Association between hemoglobin levels and dialysis dependence in patients with acute kidney injury requiring continuous kidney replacement therapy: a multicenter retrospective cohort study

Association between hemoglobin levels and dialysis dependence in patients with acute kidney injury requiring continuous kidney replacement therapy: a multicenter 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 Association between hemoglobin levels and dialysis dependence in patients with acute kidney injury requiring continuous kidney replacement therapy: a multicenter retrospective cohort study Hyun Lee Ko, Jiyun Jung, Sung Woo Lee, Yong Chul Kim, Jeong‑Hoon Lim, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6508688/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 Anemia in patients with acute kidney injury (AKI) requiring continuous kidney replacement therapy (CKRT) significantly contributes to increased mortality and morbidity. However, its impact on dialysis dependence remains unclear. This study explored the association between anemia and dialysis dependence in patients with severe AKI undergoing CKRT. Methods In this retrospective cohort study, we included 2755 patients with AKI who underwent CKRT at four medical centers between 2006 and 2021. The primary exposure was the average hemoglobin (Hb) level during CKRT, with patients categorized into anemic (Hb 8.44 g/dL) groups. Dialysis dependence was defined at the time of hospital discharge. The odds ratio for dialysis dependence in the anemic group was calculated by adjusting for demographics and laboratory data. The impact of the duration of anemia was also assessed. Results Overall, 61.4% of patients were males, with a mean age of 65.5 years. The average duration of CKRT was 7.9 d, and 64.7% of the patients were dialysis-dependent at hospital discharge. A U-shaped relationship was found between Hb levels and dialysis dependence, with 8.44 g/dL as the critical threshold. Patients in the anemia group had a 57% increased risk of dialysis dependence, particularly among women, those under 65 years, patients with non-septic AKI, and those with lower Charlson comorbidity index scores. Each additional day of anemia increased the risk by 4%. Conclusion Hb levels < 8.44 g/dL during CKRT were associated with increased dialysis dependence. These findings highlight the significance of Hb thresholds for improving kidney recovery outcomes. Anemia Complete blood count Dialysis Multicenter study Retrospective study Figures Figure 1 Figure 2 Figure 3 Introduction Acute kidney injury (AKI) is a common condition among critically ill patients, with a rising incidence of severe cases requiring dialysis [ 1 ]. Continuous kidney replacement therapy (CKRT) is widely used to remove fluid and manage acid-based and electrolyte derangements while maintaining hemodynamic stability. Despite its efficacy, CKRT is associated with alarmingly high mortality rates, estimated at around 60% [ 2 , 3 ]. While survival from AKI is a critical milestone, it often marks the beginning of additional long-term challenges. Among survivors, incomplete recovery of kidney function and persistent dialysis dependence remain major concerns [ 4 ]. AKI has been strongly linked to the onset of new chronic kidney disease (CKD) [ 5 ], progression of pre-existing CKD [ 6 ], and increased risk of end-stage kidney disease [ 7 ]. Individuals with partial kidney recovery face heightened risks of cardiovascular complications and mortality compared to those with full recovery [ 8 ]. Anemia is highly prevalent among critically ill patients [ 9 ] and is a significant risk factor for AKI [ 10 , 11 ]. Compounding conditions, such as cardiogenic shock, further exacerbate anemia [ 12 ]. CKRT can exacerbate anemia due to catheter-related bleeding, blood clotting, and blood loss [ 13 , 14 ]. This interplay highlights the need to explore the role of anemia in AKI recovery. Experimental studies suggest that anemia can impair vascular repair mechanisms in the kidneys during AKI [ 15 ], resulting in a significant loss of vascular density (30–50% in some injury models) [ 16 ]. This rarefaction of capillaries can trigger hypoxia-inducible pathways, promote inflammation, and lead to fibrosis, reducing the likelihood of complete kidney recovery [ 17 ]. Persistently low hemoglobin (Hb) levels in such contexts may amplify tissue hypoxia and exacerbate kidney damage, although this clinical relationship has been underexplored [ 15 ]. Given the high mortality rate among patients with severe AKI requiring CKRT, most studies have focused on reducing mortality through various aspects of kidney replacement therapy, such as selection [ 18 ], timing of initiation [ 19 ], and fluid balance management during treatment [ 20 , 21 ]. Few studies have aimed to improve kidney recovery in survivors [ 22 – 25 ]. Moreover, no established guidelines exist for correcting anemia to enhance dialysis independence in CKRT survivors. Achieving independence from dialysis has significant implications for the quality of life and long-term survival of patients. To address this gap, we conducted a multicenter cohort study to examine the relationship between anemia and dialysis dependence in patients with severe AKI treated with CKRT, with a particular focus on identifying Hb levels that may influence kidney recovery. Material and methods Study population This multicenter retrospective study included data from 3,576 patients diagnosed with AKI and treated with CKRT across four major hospitals: Kyungpook National University Chilgok Hospital (KNUCH; n = 136), Keimyung University Dongsan Medical Center (DSMC; n = 137), Dongguk University Ilsan Hospital (DUIH; n = 915), and Seoul National University Hospital (SNUH; n = 2388) between 2006 and 2021. A total of 821 patients were excluded due to end-stage renal disease or incomplete covariate data, resulting in a final cohort of 2,755 participants. The following demographic and laboratory information at CKRT initiation were recorded: sex, age, body mass index (BMI), presence of hypertension history, etiology of AKI (sepsis-related or other), white blood cell (WBC), albumin, creatinine, Hb, systolic blood pressure (SBP), diastolic blood pressure (DBP), red cells transfusion volumes, usage of ventilator, and CKRT setting (blood flow rate, dialysate flow rate, replacement flow rate). Additionally, we investigated the duration of CKRT, dialysis, and hospitalization, as well as the indicators related to critically ill patients, such as the Charlson comorbidity index (CCI), sequential organ failure assessment (SOFA) score excluding the renal component, and acute physiology and chronic health evaluation (APACHE II) score. The CCI is a representative clinical method for classifying the concurrent conditions of 19 diseases by assigning different weights [ 26 ]. Both SOFA (excluding the renal component) and APACHE II are widely used scoring systems that are used to predict mortality risk based on severity in five organ systems and 12 physiological variables, respectively [ 27 , 28 ]. Ethical approval This study complied with the ethical guidelines outlined in the Declaration of Helsinki. Institutional review board (IRB) approvals were obtained from all participating institutions including KNUCH (no. 2021-03-024), DSMC (no. 2021-06-057), DUIH (no. 2018-12-010), and SNUH (no. H-2111-057-1271). Exposure and outcome variable For patients admitted to the intensive care unit (ICU) and undergoing CKRT, we collected serial Hb data through daily complete blood count measurements. If a patient's Hb was measured multiple times a day, the daily average Hb level was calculated. To assess the relationship between Hb levels and dialysis dependence, we computed the average Hb level during CKRT for each patient. Dialysis dependence was defined as dependence on dialysis at the time of hospital discharge. Statistical analyses The baseline characteristics of patients who underwent CKRT were evaluated using mean and standard deviation for continuous variables, and frequency and percentile for categorical variables. Patients were categorized into two groups based on their average Hb levels during CKRT: the anemia (average Hb <8.44 g/dL) and control groups (average Hb ≥8.44 g/dL), determined through statistical methods. Differences between the two groups were evaluated using t-tests for continuous variables and chi-square tests for categorical variables. Generalized Additive Model (GAM) To explore the nonlinear effects of average Hb levels on dialysis dependence, a GAM with a binomial link was employed. Unlike a generalized linear model (GLM), GAM replaces linear components with a flexible, smooth non-linear function [29] allowing for the modeling of complex relationships. We applied the spline function on the average Hb level in the GAM with additional adjustments for sex, age, CCI, BMI, hypertension, sepsis, WBC, albumin, creatinine, SBP, DBP, SOFA (excluding the renal component), transfusion amount, and CKRT duration. A threshold Hb level was identified, representing the point at which dialysis dependence risk began to decline with increasing average Hb levels. GLM Using the identified Hb threshold, the risk of dialysis dependence for the anemia group was compared to the control group through a GLM adjusted for the same covariates. