ArticleRenal failure2025
Atherogenic index of plasma and risk of acute kidney injury in critically ill patients: a multi-cohort study with machine learning and SHAP analysis.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Atherogenic index of plasma is associated with poor prognosis in diabetic patients with acute kidney injury.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abnormal lipid metabolism poses a risk for acute kidney injury (AKI), a prevalent complication among critically ill patients. Early detection and intervention of AKI are essential; however, reliable lipid-derived predictive biomarkers remain understudied. This study aimed to investigate the relationship between the novel lipid biomarker atherogenic index of plasma (AIP) and AKI in critically ill patients and to construct an AIP-integrated early warning model to identify high-risk patients. Patient data were derived from the MIMIC-IV database (training and internal validation sets) and a local hospital cohort (external validation set). Multivariable logistic regression was used to evaluate the association between AIP and AKI. Restricted cubic spline (RCS) regression was utilized to explore potential nonlinear relationships. 13 machine learning algorithms were applied to develop and validate prediction models. Additionally, the Shapley Additive Explanations (SHAP) method enhance model interpretability. We analyzed 6,062 ICU patients from the MIMIC-IV database and 833 patients from a Chinese hospital cohort. AIP was identified as an independent risk factor for AKI in multivariable logistic regression analyses. RCS regression revealed a nonlinear association between AIP and AKI. The XGBoost + AIP model achieved superior performance with AUCs of 0.8127 (internal) and 0.7228 (external), significantly outperforming the SOFA score (AUC 0.6968). Decision curve analysis (DCA) confirmed its clinical applicability. The SHAP method provides critical validation support for the reliability of the XGBoost model. AIP serves not only as a predictive biomarker but as a metabolic phenotype, potentially enabling AI-guided precision prevention strategies in future digital twin-driven AKI care pathways.
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.