Sage Journals: Discover world-class research

Abstract

Objective

To investigate the prognostic value of red cell distribution width-to-albumin ratio (RAR) in sepsis patients with malignancies and evaluate its association with in-hospital mortality.

Methods

The retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Participants were stratified into quartiles (Q1–Q4) based on RAR levels measured within 24 hours of intensive care unit admission. The primary outcome was 28-day in-hospital all-cause mortality. Associations between RAR and clinical outcomes were assessed using Kaplan–Meier survival analysis, multivariate Cox proportional hazards regression and restricted cubic spline (RCS) modeling. Predictive performance was further evaluated through receiver operating characteristic (ROC) analysis and decision curve analysis (DCA).

Results

A total of 1686 eligible patients were included. Multivariate Cox regression revealed a significant positive association between RAR and 28-day in-hospital mortality (adjusted model 3 HR = 1.12, 95% CI: 1.08–1.16, P < 0.001). Kaplan–Meier curves demonstrated a stepwise decline in survival with increasing RAR levels (28-day survival rate of 75.26% in Q1 vs. 49.88% in Q4, P < 0.001). RCS analysis identified a linear relationship between RAR and the hazard ratios for 28-day in-hospital mortality (P for nonlinearity > 0.05). ROC analysis showed that RAR (area under the curve = 0.624) yielded better prognostic utility than red blood cell distribution width and sequential organ failure assessment, with significant differences confirmed by DeLong's test. RAR also exhibited the best calibration (slope = 1.0109; Brier score = 0.2166) and the highest net clinical benefit in DCA (22%–95%; maximum = 0.162). Subgroup analysis showed consistent results across all groups (P for interaction > 0.05).

Conclusions

RAR may serve as an independent prognostic indicator in septic patients with malignancies. Its elevation is significantly associated with increased mortality risk and may aid in the early identification of high-risk individuals and the implementation of targeted interventions. Further prospective, multicenter studies are warranted to validate its clinical applicability and dynamic monitoring potential.

Keywords

Red cell distribution width-to-albumin ratio sepsis malignancy MIMIC-IV database 28-day mortality

Introduction

Sepsis, a life-threatening clinical disorder characterized by systemic organ dysfunction, is precipitated by a dysregulated host immune reaction to invasive infection.¹ The incidence of sepsis remains alarmingly high in intensive care units (ICUs), where it contributes to over half of all ICU-related fatalities.² Besides, sepsis represents a substantial global health challenge and imposes considerable economic burdens worldwide.^3–5 An increased susceptibility to sepsis and elevated mortality risk have been demonstrated in patients with cancer, attributable to immune suppression induced by malignancies or therapeutic interventions such as chemotherapy and radiotherapy.^6–8 Despite the widespread application of conventional scoring systems such as sequential organ failure assessment (SOFA) or simplified acute physiology score II (SAPS II), their effectiveness in reflecting the complex immunological and metabolic perturbations in septic patients with malignancies remains limited.

Red blood cell distribution width (RDW), a laboratory parameter reflecting heterogeneity in erythrocyte volume, has demonstrated utility both in tracking disease activity across autoimmune disorders and as a powerful prognostic indicator of all-cause mortality among intensive care populations.^9–11 In patients with malignancies, anemia commonly arises as the clinical manifestation of multiple pathophysiological processes, including chronic inflammation, nutritional deficiencies, bone marrow suppression, and impaired iron metabolism.^12,13 These disturbances lead to ineffective erythropoiesis and greater heterogeneity in red blood cell morphology, which is reflected by elevated RDW. Albumin, synthesized within liver cells, is recognized as a negative acute phase response marker and serves as an indicator of both nutritional reserves and degree of systemic inflammation.¹⁴ Recent evidence has established a correlation between hypoalbuminemia and poor clinical outcomes in several disease cohorts.^15–17 The RDW-to-albumin ratio (RAR) could offer an integrative index that not only reflects nutritional status and inflammatory burden but also captures underlying immune dysregulation. This multifaceted measure reflects the complex interplay among metabolic, inflammatory, and immunological pathways driving disease progression, thereby emerging as a promising prognostic biomarker with validated utility across a wide range of clinical conditions.^18–21

Nevertheless, the prognostic significance of RAR among septic patients with malignancies has yet to be fully clarified. Accordingly, aim of the study was designed to systematically evaluate the potential relevance between RAR and critical clinical endpoints.

Materials and methods

Data source

This retrospective cohort study utilized data obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV, Version 3.1) database, which comprises de-identified clinical records of over 90,000 individuals hospitalized in ICUs at Beth Israel Deaconess Medical Center (Boston, MA, USA) over the period from 2008 to 2022. Ethics authorization for database utilization was granted by the Institutional Review Boards of both Massachusetts Institute of Technology (Cambridge, MA, USA) and Beth Israel Deaconess Medical Center. In compliance with patient confidentiality protocols, all personally identifiable health information underwent rigorous anonymization procedures. All methodologies adhered to pertinent guidelines and regulations (the Helsinki Declaration of 1975 as revised in 2024). Database accessibility is strictly regulated to researchers who have demonstrated mandatory completion of the Collaborative Institutional Training Initiative (CITI) certification program. Access to the database was granted to Yuhui Pan upon the successful completion of the requisite online training and evaluation procedures, as substantiated by certification number 69440345. We structured and presented this study following the guidance provided by the EQUATOR network.²²

Study population

The inclusion criteria were defined as follows: (1) individuals age ≥ 18, (2) patients fulfilling Sepsis-3 diagnostic criteria as established by the Third International Consensus on Sepsis Definitions, (3) patients with a confirmed diagnosis of malignancy. The exclusion criteria included: (1) an ICU stay of less than 24 hours, (2) repeat ICU admissions—with only the first admission being considered, (3) incomplete or missing data, and (4) absence of a malignancy diagnosis. The patient's selection workflow is illustrated in Figure 1.

Figure 1.

Flow chart of the study population.

Extraction of demographical and laboratory variables

Clinical information of enrolled patients was acquired from the MIMIC-IV database through structured query language (SQL). The gathered variables encompassed demographic characteristics (age, body mass index (BMI), etc.), physiological parameters (temperature, heart rate, etc.), laboratory indicators (RDW, albumin, blood urea nitrogen, serum potassium, etc.), comorbidities (congestive heart failure, metastatic solid tumor, etc.) and clinical severity scores (SOFA, SAPS II, etc.). All variables retrieved were derived from clinical information recorded during the initial 24 hours subsequent to ICU admission. RAR and BMI were calculated as RDW (%) / albumin (g/dL) and weight (kg)/height squared (m²), respectively.