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), which evaluates sensitivity and specificity for dialysis dependence prediction. Stratified analyses To identify vulnerable subpopulations, analyses were stratified by sex (male vs. female), age (<65 years vs. ≥65 years), AKI etiology (sepsis vs. non-sepsis), and CCI (<3 vs. ≥3). Anemia duration and dialysis dependence For each patient, we counted the number of days during CKRT when the daily Hb level was less than 8.44 g/dL. Logistic regression models, both unadjusted and adjusted, were used to assess the association between the number of anemic days and dialysis dependence. All results were analyzed using R software, and statistical significance was set at p<0.05. Results Baseline characteristics A total of 2,755 participants were included in the analysis (mean age, 65.5 years). Of these, 61.4% were male and 34.5% had a diagnosis of hypertension. The primary indication for CKRT was sepsis in 54.6% of cases. The mean APACHE II and SOFA scores in patients without renal disease were 26.7 and 10.2, respectively. The mean duration of dialysis and CKRT were 11.7 d and 7.9 d, respectively, with 77.0% of participants requiring mechanical ventilation. Table 1 summarizes the baseline characteristics of patients requiring CKRT, categorized into the anemic and control groups based on an Hb threshold of 8.44 g/dL. The anemia group was younger and had a lower prevalence of hypertension than the control group. The anemia group exhibited a higher incidence of sepsis and higher severity scores in critically ill patients. Additionally, the anemic group demonstrated lower baseline Hb and albumin levels, but higher creatinine levels. The mean duration of dialysis and CKRT were shorter in the anemic group, but the blood and dialysate flow rates in the CKRT settings were higher. No significant differences were observed between the two groups regarding the length of hospital stay, red cell transfusion volume, or mechanical ventilation dependence rate. Non-linear association between the average Hb level and dialysis dependence The relationship between average Hb levels and dialysis dependence is illustrated in Fig. 1. Below the Hb level of 8.44 g/dL, the odds ratio (OR) for dialysis dependence significantly increased to >1. While a U-shaped curve was observed, there was no statistically significant association between higher Hb levels and dialysis dependence. Risk in the anemic group compared to the control group In the risk model comparing the anemic group (Hb <8.44 g/dL) to the control group, the ROC curve analysis revealed a sensitivity of 72.8% and a specificity of 59.9%, with an AUC of 0.72, indicating a moderate level of diagnostic accuracy (Fig. 2). The anemic group demonstrated a 1.57-fold increased risk of dialysis dependence compared to the control group (Fig. 3). Subgroup analyses revealed that the increased dialysis dependence risk for the anemic group was consistent across all patient groups. However, stronger associations were observed in female patients, patients aged <65 years, patients with non-sepsis causes, and patients with high CCI scores. Association between the number of anemic days and dialysis dependence The analysis further explored the impact of the number of days with Hb levels <8.44 g/dL on dialysis dependence. In the unadjusted model, each additional day with Hb levels <8.44 g/dL was significantly associated with a 3% increase in dialysis dependence (OR: 1.03, 95% confidence interval [CI]: 1.01–1.05). After adjusting for covariates, the association remained significant, with a 4% increase in dialysis dependence for each additional day of low Hb levels (OR: 1.04, 95% CI: 1.01–1.07; Table 2). Discussion We identified a significant association between the average Hb levels and dialysis dependence in patients with AKI requiring CKRT. The threshold Hb level at which the risk of dialysis dependence increased was 8.44 g/dL. For each additional day that Hb levels remained below 8.44 g/dL, dialysis dependence increased by 4%. Effective management of complications and underlying etiologies is critical in patients with severe AKI requiring CKRT. While complications like hypotension, electrolyte imbalances, and acid-base disturbances are addressed through clinical interventions—such as vasopressors, supplementation, and fluid therapy [13] —anemia remains underexplored despite its frequent occurrence during CKRT. Guidelines, such as those provided by the Kidney Disease: Improving Global Outcomes (KDIGO) and the Acute Dialysis Quality Initiative (ADQI), provide recommendations for dialysis initiation timing and dosage but lack specific strategies for anemia management that occur frequently during CKRT. Although studies have extensively investigated anemia and survival outcomes [30, 31], few have focused on the impact of anemia and dialysis dependence [22]. In severe AKI, improving survival rates is often the primary objective, and much of the existing research has concentrated on this goal [2, 23, 25, 32]. However, increasing attention is now being given to kidney function recovery due to its role in reducing the risks of advanced chronic kidney disease (CKD) and cardiovascular events [8]. Our findings contribute to this growing body of evidence by confirming that Hb levels <8.44 g/dL during CKRT are associated with an increased risk of dialysis dependence. These findings align with prior studies. Du Cheyron et al. [33] reported that patients requiring dialysis with Hb levels <9 g/dL at admission were more likely to develop maintenance dialysis dependency than those with higher Hb levels. While our study identified a slightly lower threshold, this difference may be due to our focus on ICU patients exclusively requiring CKRT. Similarly, Hasse et al. [34] highlighted anemia as a significant risk factor for AKI following cardiac surgery and suggested that maintaining appropriate Hb levels could prevent AKI. Notably, their findings suggest a median Hb level of 7.4 g/dL in AKI patients, compared to 8.2 g/dL in those without AKI, mirroring our conclusion that maintaining higher Hb levels supports kidney recovery. Our study extends these findings by not only confirming the importance of maintaining Hb levels but also emphasizing the persistence of anemia as a critical factor. We observed that each additional day with Hb levels <8.44 g/dL further increased dialysis dependence risk. Rhee et al. [35], similarly reported that a 1 g/dL increase in Hb levels at CKRT initiation was associated with a significant reduction in dialysis dependence risk, albeit without proposing a specific Hb threshold. Our stratified analyses demonstrated that the risk of dialysis dependence associated with anemia was consistent across subgroups, including sex, age, etiology of AKI, and comorbidity burden. However, stronger associations were observed in female patients, younger patients (<65 years), those with non-sepsis-related AKI, and those with high CCI scores. These findings underscore the universal importance of anemia management during CKRT. The pathophysiological basis for the impact of anemia on kidney recovery is well-supported by prior research. The kidneys are highly perfused organs, yet their susceptibility to ischemia is amplified by factors such as plasma skimming and the structural arrangement of the tubules and vasa recta [36, 37]. Experimental studies indicate that anemia-induced hypoxia exacerbates kidney ischemia, leading to nephron cell death, fibrosis, and CKD progression [38]. Despite the reduced oxygen demand in anemia, compensatory increases in blood flow are minimal, leaving hypoxia unresolved and contributing to further kidney damage [39]. Our study has several strengths. First, it utilized large-scale and multicenter data that were collected consistently. The data focused exclusively on patients who underwent CKRT in the ICU. Second, we conducted a time-sensitive analysis using the average Hb levels throughout the CKRT period rather than relying on Hb values from a single time point. Third, this study is one of the few to demonstrate a relationship between anemia and the recovery of kidney function, offering practical insights into anemia management during CKRT. However, there are limitations to consider. First, it focused on a single ethnic group, which may have limited the generalizability of the findings. Nonetheless, homogeneity in environmental and cultural factors among participants helped reduce bias and allowed for a better understanding of this specific ethnic group. Second, the study was designed retrospectively, which may have obscured causal relationships. However, the retrospective design enabled a large-scale study where the results can serve as a foundation for future prospective research. Our study demonstrated that during CKRT, an average Hb level of <8.44 g/dL was associated with an increased risk of dialysis dependence, with each additional day of anemia compounding the risk. Conversely, maintaining Hb levels ≥8.44 g/dL reduces this risk, underscoring the importance of anemia management in CKRT patients. In conclusion, these findings provide a clinically useful Hb threshold that may guide strategies to enhance kidney recovery and reduce dialysis dependence in patients with severe AKI. Abbreviations AKI: Acute kidney injury CKRT: Continuous kidney replacement therapy CKD: Chronic kidney disease Hb: Hemoglobin KDIGO: kidney disease: improving global outcomes ADQI: acute dialysis quality initiative GAM: generalized additive model GLM: generalized linear model BMI: body mass index WBC: white blood cell count SBP: systolic blood pressure DBP: diastolic blood pressure SOFA: sequential organ failure assessment ROC: receiver operating characteristic AUC: area under the curve Declarations Ethics approval and consent to participate This study complied with the ethical guidelines outlined in the Declaration of Helsinki. Institutional review board (IRB) approvals were obtained from all participating institutions including KNUCH (no. 2021-03-024), DSMC (no. 2021-06-057), DUIH (no. 2018-12-010), and SNUH (no. H-2111-057-1271). Consent for publication Not applicable Data availability statement The data underlying this article will be shared on reasonable request to the corresponding author (J.Y.P). Acknowledgement Not applicable Funding This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2021R1I1A3052012). Author contributions research idea and study design: SWL, JYP, and EM data acquisition: YCK, JHL, JHP, WYP, KK, JS, JL, SJS data analysis/interpretation: HLK and JJ statistical analysis: HLK, JJ, SWL, JYP supervision: JYP and EM Competing interests The authors declare that they have no competing interests. References Hsu RK, McCulloch CE, Dudley RA, Lo LJ, Hsu CY. Temporal changes in incidence of dialysis-requiring AKI. J Am Soc Nephrol. 