BAR and LAR as mortality predictors in critically ill multiple myeloma patients: a MIMIC-IV study

BAR and LAR as mortality predictors in critically ill multiple myeloma patients: a MIMIC-IV 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 Article BAR and LAR as mortality predictors in critically ill multiple myeloma patients: a MIMIC-IV study Haitao Xu, Caihong Jiang, Dangui Chen, Jia Lu, Lihong Wang, Fei Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9224218/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Validated prognostic tools for critically ill multiple myeloma (MM) patients are lacking. This study evaluated the prognostic value of the blood urea nitrogen to albumin ratio (BAR) and lactate dehydrogenase to albumin ratio (LAR) for mortality in ICU-admitted MM patients using MIMIC-IV. A total of 271 adult MM patients with an index ICU admission were included. BAR and LAR were calculated from laboratory values obtained within 24 hours of ICU entry. Associations with 30-day (primary) and 365-day (secondary) all-cause mortality were assessed using Kaplan-Meier analysis, multivariable Cox regression, restricted cubic spline modeling, and subgroup analyses. After covariate adjustment, each one-unit BAR increase was associated with a 43% higher 30-day mortality hazard (HR 1.43; 95% CI 1.19–1.72; P < 0.001) and a 30% higher 365-day mortality hazard (HR 1.30; 95% CI 1.13–1.49; P < 0.001). For LAR, each one-unit increase conferred 5% (HR 1.05; 95% CI 1.02–1.07; P < 0.001) and 2% (HR 1.02; 95% CI 1.01–1.04; P = 0.004) excess hazards for 30-day and 365-day mortality, respectively. These associations were consistent across most clinical subgroups. BAR and LAR are independently predictive of short- and long-term mortality in ICU-managed MM patients and may serve as accessible bedside risk stratification tools. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Multiple myeloma Intensive care unit Blood urea nitrogen to albumin ratio Lactate dehydrogenase to albumin ratio Mortality MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Multiple myeloma (MM) is a malignant plasma cell neoplasm characterized by clonal expansion of plasma cells in the bone marrow. It accounts for approximately 1% of all cancers and 10% of hematologic malignancies worldwide.[ 1 , 2 ] MM predominantly affects older adults, with a median age at diagnosis of approximately 70 years, and its incidence has been increasing over recent decades.[ 3 , 4 ] MM is a multisystemic disease characterized by lytic bone lesions, renal impairment, hypercalcemia, cytopenias, and increased infection susceptibility, collectively imposing a substantial clinical and economic burden.[ 5 , 6 ] Patients with advanced or decompensated MM frequently require intensive care unit (ICU) admission for close monitoring and aggressive treatment.[ 7 , 8 ] Despite significant therapeutic advances—including proteasome inhibitors, immunomodulatory drugs, and monoclonal antibodies—[ 9 – 11 ] outcomes for MM patients requiring intensive care remain poor, partly due to a lack of reliable prognostic tools.[ 12 , 13 ] Therefore, identifying accessible biomarkers for prognostic assessment in this setting is clinically important. This study focuses on two composite biomarkers: the blood urea nitrogen-to-albumin ratio (BAR) and the lactate dehydrogenase-to-albumin ratio (LAR). Serum albumin reflects nutritional status and systemic inflammation,[ 14 , 15 ] blood urea nitrogen indicates renal function and protein catabolism,[ 16 ] and lactate dehydrogenase serves as a marker of tissue damage and tumor burden.[ 17 , 18 ] Emerging evidence indicates that these ratios are associated with nutritional impairment, metabolic derangement, inflammation, and tumor aggressiveness, and have shown prognostic value in critically ill and cancer populations.[ 19 – 22 ] However, the prognostic value of these indices in critically ill MM patients has not been systematically investigated. Given that MM patients frequently present with concurrent renal dysfunction, malnutrition, and high tumor burden,[ 23 , 24 ] these composite indices may provide useful insights into prognostic assessment in this population. This study aimed to determine whether BAR and LAR independently predict 30-day and 365-day all-cause mortality in ICU-admitted MM patients. To our knowledge, no previous study has evaluated the prognostic significance of both BAR and LAR in ICU-admitted MM patients, addressing an unmet need for simple, accessible risk stratification tools in this population. Using the MIMIC-IV database and multiple analytical approaches—including Kaplan-Meier analysis, Cox regression, restricted cubic spline modeling, and subgroup analyses—we sought to provide evidence for improving risk stratification and clinical decision-making in critically ill MM patients. Results Baseline characteristics of study participants Initial extraction yielded 392 hospitalization episodes involving MM patients from the MIMIC-IV repository (Fig. 1 ). When individuals had multiple ICU encounters, only the earliest admission was retained. Subsequent exclusions encompassed ICU stays shorter than 24 hours, absent survival follow-up data, physiologically implausible laboratory values (e.g., LDH), and age below 18 years. After applying all eligibility filters, 271 index ICU admissions of MM patients constituted the final analytical cohort. Imputation outcomes for missing data across 115 candidate variables are detailed in Supplementary Table S1 . Baseline characteristics by BAR quartiles Table 1 presents the demographic and clinical profiles of MM patients stratified by BAR index quartiles: Q1 (0.220–0.762), Q2 (0.763–1.407), Q3 (1.452–2.378), and Q4 (2.400–6.815). The cohort had a median age of 72 years (IQR: 65–80), with males representing 62% of the sample. Malignant cancer was the most prevalent comorbid condition (84%), followed by renal disease (41%) and heart failure (34%). Across ascending BAR quartiles (Q1 through Q4), a stepwise increase in patient age was observed (P = 0.009), accompanied by progressively higher rates of heart failure (P < 0.001), uncomplicated diabetes (P = 0.001), and renal disease (P < 0.001). Higher BAR quartiles also exhibited elevated Charlson Comorbidity Index scores (P < 0.001) and increased creatinine concentrations (P < 0.001), consistent with worsening kidney function. Body temperature was marginally higher among Q1 and Q2 patients and marginally lower in Q4, with the inter-quartile variation reaching statistical significance (P = 0.011). Although platelet counts trended lowest in Q4, this difference did not achieve statistical significance (P = 0.175). Table 1 Baseline characteristics of participants classified by BAR index quartiles Feature Overall (N = 271) Q1 (N = 69) Q2 (N = 67) Q3 (N = 67) Q4 (N = 68) P Age 72 67 73 73 76 0.009 Gender (Male) 167 (62%) 40 (58%) 42 (63%) 41 (61%) 44 (65%) 0.879 Weight 76 73 81 75 78 0.495 CHF 92 (34%) 9 (13%) 21 (31%) 29 (43%) 33 (49%) < 0.001 CPD 59 (22%) 14 (20%) 13 (19%) 17 (25%) 15 (22%) 0.851 DM without CC 52 (19%) 3 (4.3%) 15 (22%) 13 (19%) 21 (31%) 0.001 Renal disease 110 (41%) 8 (12%) 16 (24%) 43 (64%) 43 (63%) < 0.001 Malignant cancer 228 (84%) 60 (87%) 53 (79%) 56 (84%) 59 (87%) 0.596 Metastatic solid tumor 12 (4.4%) 1 (1.4%) 4 (6.0%) 4 (6.0%) 3 (4.4%) 0.469 CCI 7 [ 5 , 9 ] 5 [ 4 , 7 ] 6 [ 5 , 8 ] 8 [ 7 , 10 ] 8 [ 6 , 10 ] < 0.001 Platelets max 169 175 157 192 138 0.175 Creatinine max 1.50 [0.90, 2.90] 0.90 [0.70, 1.10] 1.20 [0.90, 1.60] 2.00 [1.50, 4.90] 3.45 [2.20, 5.35] < 0.001 Temperature max 37.17 [36.89, 37.78] 37.33 [37.00, 38.06] 37.33 [36.89, 38.22] 37.11 [36.89, 37.56] 37.06 [36.78, 37.70] 0.011 LAR 10 [ 7 , 15 ] 8 [ 6 , 13 ] 9 [ 7 , 11 ] 10 [ 6 , 16 ] 13 [ 9 , 20 ] < 0.001 BAR 1.41 [0.76, 2.40] 0.54 [0.41, 0.69] 1.00 [0.85, 1.19] 1.78 [1.51, 1.92] 3.41 [3.00, 4.26] < 0.001 Values are presented as median [Q1, Q3] or n (%). CHF, congestive heart failure; CPD, chronic pulmonary disease; DM without CC, diabetes without complications; CCI, Charlson Comorbidity Index; BAR, blood urea nitrogen to albumin ratio; LAR, lactate dehydrogenase to albumin ratio. P values from Kruskal-Wallis rank sum test or Pearson’s Chi-squared test. Baseline characteristics by LAR quartiles Supplementary Table S2 displays the demographic and clinical features of MM patients grouped according to LAR index quartiles: Q1 (3.410–6.722), Q2 (6.735–9.621), Q3 (9.821–14.458), and Q4 (15.267–62.682). Notably, the proportion of patients with metastatic solid tumors was significantly greater in the highest LAR quartile (P = 0.048). Platelet counts were most depressed in Q4 (median: 125 × 10⁹/L), and this inter-quartile disparity was statistically significant (P = 0.02), potentially reflecting myelosuppression or more advanced disease in patients with elevated LAR. Kaplan-Meier survival analysis As illustrated in Fig. 2 , Kaplan-Meier estimation demonstrated statistically significant disparities in cumulative survival across the four BAR quartile strata for both the 30-day and 365-day endpoints (log-rank P < 0.05). Individuals in Q4 of BAR experienced markedly inferior survival at both time horizons relative to those in the lower three quartiles. A parallel pattern emerged for LAR (Fig. 3 ), where Q4 patients exhibited significantly diminished survival at both observation windows compared with the remaining quartile groups (log-rank P < 0.001 and P = 0.003 for 30-day and 365-day mortality, respectively). Association between BAR and mortality The independent prognostic contribution of BAR was examined through Cox proportional hazards regression (Table 2 ). Treating BAR as a continuous predictor, each one-unit rise conferred a 33% excess hazard of 30-day death in the unadjusted model (HR = 1.33; 95% CI: 1.13–1.56; P < 0.001). Following multivariable adjustment in Model 2, the magnitude of this association strengthened (HR = 1.43; 95% CI: 1.19–1.72; P < 0.001). In quartile-based analyses relative to Q1, Q4 demonstrated a significantly heightened 30-day mortality hazard in both the unadjusted (HR = 2.97; 95% CI: 1.27–6.93; P = 0.012) and adjusted frameworks (HR = 6.19; 95% CI: 2.24–17.1; P < 0.001). A monotonically increasing risk gradient was confirmed by significant trend tests in both models (P < 0.001 each). Regarding 365-day mortality, continuous BAR was linked to a 21% higher hazard in the unadjusted analysis (HR = 1.21; 95% CI: 1.07–1.32; P = 0.002), an association that persisted and intensified upon covariate adjustment (HR = 1.30; 95% CI: 1.13–1.49; P < 0.001). On a quartile basis, Q4 showed a borderline elevation in 365-day mortality relative to Q1 before adjustment (HR = 1.61; 95% CI: 0.96–2.70; P = 0.074), which became robustly significant and appreciably amplified after controlling for covariates (HR = 2.94; 95% CI: 1.51–5.72; P = 0.001). Ordinal trend tests confirmed a graded risk escalation across quartile categories in both analytic frameworks (P for trend = 0.009 and P < 0.001, respectively). Table 2 Cox regression results of BAR and mortality (30-day and 365-day) Variable 30-day mortality 365-day mortality HR 