Association Between Red Cell Distribution Width and Prognosis in Critically Ill Patients With Aortic Valve Disease:A Retrospective Study Based on MIMIC-IV

Association Between Red Cell Distribution Width and Prognosis in Critically Ill Patients With Aortic Valve Disease:A Retrospective Study Based on MIMIC-IV | 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 Association Between Red Cell Distribution Width and Prognosis in Critically Ill Patients With Aortic Valve Disease:A Retrospective Study Based on MIMIC-IV Kaifeng Liu, Xiangsong Lan, Fuwei Liu, Jun Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6271962/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Objective To analyze the relationship between red cell distribution width(RDW)and 1-year all-cause mortality in critically ill patients with aortic stenosis(AS)and aortic regurgitation(AR),aiming to evaluate the potential of RDW as an independent prognostic indicator. Methods Patients were divided into four groups based on the quartiles of their RDW levels:Q1,Q2,Q3,and Q4.The impact of different RDW levels on 1-year all-cause mortality in patients with AS and AR was analyzed using Cox regression analysis,Kaplan-Meier survival curves,Log-Rank tests,and restricted cubic spline（RCS）analysis.The predictive performance of RDW and various clinical scores was compared using Receiver Operating Characteristic(ROC)and Decision Curve Analysis(DCA).Subgroup analyses were conducted to ensure the robustness of the results. Results A total of 2,820 patients were included in the study.Patients in the high RDW group were older,had more comorbidities,and exhibited significantly higher 1-year all-cause mortality.After adjusting for confounding factors in the multivariate Cox regression analysis,elevated RDW was significantly associated with 1-year all-cause mortality(95%CI:1.13–1.29,P<0.01).Kaplan-Meier and RCS analyses revealed that the high RDW group had the lowest survival rates,with a nonlinear relationship observed between RDW and mortality risk.RDW outperformed most traditional scoring systems in predicting 1-year mortality.Subgroup analyses showed that RDW was significantly associated with 1-year all-cause mortality across all subgroups. Conclusion RDW is an independent predictor of 1-year all-cause mortality in critically ill patients with AS and AR. Red Cell Distribution Width (RDW) Prognosis Critically Ill Patients Aortic Valve Disease Retrospective analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Aortic stenosis(AS)and aortic regurgitation(AR)are the most common cardiac valve diseases in clinical practice.The prevalence of AS and AR is significantly higher in patients with underlying conditions such as hypertension and coronary artery disease,as well as in the elderly population.By causing left ventricular outflow tract obstruction and aortic valve regurgitation,these conditions markedly increase cardiac workload,ultimately leading to heart failure 1,2 .Although surgical and interventional treatments can improve patient outcomes to some extent,the incidence of cardiovascular events and long-term mortality in patients with AS and AR remain high.Furthermore,there is currently a lack of reliable prognostic indicators for long-term outcomes in clinical practice 3,4 . Red cell distribution width(RDW)is a common hematological parameter that reflects the variation in red blood cell volume.Pathological processes such as inflammation,oxidative stress,and iron metabolism disorders may disrupt red blood cell production and lifespan,ultimately leading to changes in RDW levels 5,6 .Studies have shown that RDW can independently predict patient prognosis in various cardiovascular diseases,particularly in assessing long-term mortality and postoperative recovery 7–12 .However,existing research on RDW in patients with AS and AR is relatively limited,and the impact of RDW level variations on the prognosis of these patients remains unclear. This study based on the MIMIC-IV database,retrospectively analyzes the relationship between RDW and 1-year all-cause mortality in critically ill patients with AS and AR.The primary aim is to evaluate the potential of RDW as an independent prognostic indicator and to provide further supporting evidence for risk stratification and clinical management of patients with aortic valve disease. Material and methods 2.1 Study population This study utilized version 3.1 of the MIMIC-IV database,a large,publicly available electronic health record system that includes data from over 76,000 ICU admissions across multiple centers between 2008 and 2019 13 .The inclusion criteria were patients admitted to the ICU for the first time with a diagnosis of aortic stenosis(AS)or aortic regurgitation(AR)according to the International Classification of Diseases,9th and 10th Revisions(ICD-9&10codes:“3951”,“3952”,“3960”,“3961”,“3962”,“3963”,“7463”,“7464”,“I060”,“I061”,“I062”,“I350”,“I351”,“I352”,“Q230”,“Q231”).Exclusion criteria included missing RDW data,ICU stays of less than 24 hours,or patients younger than 18 years old. This project was approved by the Institutional Review Boards of the Massachusetts Institute of Technology(MIT)and Beth Israel Deaconess Medical Center(BIDMC)for research purposes,with a waiver of informed consent.All participants'identities were strictly anonymized.In accordance with relevant requirements,the authors completed the“Protecting Human Research Participants”course and obtained access to the database(Certification ID:60329338). 2.2 Data collection Data from MIMIC-IV were extracted using DecisionLinnc(version 1.0) softwar 14 .This study collected patients'demographic information,comorbidities,initial vital signs,and laboratory test results upon ICU admission,as well as treatment data during hospitalization.RDW was considered the primary variable of interest and was divided into quartiles(Q1,Q2,Q3,and Q4)to assess its relationship with 1-year all-cause mortality.The primary endpoint was 1-year all-cause mortality,which was confirmed and validated through patient follow-up records.In the handling of missing values, variables with less than 5% missing data were imputed using mean imputation; variables with missing rates between 5% and 20% were imputed using the K-nearest neighbors (KNN) method; and variables with missing rates exceeding 20% were excluded from the analysis. 