2013;24:37–42. Allegretti AS, Steele DJ, David-Kasdan JA, Bajwa E, Niles JL, Bhan I. Continuous renal replacement therapy outcomes in acute kidney injury and end-stage renal disease: a cohort study. Crit Care. 2013;17:R109. Prasad B, Urbanski M, Ferguson TW, Karreman E, Tangri N. Early mortality on continuous renal replacement therapy (CRRT): the prairie CRRT study. Can J Kidney Health Dis. 2016;3:36. Chawla LS, Bellomo R, Bihorac A, Goldstein SL, Siew ED, Bagshaw SM, et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol. 2017;13:241–57. Bucaloiu ID, Kirchner HL, Norfolk ER, Hartle JE 2nd, Perkins RM. Increased risk of death and de novo chronic kidney disease following reversible acute kidney injury. Kidney Int. 2012;81:477–85. Coca SG, Singanamala S, Parikh CR. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int. 2012;81:442–8. Ishani A, Xue JL, Himmelfarb J, Eggers PW, Kimmel PL, Molitoris BA, et al. Acute kidney injury increases risk of ESRD among elderly. J Am Soc Nephrol. 2009;20:223–8. Pan HC, Chen HY, Teng NC, Yeh FY, Huang TM, See CY, et al. Recovery dynamics and prognosis after dialysis for acute kidney injury. JAMA Netw Open. 2024;7: e240351. Warner MA, Hanson AC, Frank RD, Schulte PJ, Go RS, Storlie CB, et al. Prevalence of and recovery from anemia following hospitalization for critical illness among adults. JAMA Netw Open. 2020;3:e2017843. Cely JE, Mendoza EJ, Sprockel JJ, Perez LC, Mateus JM, Maldonado S, et al. Risk factors for community-acquired acute kidney injury in medical patients: a nested case-control study. Blood Purif. 2020;49:677–84. Turan A, Rivas E, Devereaux PJ, Pu X, Rodriguez-Patarroyo FA, Yalcin EK, et al. Relative contributions of anaemia and hypotension to myocardial infarction and renal injury: post hoc analysis of the POISE-2 trial. Eur J Anaesthesiol. 2023;40:365–71. Obradovic D, Loncar G, Zeymer U, Pöss J, Feistritzer HJ, Freund A, et al. Impact of anaemia and iron deficiency on outcomes in cardiogenic shock complicating acute myocardial infarction. Eur J Heart Fail. 2024;26:448–57. Kovvuru K, Velez JCQ. Complications associated with continuous renal replacement therapy. Semin Dial 2021;34:489–94. Akhoundi A, Singh B, Vela M, Chaudhary S, Monaghan M, Wilson GA, et al. Incidence of adverse events during continuous renal replacement therapy. Blood Purif. 2015;39:333–9. Basile DP, Friedrich JL, Spahic J, Knipe N, Mang H, Leonard EC, et al. Impaired endothelial proliferation and mesenchymal transition contribute to vascular rarefaction following acute kidney injury. Am J Physiol Renal Physiol. 2011;300:F721–33. Bechtel W, McGoohan S, Zeisberg EM, Mueller G, Kalbacher H, Salant DJ, et al. Methylation determines fibroblast activation and fibrogenesis in the kidney. Nat Med. 2010;16:544–50. Venkatachalam MA, Griffin KA, Lan R, Geng H, Saikumar P, Bidani AK. Acute kidney injury: a springboard for progression in chronic kidney disease. Am J Physiol Renal Physiol. 2010;298: F1078–94. Wald R, Gaudry S, da Costa BR, Adhikari NK, Bellomo R, Du B, et al. Initiation of continuous renal replacement therapy versus intermittent hemodialysis in critically ill patients with severe acute kidney injury: a secondary analysis of STARRT-AKI trial. Intensive Care Med. 2023;49:1305–16. Castro I, Relvas M, Gameiro J, Lopes JA, Monteiro-Soares M, Coentrão L. The impact of early versus late initiation of renal replacement therapy in critically ill patients with acute kidney injury on mortality and clinical outcomes: a meta-analysis. Clin Kidney J. 2022;15:1932–45. Neyra JA, Lambert J, Ortiz-Soriano V, Cleland D, Colquitt J, Adams P, et al. Assessment of prescribed vs. achieved fluid balance during continuous renal replacement therapy and mortality outcome. PLoS One. 2022;17:e0272913. Uusalo P, Hellman T, Löyttyniemi E, Peltoniemi J, Järvisalo MJ. Early restrictive fluid balance is associated with lower hospital mortality independent of acute disease severity in critically ill patients on CRRT. Sci Rep. 2021;11:18216. Duran PA, Concepcion LA. Survival after acute kidney injury requiring dialysis: long-term follow up. Hemodial Int. 2014;18 Suppl 1:S1-6. Stads S, Fortrie G, van Bommel J, Zietse R, Betjes MG. Impaired kidney function at hospital discharge and long-term renal and overall survival in patients who received CRRT. Clin J Am Soc Nephrol. 2013;8:1284–91. Franco Palacios CR, Hoxhaj R, Thigpen C, Jacob J. Factors associated with post-hospitalization dialysis dependence in ECMO patients who required continuous renal replacement therapy. Ren Fail. 2024;46:2343810. Sueyoshi K, Watanabe Y, Inoue T, Ohno Y, Nakajima H, Okada H. Predictors of long-term prognosis in acute kidney injury survivors who require continuous renal replacement therapy after cardiovascular surgery. PLoS One. 2019;14:e0211429. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–29. Palevsky PM, Zhang JH, O'Connor TZ. Intensity of renal support in critically ill patients with acute kidney injury. N Engl J Med. 2008;359:7–20. Hastie TJ. Generalized additive models. In: Chambers JM, Hastie TJ, editors. Statistical models. New York: Routledge; 2017. p. 249–307. Jenq CC, Tsai FC, Tsai TY, Hsieh SY, Lai YW, Tian YC, et al. Effect of anemia on prognosis in patients on extracorporeal membrane oxygenation. Artif Organs. 2018;42: 705–13. Jeon J, Kang D, Park H, Lee K, Lee JE, Huh W, et al. Impact of anemia requiring transfusion or erythropoiesis-stimulating agents on new-onset cardiovascular events and mortality after continuous renal replacement therapy. Sci Rep. 2024;14:6556. Delannoy B, Floccard B, Thiolliere F, Kaaki M, Badet M, Rosselli S, et al. Six-month outcome in acute kidney injury requiring renal replacement therapy in the ICU: a multicentre prospective study. Intensive Care Med. 2009;35:1907–15. du Cheyron D, Parienti JJ, Fekih-Hassen M, Daubin C, Charbonneau P. Impact of anemia on outcome in critically ill patients with severe acute renal failure. Intensive Care Med. 2005;31:1529–36. Haase M, Bellomo R, Story D, Letis A, Klemz K, Matalanis G, et al. Effect of mean arterial pressure, hemoglobin and blood transfusion during cardiopulmonary bypass on post-operative acute kidney injury. Nephrol Dial Transplant. 2012;27:153–60. Rhee H, Jang GS, An YJ, Han M, Park I, Kim IY, et al. Long-term outcomes in acute kidney injury patients who underwent continuous renal replacement therapy: a single-center experience. Clin Exp Nephrol. 2018;22:1411–1419. Brezis M, Rosen S, Silva P, Epstein FH. Renal ischemia: a new perspective. Kidney Int. 1984;26:375–83. Fu Q, Colgan SP, Shelley CS. Hypoxia: The force that drives chronic kidney disease. Clin Med Res. 2016;14:15–39. Mistry N, Mazer CD, Sled JG, Lazarus AH, Cahill LS, Solish M, et al. Red blood cell antibody-induced anemia causes differential degrees of tissue hypoxia in kidney and brain. Am J Physiol Regul Integr Comp Physiol. 2018;314:R611–r22. Habas Sr E, Al Adab A, Arryes M, Alfitori G, Farfar K, Habas AM, et al. Anemia and hypoxia impact on chronic kidney disease onset and progression: review and updates. Cureus. 