between CAR and surgical techniques. Third, static measurement of CAR at a single timepoint fails to capture dynamic perioperative fluctuations; serial monitoring in future studies may better elucidate the relationship between temporal CAR trajectories and clinical outcomes. Furthermore, given the known ethnic and regional variations in creatinine-albumin metabolism, multicenter prospective studies are required to validate the generalizability of CAR. Finally, clinical trials investigating CAR-guided perioperative albumin supplementation strategies and optimal timing for CRRT initiation could facilitate translation of these findings into clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, for patients with AVR, the CAR has been identified as a potential independent predictor of all-cause mortality, both in the short term and long term. This finding holds significant clinical relevance, as it provides healthcare providers with a valuable tool for early assessment of disease severity and identification of patients who may have a less favorable prognosis. By leveraging the predictive power of CAR, clinicians can tailor interventions more effectively, potentially improving patient outcomes. To further substantiate the prognostic value of CAR in AVR patients, there is a need for large-scale, multicenter, prospective studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFull Term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAortic Valve Replacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSerum Creatinine to Albumin Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRed Cell Distribution Width\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNeutrophil/Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRed Blood Cell Distribution Width/Albumin Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTyg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTriglyceride Glucose Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAortic Stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAortic Regurgitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTAVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTranscatheter Aortic Valve Replacement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntensive Care Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eComplete Blood Count\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAPS III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcute Physiology Score III\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSAPS II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSimplified Acute Physiology Score II\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSepsis-related Organ Failure Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOASIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOxford Acute Severity of Illness Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSIRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSystemic Inflammatory Response Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRestricted Cubic Spline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNYHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNew York Heart Association\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eB-type Natriuretic Peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN-terminal Pro B-type Natriuretic Peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAngiotensin Receptor Blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eACEI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAngiotensin-Converting Enzyme Inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCCBs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCalcium Channel Blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical Approval: The MIMIC-IV database was established with approval from the Massachusetts Institute of Technology (Cambridge, MA) and Beth Israel Deaconess Medical Center (Boston, MA). The data within the database are anonymized to protect patient privacy. Our study, being a retrospective analysis using this publicly available anonymized dataset, does not require additional ethical approval. We have adhered to the ethical standards proposed by the Helsinki Declaration of 1964.\u003c/p\u003e\n\u003cp\u003eConsent to Participate: The original data collection for the MIMIC-IV database involved patient consent. For our study, as it utilizes anonymized health records and does not pose any direct impact on patients, no further written informed consent from participants is required. The requirement for written informed consent from participants or their legal guardians/next of kin has been waived by the ethics committee/institutional review board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not contain any form of personal data; hence no publication consent is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study utilizing the MIMIC-IV database (Version 2.2) are publicly available. All information regarding the database can be found on the official MIMIC-IV database website [https://mimic.mit.edu/]. As the data used in this study are de-identified and the research is based on a retrospective analysis of the publicly available MIMIC-IV dataset, no additional data beyond what is accessible on the MIMIC-IV website will be shared. For further inquiries or specific data requests, please contact the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No.LY22H020011 and National Natural Science Foundation of China (82470697).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ.N. and R.J. wrote the main manuscript text. Y.L. and Z.G. provided methodological support. W.N. and Q.Z. provided statistical guidance and prepared the tables. L.W. and Z.M. prepared the figures. C.C. and H.Z. reviewed the manuscript; all authors were involved in the study discussions and have read and approved the final manuscript. H.Z. is the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our deepest gratitude to the participants and staff involved in the MIMIC-IV database project, whose dedication and contributions have been invaluable in facilitating this research and advancing our understanding of critical care medicine.