2.3 Statistical analysis The study population was divided into four groups based on RDW quartiles,and the Kolmogorov-Smirnov test was used to assess the normality of continuous parameters.None of the continuous variables included in the analysis met the criteria for a normal distribution.Continuous variables were expressed as medians(interquartile range,IQR)and compared between groups using the Kruskal-Wallis test.Categorical variables were presented as frequencies and percentages(%)and compared using theχ²test or Fisher's exact test.Univariate and multivariate Cox regression analyses were performed to identify variables associated with 1-year all-cause mortality in patients with aortic valve disease.To evaluate the impact of RDW on 1-year all-cause mortality,three Cox proportional hazards regression models were constructed:Unadjusted model,no variables were adjusted;Model I:adjusted for age,sex,race,and body weight;Model II:adjusted for all potential confounding factors.Kaplan-Meier survival curves and Log-Rank tests were used to analyze differences in survival times among patients with different RDW levels.Restricted cubic spline(RCS)functions were applied to model the nonlinear relationship between RDW and 1-year mortality risk.The predictive performance of RDW for 1-year all-cause mortality in patients with aortic valve disease was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC).Decision curve analysis(DCA)was conducted to evaluate the net clinical benefit of RDW across different risk thresholds.RDW's predictive performance was also compared with commonly used scoring systems,including SOFA,SAPSII,SIRS,APSIII,OASIS,Shock Index,and the Charlson Comorbidity Index.Finally,subgroup analyses were conducted to test the robustness of the results.Stratified covariates included age,sex,major comorbidities,receipt of aortic valve replacement(AVR),and type of aortic valve disease.All data analyses were performed using DecisionLinnc(version 1.0)and Stata software(version 17.0).A two-sided P<0.05 was considered statistically significant. Results Based on the inclusion and exclusion criteria,a total of 2,820 patients were included in this study(Figure 1)and divided into four groups according to RDW quartiles.The baseline characteristics are presented in Table 1.The median age of the overall cohort was 73 years,with males accounting for 62.66%,and the majority being White(77.41%).Compared with patients with lower RDW levels,those with higher RDW levels were older,had a higher proportion of females,and had a greater prevalence of comorbidities such as diabetes,chronic kidney disease,heart failure,and coronary artery disease.They also had higher clinical scores,including APSIII,SAPSII,and the Charlson Comorbidity Index.Patients in the higher RDW group had lower hemoglobin levels,higher creatinine levels,a lower proportion undergoing AVR,longer hospital stays and ICU stays,and significantly higher 1-year all-cause mortality compared with those in the lower RDW group. Table 2 summarizes the univariate and multivariate regression analyses of variables associated with 1-year all-cause mortality in patients with AS and AR.In the multivariate Cox regression analysis,after adjusting for age,sex,vital signs,laboratory parameters,and comorbidities,RDW levels remained significantly associated with 1-year all-cause mortality.Other factors significantly associated with higher 1-year all-cause mortality included age,respiratory rate,and blood urea nitrogen levels.In contrast,the use of aspirin and novel oral anticoagulants,as well as undergoing AVR,were associated with lower 1-year all-cause mortality. Primary outcomes To further explore the relationship between RDW and 1-year all-cause mortality in patients with AS and AR,three Cox proportional hazards regression models were constructed:(1)the crude model,without adjustments;(2)Model I,adjusted for age,sex,race,and body weight;and(3)Model II,adjusted for all potential confounders.The results showed that when RDW was treated as a continuous variable,each 1-unit increase in RDW was associated with an odds ratio(OR)of 1.56(95%CI:1.49–1.64,P<0.01)in the crude model.After adjusting for partial confounders in Model I,the OR slightly decreased to 1.531(95%CI:1.46–1.61,P<0.01).Further adjustment for all confounders in Model II resulted in an OR of 1.208(95%CI:1.13–1.29,P<0.01).When RDW was categorized into quartiles,the risk of 1-year mortality increased progressively with higher RDW levels(Q2 to Q4).In all three models,the Q4 group had significantly higher mortality risk compared to other groups,with a statistically significant trend(P for trend<0.0001)(Table 3). Figure 2 shows that Kaplan-Meier analysis revealed significant differences in 1-year all-cause mortality across the quartiles of red cell distribution width(RDW)(Log-rank test, P <0.0001).As RDW levels increased,the survival probability significantly decreased,with the highest RDW group(Q4)having the lowest survival rate and the steepest decline in the survival curve.The survival rates of the middle quartiles(Q2 and Q3)were between those of Q1 and Q4. After adjusting for all potential confounders in Model II,RCS analysis(Figure 3)showed a nonlinear relationship between RDW and 1-year all-cause mortality( P for nonlinearity=0.001).An increase in RDW was associated with a higher risk of 1-year mortality. Table 4 and Figure 4A summarize and illustrate the AUC,optimal cut-off values,sensitivity,and specificity of RDW and other critical illness scoring systems in predicting 1-year all-cause mortality.The Charlson Comorbidity Index had the highest predictive accuracy,with an AUC of 0.806(95%CI:0.787–0.825).RDW also demonstrated excellent predictive performance,with an AUC of 0.781(95%CI:0.761–0.801),comparable to the Charlson Index and superior to the other scoring systems.DCA results(Figure 4B)indicated that RDW provided a higher net benefit in predicting 1-year all-cause mortality.Across most risk thresholds,the RDW curve outperformed traditional scoring systems,including SOFA,SAPSII,APSIII,OASIS,and SIRS scores.Notably,in low to moderate risk thresholds(0.1–0.3),the net benefit of the RDW model was comparable to that of the Charlson Comorbidity Index. Subgroup Analysis To further investigate potential confounding variables,a risk stratification analysis was conducted,incorporating covariates such as age,sex,race,valve disease type,and comorbidities(Figure 5).The subgroup analysis demonstrated that RDW was associated with increased 1-year all-cause mortality across multiple patient subgroups. The results showed that RDW remained significantly associated with 1-year all-cause mortality in all subgroups(overall HR=1.31,95%CI:1.28–1.34,P<0.001).Notably,higher RDW levels were associated with a greater mortality risk in patients aged<75 years,those without heart failure(HF),coronary artery disease(CAD),or chronic kidney disease(CKD),as well as in those who underwent AVR(P for interaction<0.05 for all). Discussion The results of this study indicate that elevated RDW is an independent risk factor for mortality in critically ill patients with AS and AR.Multivariate Cox regression analysis demonstrated that even after adjusting for multiple potential confounders,the association between RDW and 1-year all-cause mortality remained significant.As a routinely available component of complete blood count testing,RDW offers advantages such as cost-effectiveness and efficiency,making it widely used in clinical practice.Our findings suggest that RDW can serve as a reliable and novel prognostic marker,aiding in the early identification of high-risk patients with aortic stenosis and aortic regurgitation. In recent years,multiple clinical studies have identified RDW as an independent risk factor for all-cause mortality in patients undergoing AVR.Consistent with our findings,these studies also reported that patients with higher RDW levels tend to have higher frailty and disease severity scores,poorer renal function,and lower hemoglobin levels.Additionally,they observed that patients with elevated RDW exhibited worse cardiac function 15–18 .Liang et al. 19 found in a clinical study on AS that an RDW value≥14.7%was an