2023; 5:e46737. Tables Table 1 Baseline characteristics of the 2755 patients with AKI who received CKRT Variable Total (n = 2755) Average hemoglobin during CKRT Anemic group (<8.44 g/dL) (n = 741) Control group (≥ 8.44g/dL) (n = 2124) p -value Sex (male), n (%) 1691 (61.4) 518 (60.7) 1173 (61.7) 0.67 Age, mean (SD) 65.5 (15.0) 63.5 (14.4) 66.4 (15.2) <0.01 Body mass index, mean (SD) 23.1 (4.5) 23.2 (4.2) 23.1 (4.7) 0.72 Hypertension, n (%) 950 (34.5) 230 (27.0) 720 (37.9) <0.01 Sepsis, n (%) 1503 (54.6) 530 (62.1) 973 (51.2) <0.01 Biochemical data, mean (SD) White blood cell (10³/μL) 15.2 (20.4) 15.9 (30.2) 14.9 (13.8) 0.31 Albumin (g/dL) 2.7 (0.7) 2.6 (0.6) 2.8 (0.7) <0.01 Creatinine (mg/dL) 2.9 (2.0) 3.0 (2.2) 2.8 (1.9) 0.06 Systolic blood pressure (mmHg) 113.3 (26.9) 115.4 (28.4) 112.4 (26.2) 0.01 Diastolic blood pressure (mmHg) 60.5 (15.6) 59.2 (15.8) 61.1 (15.5) <0.01 Hemoglobin (g/dL) 9.6 (2.2) 7.9 (1.5) 10.4 (2.1) <0.01 CCI, mean (SD) 3.6 (2.7) 3.7 (2.7) 3.5 (2.7) 0.16 APACHE II score, mean (SD) 26.7 (7.8) 27.2 (8.1) 26.5 (7.7) 0.04 SOFA without renal score, mean (SD) 10.2 (3.6) 10.4 (3.8) 10.0 (3.5) 0.02 Duration (days), mean (SD) CKRT 7.9 (13.3) 7.0 (13.2) 8.2 (13.3) 0.02 Dialysis 11.7 (26.8) 9.4 (21.0) 12.7 (29.0) <0.01 Hospitalization 43.1 (61.8) 40.9 (63.8) 44.1 (60.9) 0.20 Red cell transfusion volumes, mean (SD) 3.7 (7.3) 3.4 (5.0) 3.8 (8.1) 0.15 Ventilator, n (%) 2122 (77.0) 660 (77.4) 1462 (76.9) 0.81 CKRT setting, mean (SD) Blood flow rate (mL/min) 108.1 (23.3) 110.7 (23.1) 107.0 (23.3) <0.01 Dialysate flow rate (mL/h) 1222.2 (471.9) 1278.6 (480.9) 1196.9 (465.8) <0.01 Replacement flow rate (mL/h) 989.0 (608.6) 997.1 (653.5) 985.4 (587.5) 0.65 Abbreviation: AKI, acute kidney injury; CKRT, continuous kidney replacement therapy; SD, standardized deviation; CCI, Charlson’s comorbidity index; APACHE II, acute physiology and chronic health evaluation; SOFA, sequential organ failure assessment Table 2 Association between the number of days where the average hemoglobin level was <8.44 g/dL and dialysis dependence Unadjusted Adjusted* OR (95% CI) p -value OR (95% CI) p -value Every 1-day increase 1.03 (1.01, 1.05) <0.01 1.04 (1.01, 1.07) <0.01 Abbreviation: OR, odds ratio *Adjusted for sex, age, Charlson comorbidity index, body mass index, hypertension, sepsis, white blood cell count, albumin, creatinine, systolic blood pressure, diastolic blood pressure, sequential organ failure assessment (excluding the renal component), transfusion amount, and duration of continuous kidney replacement therapy. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6508688","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449426201,"identity":"d8a306e2-7729-46a2-9c08-b132bac99f57","order_by":0,"name":"Hyun Lee Ko","email":"","orcid":"","institution":"Uijeongbu Eulji Medical Center, Eulji University","correspondingAuthor":false,"prefix":"","firstName":"Hyun","middleName":"Lee","lastName":"Ko","suffix":""},{"id":449426202,"identity":"cf4b33d8-df50-481f-890a-41a046aa1f19","order_by":1,"name":"Jiyun Jung","email":"","orcid":"","institution":"Dongguk University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiyun","middleName":"","lastName":"Jung","suffix":""},{"id":449426206,"identity":"17d38ef3-8ff5-4d73-a853-917f9f8efbe3","order_by":2,"name":"Sung Woo Lee","email":"","orcid":"","institution":"Uijeongbu Eulji Medical Center, Eulji University","correspondingAuthor":false,"prefix":"","firstName":"Sung","middleName":"Woo","lastName":"Lee","suffix":""},{"id":449426207,"identity":"b748b2eb-ff1b-4200-ba36-3debda0c0565","order_by":3,"name":"Yong Chul Kim","email":"","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"Chul","lastName":"Kim","suffix":""},{"id":449426208,"identity":"95b20a79-9c64-4d5e-b269-8452f8ab99a1","order_by":4,"name":"Jeong‑Hoon Lim","email":"","orcid":"","institution":"Kyungpook National University Chilgok Hospital, Kyungpook National University","correspondingAuthor":false,"prefix":"","firstName":"Jeong‑Hoon","middleName":"","lastName":"Lim","suffix":""},{"id":449426209,"identity":"3d001761-0fa2-4855-b385-734a409652de","order_by":5,"name":"Jin Hyuk Paek","email":"","orcid":"","institution":"Keimyung University Dongsan Hospital, Keimyung University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Hyuk","lastName":"Paek","suffix":""},{"id":449426210,"identity":"40ea6878-9643-41a1-8655-4348ebf18fde","order_by":6,"name":"Woo Yeong Park","email":"","orcid":"","institution":"Keimyung University Dongsan Hospital, Keimyung University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Woo","middleName":"Yeong","lastName":"Park","suffix":""},{"id":449426211,"identity":"2b4c85b5-09f1-4ca7-8165-22f56fa80133","order_by":7,"name":"Kipyo Kim","email":"","orcid":"","institution":"Inha University Hospital, Inha University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kipyo","middleName":"","lastName":"Kim","suffix":""},{"id":449426212,"identity":"2829b7b7-522c-43aa-bdc3-419afb5593fd","order_by":8,"name":"Jeongin Song","email":"","orcid":"","institution":"Dongguk University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jeongin","middleName":"","lastName":"Song","suffix":""},{"id":449426217,"identity":"711891f3-4dd2-4752-8828-54be28f056a0","order_by":9,"name":"Jangwook Lee","email":"","orcid":"","institution":"Dongguk University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jangwook","middleName":"","lastName":"Lee","suffix":""},{"id":449426219,"identity":"65e52eb1-8c23-45eb-b2cb-3939a68c8fff","order_by":10,"name":"Sung Joon Shin","email":"","orcid":"","institution":"Dongguk University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sung","middleName":"Joon","lastName":"Shin","suffix":""},{"id":449426220,"identity":"2535a061-2ba5-4f0b-9efa-fdbcf2ae91aa","order_by":11,"name":"Jae Yoon Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYHACNghmbwDSBhakaOE5ANIiQbQWIJBIAJOE1Zuz9x57XFHBl7hd8vnVDT8KJBj427sT8Gqx7DmXbnjmDFviztk5ZTd7gA6TOHN2A14tBjdyzCQb29gSN9zOSbvBA9RiIJFLQMv9N1AtN8+k3fxDlJYbPFAtN9iP3SbOljN5aZINZ9iMd/bksN2WMZDgIeyX42ePSTZUHJPdzn782c03f2zk+Nt78WsBRiGIOOa4gYHHAM4lRkuNvQED+wMiVI+CUTAKRsFIBADOoUmPPTEsyAAAAABJRU5ErkJggg==","orcid":"","institution":"Dongguk University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jae","middleName":"Yoon","lastName":"Park","suffix":""},{"id":449426221,"identity":"8eac5aa2-bdb4-4b77-a49f-c531acebeb8d","order_by":12,"name":"Etienne Macedo","email":"","orcid":"","institution":"University of California– San Diego","correspondingAuthor":false,"prefix":"","firstName":"Etienne","middleName":"","lastName":"Macedo","suffix":""}],"badges":[],"createdAt":"2025-04-23 04:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6508688/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6508688/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82140609,"identity":"557012aa-7cf7-4151-8fea-13c8d3ad26b3","added_by":"auto","created_at":"2025-05-07 06:30:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40914,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between the average hemoglobin level during CKRT and dialysis dependence. CKRT, continuous kidney replacement therapy. Adjusted for sex, age, Charlson comorbidity index, body mass index, hypertension, sepsis, white blood cell count, albumin, creatinine, systolic blood pressure, diastolic blood pressure, sequential organ failure assessment (excluding the renal component), transfusion amount, and duration of continuous kidney replacement therapy.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6508688/v1/830dd04520edeefb8066690f.png"},{"id":82140611,"identity":"38feae0e-b2dc-4fa5-a611-d48f29205a4e","added_by":"auto","created_at":"2025-05-07 06:30:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28226,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for developing dialysis dependence based on the risk of the anemic group. ROC, receiver operating characteristic. Adjusted for sex, age, Charlson comorbidity index, body mass index, hypertension, sepsis, white blood cell count, albumin, creatinine, systolic blood pressure, diastolic blood pressure, sequential organ failure assessment (excluding the renal component), transfusion amount, and duration of continuous kidney replacement therapy.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6508688/v1/b20f5616cd2e0eba62bdca8f.png"},{"id":82143012,"identity":"306e424e-b560-435c-ae54-db370e406094","added_by":"auto","created_at":"2025-05-07 06:38:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":659768,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of dialysis dependence for the anemic group compared to the control group in all patients and subgroups stratified according to sex, age, AKI cause, and CCI. Adjusted for sex, age, Charlson comorbidity index, body mass index, hypertension, sepsis, white blood cell count, albumin, creatinine, systolic blood pressure, diastolic blood pressure, sequential organ failure assessment (excluding the renal component), transfusion amount, and duration of continuous kidney replacement therapy.\u003c/p\u003e\n\u003cp\u003eAbbreviation: AKI, acute kidney injury; CCI, Charlson comorbidity index; OR, odds ratio\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6508688/v1/63994c26d71c522e7d31f674.jpeg"},{"id":83413594,"identity":"2102932a-adb3-40d9-9c13-ab1cbdbf0e8d","added_by":"auto","created_at":"2025-05-25 16:46:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1475452,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6508688/v1/1facafce-da41-42f2-9a11-4b06ead214ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between hemoglobin levels and dialysis dependence in patients with acute kidney injury requiring continuous kidney replacement therapy: a multicenter retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute kidney injury (AKI) is a common condition among critically ill patients, with a rising incidence of severe cases requiring dialysis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Continuous kidney replacement therapy (CKRT) is widely used to remove fluid and manage acid-based and electrolyte derangements while maintaining hemodynamic stability. Despite its efficacy, CKRT is associated with alarmingly high mortality rates, estimated at around 60% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While survival from AKI is a critical milestone, it often marks the beginning of additional long-term challenges.