Clinical outcomes

The primary outcome was 28-day all-cause in-hospital mortality, while secondary endpoints included in-hospital mortality at 90, 180, and 365 days.

Statistical analysis

All statistical analyses were conducted utilizing R software (version 4.1.2). Continuous variables were presented as mean ± standard deviation (SD) in cases of normal distribution, or alternatively as median (interquartile range). Categorical variables were summarized in terms of frequencies and percentages. For group comparisons, continuous variables were analyzed utilizing the Mann–Whitney U test or Kruskal–Wallis H test. Categorical data were assessed by means of the Pearson chi-square test. To evaluate the underlying links between RAR and 28-day mortality, multivariate Cox proportional hazards regression models were employed using the survival R package to compute hazard ratios (HRs) with 95% confidence intervals (CIs). Three models were constructed: model 1 was unadjusted, while model 2 was adjusted for age, gender, and BMI; and model 3 included additional adjustments for age, gender, BMI, heart rate, SBP, respiratory rate, urine output, SpO₂, SOFA, metastatic solid tumor, sodium, potassium, magnesium, chloride, creatinine, and lactate. To explore the potential nonlinear correlation between RAR and all-cause mortality, a restricted cubic spline (RCS) regression model was implemented with the rms package in R. Kaplan–Meier (KM) survival curves were constructed to assess differences in survival probabilities among multiple RAR categories, employing the survival and survminer packages in R. Receiver operating characteristic (ROC) curve analysis with area under the curve (AUC) calculation and DeLong test were performed through the pROC R package to examine the predictive validity of SOFA scores, RDW, and RAR regarding 28-day mortality risk. The optimal cut-off value for the ROC was determined using the Youden index. Model calibration was assessed by calculating the calibration slope and Brier score utilizing rms and survival (brier function) R package, respectively. Decision curve analysis (DCA) was performed using the ggDCA package to assess clinical usefulness. To assess the robustness of the RAR index as a prognostic indicator for the primary outcome, stratified analyses were performed across subgroups based on gender, age (<65 and ≥65 years), congestive heart failure, myocardial infarction, hypertension, and metastatic solid tumors. Interactions between the RAR and stratification variables were tested using likelihood ratio methods. Statistical significance was defined as P-value < 0.05.

Results

Baseline characteristics

Clinical data from a total of 1686 patients who fulfilled the predefined inclusion and exclusion criteria were extracted from the MIMIC-IV database. The baseline clinical profiles of septic patients with malignancies stratified by RAR quartiles were summarized in Table 1. Study subjects were classified into four groups according to RAR measured at the time of ICU admission (quartile Q1: 2.826–4.786; Q2: 4.788–5.818; Q3: 5.818–7.240; Q4: 7.240–19.556). Among these patients, 1034 (61.3%) were male. The comorbidities comprised congestive heart failure in 386 patients (22.9%), paraplegia in 63 patients (3.7%), renal disease in 359 patients (21.3%), metastatic solid tumor in 613 patients (36.4%), severe liver disease in 240 patients (14.2%), diabetes in 488 patients (28.9%), chronic pulmonary disease in 423 patients (25.1%), atrial fibrillation in 310 patients (18.4%), and myocardial infarction in 193 patients (11.4%). Patients in Q4 had higher heart rates, respiratory rates, prothrombin time (PT), partial thromboplastin time (PTT), blood urea nitrogen (BUN), neutrophils, RDW, serum lactate, SOFA scores, Acute Physiology Score III (APSIII), and Oxford Acute Severity of Illness Score (OASIS) compared to the other groups. Mortality rates (in-hospital 28-day mortality, in-hospital 90-day mortality, in-hospital 180-day mortality, and in-hospital 365-day mortality) were also higher in Q4 group.

Table 1.

Characteristics and outcomes of enrolled patients categorized by RAR.