independent predictor of both short-term and long-term mortality risk in AS patients..However,most of these studies used fixed RDW thresholds to categorize patient groups,which led to an imbalance in the number of patients between groups,potentially introducing selection bias.This study is the first to include both AS and AR patients and further validates the prognostic impact of RDW levels in a larger patient cohort.By stratifying RDW into quartiles and constructing multiple models,this study minimized the influence of confounding factors and the bias caused by baseline characteristic imbalances between groups,clarifying the dose-response relationship between RDW levels and all-cause mortality.Comparison with commonly used scoring systems,including SOFA,SAPSII,SIRS,APSIII,OASIS,Shock Index,and the Charlson Comorbidity Index,demonstrated that RDW outperformed most of these scores in predicting long-term mortality.These findings highlight the significant clinical value of RDW in assessing the long-term prognosis of AS and AR patients. Elevated RDW levels are typically closely associated with chronic inflammation and oxidative stress 20,21 .Elevated RDW levels are typically closely associated with chronic inflammation and oxidative stress 22,23 ..This suggests that an increase in RDW may,to some extent,reflect a patient's comorbidity burden and nutritional status.Additionally,RDW is a sensitive indicator of iron deficiency anemia 6,24 ,allowing for earlier detection of anemia.Iron deficiency anemia is highly prevalent in HF patients and significantly impacts their prognosis 25 .Since AS and AR often lead to cardiac dysfunction in the late stages of the disease 26 ,the mechanism by which elevated RDW affects the long-term prognosis of patients with aortic valve disease may be related to anemia and heart failure.However,research on the underlying mechanisms remains limited. The results of the subgroup analysis were consistent with the primary analysis;however,the increased mortality risk associated with elevated RDW was more pronounced in patients younger than 75 years,as well as those without HF,CAD,or CKD.In younger patients,RDW may serve as a more sensitive indicator of disease status.Conversely,in elderly patients,the strong age dependency of RDW may attenuate its ability to reflect disease severity,thereby affecting its predictive accuracy for mortality risk 27 .Early studies have found that in diseases with high mortality risk and strong oxidative stress,such as HF,CAD,and CKD,the mortality risk reflected by RDW may be attenuated due to the direct impact of the disease itself,as well as the overlapping effects of inflammation and oxidative stress processes 28–30 .However,RDW remains an important systemic health indicator,and its predictive value for mortality risk may be more pronounced,particularly in the early stages of certain diseases. As a cost-effective and readily available biomarker,RDW outperforms several traditional disease scoring systems in predicting long-term mortality in critically ill patients with AS and AR.This provides new evidence for the clinical utility of RDW in managing such patients and lays the foundation for future RDW-based precision treatment strategies.This study is the first to extend the evaluation of RDW to AR patients,further supporting the feasibility of using RDW as a risk stratification tool upon hospital admission for critically ill AS and AR patients. Strengths and Limitations This study has several strengths.First,it is based on the large-scale MIMIC-IV database,which provides broad sample coverage.Compared to previous similar studies,the larger analysis scale enhances the generalizability of the findings.Second,by stratifying patients into quartiles based on RDW levels,the study ensured a balanced sample size across groups.Additionally,Kaplan-Meier survival analysis and RCS functions were utilized to systematically assess the dose-response relationship between RDW and 1-year all-cause mortality,highlighting the significance of RDW as an independent prognostic indicator. This study has certain limitations.First,as a single-center retrospective study,it is challenging to establish a causal relationship between variables and outcomes.Additionally,the study population primarily consists of White patients,which may limit the generalizability of the findings to a global population.Second,this study only analyzed the first RDW measurement obtained after ICU admission and did not investigate the impact of dynamic changes in RDW on prognosis.Although multivariable regression models were used to adjust for multiple potential confounders,the influence of unmeasured confounding factors on the accuracy of the results cannot be ruled out.Finally,while RDW demonstrated strong prognostic value in this study,it has not yet been systematically compared with other indicators that more directly reflect cardiac function,such as echocardiography or BNP.Further validation in additional cohort studies is required in the future. Conclusion This study demonstrates that RDW is an independent predictor of 1-year all-cause mortality in patients with severe AS and AR, and it systematically evaluates the prognostic relevance of RDW in AS and AR patients for the first time. These findings suggest that RDW can serve as a simple and cost-effective tool for risk stratification and clinical assessment at the time of admission in AS and AR patients. Future large-scale prospective studies with long-term follow-up are needed to further validate these results and clarify the mechanisms of RDW in different pathological conditions. Declarations Acknowledgements Not applicable. Author contributions Kaifeng Liu and Jun Luo contributed to design this study. Kaifeng Liu and Xiangsong Lan contributed to collect and integrate data. Fuwei Liu analyzed the results and wrote the manuscript. Jun Luo provided overall supervision and critically revised the manuscript. All authors read and approved the final manuscript. Funding None. Data availability The datasets that were used and evaluated in this study can be obtained from https://physionet.org/content/mimiciv/0.4/. Ethics approval and consent to participate The study was performed according to the guidelines of the Helsinki Declaration. The use of the MIMIC-IV database was approved by the review committee of Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The data is publicly available (in the MIMIC-IV database), the ethical approval statement and the requirement for informed consent were waived for this study. Consent for publication Not applicable. Clinical trial number Not applicable. Competing interests The authors declare no competing interests. References Czarnecki A, Qiu F, Koh M, et al. Trends in the incidence and outcomes of patients with aortic stenosis hospitalization. Am Heart J . 2018;199:144-149. doi:10.1016/j.ahj.2018.02.010 Wenzel JP, Petersen E, Nikorowitsch J, et al. 