95% CI P HR 95% CI P Model 1 Continuous 1.33 1.13, 1.56 < 0.001 1.21 1.07, 1.37 0.002 Q1 Ref Ref Q2 0.59 0.19, 1.87 0.4 0.72 0.40, 1.29 0.3 Q3 2.16 0.90, 5.22 0.086 1.25 0.74, 2.12 0.4 Q4 2.97 1.27, 6.93 0.012 1.61 0.96, 2.70 0.074 P for trend 1.63 1.24, 2.14 < 0.001 1.26 1.06, 1.49 0.009 Model 2 Continuous 1.43 1.19, 1.72 < 0.001 1.30 1.13, 1.49 < 0.001 Q1 Ref Ref Q2 0.55 0.17, 1.80 0.3 0.83 0.45, 1.53 0.5 Q3 2.96 1.10, 7.93 0.031 1.77 0.96, 3.27 0.069 Q4 6.19 2.24, 17.1 < 0.001 2.94 1.51, 5.72 0.001 P for trend 2.18 1.54, 3.08 < 0.001 1.53 1.22, 1.92 < 0.001 Model 1: unadjusted; Model 2: adjusted for age, weight, malignant tumor, metastatic solid tumor, Charlson Comorbidity Index, body temperature, platelets, and creatinine. CI, confidence interval; HR, hazard ratio; BAR, blood urea nitrogen to albumin ratio; Ref, reference. Association between LAR and mortality An analogous Cox regression approach was applied to evaluate LAR as a mortality predictor (Table 3 ). Modeled continuously, each one-unit LAR increment conferred a 5% excess 30-day mortality hazard in the unadjusted analysis (HR = 1.05; 95% CI: 1.03–1.07; P < 0.001). This relationship endured after multivariable correction in Model 2 (HR = 1.05; 95% CI: 1.02–1.07; P < 0.001). When stratified by quartiles, Q4 carried a markedly elevated 30-day mortality risk versus Q1 in both the unadjusted (HR = 3.63; 95% CI: 1.56–8.42; P = 0.003) and covariate-adjusted analyses (HR = 4.14; 95% CI: 1.71–10.0; P = 0.002). Trend tests were strongly significant across both models (P < 0.001), consistent with a progressive dose-response gradient linking LAR to early mortality. For the 365-day endpoint, each LAR unit increment translated to a 3% risk increase before adjustment (HR = 1.03; 95% CI: 1.01–1.05; P < 0.001), modestly attenuated yet remaining significant after covariate correction (HR = 1.02; 95% CI: 1.01–1.04; P = 0.004). In quartile-stratified analyses, Q4 exhibited significantly higher 365-day mortality than Q1 across both unadjusted (HR = 1.87; 95% CI: 1.17–2.98; P = 0.008) and adjusted models (HR = 1.85; 95% CI: 1.14–3.00; P = 0.012), accompanied by statistically significant ordinal trends (P for trend = 0.011 and P = 0.021, respectively). Table 3 Cox regression results of LAR and mortality (30-day and 365-day) Variable 30-day mortality 365-day mortality HR 95% CI P HR 95% CI P Model 1 Continuous 1.05 1.03, 1.07 < 0.001 1.03 1.01, 1.05 0.9 0.87 0.50, 1.50 0.6 Q3 2.05 0.83, 5.08 0.12 0.82 0.48, 1.39 0.5 Q4 3.63 1.56, 8.42 0.003 1.87 1.17, 2.98 0.008 P for trend 1.62 1.24, 2.12 < 0.001 1.24 1.05, 1.46 0.011 Model 2 Continuous 1.05 1.02, 1.07 0.9 Q3 2.17 0.87, 5.42 0.10 0.93 0.54, 1.59 0.8 Q4 4.14 1.71, 10.0 0.002 1.85 1.14, 3.00 0.012 P for trend 1.67 1.26, 2.21 < 0.001 1.21 1.03, 1.43 0.021 Model 1: unadjusted; Model 2: adjusted for age, weight, malignant tumor, metastatic solid tumor, Charlson Comorbidity Index, body temperature, platelets, and creatinine. CI, confidence interval; HR, hazard ratio; LAR, lactate dehydrogenase to albumin ratio; Ref, reference. Nonlinear relationship assessment Restricted cubic splines (RCS) analysis was performed to evaluate potential nonlinear relationships between the hematologic indices and mortality outcomes. For BAR (Fig. 4 ), the analysis demonstrated a significant nonlinear association with 30-day all-cause mortality risk. In the unadjusted model (Model 1), BAR showed a significant nonlinear relationship with mortality risk (P for non-linearity < 0.05), with relatively low risk when BAR < 1.5 and a rapid increase in risk as BAR levels rose. In the adjusted model (Model 2), this trend was more pronounced (P for non-linearity 2). An analogous RCS evaluation for LAR (Fig. 5 ) likewise identified a significant positive association with both early and late mortality. Elevated LAR values were uniformly linked to amplified mortality hazard regardless of adjustment status (all models P < 0.05). Nonlinearity testing again proved non-significant (P ≥ 0.05), confirming a predominantly linear LAR–mortality relationship. Subgroup analyses To evaluate whether the observed prognostic associations were robust across clinically meaningful patient subsets, pre-specified stratified analyses were conducted (Supplementary Fig. S1 and S2). For BAR, the adjusted hazard ratios consistently exceeded unity across every pre-defined stratum for both time endpoints, with the majority reaching statistical significance. The strongest point estimate emerged among patients with uncomplicated diabetes, where the 30-day mortality HR reached 3.07 (95% CI: 1.01–9.30; P = 0.047), suggesting heightened vulnerability to BAR elevation in this subgroup. Effect magnitudes were broadly comparable across the remaining strata defined by age, sex, heart failure, chronic lung disease, and renal disease. No statistically significant interaction effects were detected for any subgroup variable (all P for interaction > 0.05), indicating stable predictive performance of BAR across heterogeneous patient profiles. For LAR, elevated values were associated with increased hazards for both endpoints in the majority of examined strata, with point estimates exceeding 1.0 and reaching statistical significance in most instances. However, formal interaction testing identified statistically significant effect modification by age for the 365-day endpoint (P for interaction = 0.032) and by diabetes status for the same outcome (P for interaction = 0.041). The remaining stratification variables (sex, heart failure, chronic lung disease) exhibited no significant effect modification (P for interaction > 0.05), supporting overall consistency of the LAR–mortality association across most clinically relevant subpopulations. Discussion In this retrospective evaluation of 271 critically ill MM patients sourced from the MIMIC-IV database, we found that both BAR and LAR independently predict early and late mortality. In particular, after comprehensive covariate adjustment, every unit elevation in BAR corresponded to a 43% higher hazard of 30-day death and a 30% greater hazard of 365-day death. Analogously, each unit increment in LAR translated to 5% and 2% excess risks of 30-day and 365-day mortality, respectively. Gradient analyses corroborated that individuals occupying the uppermost quartiles of either index faced substantially heightened mortality relative to those in the lowest quartiles, with these relationships demonstrating stability across the majority of clinical strata examined. To our knowledge, this is the first systematic evaluation of the prognostic value of both BAR and LAR in ICU-managed MM patients. These findings are consistent with established MM pathobiology. BAR captures two key dimensions of critical illness: impaired renal clearance (elevated BUN) and the nutritional-inflammatory status (low albumin). Renal injury is common in MM, affecting up to 50% of patients at diagnosis, primarily due to light chain cast nephropathy, hypercalcemia-induced tubular damage, and amyloid deposition.[ 23 ] Hypoalbuminemia in this setting reflects not only malnutrition but also systemic inflammation and impaired hepatic synthetic function, both of which independently predict poor outcomes in critically ill patients.[ 14 , 15 ] Prior investigations have validated the prognostic utility of BAR in septic patients,[ 19 , 42 ] acute pancreatitis,[ 36 , 37 ] and chronic heart failure,[ 41 ] disorders sharing common pathophysiological substrates of renal-metabolic derangement. Our findings extend this evidence to ICU-managed MM patients, suggesting that BAR may capture a composite risk profile beyond what BUN or albumin alone conveys. Notably, RCS modeling revealed a significant nonlinear BAR–30-day mortality relationship, with a marked acceleration of risk above a BAR of approximately 2, suggesting a potential clinical threshold for acute deterioration.[ 40 ] In contrast, the largely linear BAR–365-day mortality relationship suggests that worsening BUN-albumin imbalance gradually accumulates long-term risk without a clear threshold. For LAR, LDH serves as a well-established surrogate marker for tumor burden and tissue damage in hematological malignancies,[ 17 , 18 ] while the albumin denominator again captures the inflammatory-nutritional axis. The prognostic value of LAR has been demonstrated in diffuse large B-cell lymphoma,[ 38 ] colorectal cancer,[ 43 ] and non-small cell lung cancer.[ 44 ] A recent pooled analysis further substantiated the prognostic value of LAR across a spectrum of malignant diseases.[ 39 ] In the present cohort, the essentially linear dose-response gradient linking LAR to both mortality horizons implies that each incremental LAR rise justifies proportionally heightened clinical attention. Compared with conventional severity scores such as APACHE II[ 33 ] and SOFA,[ 27 ] BAR and LAR offer practical advantages: they can be calculated from routine admission laboratory tests and may dynamically reflect changes in systemic stress, nutritional status, and tumor activity, potentially facilitating bedside decision-making. The subgroup analyses provided information on the generalizability and population-specific performance of these biomarkers. For BAR, the prognostic association was consistent across all pre-specified strata, with no significant interaction effects (all P for interaction > 0.05), supporting broad clinical applicability. In the diabetic subgroup without complications, BAR yielded the largest hazard ratio for 30-day mortality (HR = 3.07; 95% CI: 1.01–9.30; P = 0.047), possibly due to the combined effects of diabetic nephropathy and metabolic disturbance on BUN accumulation, which may enhance BAR’s prognostic sensitivity in this subgroup. Regarding LAR, although its overall prognostic utility remained stable across most strata, noteworthy effect modification was observed for age (P for interaction = 0.032) and diabetic status (P for interaction = 0.041) in relation to the 365-day mortality endpoint. The age-dependent interaction may stem from differences in tumor biology and therapeutic tolerance between younger and older MM patients; the former typically undergo more aggressive chemotherapy and possess greater physiological resilience,[ 3 , 4 ] potentially modifying the prognostic impact of LAR. The diabetes interaction likely reflects the metabolic interplay between glucose dysregulation, altered LDH metabolism, and accelerated protein catabolism. These findings suggest that while both indices are broadly applicable, clinicians should consider population-specific thresholds guided by established staging systems,[ 34 , 35 ] particularly for older patients and those with metabolic comorbidities, to optimize risk stratification and guide treatment decisions. This study has several limitations. First, the retrospective single-center design using the MIMIC-IV database may introduce selection bias and limit generalizability to other settings or populations.