ArticleFrontiers in nutrition2026
LDAR is superior to other albumin-derived indices in predicting 28-day ICU mortality in critically ill patients with intracerebral hemorrhage: a two-cohort study.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Spontaneous intracerebral hemorrhage (ICH) carries a high risk of poor outcomes, making early identification of high-risk patients crucial for improving prognosis. Albumin-derived composite indices can reflect the systemic inflammatory, nutritional, and metabolic status, but their predictive performance in the ICH population lacks systematic comparison. This study aimed to evaluate the prognostic value of six albumin-derived composite indices for 28-day ICU mortality in critically ill patients with ICH. Methods: This study included 1,638 adult patients with first-time ICH admission from the MIMIC-IV database as the derivation cohort, and 493 patients with ICH from the People's Hospital of Xingyi City as the external validation cohort. Clinical data within 24 h of admission were collected. Six indices were calculated: red blood cell distribution width to albumin ratio (RAR), creatinine to albumin ratio (CAR), anion gap to albumin ratio (AGAR), total bilirubin to albumin ratio (TAR), blood urea nitrogen to albumin ratio (UAR), and lactate dehydrogenase to albumin ratio (LDAR, log-transformed for analysis). Cox regression, restricted cubic splines (RCS), and Kaplan-Meier (KM) curves were used to analyze the association between each index and 28-day ICU mortality. Predictive performance was compared using receiver operating characteristic (ROC) curves. The incremental predictive value of log(LDAR) beyond traditional critical care scores was also assessed. Results: In the derivation cohort, 295 patients (18.0%) died within 28 days. After multivariable adjustment, log(LDAR) (HR = 1.68, 95%CI: 1.35-2.10), RAR (HR = 1.09, 95%CI: 1.01-1.19), and TAR (HR = 1.27, 95%CI: 1.16-1.40) were independent risk factors for 28-day mortality. RCS analysis revealed nonlinear associations with mortality risk for log(LDAR), TAR, and UAR (all P-non-linear < 0.05), whereas RAR showed a linear positive correlation (P-non-linear = 0.467). ROC curve analysis demonstrated that log(LDAR) had the highest predictive efficacy (AUC = 0.695), significantly outperforming the other indices (all DeLong test Conclusion: Among six albumin-derived composite indices, log(LDAR) offers superior predictive value for 28-day ICU mortality in critically ill ICH patients. It significantly enhances the risk stratification capability of traditional critical illness scores and may serve as a useful tool for early clinical screening.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.