Characteristic	Total (n = 1686)	Q1 (n = 421)	Q2 (n = 422)	Q3 (n = 422)	Q4 (n = 421)	P-value
Age (years)	68.2 (60.6,77.5)	67.8 (60.8,77.3)	68.9 (60.9,77.8)	67.9 (59.6,77.0)	68.6 (60.8,77.7)	0.843
BMI (kg/m²)	26.6 (22.9,31.3)	27.5 (23.2,32.3)	27.4 (24.3,31.2)	26.2 (22.7,31.1)	26.0 (21.9,30.4)	0.014
Gender: male (n, %)	1034 (61.3)	276 (65.6)	259 (61.4)	253 (60.0)	246 (58.4)	0.172
Heart rate (rpm)	96.0 (82.0,111.0)	92.0 (79.0,106.0)	95.0 (82.3,112.8)	98.0 (83.0,113.0)	99.0 (83.5,113.0)	<0.001
Respiratory rate (rpm)	20.0 (16.0,25.0)	20.0 (16.0,25.0)	20.0 (16.0,24.0)	20.0 (17.0,25.0)	21.0 (17.0,25.0)	0.263
Temperature (°C)	36.8 (36.5,37.2)	36.8 (36.5,37.3)	36.9 (36.6,37.2)	36.8 (36.5,37.2)	36.7 (36.4,37.0)	<0.001
SBP (mmHg)	117.0 (102.0,135.0)	124.0 (108.0,143.0)	117.5 (102.0,136.0)	117.0 (102.0,134.0)	109.0 (95.5,128.5)	<0.001
DBP (mmHg)	65.0 (55.0,77.0)	68.0 (58.0,80.5)	65.0 (55.0,76.0)	63.0 (55.0,75.0)	64.0 (54.0,74.5)	<0.001
MBP (mmHg)	79.0 (68.0,92.0)	84.0 (73.0,97.5)	79.5 (70.0,91.0)	77.0 (67.0,90.0)	75.0 (65.0,88.0)	<0.001
Urine output (mL)	1320.0 (709.0,2236.3)	1565.0 (860.0,2432.5)	1340.0 (714.5,2410.0)	1312.5 (700.0,2241.3)	1055.0 (583.5,1860.0)	<0.001
SpO₂ (%)	97.0 (94.0,100.0)	98.0 (95.0,100.0)	98.0 (95.0,100.0)	97.0 (94.0,100.0)	97.0 (94.0,100.0)	0.131
SOFA	4.0 (2.0,5.0)	3.0 (2.0,4.0)	3.0 (2.0,5.0)	4.0 (2.0,5.0)	4.0 (3.0,6.0)	<0.001
SAPS II	47.0 (37.0,58.0)	42.0 (34.0,51.0)	45.0 (37.0,55.0)	48.0 (39.0,58.0)	53.0 (42.0,65.0)	<0.001
APS III	55.0 (42.0,71.0)	46.0 (34.0,57.0)	52.0 (41.0,66.0)	58.0 (44.8,71.0)	67.0 (52.0,88.0)	<0.001
OASIS	35.0 (29.0,41.0)	32.0 (28.0,38.5)	35.0 (29.0,40.0)	35.5 (29.0,41.0)	37.0 (31.0,44.0)	<0.001
GCS	15.0 (14.4,15.0)	15.0 (14.4,15.0)	15.0 (14.6,15.0)	15.0 (14.5,15.0)	15.0 (14.2,15.0)	0.040
Comorbidities
Congestive heart failure (n, %)	386 (22.9)	87 (20.7)	112 (26.5)	102 (24.2)	85 (20.2)	0.090
Paraplegia (n, %)	63 (3.7)	28 (6.7)	18 (4.3)	6 (1.4)	11 (2.6)	<0.001
Renal disease (n, %)	359 (21.3)	72 (17.1)	100 (23.7)	96 (22.7)	91 (21.6)	0.093
Metastatic solid tumor (n, %)	613 (36.4)	113 (26.8)	145 (34.4)	161 (38.2)	194 (46.1)	<0.001
Severe liver disease (n, %)	240 (14.2)	50 (11.9)	51 (12.1)	70 (16.6)	69 (16.4)	0.071
Diabetes (n, %)	488 (28.9)	110 (26.1)	139 (32.9)	123 (29.1)	116 (27.6)	0.151
Chronic pulmonary disease (n, %)	423 (25.1)	118 (28.0)	119 (28.2)	103 (24.4)	83 (19.7)	0.014
Atrial fibrillation (n, %)	310 (18.4)	69 (16.4)	92 (21.8)	81 (19.2)	68 (16.2)	0.113
Myocardial infarct (n, %)	193 (11.4)	54 (12.8)	56 (13.3)	48 (11.4)	35 (8.3)	0.100
Obesity (n, %)	152 (9.0)	42 (10.0)	42 (10.0)	34 (8.1)	34 (8.1)	0.604
Hypertension (n, %)	1022 (60.6)	262 (62.2)	271 (64.2)	250 (59.2)	239 (56.8)	0.127
RBC (×10¹²/L)	3.2 (2.7,3.8)	3.7 (3.1,4.2)	3.3 (2.8,3.8)	3.1 (2.7,3.7)	2.9 (2.5,3.5)	<0.001
WBC (×10⁹/L)	10.7 (6.0,17.0)	10.6 (6.5,15.3)	9.8 (5.5,16.4)	11.0 (6.2,17.8)	11.2 (6.0,19.6)	0.158
Platelet (×10⁹/L)	162.0 (91.5,270.5)	172.0 (111.0,256.0)	157.0 (84.8,270.0)	163.5 (91.0,272.3)	151.0 (71.5,281.5)	0.202
PT (s)	15.4 (13.5,18.5)	14.0 (12.5,16.5)	15.1 (13.1,17.1)	15.8 (14.1,19.3)	17.0 (14.6,20.5)	<0.001
PTT (s)	32.1 (27.7,39.0)	30.4 (26.7,35.9)	30.9 (27.0,36.6)	32.2 (27.9,40.2)	34.4 (30.0,44.0)	<0.001
Glu (mg/dL)	133.0 (106.3,178.0)	142.0 (111.0,182.0)	143.0 (115.8,193.3)	133.0 (107.0,170.3)	119.0 (94.0,157.5)	<0.001
RDW (%)	16.0 (14.6,18.1)	14.1 (13.3,15.1)	15.5 (14.5,16.8)	16.8 (15.6,18.4)	18.6(17.1,20.8)	<0.001
Hemoglobin (g/dL)	9.6 (8.3,11.3)	11.2 (9.6,12.6)	9.8 (8.6,11.3)	9.3 (8.2,10.7)	8.7 (7.6,9.9)	<0.001
Albumin (g/dL)	2.8 (2.4,3.2)	3.4 (3.2,3.7)	2.9 (2.7,3.2)	2.6 (2.4,2.9)	2.1 (1.9,2.4)	<0.001
Globulin (g/dL)	2.5 (2.0,3.2)	2.6 (2.1,3.2)	2.6 (1.9,3.4)	2.3 (1.7,3.0)	2.7 (1.9,4.1)	0.638
AST (U/L)	47.5 (24.0,133.3)	44.5 (23.0,128.5)	46.0 (24.5,158.5)	44.0 (24.0,118.3)	55.5 (24.0,144.0)	0.730
ALT (U/L)	33.0 (16.0,92.0)	33.0 (16.0,101.5)	34.0 (15.0,112.3)	32.0 (17.0,80.0)	32.0 (15.0,80.5)	0.414
BUN (mg/dL)	23.0 (15.0,40.0)	20.0 (14.0,31.0)	22.0 (15.0,39.0)	25.0 (16.0,43.0)	27.0 (16.0,49.0)	<0.001
Scr (mg/dL)	1.1 (0.8,1.8)	1.0 (0.8,1.5)	1.1 (0.8,1.7)	1.2 (0.8,1.8)	1.1 (0.8,1.9)	0.282
HDL (mg/dL)	31.5 (23.0,41.8)	41.0 (30.5,52.0)	29.5 (23.3,36.8)	24.0 (16.0,32.0)	25.5 (18.0,33.3)	<0.001
LDL (mg/dL)	55.0 (20.8,90.5)	83.5 (57.0,110.0)	84.0 (84.0,84.0)	53.0 (53.0,53.0)	19.5 (17.0,22.0)	0.232
TG (mg/dL)	125.0 (86.0,182.0)	122.0 (85.5,163.5)	145.0 (96.3,194.5)	106.0 (86.0,183.0)	128.0 (80.5,186.8)	0.239
Sodium (mmol/L)	138.0 (134.0,141.0)	138.0 (135.0,141.0)	138.0 (135.0,141.0)	137.0 (134.0,140.0)	137.0 (133.0,140.0)	0.001
Potassium (mmol/L)	4.2 (3.7,4.7)	4.1 (3.7,4.5)	4.3 (3.8,4.7)	4.2 (3.7,4.8)	4.1 (3.7,4.8)	0.029
Calcium (mmol/L)	8.2 (7.6,8.8)	8.6 (8.2,9.0)	8.2 (7.7,8.8)	8.1 (7.6,8.6)	7.7 (7.2,8.3)	<0.001
Magnesium (mmol/L)	1.9 (1.7,2.2)	1.9 (1.7,2.2)	1.9 (1.7,2.1)	1.9 (1.7,2.2)	1.9 (1.6,2.2)	0.798
Chloride (mmol/L)	103.0 (99.0,107.0)	103.0 (99.0,106.0)	103.0 (99.0,106.0)	103.0 (99.0,107.0)	103.0 (99.0,109.0)	0.060
Lactate (mmol/L)	2.0 (1.3,3.3)	1.8 (1.2,2.9)	1.8 (1.2,3.2)	1.9 (1.4,3.1)	2.4 (1.6,4.2)	<0.001
Neutrophils (10⁹/L)	8.7(3.7,15.3)	8.3 (3.9,12.5)	7.4 (3.5,13.3)	9.1(3.1,16.3)	9.4(4.2,17.2)	0.020
Monocytes (10⁹/L)	0.5 (0.2,0.9)	0.5 (0.2,0.9)	0.4 (0.2,0.8)	0.5(0.2,1.0)	0.5(0.2,1.1)	0.312
Lymphocytes (10⁹/L)	0.7 (0.4,1.4)	0.9 (0.5,1.5)	0.7 (0.4,1.4)	0.7 (0.4,1.3)	0.7(0.3,1.3)	0.012
Events
28d hospital mortality (n, %)	584 (34.6)	103 (24.5)	124 (29.4)	146 (34.6)	211 (50.1)	<0.001
90d hospital mortality (n, %)	830 (49.2)	147 (34.9)	180 (42.7)	224 (53.1)	279 (66.3)	<0.001
180d hospital mortality (n, %)	940 (55.8)	169 (40.1)	208 (49.3)	254 (60.2)	309 (73.4)	<0.001
365d hospital mortality (n, %)	1066 (63.2)	198 (47.0)	246 (58.3)	284 (67.3)	338 (80.3)	<0.001