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Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xls Table 1 title：Baseline Characteristics of Patients with AS and AR, Stratified by RDW Quartiles, legend：RDW index:Q1(10.80-12.70),Q2(12.80-13.10),Q3(13.20-14.00),Q4(14.10-15.50). Abbreviations:AS,aortic stenosis; AR,aortic regurgitation; RDW,red cell distribution width; HR,heart rate; RR,respiratory rate; MBP,mean blood pressure; RBC,red blood cells; WBC,white blood cells; BUN,blood urea nitrogen; ctCO2,calculated total CO2; INR,international normalized ratio; PT,prothrombin time; PTT,partial thrombopalstin time; CKD,chronic kidney disease; HF,heart failure; CAD,coronary artery disease; COPD,chronic obstructive pulmonary disease; DOACs,direct oral anticoagulants; AVR,aortic valve replacement; CRRT,continuous renal replacement; LOS,length of stay. Table2.xls Table 2 title:Cox regression analysis of 1-year mortality. legend: Abbreviations:RDW,red cell distribution width; HR,heart rate; RR,respiratory rate; MBP,mean blood pressure; RBC,red blood cells; WBC,white blood cells; BUN,blood urea nitrogen; ctCO2,calculated total CO2; INR,international normalized ratio; PT,prothrombin time; PTT,partial thrombopalstin time; CKD,chronic kidney disease; HF,heart failure; CAD,coronary artery disease; COPD,chronic obstructive pulmonary disease; DOACs,direct oral anticoagulants; AVR,aortic valve replacement; CRRT,continuous renal replacement. Table3.xls Table 3title:Relationship between RDW and 1-years mortality, legend: RDW index quartiles:：Q1（10.80–12.70），Q2（12.80–13.10），Q3（13.20–14.00），和Q4（14.10–15.50）. Table4.xls Table 4 title: The AUCs of RDW and other critical illness scores based on ROC curves. 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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-6271962","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464142768,"identity":"42bc8074-0809-4962-9d27-40dd84f4e618","order_by":0,"name":"Kaifeng Liu","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Kaifeng","middleName":"","lastName":"Liu","suffix":""},{"id":464142769,"identity":"731bbc12-6ede-4c22-adc1-ecb1955213a1","order_by":1,"name":"Xiangsong Lan","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Xiangsong","middleName":"","lastName":"Lan","suffix":""},{"id":464142770,"identity":"f4caa7ec-0baf-46c3-b938-dd6a9e391700","order_by":2,"name":"Fuwei Liu","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Fuwei","middleName":"","lastName":"Liu","suffix":""},{"id":464142771,"identity":"ef4009a2-9f51-4f0e-ad77-1b946047f171","order_by":3,"name":"Jun Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACPiA2YGCQkONnbz5w4MMPIrSwQbRYGEv2HEs8OLOHSC1AUJG4YYaP8WEONmK0SCQfKObdIZG4QYLnw2EGHgZ5frEDhLSkJRjznpEw3i7du+FwgQWD4czZCYS05BgY87ZJyO6cc3bD4Rk8DAkGt4nUwrjhRs6DwzxsJGhRBGphIFILz7MEw7ltEqBANgAGsgRhv/CzJx8zeNtWB4rKxx8+/LCR55cmoAVkkQESR4KgchBgfkCUslEwCkbBKBi5AACx4kGT5TtnKwAAAABJRU5ErkJggg==","orcid":"","institution":"Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-03-20 18:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6271962/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6271962/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95663492,"identity":"0bd183bc-3a0b-4089-a864-211924afd9a5","added_by":"auto","created_at":"2025-11-11 16:39:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106126,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant inclusion and exclusion.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/7b046864393dde113e8b2d9b.png"},{"id":95663482,"identity":"bb642a26-31b0-4e20-a02e-6e127b85952a","added_by":"auto","created_at":"2025-11-11 16:38:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97828,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival analysis curves of RDW and 1-year mortality.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/df93460abb3fc36701086313.png"},{"id":95663453,"identity":"72316786-3fb3-48a8-9ff5-6d527386a6ab","added_by":"auto","created_at":"2025-11-11 16:38:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22253678,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline Curve of RDW and 1-year all-cause mortality\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/ebd6b0d37c50651ec3a89a39.png"},{"id":95663428,"identity":"669ec576-91f6-47f9-a755-45dd97b73897","added_by":"auto","created_at":"2025-11-11 16:38:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":196197,"visible":true,"origin":"","legend":"\u003cp\u003eA title:ROC Curves for Predictive Models of 1-Year Mortality.\u003c/p\u003e\n\u003cp\u003eB title:DAC Curves for 1-Year Mortality Prediction.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/48ce93c2a8eecb5d660d23db.png"},{"id":95663323,"identity":"044c47c0-d487-4b2b-98d5-63957f3d6c93","added_by":"auto","created_at":"2025-11-11 16:38:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":724934,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis of RDW for Predicting 1-Year Mortality.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/41390f6d9cd76809bdaa08d6.png"},{"id":95664087,"identity":"5b91f825-bc21-4161-a164-8abd0061236a","added_by":"auto","created_at":"2025-11-11 16:39:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/8d48cec1-9734-4fbf-a3f9-ea0a4ea7e235.pdf"},{"id":95663452,"identity":"0c496123-de7d-43b6-bbb1-f37469d93c63","added_by":"auto","created_at":"2025-11-11 16:38:57","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41472,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1 title：Baseline Characteristics of Patients with AS and AR, Stratified by RDW Quartiles, legend：RDW index:Q1(10.80-12.70),Q2(12.80-13.10),Q3(13.20-14.00),Q4(14.10-15.50). Abbreviations:AS,aortic stenosis; AR,aortic regurgitation; RDW,red cell distribution width; HR,heart rate; RR,respiratory rate; MBP,mean blood pressure; RBC,red blood cells; WBC,white blood cells; BUN,blood urea nitrogen; ctCO2,calculated total CO2; INR,international normalized ratio; PT,prothrombin time; PTT,partial thrombopalstin time; CKD,chronic kidney disease; HF,heart failure; CAD,coronary artery disease; COPD,chronic obstructive pulmonary disease; DOACs,direct oral anticoagulants; AVR,aortic valve replacement; CRRT,continuous renal replacement; LOS,length of stay.\u003c/p\u003e","description":"","filename":"Table1.xls","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/9fe198adf4304de728b2ed9f.xls"},{"id":95663382,"identity":"99cda912-00d0-47bb-9a28-8a525ed916df","added_by":"auto","created_at":"2025-11-11 16:38:49","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32768,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2 title:Cox regression analysis of 1-year mortality. legend: Abbreviations:RDW,red cell distribution width; HR,heart rate; RR,respiratory rate; MBP,mean blood pressure; RBC,red blood cells; WBC,white blood cells; BUN,blood urea nitrogen; ctCO2,calculated total CO2; INR,international normalized ratio; PT,prothrombin time; PTT,partial thrombopalstin time; CKD,chronic kidney disease; HF,heart failure; CAD,coronary artery disease; COPD,chronic obstructive pulmonary disease; DOACs,direct oral anticoagulants; AVR,aortic valve replacement; CRRT,continuous renal replacement.\u003c/p\u003e","description":"","filename":"Table2.xls","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/a2559670e1dea461b9b6e1aa.xls"},{"id":95663432,"identity":"e4f761dc-e81f-4a0f-936d-a7e91b393099","added_by":"auto","created_at":"2025-11-11 16:38:54","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23552,"visible":true,"origin":"","legend":"\u003cp\u003eTable 3title:Relationship between RDW and 1-years mortality, legend: RDW index quartiles:：Q1（10.80–12.70），Q2（12.80–13.10），Q3（13.20–14.00），和Q4（14.10–15.50）.