\u003c/p\u003e \u003cp\u003eAmong survivors, incomplete recovery of kidney function and persistent dialysis dependence remain major concerns [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. AKI has been strongly linked to the onset of new chronic kidney disease (CKD) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], progression of pre-existing CKD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and increased risk of end-stage kidney disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Individuals with partial kidney recovery face heightened risks of cardiovascular complications and mortality compared to those with full recovery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnemia is highly prevalent among critically ill patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and is a significant risk factor for AKI [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Compounding conditions, such as cardiogenic shock, further exacerbate anemia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. CKRT can exacerbate anemia due to catheter-related bleeding, blood clotting, and blood loss [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This interplay highlights the need to explore the role of anemia in AKI recovery. Experimental studies suggest that anemia can impair vascular repair mechanisms in the kidneys during AKI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], resulting in a significant loss of vascular density (30\u0026ndash;50% in some injury models) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This rarefaction of capillaries can trigger hypoxia-inducible pathways, promote inflammation, and lead to fibrosis, reducing the likelihood of complete kidney recovery [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Persistently low hemoglobin (Hb) levels in such contexts may amplify tissue hypoxia and exacerbate kidney damage, although this clinical relationship has been underexplored [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the high mortality rate among patients with severe AKI requiring CKRT, most studies have focused on reducing mortality through various aspects of kidney replacement therapy, such as selection [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], timing of initiation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and fluid balance management during treatment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Few studies have aimed to improve kidney recovery in survivors [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, no established guidelines exist for correcting anemia to enhance dialysis independence in CKRT survivors. Achieving independence from dialysis has significant implications for the quality of life and long-term survival of patients. To address this gap, we conducted a multicenter cohort study to examine the relationship between anemia and dialysis dependence in patients with severe AKI treated with CKRT, with a particular focus on identifying Hb levels that may influence kidney recovery.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis multicenter retrospective study included data from 3,576 patients diagnosed with AKI and treated with CKRT across four major hospitals: Kyungpook National University Chilgok Hospital (KNUCH; n\u0026thinsp;=\u0026thinsp;136), Keimyung University Dongsan Medical Center (DSMC; n\u0026thinsp;=\u0026thinsp;137), Dongguk University Ilsan Hospital (DUIH; n\u0026thinsp;=\u0026thinsp;915), and Seoul National University Hospital (SNUH; n\u0026thinsp;=\u0026thinsp;2388) between 2006 and 2021. A total of 821 patients were excluded due to end-stage renal disease or incomplete covariate data, resulting in a final cohort of 2,755 participants. The following demographic and laboratory information at CKRT initiation were recorded: sex, age, body mass index (BMI), presence of hypertension history, etiology of AKI (sepsis-related or other), white blood cell (WBC), albumin, creatinine, Hb, systolic blood pressure (SBP), diastolic blood pressure (DBP), red cells transfusion volumes, usage of ventilator, and CKRT setting (blood flow rate, dialysate flow rate, replacement flow rate). Additionally, we investigated the duration of CKRT, dialysis, and hospitalization, as well as the indicators related to critically ill patients, such as the Charlson comorbidity index (CCI), sequential organ failure assessment (SOFA) score excluding the renal component, and acute physiology and chronic health evaluation (APACHE II) score. The CCI is a representative clinical method for classifying the concurrent conditions of 19 diseases by assigning different weights [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Both SOFA (excluding the renal component) and APACHE II are widely used scoring systems that are used to predict mortality risk based on severity in five organ systems and 12 physiological variables, respectively [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with the ethical guidelines outlined in the Declaration of Helsinki. Institutional review board (IRB) approvals were obtained from all participating institutions including KNUCH (no. 2021-03-024), DSMC (no. 2021-06-057), DUIH (no. 2018-12-010), and SNUH (no. H-2111-057-1271).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure and outcome variable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor patients admitted to the intensive care unit (ICU) and undergoing CKRT, we collected serial Hb data through daily complete blood count measurements. If a patient's Hb was measured multiple times a day, the daily average Hb level was calculated. To assess the relationship between Hb levels and dialysis dependence, we computed the average Hb level during CKRT for each patient. Dialysis dependence was defined as dependence on dialysis at the time of hospital discharge.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of patients who underwent CKRT were evaluated using mean and standard deviation for continuous variables, and frequency and percentile for categorical variables. Patients were categorized into two groups based on their average Hb levels during CKRT: the anemia (average Hb \u0026lt;8.44 g/dL) and control groups (average Hb ≥8.44 g/dL), determined through statistical methods. Differences between the two groups were evaluated using t-tests for continuous variables and chi-square tests for categorical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneralized Additive Model (GAM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the nonlinear effects of average Hb levels on dialysis dependence, a GAM with a binomial link was employed. Unlike a generalized linear model (GLM), GAM replaces linear components with a flexible, smooth non-linear function [29] allowing for the modeling of complex relationships. We applied the spline function on the average Hb level in the GAM with additional adjustments for sex, age, CCI, BMI, hypertension, sepsis, WBC, albumin, creatinine, SBP, DBP, SOFA (excluding the renal component), transfusion amount, and CKRT duration. A threshold Hb level was identified, representing the point at which dialysis dependence risk began to decline with increasing average Hb levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGLM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the identified Hb threshold, the risk of dialysis dependence for the anemia group was compared to the control group through a GLM adjusted for the same covariates. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), which evaluates sensitivity and specificity for dialysis dependence prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify vulnerable subpopulations, analyses were stratified by sex (male vs. female), age (\u0026lt;65 years vs. ≥65 years), AKI etiology (sepsis vs. non-sepsis), and CCI (\u0026lt;3 vs. ≥3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnemia duration and dialysis dependence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each patient, we counted the number of days during CKRT when the daily Hb level was less than 8.44 g/dL. Logistic regression models, both unadjusted and adjusted, were used to assess the association between the number of anemic days and dialysis dependence.\u003c/p\u003e\n\u003cp\u003eAll results were analyzed using R software, and statistical significance was set at p\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2,755 participants were included in the analysis (mean age, 65.5 years). Of these, 61.4% were male and 34.5% had a diagnosis of hypertension. The primary indication for CKRT was sepsis in 54.6% of cases. The mean APACHE II and SOFA scores in patients without renal disease were 26.7 and 10.2, respectively. The mean duration of dialysis and CKRT were 11.7 d and 7.9 d, respectively, with 77.0% of participants requiring mechanical ventilation. Table 1 summarizes the baseline characteristics of patients requiring CKRT, categorized into the anemic and control groups based on an Hb threshold of 8.44 g/dL. The anemia group was younger and had a lower prevalence of hypertension than the control group. The anemia group exhibited a higher incidence of sepsis and higher severity scores in critically ill patients. Additionally, the anemic group demonstrated lower baseline Hb and albumin levels, but higher creatinine levels. The mean duration of dialysis and CKRT were shorter in the anemic group, but the blood and dialysate flow rates in the CKRT settings were higher. No significant differences were observed between the two groups regarding the length of hospital stay, red cell transfusion volume, or mechanical ventilation dependence rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-linear association between the average Hb level and dialysis dependence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between average Hb levels and dialysis dependence is illustrated in Fig. 1. Below the Hb level of 8.44 g/dL, the odds ratio (OR) for dialysis dependence significantly increased to \u0026gt;1. While a U-shaped curve was observed, there was no statistically significant association between higher Hb levels and dialysis dependence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk in the anemic group compared to the control group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the risk model comparing the anemic group (Hb \u0026lt;8.44 g/dL) to the control group, the ROC curve analysis revealed a sensitivity of 72.8% and a specificity of 59.9%, with an AUC of 0.72, indicating a moderate level of diagnostic accuracy (Fig. 2). The anemic group demonstrated a 1.57-fold increased risk of dialysis dependence compared to the control group (Fig. 3). Subgroup analyses revealed that the increased dialysis dependence risk for the anemic group was consistent across all patient groups. However, stronger associations were observed in female patients, patients aged \u0026lt;65 years, patients with non-sepsis causes, and patients with high CCI scores.