[ 45 ] Second, despite multivariable adjustment, unmeasured confounders—particularly MM-specific treatments such as chemotherapy, autologous stem cell transplantation, or immunotherapy—may have influenced the results. Third, external validation in independent cohorts or prospective studies was not performed, which is necessary to confirm these findings. Fourth, the modest sample size (n = 271) may have limited statistical power for detecting interaction effects in smaller subgroups. Larger, prospective multicenter studies are needed to validate these results and establish optimal clinical thresholds. In conclusion, BAR and LAR are independent prognostic markers for 30-day and 365-day all-cause mortality in ICU-admitted MM patients, with dose-dependent risk associations that are consistent across most clinical subgroups. As simple indices derived from routine laboratory tests, BAR and LAR may serve as practical bedside tools for early risk stratification in this high-acuity population. Incorporating these indices into risk assessment algorithms may help identify high-risk patients, guide treatment intensity, and potentially improve outcomes. Prospective multicenter studies and mechanistic investigations are needed to confirm these findings and explore whether combining BAR and LAR into a prognostic scoring system could further improve risk prediction in critically ill cancer patients. Methods Source of data This retrospective investigation drew upon data housed in the freely accessible Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.2) repository.[ 25 ] As an expanded successor to the earlier MIMIC-III platform, MIMIC-IV incorporates updated data elements and restructured relational tables. The database captures comprehensive clinical records spanning more than 190,000 individuals and 450,000 hospital encounters documented from 2008 through 2022 at Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts. Recorded variables encompass demographic profiles, laboratory measurements, pharmacological treatments, physiological parameters, procedural interventions, diagnostic classifications, therapeutic regimens, and longitudinal survival data. The MIMIC-IV project operates under Institutional Review Board approval granted by MIT and BIDMC (protocols MIT-0403000206 and BIDMC-2001P001699); all records are fully de-identified. The investigators completed the required NIH human research protection training and CITI certification prior to accessing the database. Individual informed consent was not required because the dataset contains no identifiable patient information. Study design and population We queried the MIMIC-IV database to identify adult individuals (age ≥ 18 years) who underwent ICU admission with a documented diagnosis of MM. Diagnostic ascertainment of MM relied on International Classification of Diseases coding algorithms encompassing both ICD-9 and ICD-10 nomenclatures.[ 26 ] The following inclusion criteria governed participant selection: (1) confirmed MM diagnosis with concurrent ICU admission; (2) minimum age of 18 years; (3) retention of only the initial ICU episode for patients with repeated admissions; and (4) presence of comprehensive clinical records obtained within the first 24 hours post-ICU entry, accompanied by unambiguous survival outcome documentation. To maintain data integrity, individuals exhibiting physiologically implausible extremes in clinical parameters were additionally excluded. Furthermore, patients whose ICU stay was curtailed by discharge or death within the first 24 hours were removed from the analytical cohort. Data extraction All data retrieval was accomplished through Structured Query Language (SQL) queries executed against the MIMIC-IV relational database. The variables interrogated were organized into the following domains: (1) Demographic characteristics: age, sex, body weight, and self-reported race/ethnicity. (2) Comorbid conditions (ascertained via ICD coding): myocardial infarction, heart failure, peripheral arterial disease, cerebrovascular events, dementia, chronic lung disease, peptic ulcer, non-severe hepatic disease, uncomplicated and complicated diabetes mellitus, paraplegia, chronic kidney disease, primary malignancy, advanced liver disease, and metastatic solid neoplasm. (3) Hemodynamic and physiological measurements: heart rate, systolic and diastolic blood pressure (SBP, DBP), mean arterial pressure (MAP), respiratory rate, core temperature, pulse oximetry (SpO₂), and capillary blood glucose. (4) Biochemical and hematological parameters: hematocrit, hemoglobin concentration, platelet count, total leukocyte count (WBC), anion gap, serum bicarbonate, BUN, ionized calcium, chloride, serum creatinine, sodium, potassium, coagulation profile including prothrombin time (PT) and activated partial thromboplastin time (APTT), LDH, and serum albumin. (5) Illness severity indices: the Sequential Organ Failure Assessment (SOFA) score,[ 27 ] with its constituent subsystem components (coagulation, cardiovascular, neurological, and renal domains); and the Charlson Comorbidity Index (CCI).[ 28 ] Any variable exhibiting a missing data proportion exceeding 20% was omitted from analytical consideration. Where missingness fell below this threshold, imputation was carried out using a random forest–based method available in the mice R package.[ 29 ] Calculation of hematologic indices The key exposure variables were two composite hematologic ratios calculated from laboratory data obtained at the time of the initial ICU admission. These ratios were the lactate dehydrogenase to albumin ratio (LAR) and the blood urea nitrogen to albumin ratio (BAR). The formulae were as follows: LAR = serum lactate dehydrogenase (U/L) divided by serum albumin (g/L); BAR = blood urea nitrogen (mg/dL) divided by serum albumin (g/L). Outcomes We designated 30-day all-cause mortality after the index ICU admission as the primary outcome; the secondary outcome was 365-day all-cause mortality measured from the same time point. Statistical analysis The Kaplan-Meier method was used to estimate time-to-event distributions for primary and secondary endpoints, with patients grouped by their hematologic composite index values.[ 30 ] Study participants were allocated into four strata corresponding to the quartile boundaries of BAR and LAR distributions. Between-stratum survival disparities were evaluated using the log-rank test. Crude Cox regression was first performed to estimate the unadjusted association between each composite index and both mortality endpoints.[ 31 ] Subsequent multivariable Cox models included covariates selected on the basis of clinical relevance or demonstrable univariate prognostic association. Model 1 evaluated the hematologic indices in an unadjusted framework. Model 2 incorporated adjustments for age, body weight, presence of malignant neoplasm, metastatic disease, Charlson Comorbidity Index, body temperature, platelet count, and serum creatinine. Across both models, the lowest quartile of each index constituted the referent category. Both continuous and quartile-based parameterizations of each index were employed to characterize potential graded relationships with outcome risk. Ordered trend tests across quartile strata were performed to assess the presence of monotonic risk gradients. RCS regression with pre-specified knot placement was used to examine whether the hematologic indices exhibited nonlinear associations with all-cause mortality.[ 32 ] Three knot positions were specified across the empirical distribution of each index. Adjustment covariates including age, sex, and additional clinical variables were incorporated to mitigate confounding. The presence of nonlinearity was formally evaluated through likelihood ratio testing. Findings were depicted graphically as dose-response curves relating each index to mortality hazard, accompanied by 95% confidence bands. Pre-planned stratified analyses were further performed by age group (< 65 vs. ≥65 years), sex, and selected comorbidities (heart failure, chronic lung disease, uncomplicated diabetes, renal disease) to test the consistency of the primary findings across clinically meaningful patient subsets. Potential effect modification was explored by adding product interaction terms to the respective Cox models. All hypothesis tests were two-sided, with a significance level of α = 0.05. Analyses were carried out in R (version 4.3.3; R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were expressed as counts and percentages. Normality of continuous data was assessed using Shapiro-Wilk or Anderson-Darling testing. Normally distributed continuous variables were summarized as mean ± SD; non-normally distributed ones as median (IQR, 25th–75th percentile). Group-level comparisons of categorical data used the χ2 test or Fisher exact test. Continuous variables were compared between groups with the Student t-test (parametric) or the Wilcoxon rank-sum test (non-parametric), as dictated by distributional properties. Data Availability The datasets analysed in this study are available in the MIMIC-IV repository (version 2.2), accessible at https://physionet.org/content/mimiciv/2.2/ . Access to MIMIC-IV requires completion of a recognized course in human research subject protection and a signed data use agreement. The SQL queries and R scripts used for data extraction and analysis are available from the corresponding author upon reasonable request. Declarations Acknowledgements The authors gratefully acknowledge the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC) for maintaining and providing open access to the MIMIC-IV database. Funding This work was supported by the Research Project of the Education Department of Anhui Province (Grant No. 2025AHGXZK31419). Author Contributions H.X. and C.J. contributed equally to this work and share first authorship. H.X. conceived and designed the study, performed data extraction and statistical analysis, interpreted the results, and drafted the manuscript. C.J. contributed to study design, data extraction, and critical revision of the manuscript. D.C. and J.L. contributed to data collection and management. L.W. contributed to data curation and quality control. F.C. contributed to methodology review and validation. L.Z. supervised the study and provided critical revision. All authors reviewed and approved the final manuscript. Additional Information Supplementary Information accompanies this paper. Correspondence and requests for materials should be addressed to H.X. ( [email protected] ). Competing Interests The authors declare no competing interests. References Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. CA Cancer J. Clin. 74, 12–49 (2024). Padala, S. A. et al. 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Multiple myeloma: EHA-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 32, 309–322 (2021). Moreau, P. et al. Treatment of relapsed and refractory multiple myeloma: recommendations from the International Myeloma Working Group. Lancet Oncol. 