RAR Q1: 2.826–4.786; Q2: 4.788–5.818; Q3: 5.818–7.240; Q4: 7.240–19.556.

ALT: alanine aminotransferase; APSIII: Acute Physiology Score III; AST: aspartate aminotransferase; BMI: body mass index; BUN: blood urea nitrogen; DBP: diastolic blood pressure; GCS: Glasgow Coma Scale; Glu: glucose; HDL: high-density lipoprotein; LDL: low-density lipoprotein; MBP: mean blood pressure; OASIS: Oxford Acute Severity of Illness Score; PT: prothrombin time; PTT: partial thromboplastin time; RAR: red cell distribution width-to-albumin ratio; RBC: red blood cell; RDW: red cell distribution width; SAPS II: simplified acute physiology score II; SBP: systolic blood pressure; Scr: serum creatinine; SOFA: sequential organ failure assessment; TG: triglyceride; WBC: white blood cell.

Baseline characteristic differences between survivor group and nonsurvivor group were presented in Table 2. Individuals in nonsurvivor cohort were more frequently male and exhibited elevated levels of heart rates, respiratory rates, PT, PTT, BUN, serum potassium, low-density lipoprotein (LDH), white blood cell (WBC), neutrophils, RDW, serum lactate, triglyceride (TG), LDL as well as higher illness severity scores. RAR levels in nonsurvivor group were markedly elevated compared to those observed in survivor group (6.4 vs. 5.6, P ˂ 0.001).

Table 2.

Baseline characteristics of the survivors and nonsurvivors groups.