\u003c/p\u003e","description":"","filename":"Table3.xls","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/32f26278f1f1d6bb83bcb476.xls"},{"id":95663489,"identity":"4cb26240-c8b7-4b4c-adb7-18ef54248999","added_by":"auto","created_at":"2025-11-11 16:39:00","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":22016,"visible":true,"origin":"","legend":"\u003cp\u003eTable 4 title: The AUCs of RDW and other critical illness scores based on ROC curves.\u003c/p\u003e","description":"","filename":"Table4.xls","url":"https://assets-eu.researchsquare.com/files/rs-6271962/v1/ff9cdd1e63075dc062ec481d.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Red Cell Distribution Width and Prognosis in Critically Ill Patients With Aortic Valve Disease:A Retrospective Study Based on MIMIC-IV","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAortic stenosis(AS)and aortic regurgitation(AR)are the most common cardiac valve diseases in clinical practice.The prevalence of AS and AR is significantly higher in patients with underlying conditions such as hypertension and coronary artery disease,as well as in the elderly population.By causing left ventricular outflow tract obstruction and aortic valve regurgitation,these conditions markedly increase cardiac workload,ultimately leading to heart failure\u003csup\u003e1,2\u003c/sup\u003e.Although surgical and interventional treatments can improve patient outcomes to some extent,the incidence of cardiovascular events and long-term mortality in patients with AS and AR remain high.Furthermore,there is currently a lack of reliable prognostic indicators for long-term outcomes in clinical practice\u003csup\u003e3,4\u003c/sup\u003e .\u003c/p\u003e\n\u003cp\u003eRed cell distribution width(RDW)is a common hematological parameter that reflects the variation in red blood cell volume.Pathological processes such as inflammation,oxidative stress,and iron metabolism disorders may disrupt red blood cell production and lifespan,ultimately leading to changes in RDW levels\u003csup\u003e5,6\u003c/sup\u003e.Studies have shown that RDW can independently predict patient prognosis in various cardiovascular diseases,particularly in assessing long-term mortality and postoperative recovery\u003csup\u003e7\u0026ndash;12\u003c/sup\u003e.However,existing research on RDW in patients with AS and AR is relatively limited,and the impact of RDW level variations on the prognosis of these patients remains unclear.\u003c/p\u003e\n\u003cp\u003eThis study based on the MIMIC-IV database,retrospectively analyzes the relationship between RDW and 1-year all-cause mortality in critically ill patients with AS and AR.The primary aim is to evaluate the potential of RDW as an independent prognostic indicator and to provide further supporting evidence for risk stratification and clinical management of patients with aortic valve disease.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003ch3\u003e2.1 Study population\u003c/h3\u003e\n\u003cp\u003eThis study utilized version 3.1 of the MIMIC-IV database,a large,publicly available electronic health record system that includes data from over 76,000 ICU admissions across multiple centers between 2008 and 2019\u003csup\u003e13\u003c/sup\u003e.The inclusion criteria were patients admitted to the ICU for the first time with a diagnosis of aortic stenosis(AS)or aortic regurgitation(AR)according to the International Classification of Diseases,9th and 10th Revisions(ICD-9\u0026amp;10codes:“3951”,“3952”,“3960”,“3961”,“3962”,“3963”,“7463”,“7464”,“I060”,“I061”,“I062”,“I350”,“I351”,“I352”,“Q230”,“Q231”).Exclusion criteria included missing RDW data,ICU stays of less than 24 hours,or patients younger than 18 years old.\u003c/p\u003e\n\u003cp\u003eThis project was approved by the Institutional Review Boards of the Massachusetts Institute of Technology(MIT)and Beth Israel Deaconess Medical Center(BIDMC)for research purposes,with a waiver of informed consent.All participants'identities were strictly anonymized.In accordance with relevant requirements,the authors completed the“Protecting Human Research Participants”course and obtained access to the database(Certification ID:60329338).\u003c/p\u003e\n\u003ch3\u003e2.2 Data collection\u003c/h3\u003e\n\u003cp\u003eData from MIMIC-IV were extracted using DecisionLinnc(version 1.0) softwar\u003csup\u003e14\u003c/sup\u003e.This study collected patients'demographic information,comorbidities,initial vital signs,and laboratory test results upon ICU admission,as well as treatment data during hospitalization.RDW was considered the primary variable of interest and was divided into quartiles(Q1,Q2,Q3,and Q4)to assess its relationship with 1-year all-cause mortality.The primary endpoint was 1-year all-cause mortality,which was confirmed and validated through patient follow-up records.In the handling of missing values, variables with less than 5% missing data were imputed using mean imputation; variables with missing rates between 5% and 20% were imputed using the K-nearest neighbors (KNN) method; and variables with missing rates exceeding 20% were excluded from the analysis.\u003c/p\u003e\n\u003ch3\u003e2.3 Statistical analysis\u003c/h3\u003e\n\u003cp\u003eThe study population was divided into four groups based on RDW quartiles,and the Kolmogorov-Smirnov test was used to assess the normality of continuous parameters.None of the continuous variables included in the analysis met the criteria for a normal distribution.Continuous variables were expressed as medians(interquartile range,IQR)and compared between groups using the Kruskal-Wallis test.Categorical variables were presented as frequencies and percentages(%)and compared using theχ²test or Fisher's exact test.Univariate and multivariate Cox regression analyses were performed to identify variables associated with 1-year all-cause mortality in patients with aortic valve disease.To evaluate the impact of RDW on 1-year all-cause mortality,three Cox proportional hazards regression models were constructed:Unadjusted model,no variables were adjusted;Model I:adjusted for age,sex,race,and body weight;Model II:adjusted for all potential confounding factors.Kaplan-Meier survival curves and Log-Rank tests were used to analyze differences in survival times among patients with different RDW levels.Restricted cubic spline(RCS)functions were applied to model the nonlinear relationship between RDW and 1-year mortality risk.The predictive performance of RDW for 1-year all-cause mortality in patients with aortic valve disease was assessed using receiver operating characteristic(ROC)curves and the area under the curve(AUC).Decision curve analysis(DCA)was conducted to evaluate the net clinical benefit of RDW across different risk thresholds.RDW's predictive performance was also compared with commonly used scoring systems,including SOFA,SAPSII,SIRS,APSIII,OASIS,Shock Index,and the Charlson Comorbidity Index.Finally,subgroup analyses were conducted to test the robustness of the results.Stratified covariates included age,sex,major comorbidities,receipt of aortic valve replacement(AVR),and type of aortic valve disease.All data analyses were performed using DecisionLinnc(version 1.0)and Stata software(version 17.0).A two-sided P\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBased on the inclusion and exclusion criteria,a total of 2,820 patients were included in this study(Figure 1)and divided into four groups according to RDW quartiles.The baseline characteristics are presented in Table 1.The median age of the overall cohort was 73 years,with males accounting for 62.66%,and the majority being White(77.41%).Compared with patients with lower RDW levels,those with higher RDW levels were older,had a higher proportion of females,and had a greater prevalence of comorbidities such as diabetes,chronic kidney disease,heart failure,and coronary artery disease.They also had higher clinical scores,including APSIII,SAPSII,and the Charlson Comorbidity Index.Patients in the higher RDW group had lower hemoglobin levels,higher creatinine levels,a lower proportion undergoing AVR,longer hospital stays and ICU stays,and significantly higher 1-year all-cause mortality compared with those in the lower RDW group.