22, e105–e118 (2021). San-Miguel, J. et al. Cilta-cel or standard care in lenalidomide-refractory multiple myeloma. N. Engl. J. Med. 389, 335–347 (2023). Chen, C. L. et al. Outcomes and prognostic factors in critical patients with hematologic malignancies. J. Clin. Med. 12, 958 (2023). Bıkmaz, Ş. G. A. et al. Risk factors for ICU mortality in patients with hematological malignancies: a single-center, retrospective cohort study from Turkey. Turk. J. Med. Sci. 53, 340–351 (2023). Soeters, P. B., Wolfe, R. R. & Shenkin, A. Hypoalbuminemia: pathogenesis and clinical significance. JPEN J. Parenter. Enteral Nutr. 43, 181–193 (2019). Keller, U. Nutritional laboratory markers in malnutrition. J. Clin. Med. 8, 775 (2019). Weiner, I. D., Mitch, W. E. & Sands, J. M. Urea and ammonia metabolism and the control of renal nitrogen excretion. Clin. J. Am. Soc. Nephrol. 10, 1444–1458 (2015). Comandatore, A. et al. Lactate dehydrogenase and its clinical significance in pancreatic and thoracic cancers. Semin. Cancer Biol. 86, 93–100 (2022). Mishra, D. & Banerjee, D. Lactate dehydrogenases as metabolic links between tumor and stroma in the tumor microenvironment. Cancers (Basel) 11, 750 (2019). Wang, Y. et al. Prognostic impact of blood urea nitrogen to albumin ratio on patients with sepsis: a retrospective cohort study. Sci. Rep. 13, 10013 (2023). Hu, Y. et al. Nomograms based on lactate dehydrogenase to albumin ratio for predicting survival in colorectal cancer. Int. J. Med. Sci. 19, 1003–1012 (2022). Yang, F. et al. Prognostic value of blood urea nitrogen to serum albumin ratio for acute kidney injury and in-hospital mortality in intensive care unit patients with intracerebral haemorrhage: a retrospective cohort study using the MIMIC-IV database. BMJ Open 13, e069503 (2023). Zhang, L. et al. Blood urea nitrogen to serum albumin ratio: a novel mortality indicator in intensive care unit patients with coronary heart disease. Sci. Rep. 14, 7466 (2024). Dimopoulos, M. A. et al. Management of multiple myeloma-related renal impairment: recommendations from the International Myeloma Working Group. Lancet Oncol. 24, e293–e311 (2023). Arends, J. et al. Cancer cachexia in adult patients: ESMO Clinical Practice Guidelines. ESMO Open 6, 100092 (2021). Johnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023). Gupta, M. et al. An extensive data processing pipeline for MIMIC-IV. Proc. Mach. Learn. Res. 193, 311–325 (2022). Singer, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315, 801–810 (2016). Charlson, M. E., Carrozzino, D., Guidi, J. & Patierno, C. Charlson Comorbidity Index: a critical review of clinimetric properties. Psychother. Psychosom. 91, 8–35 (2022). van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate Imputation by Chained Equations in R. J. Stat. Softw. 45, 1–67 (2011). Bland, J. M. & Altman, D. G. The logrank test. BMJ 328, 1073 (2004). George, B., Seals, S. & Aban, I. Survival analysis and regression models. J. Nucl. Cardiol. 21, 686–694 (2014). Gauthier, J., Wu, Q. V. & Gooley, T. A. Cubic splines to model relationships between continuous variables and outcomes: a guide for clinicians. Bone Marrow Transplant. 55, 675–680 (2020). Knaus, W. A., Draper, E. A., Wagner, D. P. & Zimmerman, J. E. APACHE II: a severity of disease classification system. Crit. Care Med. 13, 818–829 (1985). Palumbo, A. et al. Revised International Staging System for multiple myeloma: a report from International Myeloma Working Group. J. Clin. Oncol. 33, 2863–2869 (2015). D'Agostino, M. et al. Second revision of the International Staging System (R2-ISS) for overall survival in multiple myeloma: a European Myeloma Network (EMN) report within the HARMONY project. J. Clin. Oncol. 40, 3406–3418 (2022). Li, W. et al. Blood urea nitrogen to albumin ratio as predictor of mortality among acute pancreatitis patients in ICU: a retrospective cohort study. PLoS One 20, e0323321 (2025). Kuang, M. et al. Blood urea nitrogen to albumin ratio as a robust predictor of in-hospital mortality in patients with predicted severe acute pancreatitis: a retrospective multicenter observational study. Int. J. Surg. 110, 1295–1307 (2024). Cai, Y. et al. Prognostic value of lactate dehydrogenase, serum albumin and the lactate dehydrogenase/albumin ratio in patients with diffuse large B-cell lymphoma. Hematology 29, 2293514 (2024). Purwati, D. D., Utami, M. D. T., Kurniawan, R. B., Wungu, C. D. K. & Amin, I. M. Deciphering the potential of the lactate dehydrogenase-to-albumin ratio as a prognostic biomarker in malignancy: a systematic review and meta-analysis. Postgrad. Med. 137, 486–495 (2025). Schuster, N. A., Rijnhart, J. J. M., Twisk, J. W. R. & Heymans, M. W. Modeling non-linear relationships in epidemiological data: the application and interpretation of spline models. Front. Epidemiol. 2, 975380 (2022). Lin, Z. et al. Blood urea nitrogen to serum albumin ratio as a new prognostic indicator in critical patients with chronic heart failure. ESC Heart Fail. 9, 1360–1369 (2022). Min, J. et al. The correlation study between blood urea nitrogen to serum albumin ratio and prognosis of patients with sepsis during hospitalization. BMC Infect. Dis. 22, 404 (2022). Shu, X. P. et al. Effect of serum lactate dehydrogenase-to-albumin ratio (LAR) on the short-term outcomes and long-term prognosis of colorectal cancer after radical surgery. BMC Cancer 23, 915 (2023). Zhang, J. et al. Prognostic value of the lactate dehydrogenase to albumin ratio in advanced non-small cell lung cancer patients treated with the first-line PD-1 checkpoint inhibitors combined with chemotherapy. Front. Immunol. 16, 1535108 (2025). Hernán, M. A. & Robins, J. M. Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183, 758–764 (2016). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9224218","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615825326,"identity":"392ebdf0-8a2a-477d-a212-9524566eb5bc","order_by":0,"name":"Haitao Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDCCAyBUwMDAz97Y+PAD8VoMGBgkew43G0sQq4UBpMXgRnqbAA8xOviOnz144IPB4cQNNx+2MUgw2MnpNhDQInkmL+HgDIO0xJm3E9seFDAkG5sdIKDF4ECOwWEeA5vEvtuJ7QYSDAcStxHUcv6NweE/BhKJDTcPtknwEKXlBtAWBqAtE24wEqlF8sYbg4M9BmnGM3sSgYFsQIRf+M7nGH/4UXFYtp/9+MOHHyrs5AhqQXcnacpHwSgYBaNgFOAAAOHCS3/oLnvsAAAAAElFTkSuQmCC","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to China Pharmaceutical University","correspondingAuthor":true,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Xu","suffix":""},{"id":615825327,"identity":"40681f3c-f6e5-4b2b-b175-02c6108d047b","order_by":1,"name":"Caihong Jiang","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Caihong","middleName":"","lastName":"Jiang","suffix":""},{"id":615825329,"identity":"631108f6-436f-4445-82d9-24957ab0d145","order_by":2,"name":"Dangui Chen","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Dangui","middleName":"","lastName":"Chen","suffix":""},{"id":615825332,"identity":"92010afc-05c3-4e0c-b264-65927acecc71","order_by":3,"name":"Jia Lu","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Lu","suffix":""},{"id":615825335,"identity":"fe41ff09-eb2e-446b-b645-cab83cdf4eea","order_by":4,"name":"Lihong Wang","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Lihong","middleName":"","lastName":"Wang","suffix":""},{"id":615825338,"identity":"5e72e997-381e-44cd-afa0-fa7e215a0328","order_by":5,"name":"Fei Chen","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Chen","suffix":""},{"id":615825340,"identity":"814f02a8-f184-4119-a48b-f41c5c68de18","order_by":6,"name":"Long Zhong","email":"","orcid":"","institution":"Anqing Municipal Hospital, Anqing Hospital Affiliated to China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2026-03-25 14:10:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9224218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9224218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106206681,"identity":"17a2386e-b2c7-4658-9e2e-1944d0d41814","added_by":"auto","created_at":"2026-04-06 05:51:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":207877,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for MIMIC analysis. MIMIC, Medical Information Mart for Intensive Care; N, Numbers; ICU, Intensive Care Unit; RCS, Restricted Cubic Spline.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9224218/v1/50a5579e9995dcc3732e3092.png"},{"id":106415152,"identity":"cec0f0cf-b46c-4997-af0a-376c991c7d73","added_by":"auto","created_at":"2026-04-08 10:33:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77704,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for mortality stratified by BAR quartiles. (a) 30-day mortality; (b) 365-day mortality. Patients were divided into four groups according to BAR quartiles (Q1–Q4). Log-rank test was used for comparison among groups. BAR, blood urea nitrogen-to-albumin ratio; Q, quartile.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9224218/v1/aa3e80489bc04bb08c77b89a.png"},{"id":106206685,"identity":"2fcb5aac-82cd-4982-83dc-d648c8427345","added_by":"auto","created_at":"2026-04-06 05:51:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78546,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for mortality stratified by LAR quartiles. (a) 30-day mortality; (b) 365-day mortality. Patients were divided into four groups according to LAR quartiles (Q1–Q4). Log-rank test was used for comparison among groups. LAR, lactate dehydrogenase-to-albumin ratio; Q, quartile.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9224218/v1/1817128ef20b39cc8bfd8302.png"},{"id":106206683,"identity":"3de480b6-8056-435a-839c-c361f284b5e0","added_by":"auto","created_at":"2026-04-06 05:51:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":132665,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analysis of the association between BAR and mortality. (a–b) 30-day mortality; (c–d) 365-day mortality. Model 1: unadjusted; Model 2: adjusted for age, weight, malignant cancer, metastatic solid tumor, Charlson Comorbidity Index, body temperature, platelet count, and creatinine. The solid lines represent hazard ratios, and the shaded areas represent 95% confidence intervals. BAR, blood urea nitrogen-to-albumin ratio; RCS, restricted cubic spline.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9224218/v1/bd0c4590ed0d2c27060b0c9c.png"},{"id":106206684,"identity":"636070c6-02f1-4a05-93d4-003abcc08145","added_by":"auto","created_at":"2026-04-06 05:51:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":136521,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analysis of the association between LAR and mortality. (a–b) 30-day mortality; (c–d) 365-day mortality. Model 1: unadjusted; Model 2: adjusted for age, weight, malignant cancer, metastatic solid tumor, Charlson Comorbidity Index, body temperature, platelet count, and creatinine. The solid lines represent hazard ratios, and the shaded areas represent 95% confidence intervals. LAR, lactate dehydrogenase-to-albumin ratio; RCS, restricted cubic spline.