Characteristic	Total (n = 1686)	Survivors (n = 1102)	Nonsurvivors (n = 584)	P-value
Age (years)	68.2 (60.6,77.5)	67.1 (59.8.76.1)	70.9 (62.9.80.3)	<0.001
BMI (kg/m²)	26.6 (22.9,31.3)	27.1 (23.5,31.8)	26.0 (22.1,30.5)	0.007
Gender: male (n, %)	1034 (61.3)	666 (60.4)	368 (63.0)	0.326
Heart rate (rpm)	96.0 (82.0,111.0)	94.0 (80.0,110.0)	99.0 (85.0,113.0)	<0.001
Respiratory rate (rpm)	20.0 (16.0,25.0)	19.0 (16.0,24.0)	22.0 (18.0,26.0)	<0.001
Temperature (°C)	36.8 (36.5,37.2)	36.8 (36.5,37.2)	36.7 (36.4,37.1)	<0.001
SBP (mmHg)	117.0 (102.0,135.0)	119.0 (102.0,137.0)	115.0 (101.0,132.0)	0.009
DBP (mmHg)	65.0 (55.0,77.0)	64.5 (55.0,76.0)	66.0 (56.0,78.0)	0.162
MBP (mmHg)	79.0 (68.0,92.0)	79.0 (68.0,92.0)	79.0 (68.0,92.0)	0.555
Urine output (mL)	1320.0 (709.0,2236.3)	1555.0 (850.0,2485.0)	986.0 (513.0,1635.0)	<0.001
SpO₂ (%)	97.0 (94.0,100.0)	98.0 (95.0,100.0)	97.0 (94.0,99.0)	<0.001
SOFA	4.0 (2.0,5.0)	3.0 (2.0,5.0)	4.0 (3.0,5.0)	<0.001
SAPS II	47.0 (37.0,58.0)	43.0 (35.0,54.0)	54.0 (44.0,64.8)	<0.001
APS III	55.0 (42.0,71.0)	50.0 (39.0,63.0)	66.0 (52.0,84.0)	<0.001
OASIS	35.0 (29.0,41.0)	33.0 (28.0,39.0)	38.0 (32.0,44.8)	<0.001
GCS	15.0 (14.4,15.0)	15.0 (14.6,15.0)	15.0 (14.0,15.0)	<0.001
Comorbidities
Congestive heart failure (n, %)	386 (22.9)	234 (21.2)	152 (26.0)	0.030
Paraplegia (n, %)	63 (3.7)	38 (3.4)	25 (4.3)	0.470
Renal disease (n, %)	359 (21.3)	225 (20.4)	134 (22.9)	0.253
Metastatic solid tumor (n, %)	613 (36.4)	337 (30.6)	276 (47.3)	<0.001
Severe liver disease (n, %)	240 (14.2)	151 (13.7)	89 (15.2)	0.432
Diabetes (n, %)	488 (28.9)	334 (30.3)	154 (26.4)	0.101
Chronic pulmonary disease (n, %)	423 (25.1)	262 (23.8)	161 (27.6)	0.099
Atrial fibrillation (n, %)	310 (18.4)	192 (17.4)	118 (20.2)	0.181
Myocardial infarct (n, %)	193 (11.4)	112 (10.2)	81 (13.9)	0.028
Obesity (n, %)	152 (9.0)	114 (10.3)	38 (6.5)	0.011
Hypertension (n, %)	1022 (60.6)	664 (60.3)	358 (61.3)	0.714
RBC (×10¹²/L)	3.2 (2.7,3.8)	3.3 (2.7,3.9)	3.2 (2.7,3.8)	0.231
WBC (×10⁹/L)	10.7 (6.0,17.0)	10.3 (5.8,16.4)	11.3 (6.5,18.8)	0.005
Platelet (×10⁹/L)	162.0 (91.5,270.5)	163.5 (95.0,273.0)	156.0 (81.0,264.0)	0.116
PT (s)	15.4 (13.5,18.5)	14.9 (13.2,17.7)	16.4 (14.1,20.5)	<0.001
PTT (s)	32.1 (27.7,39.0)	31.2 (27.4,37.4)	33.4 (28.6,43.7)	<0.001
Glu (mg/dL)	133.0 (106.3,178.0)	137.0 (110.0,182.0)	129.0 (100.0,166.8)	<0.001
RDW(%)	16.0 (14.6,18.1)	15.7 (14.3,17.7)	16.7 (15.0,18.9)	<0.001
Hemoglobin (g/dL)	9.6 (8.3,11.3)	9.6 (8.4,11.4)	9.5 (8.2,11.2)	0.100
Albumin (g/dL)	2.8 (2.4,3.2)	2.9 (2.5,3.3)	2.6 (2.2,3.1)	<0.001
Globulin (g/dL)	2.5 (2.0,3.2)	2.5 (2.0,3.2)	2.6 (1.9,3.2)	0.882
AST (U/L)	47.5 (24.0,133.3)	45.0 (23.0,127.0)	54.0 (26.0,149.0)	0.050
ALT (U/L)	33.0 (16.0,92.0)	32.0 (16.0,97.3)	33.0 (16.0,87.0)	0.693
BUN (mg/dL)	23.0 (15.0,40.0)	21.0 (14.0,33.0)	32.0 (18.0,52.0)	<0.001
Scr (mg/dL)	1.1 (0.8,1.8)	1.0 (0.7,1.6)	1.3 (0.8,2.0)	<0.001
HDL (mg/dL)	31.5 (23.0,41.8)	30.0 (23.0,41.3)	35.5 (23.3,43.3)	0.400
LDL (mg/dL)	55.0 (20.8,90.5)	55.0 (26.0,96.8)	53.0 (22.0,84.0)	0.106
TG (mg/dL)	125.0 (86.0,182.0)	117.5 (81.8,161.5)	130.0 (97.0,202.5)	0.044
Sodium (mmol/L)	138.0 (134.0,141.0)	138.0 (134.0,141.0)	137.0 (133.0,141.0)	<0.001
Potassium (mmol/L)	4.2(3.7,4.7)	4.1(3.7,4.6)	4.3(3.8,4.9)	<0.001
Calcium (mmol/L)	8.2 (7.6,8.8)	8.2 (7.6,8.8)	8.1 (7.5,8.7)	0.055
Magnesium (mmol/L)	1.9 (1.7,2.2)	1.9 (1.6,2.1)	2.0 (1.7,2.3)	<0.001
Chloride (mmol/L)	103.0 (99.0,107.0)	103.0 (100.0,107.0)	101.5 (98.0,107.0)	<0.001
Lactate (mmol/L)	2.0 (1.3,3.3)	1.8 (1.3,2.9)	2.4 (1.5,4.0)	<0.001
Neutrophils (10⁹/L)	8.7 (3.7,15.3)	8.4 (3.6,14.8)	9.5 (4.0,16.1)	0.131
Monocytes (10⁹/L)	0.5 (0.2,0.9)	0.5 (0.2,0.9)	0.5 (0.2,1.0)	0.330
Lymphocytes (10⁹/L)	0.7 (0.4,1.4)	0.8 (0.4,1.4)	0.7 (0.3,1.3)	0.070
RAR (%/g/dL)	5.8 (4.8,7.2)	5.6 (4.6,6.8)	6.4 (5.2,8.1)	<0.001

Clinical outcomes

The incidence of clinical endpoints across RAR quartiles groups was assessed utilizing KM survival curves, as depicted in Figure 2. KM analysis of 28-day in-hospital survival probabilities among septic patients with malignancies demonstrated a graded survival trend with statistical significance (P < 0.001), whereby patients in the lowest RAR quartile (Q1) exhibited the most favorable outcomes and progressive mortality risk elevation identified across successively higher quartiles, culminating in the poorest survival rates within the highest quartile (Q4). By day 28 of hospitalization, patients in the Q1 quartile exhibited a survival rate of 75.26%, whereas those in the Q4 quartile showed a markedly lower rate of 49.88%. Parallel survival trends were detected when constructing KM survival curves at 90-day, 180-day, and 365-day in-hospital mortality.

Figure 2.

Kaplan–Meier survival analysis curves for all-cause mortality. Kaplan–Meier curves showing cumulative probability of all-cause mortality according to groups at 28 days (a), 90 days (b), 180 days (c), and 365 days (d).

