Background: The lactate albumin ratio (LAR), a simple prognostic marker used in intensive care units (ICUs), combines lactate and serum albumin levels to predict patient outcomes. Despite its potential, the predictive accuracy of the LAR remains insufficiently explored. This study aimed to assess the usefulness of the LAR in predicting mortality among patients in the ICU.

Methods: This retrospective study conducted a secondary analysis of prospectively obtained clinical data from the Japanese Intensive Care Patient Database. We included all patients admitted to ICUs between 2015 and 2021, excluding those under the age of 16 years. The main outcome was in-hospital mortality. The LAR predictive value for this outcome was assessed by examining the area under the receiver operating characteristic curve and comparing it against prognostic indicators such as age, lactate, albumin and Sequential Organ Failure Assessment score. LAR shape was assessed using unrestricted spline curves, and the optimal cut-off value was identified from sensitivity and negative likelihood ratio. Subgroup analysis was used to evaluate the predictive accuracy of the LAR across different patient attributes and clinical scenarios.

Results: Of 2 34 774 cases analysed, in-hospital mortality was 8.8% (20 723 deaths). The LAR had an area under the curve of 0.761 (95%CI 0.757 to 0.765), indicating a fair predictive performance for in-hospital mortality. Unrestricted spline curves demonstrated that LAR can predict mortality through a monotonic positive dose-response relationship with 0.4 as the optimal cut-off value. In subgroup analysis, areas under the curve were significantly higher in subgroups defined by younger age, female sex, unplanned ICU admission, non-surgical patients, non-infectious patients, non-heart failure patients and lack of end-stage renal disease.

Conclusions: The LAR might be a useful predictor for screening mortality in ICU patients. However, further research to establish appropriate cut-off values for the LAR and identify the optimal target population is warranted.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667448PMC
http://dx.doi.org/10.1136/bmjopen-2024-088926DOI Listing

Publication Analysis

Top Keywords

predictive accuracy
12
lactate albumin
12
intensive care
12
in-hospital mortality
12
lar
10
albumin ratio
8
care units
8
accuracy lar
8
unrestricted spline
8
spline curves
8

Similar Publications

Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.

Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.

View Article and Find Full Text PDF

Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.

J Imaging Inform Med

January 2025

School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.

Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.

View Article and Find Full Text PDF

We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.

View Article and Find Full Text PDF

Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.

View Article and Find Full Text PDF

Predictive model performance may deteriorate when applied to data sources that were not used for training, thus, external validation is a key step in successful model deployment. As access to patient-level external data sources is typically limited, we recently proposed a method that estimates external model performance using only external summary statistics. Here, we benchmark the proposed method on multiple tasks using five large heterogeneous US data sources, where each, in turn, plays the role of an internal source and the remaining-external.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!