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9224218/v1/ddf3087088d781fbc8c974c6.png"},{"id":106416496,"identity":"6177efa2-0629-4840-8911-3c9dc907b74a","added_by":"auto","created_at":"2026-04-08 10:46:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1406761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9224218/v1/565e6f05-2c8f-4218-84b4-0a2679c8c63c.pdf"},{"id":106206680,"identity":"f33d890f-e3b4-42f4-8489-cc1e35542328","added_by":"auto","created_at":"2026-04-06 05:51:21","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":149694,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-9224218/v1/a81016c512f1fbc62d68399d.csv"},{"id":106403478,"identity":"d2e26d2c-2393-4a97-a445-ef517e86eab4","added_by":"auto","created_at":"2026-04-08 09:14:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":346576,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigs.docx","url":"https://assets-eu.researchsquare.com/files/rs-9224218/v1/6cc09defb2fcc32ce491dab1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"BAR and LAR as mortality predictors in critically ill multiple myeloma patients: a MIMIC-IV study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is a malignant plasma cell neoplasm characterized by clonal expansion of plasma cells in the bone marrow. It accounts for approximately 1% of all cancers and 10% of hematologic malignancies worldwide.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] MM predominantly affects older adults, with a median age at diagnosis of approximately 70 years, and its incidence has been increasing over recent decades.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] MM is a multisystemic disease characterized by lytic bone lesions, renal impairment, hypercalcemia, cytopenias, and increased infection susceptibility, collectively imposing a substantial clinical and economic burden.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Patients with advanced or decompensated MM frequently require intensive care unit (ICU) admission for close monitoring and aggressive treatment.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Despite significant therapeutic advances\u0026mdash;including proteasome inhibitors, immunomodulatory drugs, and monoclonal antibodies\u0026mdash;[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] outcomes for MM patients requiring intensive care remain poor, partly due to a lack of reliable prognostic tools.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Therefore, identifying accessible biomarkers for prognostic assessment in this setting is clinically important.\u003c/p\u003e \u003cp\u003eThis study focuses on two composite biomarkers: the blood urea nitrogen-to-albumin ratio (BAR) and the lactate dehydrogenase-to-albumin ratio (LAR). Serum albumin reflects nutritional status and systemic inflammation,[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] blood urea nitrogen indicates renal function and protein catabolism,[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and lactate dehydrogenase serves as a marker of tissue damage and tumor burden.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Emerging evidence indicates that these ratios are associated with nutritional impairment, metabolic derangement, inflammation, and tumor aggressiveness, and have shown prognostic value in critically ill and cancer populations.[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] However, the prognostic value of these indices in critically ill MM patients has not been systematically investigated. Given that MM patients frequently present with concurrent renal dysfunction, malnutrition, and high tumor burden,[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] these composite indices may provide useful insights into prognostic assessment in this population.\u003c/p\u003e \u003cp\u003eThis study aimed to determine whether BAR and LAR independently predict 30-day and 365-day all-cause mortality in ICU-admitted MM patients. To our knowledge, no previous study has evaluated the prognostic significance of both BAR and LAR in ICU-admitted MM patients, addressing an unmet need for simple, accessible risk stratification tools in this population. Using the MIMIC-IV database and multiple analytical approaches\u0026mdash;including Kaplan-Meier analysis, Cox regression, restricted cubic spline modeling, and subgroup analyses\u0026mdash;we sought to provide evidence for improving risk stratification and clinical decision-making in critically ill MM patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eInitial extraction yielded 392 hospitalization episodes involving MM patients from the MIMIC-IV repository (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When individuals had multiple ICU encounters, only the earliest admission was retained. Subsequent exclusions encompassed ICU stays shorter than 24 hours, absent survival follow-up data, physiologically implausible laboratory values (e.g., LDH), and age below 18 years. After applying all eligibility filters, 271 index ICU admissions of MM patients constituted the final analytical cohort. Imputation outcomes for missing data across 115 candidate variables are detailed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline characteristics by BAR quartiles\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic and clinical profiles of MM patients stratified by BAR index quartiles: Q1 (0.220\u0026ndash;0.762), Q2 (0.763\u0026ndash;1.407), Q3 (1.452\u0026ndash;2.378), and Q4 (2.400\u0026ndash;6.815). The cohort had a median age of 72 years (IQR: 65\u0026ndash;80), with males representing 62% of the sample. Malignant cancer was the most prevalent comorbid condition (84%), followed by renal disease (41%) and heart failure (34%). Across ascending BAR quartiles (Q1 through Q4), a stepwise increase in patient age was observed (P\u0026thinsp;=\u0026thinsp;0.009), accompanied by progressively higher rates of heart failure (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), uncomplicated diabetes (P\u0026thinsp;=\u0026thinsp;0.001), and renal disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher BAR quartiles also exhibited elevated Charlson Comorbidity Index scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and increased creatinine concentrations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with worsening kidney function. Body temperature was marginally higher among Q1 and Q2 patients and marginally lower in Q4, with the inter-quartile variation reaching statistical significance (P\u0026thinsp;=\u0026thinsp;0.011). Although platelet counts trended lowest in Q4, this difference did not achieve statistical significance (P\u0026thinsp;=\u0026thinsp;0.175).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants classified by BAR index quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;271)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM without CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic solid tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50 [0.90, 2.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 [0.70, 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 [0.90, 1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00 [1.50, 4.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.45 [2.20, 5.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.17 [36.89, 37.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.33 [37.00, 38.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.33 [36.89, 38.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.11 [36.89, 37.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.06 [36.78, 37.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 [0.76, 2.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54 [0.41, 0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 [0.85, 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78 [1.51, 1.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.41 [3.00, 4.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are presented as median [Q1, Q3] or n (%). CHF, congestive heart failure; CPD, chronic pulmonary disease; DM without CC, diabetes without complications; CCI, Charlson Comorbidity Index; BAR, blood urea nitrogen to albumin ratio; LAR, lactate dehydrogenase to albumin ratio. P values from Kruskal-Wallis rank sum test or Pearson\u0026rsquo;s Chi-squared test.\u003c/p\u003e\n\u003ch3\u003eBaseline characteristics by LAR quartiles\u003c/h3\u003e\n\u003cp\u003e Supplementary Table S2 displays the demographic and clinical features of MM patients grouped according to LAR index quartiles: Q1 (3.410\u0026ndash;6.722), Q2 (6.735\u0026ndash;9.621), Q3 (9.821\u0026ndash;14.458), and Q4 (15.267\u0026ndash;62.682). Notably, the proportion of patients with metastatic solid tumors was significantly greater in the highest LAR quartile (P\u0026thinsp;=\u0026thinsp;0.048). Platelet counts were most depressed in Q4 (median: 125 \u0026times; 10⁹/L), and this inter-quartile disparity was statistically significant (P\u0026thinsp;=\u0026thinsp;0.02), potentially reflecting myelosuppression or more advanced disease in patients with elevated LAR.\u003c/p\u003e\n\u003ch3\u003eKaplan-Meier survival analysis\u003c/h3\u003e\n\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Kaplan-Meier estimation demonstrated statistically significant disparities in cumulative survival across the four BAR quartile strata for both the 30-day and 365-day endpoints (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Individuals in Q4 of BAR experienced markedly inferior survival at both time horizons relative to those in the lower three quartiles. A parallel pattern emerged for LAR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), where Q4 patients exhibited significantly diminished survival at both observation windows compared with the remaining quartile groups (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and P\u0026thinsp;=\u0026thinsp;0.003 for 30-day and 365-day mortality, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between BAR and mortality\u003c/h2\u003e \u003cp\u003eThe independent prognostic contribution of BAR was examined through Cox proportional hazards regression (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Treating BAR as a continuous predictor, each one-unit rise conferred a 33% excess hazard of 30-day death in the unadjusted model (HR\u0026thinsp;=\u0026thinsp;1.33; 95% CI: 1.13\u0026ndash;1.56; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Following multivariable adjustment in Model 2, the magnitude of this association strengthened (HR\u0026thinsp;=\u0026thinsp;1.43; 95% CI: 1.19\u0026ndash;1.72; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn quartile-based analyses relative to Q1, Q4 demonstrated a significantly heightened 30-day mortality hazard in both the unadjusted (HR\u0026thinsp;=\u0026thinsp;2.97; 95% CI: 1.27\u0026ndash;6.93; P\u0026thinsp;=\u0026thinsp;0.012) and adjusted frameworks (HR\u0026thinsp;=\u0026thinsp;6.19; 95% CI: 2.24\u0026ndash;17.1; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A monotonically increasing risk gradient was confirmed by significant trend tests in both models (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 each).