As detailed in Table 3, the correlation of RAR with in-hospital mortality was examined through Cox proportional hazards regression. When considered as a continuous variable, RAR emerged as a significant independent predictor across all models: model 1 (HR = 1.18, 95% CI: 1.15–1.22, P ˂ 0.001), model 2 (HR = 1.15, 95% CI: 1.10–1.20, P ˂ 0.001), and model 3 (HR = 1.12, 95% CI: 1.08–1.16, P ˂ 0.001). Moreover, when RAR was stratified into quartiles in model 1, the HR for Q2, Q3, and Q4, relative to Q1, were 1.30 (95% CI: 1.00–1.67, P= 0.047), 1.58 (95% CI: 1.23–2.02, P < 0.001), and 2.65 (95% CI: 2.10–3.34, P < 0.001), respectively. This upward trend persisted even after adjustment for multiple covariates—including age, gender, BMI, heart rate, SBP, respiratory rate, urine output, SpO₂, SOFA, metastatic solid tumor, sodium, potassium, magnesium, chloride, creatinine, and lactate—demonstrating that higher RAR quartiles were associated with a progressively increased 28-day mortality.

Table 3.

Cox proportional hazard ratios (HR) for all-cause mortality.

Groups	Model 1 HR (95% CI)	p-Value	Model 2 HR (95% CI)	p-Value	Model 3 HR (95% CI)	P-value
RAR (continuous variable per unit)	1.18 [1.15,1.22]	<0.001	1.15 [1.10,1.20]	<0.001	1.12 [1.08,1.16]	<0.001
Q1	Ref		Ref		Ref
Q2	1.30[1.00,1.67]	0.047	1.30[1.00,1.67]	0.047	1.15 [0.88,1.50]	0.300
Q3	1.58 [1.23,2.02]	<0.001	1.60[1.25,2.05]	<0.001	1.33 [1.03,1.73]	0.031
Q4	2.65 [2.10,3.34]	<0.001	2.71 [2.15,3.41]	<0.001	1.85 [1.43,2.39]	<0.001

Model 1 was unadjusted.

Model 2 was adjusted for age, gender and BMI.

Model 3 was adjusted for age, gender, BMI, heart rate, SBP, respiratory rate, urine output, SpO₂, SOFA, metastatic solid tumor, sodium, potassium, magnesium, chloride, creatinine, and lactate.

BMI: body mass index; SBP: systolic blood pressure; SOFA: sequential organ failure assessment.

RCS regression model

After adjustment for the aforementioned covariates, a statistically significant linear dose–response relationship was identified between RAR and the hazard ratios for 28-day in-hospital mortality (P for nonlinearity > 0.05, Figure 3).

Figure 3.

Restricted cubic spline curve of RAR hazard ratio for 28-days in-hospital mortality. Heavy central lines represent the estimated adjusted hazard ratios, with shaded ribbons denoting 95% confidence intervals. The horizontal dotted lines represent the hazard ratio of 1.0. RAR: red cell distribution width-to-albumin ratio.

Comprehensive evaluation of prognosis model

As depicted in Figure 4(a), the optimal cut-off value of RAR for predicting 28-day in-hospital mortality was determined to be 6.289 based on Youden index. The AUC for RAR was calculated as 0.624 (95% CI: 0.596–0.652), which exceeded the predictive performance of SOFA [0.561 (95% CI: 0.533–0.590)] and RDW [0.600 (95% CI: 0.572–0.629)]. DeLong's test confirmed the statistical significance of these differences (RAR vs. SOFA: P ˂ 0.001; RAR vs. RDW: P = 0.045; RDW vs. SOFA: P = 0.034). In terms of calibration, RAR also demonstrated the most favorable calibration slope (1.0109) and the lowest Brier score (0.2166), compared with RDW (1.0212, 0.2203) and SOFA (1.1486, 0.2243), indicating better agreement between predicted probabilities and observed outcomes. Furthermore, DCA was conducted to assess the clinical utility of the models across a range of threshold probabilities (Figure 4(b)). RAR exhibited a broader range of net benefit (22%–95%) and the highest maximum net benefit (0.162), exhibiting better performance than RDW (23%–87%; maximum net benefit = 0.154) and SOFA (32%–72%; maximum net benefit = 0.050).

Figure 4.

Evaluation of clinical prognostic performance of RAR in predicting 28-day in-hospital mortality among septic patients with malignancies. (a) ROC curves of RAR, RDW, and SOFA for predicting 28-day all-cause in-hospital mortality in septic patients with malignancies. (b) DCA of RAR, RDW, and SOFA for predicting 28-day all-cause in-hospital mortality in septic patients with malignancies. DCA: decision curve analysis; RAR: red cell distribution width-to-albumin ratio; RDW: red cell distribution width; ROC: receiver operating characteristic; SOFA: sequential organ failure assessment.

Subgroup analysis

Subgroup analyses identified no statistically significant interaction effects in relation to age, gender, congestive heart failure, myocardial infarction, hypertension, and metastatic solid tumor. The relationship between RAR and 28-day mortality in septic patients with malignancies remained stable across these subgroups (P for interaction > 0.05, Figure 5).

Figure 5.

Forest plots of hazard ratios for the 28-day in-hospital mortality in different subgroups.

Discussion

The coexistence of sepsis and cancer is frequently observed and associated with adverse clinical outcomes.²³ Epidemiological evidence has consistently demonstrated that sepsis persists as a principal contributor to global cancer mortality, despite substantial progress in oncological therapeutic strategies.^24,25 Sepsis may be aggravated by cancer-induced immune dysfunction and dysregulated inflammatory responses, both of which collectively complicate critical care approaches and increase mortality risk. Accordingly, there is an urgent need for the identification of a clinical biomarker capable of reliably predicting the prognosis of septic patients with malignancies.

This study revealed a statistically significant linear association of RAR with 28-day mortality among patients with sepsis and malignancies. Specifically, each unit increment in RAR was correlated with an 13% elevation in 28-day mortality risk (95% CI: 1.07, 1.19). This association remained robust after adjustment for potential confounders, underscoring the independent prognostic value of the RAR index. Furthermore, subgroup analyses revealed no statistically significant interaction effects, and KM analyses confirmed the stability of this association across extended endpoints, including 90-day, 180-day, and 365-day in-hospital mortality.