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between the number of anemic days and dialysis dependence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis further explored the impact of the number of days with Hb levels \u0026lt;8.44 g/dL on dialysis dependence. In the unadjusted model, each additional day with Hb levels \u0026lt;8.44 g/dL was significantly associated with a 3% increase in dialysis dependence (OR: 1.03, 95% confidence interval [CI]: 1.01–1.05). After adjusting for covariates, the association remained significant, with a 4% increase in dialysis dependence for each additional day of low Hb levels (OR: 1.04, 95% CI: 1.01–1.07; Table 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe identified a significant association between the average Hb levels and dialysis dependence in patients with AKI requiring CKRT. The threshold Hb level at which the risk of dialysis dependence increased was 8.44 g/dL. For each additional day that Hb levels remained below 8.44 g/dL, dialysis dependence increased by 4%.\u003c/p\u003e\n\u003cp\u003eEffective management of complications and underlying etiologies is critical in patients with severe AKI requiring CKRT. While complications like hypotension, electrolyte imbalances, and acid-base disturbances are addressed through clinical interventions—such as vasopressors, supplementation, and fluid therapy [13] —anemia remains underexplored despite its frequent occurrence during CKRT. Guidelines, such as those provided by the Kidney Disease: Improving Global Outcomes (KDIGO) and the Acute Dialysis Quality Initiative (ADQI), provide recommendations for dialysis initiation timing and dosage but lack specific strategies for anemia management that occur frequently during CKRT. Although studies have extensively investigated anemia and survival outcomes [30, 31], few have focused on the impact of anemia and dialysis dependence [22]. In severe AKI, improving survival rates is often the primary objective, and much of the existing research has concentrated on this goal [2, 23, 25, 32]. However, increasing attention is now being given to kidney function recovery due to its role in reducing the risks of advanced chronic kidney disease (CKD) and cardiovascular events [8]. Our findings contribute to this growing body of evidence by confirming that Hb levels \u0026lt;8.44 g/dL during CKRT are associated with an increased risk of dialysis dependence.\u003c/p\u003e\n\u003cp\u003eThese findings align with prior studies. Du Cheyron et al. [33] reported that patients requiring dialysis with Hb levels \u0026lt;9 g/dL at admission were more likely to develop maintenance dialysis dependency than those with higher Hb levels. While our study identified a slightly lower threshold, this difference may be due to our focus on ICU patients exclusively requiring CKRT. Similarly, Hasse et al. [34] highlighted anemia as a significant risk factor for AKI following cardiac surgery and suggested that maintaining appropriate Hb levels could prevent AKI. Notably, their findings suggest a median Hb level of 7.4 g/dL in AKI patients, compared to 8.2 g/dL in those without AKI, mirroring our conclusion that maintaining higher Hb levels supports kidney recovery. Our study extends these findings by not only confirming the importance of maintaining Hb levels but also emphasizing the persistence of anemia as a critical factor. We observed that each additional day with Hb levels \u0026lt;8.44 g/dL further increased dialysis dependence risk. Rhee et al. [35], similarly reported that a 1 g/dL increase in Hb levels at CKRT initiation was associated with a significant reduction in dialysis dependence risk, albeit without proposing a specific Hb threshold. Our stratified analyses demonstrated that the risk of dialysis dependence associated with anemia was consistent across subgroups, including sex, age, etiology of AKI, and comorbidity burden. However, stronger associations were observed in female patients, younger patients (\u0026lt;65 years), those with non-sepsis-related AKI, and those with high CCI scores. These findings underscore the universal importance of anemia management during CKRT.\u003c/p\u003e\n\u003cp\u003eThe pathophysiological basis for the impact of anemia on kidney recovery is well-supported by prior research. The kidneys are highly perfused organs, yet their susceptibility to ischemia is amplified by factors such as plasma skimming and the structural arrangement of the tubules and vasa recta [36, 37]. Experimental studies indicate that anemia-induced hypoxia exacerbates kidney ischemia, leading to nephron cell death, fibrosis, and CKD progression [38]. Despite the reduced oxygen demand in anemia, compensatory increases in blood flow are minimal, leaving hypoxia unresolved and contributing to further kidney damage [39].\u003c/p\u003e\n\u003cp\u003eOur study has several strengths. First, it utilized large-scale and multicenter data that were collected consistently. The data focused exclusively on patients who underwent CKRT in the ICU. Second, we conducted a time-sensitive analysis using the average Hb levels throughout the CKRT period rather than relying on Hb values from a single time point. Third, this study is one of the few to demonstrate a relationship between anemia and the recovery of kidney function, offering practical insights into anemia management during CKRT. \u003c/p\u003e\n\u003cp\u003eHowever, there are limitations to consider. First, it focused on a single ethnic group, which may have limited the generalizability of the findings. Nonetheless, homogeneity in environmental and cultural factors among participants helped reduce bias and allowed for a better understanding of this specific ethnic group. Second, the study was designed retrospectively, which may have obscured causal relationships. However, the retrospective design enabled a large-scale study where the results can serve as a foundation for future prospective research.\u003c/p\u003e\n\u003cp\u003eOur study demonstrated that during CKRT, an average Hb level of \u0026lt;8.44 g/dL was associated with an increased risk of dialysis dependence, with each additional day of anemia compounding the risk. Conversely, maintaining Hb levels ≥8.44 g/dL reduces this risk, underscoring the importance of anemia management in CKRT patients. In conclusion, these findings provide a clinically useful Hb threshold that may guide strategies to enhance kidney recovery and reduce dialysis dependence in patients with severe AKI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKI:\u0026nbsp; \u0026nbsp;\u0026nbsp;Acute kidney injury\u003c/p\u003e\n\u003cp\u003eCKRT:\u0026nbsp;Continuous kidney replacement therapy\u003c/p\u003e\n\u003cp\u003eCKD:\u0026nbsp;\u0026nbsp;Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eHb: Hemoglobin\u003c/p\u003e\n\u003cp\u003eKDIGO: kidney disease: improving global outcomes\u003c/p\u003e\n\u003cp\u003eADQI:\u0026nbsp;acute dialysis quality initiative\u003c/p\u003e\n\u003cp\u003eGAM:\u0026nbsp;\u0026nbsp;generalized additive model\u003c/p\u003e\n\u003cp\u003eGLM:\u0026nbsp;\u0026nbsp;generalized linear model\u003c/p\u003e\n\u003cp\u003eBMI:\u0026nbsp; \u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eWBC:\u0026nbsp;\u0026nbsp;white blood cell count\u003c/p\u003e\n\u003cp\u003eSBP:\u0026nbsp; \u0026nbsp;\u0026nbsp;systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP:\u0026nbsp; \u0026nbsp;diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eSOFA:\u0026nbsp;sequential organ failure assessment\u003c/p\u003e\n\u003cp\u003eROC:\u0026nbsp; \u0026nbsp;receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC: \u0026nbsp;area under the curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with the ethical guidelines outlined in the Declaration of Helsinki. Institutional review board (IRB) approvals were obtained from all participating institutions including KNUCH (no. 2021-03-024), DSMC (no. 2021-06-057), DUIH (no. 2018-12-010), and SNUH (no. H-2111-057-1271).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author (J.Y.P).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2021R1I1A3052012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eresearch idea and study design: SWL, JYP, and EM\u003c/p\u003e\n\u003cp\u003edata acquisition: YCK, JHL, JHP, WYP, KK, JS, JL, SJS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;data analysis/interpretation: HLK and JJ\u003c/p\u003e\n\u003cp\u003estatistical analysis: HLK, JJ, SWL, JYP\u003c/p\u003e\n\u003cp\u003esupervision: JYP and EM\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"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHsu RK, McCulloch CE, Dudley RA, Lo LJ, Hsu CY. Temporal changes in incidence of dialysis-requiring AKI. J Am Soc Nephrol. 