\u003c/p\u003e \u003cp\u003eRegarding 365-day mortality, continuous BAR was linked to a 21% higher hazard in the unadjusted analysis (HR\u0026thinsp;=\u0026thinsp;1.21; 95% CI: 1.07\u0026ndash;1.32; P\u0026thinsp;=\u0026thinsp;0.002), an association that persisted and intensified upon covariate adjustment (HR\u0026thinsp;=\u0026thinsp;1.30; 95% CI: 1.13\u0026ndash;1.49; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On a quartile basis, Q4 showed a borderline elevation in 365-day mortality relative to Q1 before adjustment (HR\u0026thinsp;=\u0026thinsp;1.61; 95% CI: 0.96\u0026ndash;2.70; P\u0026thinsp;=\u0026thinsp;0.074), which became robustly significant and appreciably amplified after controlling for covariates (HR\u0026thinsp;=\u0026thinsp;2.94; 95% CI: 1.51\u0026ndash;5.72; P\u0026thinsp;=\u0026thinsp;0.001). Ordinal trend tests confirmed a graded risk escalation across quartile categories in both analytic frameworks (P for trend\u0026thinsp;=\u0026thinsp;0.009 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox regression results of BAR and mortality (30-day and 365-day)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e30-day mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e365-day mortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13, 1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.07, 1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19, 1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.40, 1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90, 5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74, 2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27, 6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96, 2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24, 2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06, 1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19, 1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13, 1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17, 1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45, 1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10, 7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96, 3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.24, 17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.51, 5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54, 3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.22, 1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: unadjusted; Model 2: adjusted for age, weight, malignant tumor, metastatic solid tumor, Charlson Comorbidity Index, body temperature, platelets, and creatinine. CI, confidence interval; HR, hazard ratio; BAR, blood urea nitrogen to albumin ratio; Ref, reference.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between LAR and mortality\u003c/h3\u003e\n\u003cp\u003eAn analogous Cox regression approach was applied to evaluate LAR as a mortality predictor (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Modeled continuously, each one-unit LAR increment conferred a 5% excess 30-day mortality hazard in the unadjusted analysis (HR\u0026thinsp;=\u0026thinsp;1.05; 95% CI: 1.03\u0026ndash;1.07; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This relationship endured after multivariable correction in Model 2 (HR\u0026thinsp;=\u0026thinsp;1.05; 95% CI: 1.02\u0026ndash;1.07; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWhen stratified by quartiles, Q4 carried a markedly elevated 30-day mortality risk versus Q1 in both the unadjusted (HR\u0026thinsp;=\u0026thinsp;3.63; 95% CI: 1.56\u0026ndash;8.42; P\u0026thinsp;=\u0026thinsp;0.003) and covariate-adjusted analyses (HR\u0026thinsp;=\u0026thinsp;4.14; 95% CI: 1.71\u0026ndash;10.0; P\u0026thinsp;=\u0026thinsp;0.002). Trend tests were strongly significant across both models (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with a progressive dose-response gradient linking LAR to early mortality.\u003c/p\u003e \u003cp\u003eFor the 365-day endpoint, each LAR unit increment translated to a 3% risk increase before adjustment (HR\u0026thinsp;=\u0026thinsp;1.03; 95% CI: 1.01\u0026ndash;1.05; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), modestly attenuated yet remaining significant after covariate correction (HR\u0026thinsp;=\u0026thinsp;1.02; 95% CI: 1.01\u0026ndash;1.04; P\u0026thinsp;=\u0026thinsp;0.004). In quartile-stratified analyses, Q4 exhibited significantly higher 365-day mortality than Q1 across both unadjusted (HR\u0026thinsp;=\u0026thinsp;1.87; 95% CI: 1.17\u0026ndash;2.98; P\u0026thinsp;=\u0026thinsp;0.008) and adjusted models (HR\u0026thinsp;=\u0026thinsp;1.85; 95% CI: 1.14\u0026ndash;3.00; P\u0026thinsp;=\u0026thinsp;0.012), accompanied by statistically significant ordinal trends (P for trend\u0026thinsp;=\u0026thinsp;0.011 and P\u0026thinsp;=\u0026thinsp;0.021, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox regression results of LAR and mortality (30-day and 365-day)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e30-day mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e365-day mortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03, 1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01, 1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37, 3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50, 1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83, 5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48, 1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56, 8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.17, 2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24, 2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05, 1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02, 1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01, 1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42, 3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56, 1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87, 5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.54, 1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.71, 10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.14, 3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26, 2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.03, 1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: unadjusted; Model 2: adjusted for age, weight, malignant tumor, metastatic solid tumor, Charlson Comorbidity Index, body temperature, platelets, and creatinine. CI, confidence interval; HR, hazard ratio; LAR, lactate dehydrogenase to albumin ratio; Ref, reference.\u003c/p\u003e\n\u003ch3\u003eNonlinear relationship assessment\u003c/h3\u003e\n\u003cp\u003eRestricted cubic splines (RCS) analysis was performed to evaluate potential nonlinear relationships between the hematologic indices and mortality outcomes. For BAR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the analysis demonstrated a significant nonlinear association with 30-day all-cause mortality risk. In the unadjusted model (Model 1), BAR showed a significant nonlinear relationship with mortality risk (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with relatively low risk when BAR\u0026thinsp;\u0026lt;\u0026thinsp;1.5 and a rapid increase in risk as BAR levels rose. In the adjusted model (Model 2), this trend was more pronounced (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with extremely low risk in the lower BAR range and significantly elevated risk in the higher BAR range (\u0026gt;\u0026thinsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn analogous RCS evaluation for LAR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) likewise identified a significant positive association with both early and late mortality. Elevated LAR values were uniformly linked to amplified mortality hazard regardless of adjustment status (all models P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Nonlinearity testing again proved non-significant (P\u0026thinsp;\u0026ge;\u0026thinsp;0.05), confirming a predominantly linear LAR\u0026ndash;mortality relationship.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eTo evaluate whether the observed prognostic associations were robust across clinically meaningful patient subsets, pre-specified stratified analyses were conducted (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). For BAR, the adjusted hazard ratios consistently exceeded unity across every pre-defined stratum for both time endpoints, with the majority reaching statistical significance. The strongest point estimate emerged among patients with uncomplicated diabetes, where the 30-day mortality HR reached 3.07 (95% CI: 1.01\u0026ndash;9.30; P\u0026thinsp;=\u0026thinsp;0.047), suggesting heightened vulnerability to BAR elevation in this subgroup. Effect magnitudes were broadly comparable across the remaining strata defined by age, sex, heart failure, chronic lung disease, and renal disease. No statistically significant interaction effects were detected for any subgroup variable (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating stable predictive performance of BAR across heterogeneous patient profiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor LAR, elevated values were associated with increased hazards for both endpoints in the majority of examined strata, with point estimates exceeding 1.0 and reaching statistical significance in most instances. However, formal interaction testing identified statistically significant effect modification by age for the 365-day endpoint (P for interaction\u0026thinsp;=\u0026thinsp;0.032) and by diabetes status for the same outcome (P for interaction\u0026thinsp;=\u0026thinsp;0.041). The remaining stratification variables (sex, heart failure, chronic lung disease) exhibited no significant effect modification (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), supporting overall consistency of the LAR\u0026ndash;mortality association across most clinically relevant subpopulations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective evaluation of 271 critically ill MM patients sourced from the MIMIC-IV database, we found that both BAR and LAR independently predict early and late mortality. In particular, after comprehensive covariate adjustment, every unit elevation in BAR corresponded to a 43% higher hazard of 30-day death and a 30% greater hazard of 365-day death. Analogously, each unit increment in LAR translated to 5% and 2% excess risks of 30-day and 365-day mortality, respectively. Gradient analyses corroborated that individuals occupying the uppermost quartiles of either index faced substantially heightened mortality relative to those in the lowest quartiles, with these relationships demonstrating stability across the majority of clinical strata examined. To our knowledge, this is the first systematic evaluation of the prognostic value of both BAR and LAR in ICU-managed MM patients.\u003c/p\u003e \u003cp\u003eThese findings are consistent with established MM pathobiology. BAR captures two key dimensions of critical illness: impaired renal clearance (elevated BUN) and the nutritional-inflammatory status (low albumin). Renal injury is common in MM, affecting up to 50% of patients at diagnosis, primarily due to light chain cast nephropathy, hypercalcemia-induced tubular damage, and amyloid deposition.[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] Hypoalbuminemia in this setting reflects not only malnutrition but also systemic inflammation and impaired hepatic synthetic function, both of which independently predict poor outcomes in critically ill patients.