An expanding range of evidence has highlighted the prognostic utility of RAR across diverse pathological conditions, including chronic obstructive pulmonary disease, chronic kidney disease, heart failure, and sepsis.^19,26–29 RDW, which quantifies heterogeneity in erythrocyte volume distribution, is mechanistically implicated in systemic inflammatory processes and oxidative stress pathways, whereas albumin, characterized as negative acute phase response protein, serves as a marker for both nutritional deprivation and inflammatory dysregulation. Elevated RAR values have been demonstrated to correlate with excess mortality risk in critical care populations.^30,31 Nevertheless, the prognostic significance of RAR in septic patients with malignancies has not been thoroughly investigated. The present study addresses this gap by demonstrating a notable correlation between RAR and mortality in this patient population, thereby reinforcing the clinical relevance of this biomarker in the context of sepsis and cancer comorbidities.

Both malignant neoplasms and sepsis provoke complex and overlapping inflammatory responses that drive a cascade of pathophysiological alterations reflected partially by the RAR. In malignancy, persistent low-grade inflammation—mediated by cytokines such as vascular endothelial growth factor and transforming growth factor β—induces oxidative stress and disrupts iron homeostasis, thereby impairing erythropoiesis and increasing RDW.^32,33 In parallel, tumor-associated inflammation contributes to vascular dysfunction and capillary leakage, further exacerbating hypoalbuminemia.^34,35 Sepsis amplifies these processes through a robust acute-phase response, during which inflammatory mediators suppress hepatic albumin synthesis, enhance vascular permeability, and upregulate endothelial adhesion molecules.³⁶ Beyond these primary pathways, additional mechanisms may also contribute to elevated RAR levels. First, oxidative stress in both cancer and sepsis accelerates erythrocytes turnover and promotes the release of morphologically abnormal red blood cells, resulting in elevated RDW.³⁷ Second, systemic inflammation induces endothelial activation and a prothrombotic state—through pathways such as tissue factor expression and disseminated intravascular coagulation—which may alter albumin distribution and degradation. Third, sustained inflammation increases metabolic demands and promotes protein catabolism, further depleting serum albumin concentrations through Warburg effect.^38,39 Collectively, these interrelated mechanisms underscore RAR as an integrated reflection of inflammation, oxidative stress, and metabolic disruption, driven by sepsis and cancer, capturing a shared pathophysiological axis characterized by increasing erythrocyte heterogeneity and hypoalbuminemia. This convergence of mechanisms provides a biological rationale for the prognostic utility of RAR in septic patients with malignancies.

The present study revealed that RAR exhibits superior predictive accuracy compared to conventional scoring tools (SOFA) in forecasting 28-day in-hospital mortality. The attempt to incorporating RAR into existing ICU scoring models like SOFA or SAPS II may enhance early risk stratification and optimize the allocation of clinical resources. Early detection of elevated RAR (≥6.289) may facilitate timely recognition of patients with increased risk of adverse clinical outcomes. Proactive therapeutic measures, such as stabilizing hemodynamics, enhancement of nutritional support, and vigilant surveillance, could potentially reduce mortality for patients with high risk. Continuous monitoring of RAR level dynamics may provide valuable information regarding disease progression and therapeutic efficacy; for instance, a persistent upward trend may reflect clinical worsening, indicating the necessity for escalated interventions, whereas a downward shift may suggest improvement, thereby supporting decisions to de-escalate care. Accordingly, RAR may function not only as a prognostic biomarker but also a dynamic tool for tracking clinical course.

Limitations

Several limitations warrant careful consideration. Firstly, the retrospective design inherent to this research introduced unavoidable risks of selection and confounding biases. Independent external validation will be required to strengthen and substantiate the conclusions. Secondly, the exclusive reliance on baseline RAR measurements precluded an evaluation of temporal trends, which might have offered additional insight into its prognostic ability. Third, MIMIC-IV database lacked detailed information regarding specific causes of death, thereby restricting the outcome assessment to all-cause mortality. Fourthly, patients with missing RDW and albumin data were excluded, which may have introduced selection bias, thereby potentially limiting the external validity of the results. Finally, the conclusions of this study require further validation through multicenter, prospective investigations conducted with rigorous methodological design.

Conclusions

In conclusion, the present study broadened the applicability of RAR to septic patients with malignancies and indicated that it might serve as a promising marker for risk stratification regarding in-hospital mortality in this population. Ongoing surveillance of RAR may assist in clinical decision making and the optimization of disease management.

Supplemental Material

sj-pdf-1-sci-10.1177_00368504251370990 - Supplemental material for Prognostic value of red cell distribution width-to-albumin ratio in septic patients with malignancies: A retrospective cohort study based on the MIMIC-IV database

Supplemental material, sj-pdf-1-sci-10.1177_00368504251370990 for Prognostic value of red cell distribution width-to-albumin ratio in septic patients with malignancies: A retrospective cohort study based on the MIMIC-IV database by Xingpeng Yang, Yuhui Pan, Pengyue Zhao, Ning Chen, Yizhao Ma, Yichen Bao, Lin Qi and Xiaohui Du in Science Progress

Footnotes

Acknowledgements

The authors sincerely thank the open-access MIMIC-IV database.

ORCID iDs

Xingpeng Yang

Yuhui Pan

Ethical consideration

The present study was conducted in compliance with the principles of the Declaration of Helsinki. Access to the MIMIC-IV database was granted with the approval of the institutional review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. As the database is de-identified and publicly accessible, the need for institutional ethical approval and individual informed consent was waived.

Authors’ contributions

XY and XD contributed to the conception of the study. YP extracted the data from MIMIC database. XY and YP performed the data analysis. PZ and NC assisted with the analysis and results. XY wrote the manuscript. YM, YB, LQ, and XD revised the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (grant number 81871317, 82372158).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental material

Supplemental material for this article is available online.

References

Singer

Deutschman

Seymour

, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 2016; 315: 801–810.

Markwart

Saito

Harder

, et al. Epidemiology and burden of sepsis acquired in hospitals and intensive care units: a systematic review and meta-analysis. Intensive Care Med 2020; 46: 1536–1551.

Darbà

Marsà

. Epidemiology, management and costs of sepsis in Spain (2008–2017): a retrospective multicentre study. Curr Med Res Opin 2020; 36: 1089–1095.

Paoli

Reynolds

Sinha

, et al. Epidemiology and costs of sepsis in the United States-an analysis based on timing of diagnosis and severity level. Crit Care Med 2018; 46: 1889–1897.

Wienholts

Nijssen

Sharabiany

, et al. Economic burden of pelvic sepsis after anastomotic leakage following rectal cancer surgery: a retrospective cost-of-illness analysis. Colorectal Dis 2024; 26: 1922–1930.

