ArticleAmerican journal of translational research2026
Prediction of hospital length of stay in decompensated cirrhosis using hematologic and inflammatory indices.
Article in American journal of translational research, 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
objectiveTo identify predictors of prolonged hospital length of stay (LOS) in decompensated cirrhosis (DC) patients and to develop a prediction model based on hematologic and inflammatory indices.
methodsWe retrospectively studied 338 hospitalized patients with DC. Admission blood indices reflecting hematologic status (hemoglobin, hematocrit, red blood cell (RBC) count, platelets, mean corpuscular volume, red cell distribution width [RDW]) and inflammation (monocyte-to-lymphocyte ratio [MLR], neutrophil-monocyte-lymphocyte ratio [NMLR], systemic inflammation response index [SIRI]) were compared between patients with LOS ≥ 7 days vs < 7 days. Multivariable logistic regression and machine learning were used to identify independent predictors of LOS ≥ 7 days, with model performance assessed by area under the receiver operating characteristic curve (AUC) on a held-out test set.
resultsOf 338 patients (median age 58 years, 33% female), 52% had LOS ≥ 7 days. Prolonged LOS was associated with more severe cytopenias (lower hemoglobin, hematocrit, RBC count, platelets) and heightened inflammation (higher RDW and inflammatory ratios; all P < 0.05). Nonlinear thresholds were observed. An admission RBC count < 5.2 × 10
conclusionsAdmission hematologic and inflammatory markers are independent predictors of prolonged LOS in DC. A machine learning model derived from routinely available low-cost tests provided moderate risk stratification for identifying patients at increased risk and supporting early clinical management.
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.