[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] Prior investigations have validated the prognostic utility of BAR in septic patients,[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] acute pancreatitis,[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] and chronic heart failure,[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] disorders sharing common pathophysiological substrates of renal-metabolic derangement. Our findings extend this evidence to ICU-managed MM patients, suggesting that BAR may capture a composite risk profile beyond what BUN or albumin alone conveys. Notably, RCS modeling revealed a significant nonlinear BAR–30-day mortality relationship, with a marked acceleration of risk above a BAR of approximately 2, suggesting a potential clinical threshold for acute deterioration.[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e] In contrast, the largely linear BAR–365-day mortality relationship suggests that worsening BUN-albumin imbalance gradually accumulates long-term risk without a clear threshold. For LAR, LDH serves as a well-established surrogate marker for tumor burden and tissue damage in hematological malignancies,[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] while the albumin denominator again captures the inflammatory-nutritional axis. The prognostic value of LAR has been demonstrated in diffuse large B-cell lymphoma,[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] colorectal cancer,[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] and non-small cell lung cancer.[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e] A recent pooled analysis further substantiated the prognostic value of LAR across a spectrum of malignant diseases.[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] In the present cohort, the essentially linear dose-response gradient linking LAR to both mortality horizons implies that each incremental LAR rise justifies proportionally heightened clinical attention. Compared with conventional severity scores such as APACHE II[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] and SOFA,[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] BAR and LAR offer practical advantages: they can be calculated from routine admission laboratory tests and may dynamically reflect changes in systemic stress, nutritional status, and tumor activity, potentially facilitating bedside decision-making.\u003c/p\u003e \u003cp\u003eThe subgroup analyses provided information on the generalizability and population-specific performance of these biomarkers. For BAR, the prognostic association was consistent across all pre-specified strata, with no significant interaction effects (all P for interaction \u0026gt; 0.05), supporting broad clinical applicability. In the diabetic subgroup without complications, BAR yielded the largest hazard ratio for 30-day mortality (HR = 3.07; 95% CI: 1.01–9.30; P = 0.047), possibly due to the combined effects of diabetic nephropathy and metabolic disturbance on BUN accumulation, which may enhance BAR’s prognostic sensitivity in this subgroup. Regarding LAR, although its overall prognostic utility remained stable across most strata, noteworthy effect modification was observed for age (P for interaction = 0.032) and diabetic status (P for interaction = 0.041) in relation to the 365-day mortality endpoint. The age-dependent interaction may stem from differences in tumor biology and therapeutic tolerance between younger and older MM patients; the former typically undergo more aggressive chemotherapy and possess greater physiological resilience,[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e] potentially modifying the prognostic impact of LAR. The diabetes interaction likely reflects the metabolic interplay between glucose dysregulation, altered LDH metabolism, and accelerated protein catabolism. These findings suggest that while both indices are broadly applicable, clinicians should consider population-specific thresholds guided by established staging systems,[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] particularly for older patients and those with metabolic comorbidities, to optimize risk stratification and guide treatment decisions.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the retrospective single-center design using the MIMIC-IV database may introduce selection bias and limit generalizability to other settings or populations.[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e] Second, despite multivariable adjustment, unmeasured confounders—particularly MM-specific treatments such as chemotherapy, autologous stem cell transplantation, or immunotherapy—may have influenced the results. Third, external validation in independent cohorts or prospective studies was not performed, which is necessary to confirm these findings. Fourth, the modest sample size (n = 271) may have limited statistical power for detecting interaction effects in smaller subgroups. Larger, prospective multicenter studies are needed to validate these results and establish optimal clinical thresholds.\u003c/p\u003e \u003cp\u003eIn conclusion, BAR and LAR are independent prognostic markers for 30-day and 365-day all-cause mortality in ICU-admitted MM patients, with dose-dependent risk associations that are consistent across most clinical subgroups. As simple indices derived from routine laboratory tests, BAR and LAR may serve as practical bedside tools for early risk stratification in this high-acuity population. Incorporating these indices into risk assessment algorithms may help identify high-risk patients, guide treatment intensity, and potentially improve outcomes. Prospective multicenter studies and mechanistic investigations are needed to confirm these findings and explore whether combining BAR and LAR into a prognostic scoring system could further improve risk prediction in critically ill cancer patients.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eSource of data\u003c/h2\u003e\u003cp\u003eThis retrospective investigation drew upon data housed in the freely accessible Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.2) repository.[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] As an expanded successor to the earlier MIMIC-III platform, MIMIC-IV incorporates updated data elements and restructured relational tables. The database captures comprehensive clinical records spanning more than 190,000 individuals and 450,000 hospital encounters documented from 2008 through 2022 at Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts. Recorded variables encompass demographic profiles, laboratory measurements, pharmacological treatments, physiological parameters, procedural interventions, diagnostic classifications, therapeutic regimens, and longitudinal survival data. The MIMIC-IV project operates under Institutional Review Board approval granted by MIT and BIDMC (protocols MIT-0403000206 and BIDMC-2001P001699); all records are fully de-identified. The investigators completed the required NIH human research protection training and CITI certification prior to accessing the database. Individual informed consent was not required because the dataset contains no identifiable patient information.\u003c/p\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eWe queried the MIMIC-IV database to identify adult individuals (age ≥ 18 years) who underwent ICU admission with a documented diagnosis of MM. Diagnostic ascertainment of MM relied on International Classification of Diseases coding algorithms encompassing both ICD-9 and ICD-10 nomenclatures.[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] The following inclusion criteria governed participant selection: (1) confirmed MM diagnosis with concurrent ICU admission; (2) minimum age of 18 years; (3) retention of only the initial ICU episode for patients with repeated admissions; and (4) presence of comprehensive clinical records obtained within the first 24 hours post-ICU entry, accompanied by unambiguous survival outcome documentation. To maintain data integrity, individuals exhibiting physiologically implausible extremes in clinical parameters were additionally excluded. Furthermore, patients whose ICU stay was curtailed by discharge or death within the first 24 hours were removed from the analytical cohort.\u003c/p\u003e\u003ch2\u003eData extraction\u003c/h2\u003e\u003cp\u003eAll data retrieval was accomplished through Structured Query Language (SQL) queries executed against the MIMIC-IV relational database. The variables interrogated were organized into the following domains: (1) Demographic characteristics: age, sex, body weight, and self-reported race/ethnicity. (2) Comorbid conditions (ascertained via ICD coding): myocardial infarction, heart failure, peripheral arterial disease, cerebrovascular events, dementia, chronic lung disease, peptic ulcer, non-severe hepatic disease, uncomplicated and complicated diabetes mellitus, paraplegia, chronic kidney disease, primary malignancy, advanced liver disease, and metastatic solid neoplasm. (3) Hemodynamic and physiological measurements: heart rate, systolic and diastolic blood pressure (SBP, DBP), mean arterial pressure (MAP), respiratory rate, core temperature, pulse oximetry (SpO₂), and capillary blood glucose. (4) Biochemical and hematological parameters: hematocrit, hemoglobin concentration, platelet count, total leukocyte count (WBC), anion gap, serum bicarbonate, BUN, ionized calcium, chloride, serum creatinine, sodium, potassium, coagulation profile including prothrombin time (PT) and activated partial thromboplastin time (APTT), LDH, and serum albumin. (5) Illness severity indices: the Sequential Organ Failure Assessment (SOFA) score,[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] with its constituent subsystem components (coagulation, cardiovascular, neurological, and renal domains); and the Charlson Comorbidity Index (CCI).[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] Any variable exhibiting a missing data proportion exceeding 20% was omitted from analytical consideration. Where missingness fell below this threshold, imputation was carried out using a random forest–based method available in the mice R package.[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003ch2\u003eCalculation of hematologic indices\u003c/h2\u003e\u003cp\u003eThe key exposure variables were two composite hematologic ratios calculated from laboratory data obtained at the time of the initial ICU admission. These ratios were the lactate dehydrogenase to albumin ratio (LAR) and the blood urea nitrogen to albumin ratio (BAR). The formulae were as follows: LAR = serum lactate dehydrogenase (U/L) divided by serum albumin (g/L); BAR = blood urea nitrogen (mg/dL) divided by serum albumin (g/L).\u003c/p\u003e\u003ch2\u003eOutcomes\u003c/h2\u003e\u003cp\u003eWe designated 30-day all-cause mortality after the index ICU admission as the primary outcome; the secondary outcome was 365-day all-cause mortality measured from the same time point.