AbuSara

Nazer

Hawari

. ICU readmission of patients with cancer: incidence, risk factors and mortality. J Crit Care 2019; 51: 84–87.

Evans

Rhodes

Alhazzani

, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Crit Care Med 2021; 49: e1063–e143.

Angus

Linde-Zwirble

Lidicker

, et al. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001; 29: 1303–1310.

Rodríguez-Carrio

Alperi-López

López

, et al. Red cell distribution width is associated with endothelial progenitor cell depletion and vascular-related mediators in rheumatoid arthritis. Atherosclerosis 2015; 240: 131–136.

10.

Liao

Cao

, et al. Diagnostic value of RDW for the prediction of mortality in adult sepsis patients: a systematic review and meta-analysis. Front Immunol 2022; 13: 997853.

11.

Bazick

Chang

Mahadevappa

, et al. Red cell distribution width and all-cause mortality in critically ill patients. Crit Care Med 2011; 39: 1913–1921.

12.

Weiss

Ganz

Goodnough

. Anemia of inflammation. Blood 2019; 133: 40–50.

13.

Chaber

Helwich

Lauterbach

, et al. Diagnosis and treatment of iron deficiency and iron deficiency anemia in children and adolescents: recommendations of the Polish Pediatric Society, the Polish Society of pediatric oncology and hematology, the Polish Society of neonatology, and the Polish Society of family medicine. Nutrients 2024; 16: 3623.

14.

Sheinenzon

Shehadeh

Michelis

, et al. Serum albumin levels and inflammation. Int J Biol Macromol 2021; 184: 857–862.

15.

Gupta

Lis

. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J 2010; 9: 69.

16.

Arnau-Barrés

Güerri-Fernández

Luque

, et al. Serum albumin is a strong predictor of sepsis outcome in elderly patients. Eur J Clin Microbiol Infect Dis 2019; 38: 743–746.

17.

Qiu

Hao

Huang

, et al. Serum albumin for short-term poor prognosis in patients with acute pulmonary embolism: a clinical study based on a database. Angiology 2025; 76: 458–465.

18.

Long

Liu

, et al. Red blood cell distribution width-to-albumin ratio is associated with all-cause mortality in cancer patients. J Clin Lab Anal 2022; 36: e24423.

19.

Huo

Chen

, et al. Association between red blood cell distribution width to albumin ratio and prognosis of patients with sepsis: a retrospective cohort study. Front Nutr 2022; 9: 1019502.

20.

Wang

, et al. The red blood cell distribution width-albumin ratio: a promising predictor of mortality in heart failure patients—a cohort study. Clin Chim Acta 2022; 527: 38–46.

21.

Xie

, et al. The red blood cell distribution width to albumin ratio was a potential prognostic biomarker for acute respiratory failure: a retrospective study. BMC Med Inform Decis Mak 2024; 24: 253.

22.

von Elm

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007; 147: 573–577.

23.

Danai

Moss

Mannino

, et al. The epidemiology of sepsis in patients with malignancy. Chest 2006; 129: 1432–1440.

24.

, et al. Overall and geographic pattern of incidence, fatality and mortality of sepsis among hospitalized non-child cancer patients in China: a nationwide cross-sectional study. Sci Bull (Beijing) 2024; 69: 1637–1641.

25.

Moore

Akinyemiju

Bartolucci

, et al. A prospective study of cancer survivors and risk of sepsis within the REGARDS cohort. Cancer Epidemiol 2018; 55: 30–38.

26.

Zhou

Tian

Zhai

, et al. Association between red blood cell distribution width-to-albumin ratio and prognosis in non-ischaemic heart failure. ESC Heart Fail 2024; 11: 1110–1120.

27.

Yang

Huang

, et al. Relationship between red blood cell distribution width-to-albumin ratio and outcome of septic patients with atrial fibrillation: a retrospective cohort study. BMC Cardiovasc Disord 2022; 22: 538.

28.

Kimura

Tanaka

Saito

, et al. Impact of red blood cell distribution width-albumin ratio on prognosis of patients with CKD. Sci Rep 2023; 13: 15774.

29.

Qiu

Wang

Shen

, et al. Association between red blood cell distribution width-albumin ratio and hospital mortality in chronic obstructive pulmonary disease patients admitted to the intensive care unit: a retrospective study. Int J Chron Obstruct Pulmon Dis 2022; 17: 1797–1809.

30.

Chen

Guan

Yan

, et al. Prognostic value of red blood cell distribution width-to-albumin ratio in ICU patients with coronary heart disease and diabetes mellitus. Front Endocrinol (Lausanne) 2024; 15: 1359345.

31.

Chen

, et al. Association between the red blood cell distribution width and 30-day mortality in intensive care patients undergoing cardiac surgery: a retrospective observational study based on the medical information mart for intensive care-IV database. Ann Lab Med 2024; 44: 401–409.

32.

Fraenkel

. Anemia of inflammation: a review. Med Clin North Am 2017; 101: 285–296.

33.

Chen

Han

, et al. Hypoxic microenvironment in cancer: molecular mechanisms and therapeutic interventions. Signal Transduct Target Ther 2023; 8: 70.

34.

Viallard

Larrivée

. Tumor angiogenesis and vascular normalization: alternative therapeutic targets. Angiogenesis 2017; 20: 409–426.

35.

Di Castelnuovo

Bonaccio

Costanzo

, et al. The association between hypoalbuminemia and risk of death due to cancer and vascular disease in individuals aged 65 years and older: findings from the prospective Moli-sani cohort study. EClinicalMedicine 2024; 72: 102627.

36.

Kumpers

Gueler

David

, et al. The synthetic tie2 agonist peptide vasculotide protects against vascular leakage and reduces mortality in murine abdominal sepsis. Crit Care 2011; 15: R261.

37.

Gyawali

Richards

Bwititi

, et al. Association of abnormal erythrocyte morphology with oxidative stress and inflammation in metabolic syndrome. Blood Cells Mol Dis 2015; 54: 360–363.

38.

Zhu

Fan

Hou

, et al. Lactylation in tumor microenvironment and immunotherapy resistance: new mechanisms and challenges. Cancer Lett 2025; 627: 217835.

39.

Zhong

Wang

, et al. Warburg effect in colorectal cancer: the emerging roles in tumor microenvironment and therapeutic implications. J Hematol Oncol 2022; 15: 160.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.15 MB