\u003c/p\u003e\n\u003cp\u003eTable 2 summarizes the univariate and multivariate regression analyses of variables associated with 1-year all-cause mortality in patients with AS and AR.In the multivariate Cox regression analysis,after adjusting for age,sex,vital signs,laboratory parameters,and comorbidities,RDW levels remained significantly associated with 1-year all-cause mortality.Other factors significantly associated with higher 1-year all-cause mortality included age,respiratory rate,and blood urea nitrogen levels.In contrast,the use of aspirin and novel oral anticoagulants,as well as undergoing AVR,were associated with lower 1-year all-cause mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore the relationship between RDW and 1-year all-cause mortality in patients with AS and AR,three Cox proportional hazards regression models were constructed:(1)the crude model,without adjustments;(2)Model I,adjusted for age,sex,race,and body weight;and(3)Model II,adjusted for all potential confounders.The results showed that when RDW was treated as a continuous variable,each 1-unit increase in RDW was associated with an odds ratio(OR)of 1.56(95%CI:1.49\u0026ndash;1.64,P\u0026lt;0.01)in the crude model.After adjusting for partial confounders in Model I,the OR slightly decreased to 1.531(95%CI:1.46\u0026ndash;1.61,P\u0026lt;0.01).Further adjustment for all confounders in Model II resulted in an OR of 1.208(95%CI:1.13\u0026ndash;1.29,P\u0026lt;0.01).When RDW was categorized into quartiles,the risk of 1-year mortality increased progressively with higher RDW levels(Q2 to Q4).In all three models,the Q4 group had significantly higher mortality risk compared to other groups,with a statistically significant trend(P for trend\u0026lt;0.0001)(Table 3).\u003c/p\u003e\n\u003cp\u003eFigure 2 shows that Kaplan-Meier analysis revealed significant differences in 1-year all-cause mortality across the quartiles of red cell distribution width(RDW)(Log-rank test,\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001).As RDW levels increased,the survival probability significantly decreased,with the highest RDW group(Q4)having the lowest survival rate and the steepest decline in the survival curve.The survival rates of the middle quartiles(Q2 and Q3)were between those of Q1 and Q4.\u003c/p\u003e\n\u003cp\u003eAfter adjusting for all potential confounders in Model II,RCS analysis(Figure 3)showed a nonlinear relationship between RDW and 1-year all-cause mortality(\u003cem\u003eP\u003c/em\u003e for nonlinearity=0.001).An increase in RDW was associated with a higher risk of 1-year mortality.\u003c/p\u003e\n\u003cp\u003eTable 4 and Figure 4A summarize and illustrate the AUC,optimal cut-off values,sensitivity,and specificity of RDW and other critical illness scoring systems in predicting 1-year all-cause mortality.The Charlson Comorbidity Index had the highest predictive accuracy,with an AUC of 0.806(95%CI:0.787\u0026ndash;0.825).RDW also demonstrated excellent predictive performance,with an AUC of 0.781(95%CI:0.761\u0026ndash;0.801),comparable to the Charlson Index and superior to the other scoring systems.DCA results(Figure 4B)indicated that RDW provided a higher net benefit in predicting 1-year all-cause mortality.Across most risk thresholds,the RDW curve outperformed traditional scoring systems,including SOFA,SAPSII,APSIII,OASIS,and SIRS scores.Notably,in low to moderate risk thresholds(0.1\u0026ndash;0.3),the net benefit of the RDW model was comparable to that of the Charlson Comorbidity Index.\u003c/p\u003e\n\u003ch3\u003eSubgroup Analysis\u003c/h3\u003e\n\u003cp\u003eTo further investigate potential confounding variables,a risk stratification analysis was conducted,incorporating covariates such as age,sex,race,valve disease type,and comorbidities(Figure 5).The subgroup analysis demonstrated that RDW was associated with increased 1-year all-cause mortality across multiple patient subgroups.\u003c/p\u003e\n\u003cp\u003eThe results showed that RDW remained significantly associated with 1-year all-cause mortality in all subgroups(overall HR=1.31,95%CI:1.28\u0026ndash;1.34,P\u0026lt;0.001).Notably,higher RDW levels were associated with a greater mortality risk in patients aged\u0026lt;75 years,those without heart failure(HF),coronary artery disease(CAD),or chronic kidney disease(CKD),as well as in those who underwent AVR(P for interaction\u0026lt;0.05 for all).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study indicate that elevated RDW is an independent risk factor for mortality in critically ill patients with AS and AR.Multivariate Cox regression analysis demonstrated that even after adjusting for multiple potential confounders,the association between RDW and 1-year all-cause mortality remained significant.As a routinely available component of complete blood count testing,RDW offers advantages such as cost-effectiveness and efficiency,making it widely used in clinical practice.Our findings suggest that RDW can serve as a reliable and novel prognostic marker,aiding in the early identification of high-risk patients with aortic stenosis and aortic regurgitation.\u003c/p\u003e\n\u003cp\u003eIn recent years,multiple clinical studies have identified RDW as an independent risk factor for all-cause mortality in patients undergoing AVR.Consistent with our findings,these studies also reported that patients with higher RDW levels tend to have higher frailty and disease severity scores,poorer renal function,and lower hemoglobin levels.Additionally,they observed that patients with elevated RDW exhibited worse cardiac function\u003csup\u003e15\u0026ndash;18\u003c/sup\u003e.Liang et al.\u003csup\u003e19\u003c/sup\u003efound in a clinical study on AS that an RDW value\u0026ge;14.7%was an independent predictor of both short-term and long-term mortality risk in AS patients..However,most of these studies used fixed RDW thresholds to categorize patient groups,which led to an imbalance in the number of patients between groups,potentially introducing selection bias.This study is the first to include both AS and AR patients and further validates the prognostic impact of RDW levels in a larger patient cohort.By stratifying RDW into quartiles and constructing multiple models,this study minimized the influence of confounding factors and the bias caused by baseline characteristic imbalances between groups,clarifying the dose-response relationship between RDW levels and all-cause mortality.Comparison with commonly used scoring systems,including SOFA,SAPSII,SIRS,APSIII,OASIS,Shock Index,and the Charlson Comorbidity Index,demonstrated that RDW outperformed most of these scores in predicting long-term mortality.These findings highlight the significant clinical value of RDW in assessing the long-term prognosis of AS and AR patients.