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe Kaplan-Meier method was used to estimate time-to-event distributions for primary and secondary endpoints, with patients grouped by their hematologic composite index values.[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] Study participants were allocated into four strata corresponding to the quartile boundaries of BAR and LAR distributions. Between-stratum survival disparities were evaluated using the log-rank test. Crude Cox regression was first performed to estimate the unadjusted association between each composite index and both mortality endpoints.[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] Subsequent multivariable Cox models included covariates selected on the basis of clinical relevance or demonstrable univariate prognostic association. Model 1 evaluated the hematologic indices in an unadjusted framework. Model 2 incorporated adjustments for age, body weight, presence of malignant neoplasm, metastatic disease, Charlson Comorbidity Index, body temperature, platelet count, and serum creatinine. Across both models, the lowest quartile of each index constituted the referent category. Both continuous and quartile-based parameterizations of each index were employed to characterize potential graded relationships with outcome risk. Ordered trend tests across quartile strata were performed to assess the presence of monotonic risk gradients.\u003c/p\u003e\u003cp\u003eRCS regression with pre-specified knot placement was used to examine whether the hematologic indices exhibited nonlinear associations with all-cause mortality.[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] Three knot positions were specified across the empirical distribution of each index. Adjustment covariates including age, sex, and additional clinical variables were incorporated to mitigate confounding. The presence of nonlinearity was formally evaluated through likelihood ratio testing. Findings were depicted graphically as dose-response curves relating each index to mortality hazard, accompanied by 95% confidence bands.\u003c/p\u003e\u003cp\u003ePre-planned stratified analyses were further performed by age group (\u0026lt; 65 vs. ≥65 years), sex, and selected comorbidities (heart failure, chronic lung disease, uncomplicated diabetes, renal disease) to test the consistency of the primary findings across clinically meaningful patient subsets. Potential effect modification was explored by adding product interaction terms to the respective Cox models. All hypothesis tests were two-sided, with a significance level of α = 0.05.\u003c/p\u003e\u003cp\u003eAnalyses were carried out in R (version 4.3.3; R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were expressed as counts and percentages. Normality of continuous data was assessed using Shapiro-Wilk or Anderson-Darling testing. Normally distributed continuous variables were summarized as mean ± SD; non-normally distributed ones as median (IQR, 25th–75th percentile). Group-level comparisons of categorical data used the χ2 test or Fisher exact test. Continuous variables were compared between groups with the Student t-test (parametric) or the Wilcoxon rank-sum test (non-parametric), as dictated by distributional properties.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analysed in this study are available in the MIMIC-IV repository (version 2.2), accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://physionet.org/content/mimiciv/2.2/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Access to MIMIC-IV requires completion of a recognized course in human research subject protection and a signed data use agreement. The SQL queries and R scripts used for data extraction and analysis are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC) for maintaining and providing open access to the MIMIC-IV database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Research Project of the Education Department of Anhui Province (Grant No. 2025AHGXZK31419).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.X. and C.J. contributed equally to this work and share first authorship. H.X. conceived and designed the study, performed data extraction and statistical analysis, interpreted the results, and drafted the manuscript. C.J. contributed to study design, data extraction, and critical revision of the manuscript. D.C. and J.L. contributed to data collection and management. L.W. contributed to data curation and quality control. F.C. contributed to methodology review and validation. L.Z. supervised the study and provided critical revision. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Information accompanies this paper. Correspondence and requests for materials should be addressed to H.X. ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R. L., Giaquinto, A. N. \u0026amp; Jemal, A. Cancer statistics, 2024. CA Cancer J. Clin. 74, 12\u0026ndash;49 (2024).\u003c/li\u003e\n\u003cli\u003ePadala, S. A. et al. Epidemiology, staging, and management of multiple myeloma. Med. Sci. (Basel) 9, 3 (2021).\u003c/li\u003e\n\u003cli\u003eHuang, J. et al. The epidemiological landscape of multiple myeloma: a global cancer registry estimate of disease burden, risk factors, and temporal trends. Lancet Haematol. 9, e670\u0026ndash;e677 (2022).\u003c/li\u003e\n\u003cli\u003eCowan, A. J. et al. Diagnosis and management of multiple myeloma: a review. JAMA 327, 464\u0026ndash;477 (2022).\u003c/li\u003e\n\u003cli\u003eRajkumar, S. V. 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Nomograms based on lactate dehydrogenase to albumin ratio for predicting survival in colorectal cancer. Int. J. Med. Sci. 19, 1003\u0026ndash;1012 (2022).\u003c/li\u003e\n\u003cli\u003eYang, F. et al. Prognostic value of blood urea nitrogen to serum albumin ratio for acute kidney injury and in-hospital mortality in intensive care unit patients with intracerebral haemorrhage: a retrospective cohort study using the MIMIC-IV database. BMJ Open 13, e069503 (2023).\u003c/li\u003e\n\u003cli\u003eZhang, L. et al. Blood urea nitrogen to serum albumin ratio: a novel mortality indicator in intensive care unit patients with coronary heart disease. Sci. Rep. 14, 7466 (2024).\u003c/li\u003e\n\u003cli\u003eDimopoulos, M. A. et al. Management of multiple myeloma-related renal impairment: recommendations from the International Myeloma Working Group. Lancet Oncol. 24, e293\u0026ndash;e311 (2023).\u003c/li\u003e\n\u003cli\u003eArends, J. et al. Cancer cachexia in adult patients: ESMO Clinical Practice Guidelines. ESMO Open 6, 100092 (2021).\u003c/li\u003e\n\u003cli\u003eJohnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023).\u003c/li\u003e\n\u003cli\u003eGupta, M. et al. An extensive data processing pipeline for MIMIC-IV. Proc. Mach. Learn. Res. 193, 311\u0026ndash;325 (2022).\u003c/li\u003e\n\u003cli\u003eSinger, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315, 801\u0026ndash;810 (2016).\u003c/li\u003e\n\u003cli\u003eCharlson, M. E., Carrozzino, D., Guidi, J. \u0026amp; Patierno, C. Charlson Comorbidity Index: a critical review of clinimetric properties. Psychother. Psychosom. 91, 8\u0026ndash;35 (2022).\u003c/li\u003e\n\u003cli\u003evan Buuren, S. \u0026amp; Groothuis-Oudshoorn, K. mice: Multivariate Imputation by Chained Equations in R. J. Stat. 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Oncol. 33, 2863\u0026ndash;2869 (2015).\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Agostino, M. et al. Second revision of the International Staging System (R2-ISS) for overall survival in multiple myeloma: a European Myeloma Network (EMN) report within the HARMONY project. J. Clin. Oncol. 40, 3406\u0026ndash;3418 (2022).\u003c/li\u003e\n\u003cli\u003eLi, W. et al. Blood urea nitrogen to albumin ratio as predictor of mortality among acute pancreatitis patients in ICU: a retrospective cohort study. PLoS One 20, e0323321 (2025).\u003c/li\u003e\n\u003cli\u003eKuang, M. et al. Blood urea nitrogen to albumin ratio as a robust predictor of in-hospital mortality in patients with predicted severe acute pancreatitis: a retrospective multicenter observational study. Int. J. Surg. 110, 1295\u0026ndash;1307 (2024).\u003c/li\u003e\n\u003cli\u003eCai, Y. et al. 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Epidemiol. 183, 758\u0026ndash;764 (2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple myeloma, Intensive care unit, Blood urea nitrogen to albumin ratio, Lactate dehydrogenase to albumin ratio, Mortality, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-9224218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9224218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eValidated prognostic tools for critically ill multiple myeloma (MM) patients are lacking. This study evaluated the prognostic value of the blood urea nitrogen to albumin ratio (BAR) and lactate dehydrogenase to albumin ratio (LAR) for mortality in ICU-admitted MM patients using MIMIC-IV. A total of 271 adult MM patients with an index ICU admission were included. BAR and LAR were calculated from laboratory values obtained within 24 hours of ICU entry. Associations with 30-day (primary) and 365-day (secondary) all-cause mortality were assessed using Kaplan-Meier analysis, multivariable Cox regression, restricted cubic spline modeling, and subgroup analyses. After covariate adjustment, each one-unit BAR increase was associated with a 43% higher 30-day mortality hazard (HR 1.43; 95% CI 1.19–1.72; P \u0026lt; 0.001) and a 30% higher 365-day mortality hazard (HR 1.30; 95% CI 1.13–1.49; P \u0026lt; 0.001). For LAR, each one-unit increase conferred 5% (HR 1.05; 95% CI 1.02–1.07; P \u0026lt; 0.001) and 2% (HR 1.02; 95% CI 1.01–1.04; P = 0.004) excess hazards for 30-day and 365-day mortality, respectively. These associations were consistent across most clinical subgroups. BAR and LAR are independently predictive of short- and long-term mortality in ICU-managed MM patients and may serve as accessible bedside risk stratification tools.\u003c/p\u003e","manuscriptTitle":"BAR and LAR as mortality predictors in critically ill multiple myeloma patients: a MIMIC-IV study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 05:51:16","doi":"10.21203/rs.3.rs-9224218/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-31T23:06:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T12:33:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-26T14:41:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T14:41:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-25T13:59:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"034e194e-2361-4efc-84f0-82ec46c45233","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65531518,"name":"Health sciences/Biomarkers"},{"id":65531519,"name":"Health sciences/Diseases"},{"id":65531520,"name":"Health sciences/Medical research"},{"id":65531521,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-06T05:51:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 05:51:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9224218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9224218","identity":"rs-9224218","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}