ArticleFrontiers in medicine2026
LDAR outperforms other albumin-derived indices in predicting 28-day ICU mortality in critically ill myocardial infarction patients: a two-cohort study.
Article in Frontiers in medicine, 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early risk stratification is crucial for improving outcomes in critically ill patients with acute myocardial infarction (AMI). Albumin-derived composite indices hold promise as convenient and effective predictive tools, but their relative efficacy and clinical utility remain unclear. Methods: This two-cohort retrospective analysis utilized a derivation cohort from the MIMIC-IV public database and an external validation cohort from the ICU of Guizhou Medical University Affiliated Hospital. Six albumin-derived composite indices were evaluated. Statistical analyses employed Cox proportional hazards regression models to assess their association with mortality. Predictive performance was compared using the area under the receiver operating characteristic curve (AUC) and Delong's test. A multivariate risk prediction model was developed based on key prognostic variables selected by multiple machine learning algorithms. Results: The study included 4,850 critically ill AMI patients (4,210 in the derivation cohort, 640 in the validation cohort). Multivariable-adjusted analysis identified the red cell distribution width to Albumin Ratio (RAR), Urea nitrogen to Albumin Ratio (UAR), and Lactate Dehydrogenase to Albumin Ratio (LDAR) as independent predictors of 28-day ICU mortality. Among these, LDAR demonstrated the strongest predictive ability, with an AUC of 0.702 in the derivation cohort, a finding robustly validated externally (AUC = 0.703). Subgroup analysis indicated consistent predictive value across most populations but revealed a significant interaction with hyperlipidemia. Incorporating LDAR into traditional critical illness scores (e.g., APACHE II, SOFA) significantly improved their predictive discrimination (all Delong's test Conclusion: Among the six albumin-derived composite indices, LDAR offers the best independent and incremental predictive value for 28-day ICU mortality in critically ill AMI patients. Its interaction with hyperlipidemia suggests potential for targeted risk stratification. The machine learning model incorporating LDAR and other variables demonstrates robust performance, providing a promising tool for the early clinical identification of high-risk patients.
Indexed as
Identifiers
What Socratic 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 Socratic graph.