\u003c/p\u003e\n\u003cp\u003eElevated RDW levels are typically closely associated with chronic inflammation and oxidative stress\u003csup\u003e20,21\u003c/sup\u003e.Elevated RDW levels are typically closely associated with chronic inflammation and oxidative stress\u003csup\u003e22,23\u003c/sup\u003e..This suggests that an increase in RDW may,to some extent,reflect a patient\u0026apos;s comorbidity burden and nutritional status.Additionally,RDW is a sensitive indicator of iron deficiency anemia\u003csup\u003e6,24\u003c/sup\u003e,allowing for earlier detection of anemia.Iron deficiency anemia is highly prevalent in HF patients and significantly impacts their prognosis\u003csup\u003e25\u003c/sup\u003e.Since AS and AR often lead to cardiac dysfunction in the late stages of the disease\u003csup\u003e26\u003c/sup\u003e,the mechanism by which elevated RDW affects the long-term prognosis of patients with aortic valve disease may be related to anemia and heart failure.However,research on the underlying mechanisms remains limited.\u003c/p\u003e\n\u003cp\u003eThe results of the subgroup analysis were consistent with the primary analysis;however,the increased mortality risk associated with elevated RDW was more pronounced in patients younger than 75 years,as well as those without HF,CAD,or CKD.In younger patients,RDW may serve as a more sensitive indicator of disease status.Conversely,in elderly patients,the strong age dependency of RDW may attenuate its ability to reflect disease severity,thereby affecting its predictive accuracy for mortality risk\u003csup\u003e27\u003c/sup\u003e.Early studies have found that in diseases with high mortality risk and strong oxidative stress,such as HF,CAD,and CKD,the mortality risk reflected by RDW may be attenuated due to the direct impact of the disease itself,as well as the overlapping effects of inflammation and oxidative stress processes\u003csup\u003e28\u0026ndash;30\u003c/sup\u003e.However,RDW remains an important systemic health indicator,and its predictive value for mortality risk may be more pronounced,particularly in the early stages of certain diseases.\u003c/p\u003e\n\u003cp\u003eAs a cost-effective and readily available biomarker,RDW outperforms several traditional disease scoring systems in predicting long-term mortality in critically ill patients with AS and AR.This provides new evidence for the clinical utility of RDW in managing such patients and lays the foundation for future RDW-based precision treatment strategies.This study is the first to extend the evaluation of RDW to AR patients,further supporting the feasibility of using RDW as a risk stratification tool upon hospital admission for critically ill AS and AR patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several strengths.First,it is based on the large-scale MIMIC-IV database,which provides broad sample coverage.Compared to previous similar studies,the larger analysis scale enhances the generalizability of the findings.Second,by stratifying patients into quartiles based on RDW levels,the study ensured a balanced sample size across groups.Additionally,Kaplan-Meier survival analysis and RCS functions were utilized to systematically assess the dose-response relationship between RDW and 1-year all-cause mortality,highlighting the significance of RDW as an independent prognostic indicator.\u003c/p\u003e\n\u003cp\u003eThis study has certain limitations.First,as a single-center retrospective study,it is challenging to establish a causal relationship between variables and outcomes.Additionally,the study population primarily consists of White patients,which may limit the generalizability of the findings to a global population.Second,this study only analyzed the first RDW measurement obtained after ICU admission and did not investigate the impact of dynamic changes in RDW on prognosis.Although multivariable regression models were used to adjust for multiple potential confounders,the influence of unmeasured confounding factors on the accuracy of the results cannot be ruled out.Finally,while RDW demonstrated strong prognostic value in this study,it has not yet been systematically compared with other indicators that more directly reflect cardiac function,such as echocardiography or BNP.Further validation in additional cohort studies is required in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that RDW is an independent predictor of 1-year all-cause mortality in patients with severe AS and AR, and it systematically evaluates the prognostic relevance of RDW in AS and AR patients for the first time. These findings suggest that RDW can serve as a simple and cost-effective tool for risk stratification and clinical assessment at the time of admission in AS and AR patients. Future large-scale prospective studies with long-term follow-up are needed to further validate these results and clarify the mechanisms of RDW in different pathological conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaifeng Liu and Jun Luo contributed to design this study. Kaifeng Liu and Xiangsong Lan contributed to collect and integrate data. Fuwei Liu analyzed the results and wrote the manuscript. Jun Luo provided overall supervision and critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets that were used and evaluated in this study can be obtained from https://physionet.org/content/mimiciv/0.4/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was performed according to the guidelines of the Helsinki Declaration. The use of the MIMIC-IV database was approved by the review committee of Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The data is publicly available (in the MIMIC-IV database), the ethical approval statement and the requirement for informed consent were waived for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCzarnecki A, Qiu F, Koh M, et al. Trends in the incidence and outcomes of patients with aortic stenosis hospitalization. \u003cem\u003eAm Heart J\u003c/em\u003e. 2018;199:144-149. doi:10.1016/j.ahj.2018.02.010\u003c/li\u003e\n\u003cli\u003eWenzel JP, Petersen E, Nikorowitsch J, et al. 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Progressive rise in red cell distribution width is associated with disease progression in ambulatory patients with chronic heart failure. \u003cem\u003eJ Card Fail\u003c/em\u003e. 2012;18(2):146-152. doi:10.1016/j.cardfail.2011.10.013\u003c/li\u003e\n\u003cli\u003eXanthopoulos A, Giamouzis G, Melidonis A, et al. Red blood cell distribution width as a prognostic marker in patients with heart failure and diabetes mellitus. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e. 2017;16(1):81. doi:10.1186/s12933-017-0563-1\u003c/li\u003e\n\u003cli\u003eJohnson AEW, Bulgarelli L, Shen L, et al. MIMIC-IV, a freely accessible electronic health record dataset. \u003cem\u003eSci Data\u003c/em\u003e. 2023;10(1):1. doi:10.1038/s41597-022-01899-x\u003c/li\u003e\n\u003cli\u003eDecisionLinnc Core Team.