2013;24:37\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eAllegretti AS, Steele DJ, David-Kasdan JA, Bajwa E, Niles JL, Bhan I. Continuous renal replacement therapy outcomes in acute kidney injury and end-stage renal disease: a cohort study. Crit Care. 2013;17:R109.\u003c/li\u003e\n\u003cli\u003ePrasad B, Urbanski M, Ferguson TW, Karreman E, Tangri N. Early mortality on continuous renal replacement therapy (CRRT): the prairie CRRT study. Can J Kidney Health Dis. 2016;3:36.\u003c/li\u003e\n\u003cli\u003eChawla LS, Bellomo R, Bihorac A, Goldstein SL, Siew ED, Bagshaw SM, et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol. 2017;13:241\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eBucaloiu ID, Kirchner HL, Norfolk ER, Hartle JE 2nd, Perkins RM. Increased risk of death and de novo chronic kidney disease following reversible acute kidney injury. Kidney Int. 2012;81:477\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eCoca SG, Singanamala S, Parikh CR. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int. 2012;81:442\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eIshani A, Xue JL, Himmelfarb J, Eggers PW, Kimmel PL, Molitoris BA, et al. Acute kidney injury increases risk of ESRD among elderly. J Am Soc Nephrol. 2009;20:223\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003ePan HC, Chen HY, Teng NC, Yeh FY, Huang TM, See CY, et al. Recovery dynamics and prognosis after dialysis for acute kidney injury. JAMA Netw Open. 2024;7: e240351.\u003c/li\u003e\n\u003cli\u003eWarner MA, Hanson AC, Frank RD, Schulte PJ, Go RS, Storlie CB, et al. Prevalence of and recovery from anemia following hospitalization for critical illness among adults. JAMA Netw Open. 2020;3:e2017843.\u003c/li\u003e\n\u003cli\u003eCely JE, Mendoza EJ, Sprockel JJ, Perez LC, Mateus JM, Maldonado S, et al. Risk factors for community-acquired acute kidney injury in medical patients: a nested case-control study. Blood Purif. 2020;49:677\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eTuran A, Rivas E, Devereaux PJ, Pu X, Rodriguez-Patarroyo FA, Yalcin EK, et al. Relative contributions of anaemia and hypotension to myocardial infarction and renal injury: post hoc analysis of the POISE-2 trial. Eur J Anaesthesiol. 2023;40:365\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eObradovic D, Loncar G, Zeymer U, P\u0026ouml;ss J, Feistritzer HJ, Freund A, et al. Impact of anaemia and iron deficiency on outcomes in cardiogenic shock complicating acute myocardial infarction. Eur J Heart Fail. 2024;26:448\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eKovvuru K, Velez JCQ. Complications associated with continuous renal replacement therapy. Semin Dial 2021;34:489\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eAkhoundi A, Singh B, Vela M, Chaudhary S, Monaghan M, Wilson GA, et al. Incidence of adverse events during continuous renal replacement therapy. Blood Purif. 2015;39:333\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eBasile DP, Friedrich JL, Spahic J, Knipe N, Mang H, Leonard EC, et al. Impaired endothelial proliferation and mesenchymal transition contribute to vascular rarefaction following acute kidney injury. Am J Physiol Renal Physiol. 2011;300:F721\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eBechtel W, McGoohan S, Zeisberg EM, Mueller G, Kalbacher H, Salant DJ, et al. Methylation determines fibroblast activation and fibrogenesis in the kidney. Nat Med. 2010;16:544\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eVenkatachalam MA, Griffin KA, Lan R, Geng H, Saikumar P, Bidani AK. Acute kidney injury: a springboard for progression in chronic kidney disease. Am J Physiol Renal Physiol. 2010;298: F1078\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eWald R, Gaudry S, da Costa BR, Adhikari NK, Bellomo R, Du B, et al. Initiation of continuous renal replacement therapy versus intermittent hemodialysis in critically ill patients with severe acute kidney injury: a secondary analysis of STARRT-AKI trial. Intensive Care Med. 2023;49:1305\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eCastro I, Relvas M, Gameiro J, Lopes JA, Monteiro-Soares M, Coentr\u0026atilde;o L. The impact of early versus late initiation of renal replacement therapy in critically ill patients with acute kidney injury on mortality and clinical outcomes: a meta-analysis. Clin Kidney J. 2022;15:1932\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eNeyra JA, Lambert J, Ortiz-Soriano V, Cleland D, Colquitt J, Adams P, et al. Assessment of prescribed vs. achieved fluid balance during continuous renal replacement therapy and mortality outcome. PLoS One. 2022;17:e0272913.\u003c/li\u003e\n\u003cli\u003eUusalo P, Hellman T, L\u0026ouml;yttyniemi E, Peltoniemi J, J\u0026auml;rvisalo MJ. Early restrictive fluid balance is associated with lower hospital mortality independent of acute disease severity in critically ill patients on CRRT. Sci Rep. 2021;11:18216.\u003c/li\u003e\n\u003cli\u003eDuran PA, Concepcion LA. Survival after acute kidney injury requiring dialysis: long-term follow up. Hemodial Int. 2014;18 Suppl 1:S1-6.\u003c/li\u003e\n\u003cli\u003eStads S, Fortrie G, van Bommel J, Zietse R, Betjes MG. Impaired kidney function at hospital discharge and long-term renal and overall survival in patients who received CRRT. Clin J Am Soc Nephrol. 2013;8:1284\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eFranco Palacios CR, Hoxhaj R, Thigpen C, Jacob J. Factors associated with post-hospitalization dialysis dependence in ECMO patients who required continuous renal replacement therapy. Ren Fail. 2024;46:2343810.\u003c/li\u003e\n\u003cli\u003eSueyoshi K, Watanabe Y, Inoue T, Ohno Y, Nakajima H, Okada H. Predictors of long-term prognosis in acute kidney injury survivors who require continuous renal replacement therapy after cardiovascular surgery. PLoS One. 2019;14:e0211429.\u003c/li\u003e\n\u003cli\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003ePalevsky PM, Zhang JH, O\u0026apos;Connor TZ. Intensity of renal support in critically ill patients with acute kidney injury. N Engl J Med. 2008;359:7\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eHastie TJ. Generalized additive models. In: Chambers JM, Hastie TJ, editors. Statistical models.\u003cem\u003e \u003c/em\u003eNew York: Routledge; 2017. p. 249\u0026ndash;307.\u003c/li\u003e\n\u003cli\u003eJenq CC, Tsai FC, Tsai TY, Hsieh SY, Lai YW, Tian YC, et al. Effect of anemia on prognosis in patients on extracorporeal membrane oxygenation. Artif Organs. 2018;42: 705\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eJeon J, Kang D, Park H, Lee K, Lee JE, Huh W, et al. Impact of anemia requiring transfusion or erythropoiesis-stimulating agents on new-onset cardiovascular events and mortality after continuous renal replacement therapy. Sci Rep. 2024;14:6556.\u003c/li\u003e\n\u003cli\u003eDelannoy B, Floccard B, Thiolliere F, Kaaki M, Badet M, Rosselli S, et al. Six-month outcome in acute kidney injury requiring renal replacement therapy in the ICU: a multicentre prospective study. Intensive Care Med. 2009;35:1907\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003edu Cheyron D, Parienti JJ, Fekih-Hassen M, Daubin C, Charbonneau P. Impact of anemia on outcome in critically ill patients with severe acute renal failure. Intensive Care Med. 2005;31:1529\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eHaase M, Bellomo R, Story D, Letis A, Klemz K, Matalanis G, et al. Effect of mean arterial pressure, hemoglobin and blood transfusion during cardiopulmonary bypass on post-operative acute kidney injury. Nephrol Dial Transplant. 2012;27:153\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eRhee H, Jang GS, An YJ, Han M, Park I, Kim IY, et al. Long-term outcomes in acute kidney injury patients who underwent continuous renal replacement therapy: a single-center experience. Clin Exp Nephrol. 2018;22:1411\u0026ndash;1419.\u003c/li\u003e\n\u003cli\u003eBrezis M, Rosen S, Silva P, Epstein FH. Renal ischemia: a new perspective. Kidney Int. 1984;26:375\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eFu Q, Colgan SP, Shelley CS. Hypoxia: The force that drives chronic kidney disease. Clin Med Res. 2016;14:15\u0026ndash;39.\u003c/li\u003e\n\u003cli\u003eMistry N, Mazer CD, Sled JG, Lazarus AH, Cahill LS, Solish M, et al. Red blood cell antibody-induced anemia causes differential degrees of tissue hypoxia in kidney and brain. Am J Physiol Regul Integr Comp Physiol. 2018;314:R611\u0026ndash;r22.\u003c/li\u003e\n\u003cli\u003eHabas Sr E, Al Adab A, Arryes M, Alfitori G, Farfar K, Habas AM, et al. Anemia and hypoxia impact on chronic kidney disease onset and progression: review and updates. Cureus. 2023; 5:e46737.