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIung B, Baron G, Butchart EG, Delahaye F, Gohlke-Barwolf C, Levang OW, Tornos P, Vanoverschelde JL, Vermeer F, Boersma E\u003cem\u003e et al\u003c/em\u003e: A prospective survey of patients with valvular heart disease in Europe: The Euro Heart Survey on Valvular Heart Disease. \u003cem\u003eEur Heart J \u003c/em\u003e2003, 24(13):1231-1243.\u003c/li\u003e\n\u003cli\u003eShahim B, Shahim A, Adamo M, Chioncel O, Benson L, Crespo-Leiro MG, Anker SD, Coats AJS, Filippatos G, Lainscak M\u003cem\u003e et al\u003c/em\u003e: Prevalence, characteristics and prognostic impact of aortic valve disease in patients with heart failure and reduced, mildly reduced, and preserved ejection fraction: An analysis of the ESC Heart Failure Long-Term Registry. \u003cem\u003eEur J Heart Fail \u003c/em\u003e2023, 25(7):1049-1060.\u003c/li\u003e\n\u003cli\u003eVahanian A, Beyersdorf F, Praz F, Milojevic M, Baldus S, Bauersachs J, Capodanno D, Conradi L, De Bonis M, De Paulis R\u003cem\u003e et al\u003c/em\u003e: 2021 ESC/EACTS Guidelines for the management of valvular heart disease. \u003cem\u003eEur Heart J \u003c/em\u003e2022, 43(7):561-632.\u003c/li\u003e\n\u003cli\u003eOtto CM, Nishimura RA, Bonow RO, Carabello BA, Erwin JP, 3rd, Gentile F, Jneid H, Krieger EV, Mack M, McLeod C\u003cem\u003e et al\u003c/em\u003e: 2020 ACC/AHA Guideline for the Management of Patients With Valvular Heart Disease: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. \u003cem\u003eCirculation \u003c/em\u003e2021, 143(5):e72-e227.\u003c/li\u003e\n\u003cli\u003eBrennan JM, Lowenstern A, Sheridan P, Boero IJ, Thourani VH, Vemulapalli S, Wang TY, Liska O, Gander S, Jager J\u003cem\u003e et al\u003c/em\u003e: Association Between Patient Survival and Clinician Variability in Treatment Rates for Aortic Valve Stenosis. \u003cem\u003eJ Am Heart Assoc \u003c/em\u003e2021, 10(16):e020490.\u003c/li\u003e\n\u003cli\u003eRouleau SG, Brady WJ, Koyfman A, Long B: Transcatheter aortic valve replacement complications: A narrative review for emergency clinicians. \u003cem\u003eAm J Emerg Med \u003c/em\u003e2022, 56:77-86.\u003c/li\u003e\n\u003cli\u003eVakhshoori M, Nemati S, Sabouhi S, Yavari B, Shakarami M, Bondariyan N, Emami SA, Shafie D: Neutrophil to lymphocyte ratio (NLR) prognostic effects on heart failure; 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14(1):19151.\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":"Serum creatinine-to-albumin ratio (CAR), Mortality, Aortic valve replacement (AVR), Prognosis, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-6313927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6313927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe identification of novel biomarkers has significantly enhanced prognostic capabilities in the context of cardiovascular diseases. Among these emerging markers, the Serum creatinine-to-albumin ratio (CAR) has garnered increasing attention as a potential prognostic indicator across a variety of clinical settings. To our knowledge, the association between short- and long-term all-cause mortality in patients with aortic valve replacement (AVR) and the CAR has not been investigated. This study discusses the role of CAR in the evaluation of patients with AVR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe performed a retrospective analysis of 700 patients who underwent AVR and whose data were extracted from the MIMIC-IV database. The main purpose is to evaluate all-cause mortality in different periods. We extracted demographic baseline data, vital signs, laboratory tests, and other relevant information from the MIMIC-IV database. Machine learning techniques were employed to select features based on the 28-Day all-cause mortality outcome of the patients. The X-tile software was used to determine the optimal threshold for the CAR. Cox regression analyses were used to investigate the relationship between the CAR and all-cause mortality. Additionally, ROC curve analysis was conducted to evaluate the predictive performance of different indicators for the outcome. Additionally, subgroup analyses were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOur analysis of 700 patients from the MIMIC-IV database who underwent aortic valve replacement revealed that the CAR is a significant predictor of 1-year all-cause mortality. The CAR ideal threshold, determined by X-tile software, was 0.43. LASSO regression, identified CAR as one of the important features in mortality prediction models. Restricted cubic spline analysis demonstrated a significant nonlinear association between the CAR and both 28-Day, 90-Day and 1-year mortality. Cox regression analysis confirmed a dose-dependent increase in all the periods mortality risk with the higher CAR groups. Kaplan-Meier survival analysis showed the lowest survival probability in the higher CAR group. ROC curves indicated that the CAR had a higher AUC for the prediction of 1-year mortality (AUC 0.655) than the other indicators did. These results suggest that the CAR is a robust and independent predictor of mortality in critically ill patients with AVR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur findings suggest that the CAR holds significant promise as a prognostic marker for 1-year mortality in patients undergoing AVR. It can serve as a tool for risk stratification and prognostic assessment in AVR patients.\u003c/p\u003e","manuscriptTitle":"Role of the Serum Creatinine to Albumin Ratio in the Evaluation short- and long-term all-cause mortality of Patients with Aortic Valve Replacement: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 05:43:41","doi":"10.21203/rs.3.rs-6313927/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":"1a15637e-a605-4c0c-a5ba-a3a1ea2d383f","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-04T15:38:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 05:43:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6313927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6313927","identity":"rs-6313927","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}