(2023). DecisionLinnc.1.0.https://www.statsape.com/.\u003c/li\u003e\n\u003cli\u003eSzekely Y, Finkelstein A, Bazan S, et al. Red blood cell distribution width as a prognostic factor in patients undergoing transcatheter aortic valve implantation. \u003cem\u003eJ Cardiol\u003c/em\u003e. 2019;74(3):212-216. doi:10.1016/j.jjcc.2019.04.005\u003c/li\u003e\n\u003cli\u003eRuan L, Zhu L, Su L, et al. Better prognosis in surgical aortic valve replacement patients with lower red cell distribution width: A MIMIC-IV database study. \u003cem\u003ePLOS ONE\u003c/em\u003e. 2024;19(7):e0306258. doi:10.1371/journal.pone.0306258\u003c/li\u003e\n\u003cli\u003eDuchnowski P, Szymański P, Orłowska-Baranowska E, Kuśmierczyk M, Hryniewiecki T. Raised red cell distribution width as a prognostic marker in aortic valve replacement surgery. \u003cem\u003eKardiol Pol\u003c/em\u003e. 2016;74(6):547-552. doi:10.5603/KP.a2015.0213\u003c/li\u003e\n\u003cli\u003eDuchnowski P, Hryniewiecki T, Stokłosa P, Kuśmierczyk M, Szymański P. Red Cell Distribution Width as a Prognostic Marker in Patients Undergoing Valve Surgery. \u003cem\u003eJ Heart Valve Dis\u003c/em\u003e. 2017;26(6):714-720.\u003c/li\u003e\n\u003cli\u003eLiang B, Tang Y, Li S, et al. Association between red blood cell distribution width and the all-cause mortality of patients with aortic stenosis: A retrospective study. \u003cem\u003eHeart Lung\u003c/em\u003e. 2024;67:191-200. doi:10.1016/j.hrtlng.2024.04.020\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Escobar A, L\u0026aacute;zaro-Garc\u0026iacute;a R, Goicolea-Ruig\u0026oacute;mez J, et al. Red Blood Cell Distribution Width is a Biomarker of Red Cell Dysfunction Associated with High Systemic Inflammation and a Prognostic Marker in Heart Failure and Cardiovascular Disease: A Potential Predictor of Atrial Fibrillation Recurrence. \u003cem\u003eHigh Blood Press Cardiovasc Prev\u003c/em\u003e. 2024;31(5):437-449. doi:10.1007/s40292-024-00662-0\u003c/li\u003e\n\u003cli\u003eParizadeh SM, Jafarzadeh-Esfehani R, Bahreyni A, et al. The diagnostic and prognostic value of red cell distribution width in cardiovascular disease; current status and prospective. \u003cem\u003eBiofactors\u003c/em\u003e. 2019;45(4):507-516. doi:10.1002/biof.1518\u003c/li\u003e\n\u003cli\u003ePodkowińska A, Formanowicz D. Chronic Kidney Disease as Oxidative Stress- and Inflammatory-Mediated Cardiovascular Disease. \u003cem\u003eAntioxidants (Basel)\u003c/em\u003e. 2020;9(8):752. doi:10.3390/antiox9080752\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Velilla N, Cambra-Contin K, Garc\u0026iacute;a-Bazt\u0026aacute;n A, Alonso-Renedo J, Herce PA, Ib\u0026aacute;\u0026ntilde;ez-Beroiz B. Change in Red Blood Cell Distribution width During the Last Years of Life in Geriatric Patients. \u003cem\u003eJ Nutr Health Aging\u003c/em\u003e. 2015;19(5):590-594. doi:10.1007/s12603-015-0470-7\u003c/li\u003e\n\u003cli\u003eBadrick T, Richardson AM, Arnott A, Lidbury BA. The early detection of anaemia and aetiology prediction through the modelling of red cell distribution width (RDW) in cross-sectional community patient data. \u003cem\u003eDiagnosis (Berl)\u003c/em\u003e. 2015;2(3):171-179. doi:10.1515/dx-2015-0010\u003c/li\u003e\n\u003cli\u003eSavarese G, Haehling S von, Butler J, Cleland JGF, Ponikowski P, Anker SD. Iron deficiency and cardiovascular disease. \u003cem\u003eEuropean Heart Journal\u003c/em\u003e. 2022;44(1):14. doi:10.1093/eurheartj/ehac569\u003c/li\u003e\n\u003cli\u003eVahanian A, Beyersdorf F, Praz F, et al. 2021 ESC/EACTS Guidelines for the management of valvular heart disease. \u003cem\u003eEur Heart J\u003c/em\u003e. 2022;43(7):561-632. doi:10.1093/eurheartj/ehab395\u003c/li\u003e\n\u003cli\u003eHoffmann JJML, Nabbe KCAM, van den Broek NMA. Effect of age and gender on reference intervals of red blood cell distribution width (RDW) and mean red cell volume (MCV). \u003cem\u003eClin Chem Lab Med\u003c/em\u003e. 2015;53(12):2015-2019. doi:10.1515/cclm-2015-0155\u003c/li\u003e\n\u003cli\u003eTonelli M, Sacks F, Arnold M, et al. Relation Between Red Blood Cell Distribution Width and Cardiovascular Event Rate in People With Coronary Disease. \u003cem\u003eCirculation\u003c/em\u003e. 2008;117(2):163-168. doi:10.1161/CIRCULATIONAHA.107.727545\u003c/li\u003e\n\u003cli\u003eJ N, E N, D R, et al. Red blood cell distribution width is longitudinally associated with mortality and anemia in heart failure patients. \u003cem\u003eCirculation journal : official journal of the Japanese Circulation Society\u003c/em\u003e. 2014;78(2). doi:10.1253/circj.cj-13-0630\u003c/li\u003e\n\u003cli\u003eCottone S, Lorito MC, Riccobene R, et al. Oxidative stress, inflammation and cardiovascular disease in chronic renal failure. \u003cem\u003eJ Nephrol\u003c/em\u003e. 2008;21(2):175-179.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Red Cell Distribution Width (RDW), Prognosis, Critically Ill Patients, Aortic Valve Disease, Retrospective analysis","lastPublishedDoi":"10.21203/rs.3.rs-6271962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6271962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze the relationship between red cell distribution width(RDW)and 1-year all-cause mortality in critically ill patients with aortic stenosis(AS)and aortic regurgitation(AR),aiming to evaluate the potential of RDW as an independent prognostic indicator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were divided into four groups based on the quartiles of their RDW levels:Q1,Q2,Q3,and Q4.The impact of different RDW levels on 1-year all-cause mortality in patients with AS and AR was analyzed using Cox regression analysis,Kaplan-Meier survival curves,Log-Rank tests,and restricted cubic spline（RCS）analysis.The predictive performance of RDW and various clinical scores was compared using Receiver Operating Characteristic(ROC)and Decision Curve Analysis(DCA).Subgroup analyses were conducted to ensure the robustness of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nA total of 2,820 patients were included in the study.Patients in the high RDW group were older,had more comorbidities,and exhibited significantly higher 1-year all-cause mortality.After adjusting for confounding factors in the multivariate Cox regression analysis,elevated RDW was significantly associated with 1-year all-cause mortality(95%CI:1.13–1.29,P\u0026lt;0.01).Kaplan-Meier and RCS analyses revealed that the high RDW group had the lowest survival rates,with a nonlinear relationship observed between RDW and mortality risk.RDW outperformed most traditional scoring systems in predicting 1-year mortality.Subgroup analyses showed that RDW was significantly associated with 1-year all-cause mortality across all subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRDW is an independent predictor of 1-year all-cause mortality in critically ill patients with AS and AR.\u003c/p\u003e","manuscriptTitle":"Association Between Red Cell Distribution Width and Prognosis in Critically Ill Patients With Aortic Valve Disease:A Retrospective Study Based on 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