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Baseline characteristics of the 2755 patients with AKI who received CKRT\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"891\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 2755)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 428px;\"\u003e\n \u003cp\u003eAverage hemoglobin during CKRT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAnemic group\u003c/p\u003e\n \u003cp\u003e(\u0026lt;8.44 g/dL)\u003c/p\u003e\n \u003cp\u003e(n = 741)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003cp\u003e(\u0026ge; 8.44g/dL)\u003c/p\u003e\n \u003cp\u003e(n = 2124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eSex (male), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1691 (61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e518 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1173 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eAge, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e65.5 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e63.5 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e66.4 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eBody mass index, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e23.1 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e23.2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e23.1 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e950 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e230 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e720 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eSepsis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e1503 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e530 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e973 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eBiochemical data, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eWhite blood cell (10\u0026sup3;/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e15.2 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e15.9 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e14.9 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e2.7 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.6 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.8 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e2.9 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.0 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2.8 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e113.3 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e115.4 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e112.4 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e60.5 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e59.2 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e61.1 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e9.6 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7.9 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e10.4 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eCCI, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e3.6 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.7 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 302px;\"\u003e\n \u003cp\u003eAPACHE II score, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e26.7 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e27.2 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e26.5 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 302px;\"\u003e\n \u003cp\u003eSOFA without renal score, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e10.2 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e10.4 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e10.0 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 302px;\"\u003e\n \u003cp\u003eDuration (days), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eCKRT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e7.9 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7.0 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e8.2 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eDialysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e11.7 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e9.4 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e12.7 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eHospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e43.1 (61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e40.9 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e44.1 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eRed cell transfusion volumes, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e3.7 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.4 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3.8 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eVentilator, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2122 (77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e660 (77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1462 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eCKRT setting, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eBlood flow rate (mL/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e108.1 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e110.7 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e107.0 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eDialysate flow rate (mL/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1222.2 (471.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1278.6 (480.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1196.9 (465.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 279px;\"\u003e\n \u003cp\u003eReplacement flow rate (mL/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e989.0 (608.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e997.1 (653.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e985.4 (587.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation: AKI, acute kidney injury; CKRT, continuous kidney replacement therapy; SD, standardized deviation; CCI, Charlson\u0026rsquo;s comorbidity index; APACHE II, acute physiology and chronic health evaluation; SOFA, sequential organ failure assessment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Association between the number of days where the average hemoglobin level was \u0026lt;8.44 g/dL and dialysis dependence\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 365px;\"\u003e\n \u003cp\u003eAdjusted*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003eEvery 1-day increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e1.03 (1.01, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e1.04 (1.01, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation: OR, odds ratio\u003c/p\u003e\n\u003cp\u003e*Adjusted for sex, age, Charlson comorbidity index, body mass index, hypertension, sepsis, white blood cell count, albumin, creatinine, systolic blood pressure, diastolic blood pressure, sequential organ failure assessment (excluding the renal component), transfusion amount, and duration of continuous kidney replacement therapy.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anemia, Complete blood count, Dialysis, Multicenter study, Retrospective study","lastPublishedDoi":"10.21203/rs.3.rs-6508688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6508688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAnemia in patients with acute kidney injury (AKI) requiring continuous kidney replacement therapy (CKRT) significantly contributes to increased mortality and morbidity. However, its impact on dialysis dependence remains unclear. This study explored the association between anemia and dialysis dependence in patients with severe AKI undergoing CKRT.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective cohort study, we included 2755 patients with AKI who underwent CKRT at four medical centers between 2006 and 2021. The primary exposure was the average hemoglobin (Hb) level during CKRT, with patients categorized into anemic (Hb\u0026thinsp;\u0026lt;\u0026thinsp;8.44 g/dL) and control (Hb\u0026thinsp;\u0026gt;\u0026thinsp;8.44 g/dL) groups. Dialysis dependence was defined at the time of hospital discharge. The odds ratio for dialysis dependence in the anemic group was calculated by adjusting for demographics and laboratory data. The impact of the duration of anemia was also assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 61.4% of patients were males, with a mean age of 65.5 years. The average duration of CKRT was 7.9 d, and 64.7% of the patients were dialysis-dependent at hospital discharge. A U-shaped relationship was found between Hb levels and dialysis dependence, with 8.44 g/dL as the critical threshold. Patients in the anemia group had a 57% increased risk of dialysis dependence, particularly among women, those under 65 years, patients with non-septic AKI, and those with lower Charlson comorbidity index scores. Each additional day of anemia increased the risk by 4%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHb levels\u0026thinsp;\u0026lt;\u0026thinsp;8.44 g/dL during CKRT were associated with increased dialysis dependence. These findings highlight the significance of Hb thresholds for improving kidney recovery outcomes.\u003c/p\u003e","manuscriptTitle":"Association between hemoglobin levels and dialysis dependence in patients with acute kidney injury requiring continuous kidney replacement therapy: a multicenter retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:30:47","doi":"10.21203/rs.3.rs-6508688/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":"f64d0865-6a19-4dce-a4b4-e9e80af8b3b8","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-25T16:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 06:30:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6508688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6508688","identity":"rs-6508688","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}