Early identification of acute kidney injury progression in critically ill patients with sepsis: interpretable machine learning approach

Clicks: 11
ID: 319040
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #22 of 129 articles by views in clinical kidney journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 129 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Background Acute kidney injury (AKI) is a common and severe complication of sepsis and is often associated with a poor prognosis. However, there is still a lack of an effective prediction model for early identification of AKI progression in critical septic patients, defined as AKI stage 1 or 2 to stage 3 within 7 days after diagnosis of sepsis-associated AKI (SA-AKI). Methods We extracted the clinical data of patients with SA-AKI from the MIMIC datasets, eICU-CRD, and SICdb, with the MIMIC-IV (version 3.1) database used for training and internal validation, the MIMIC-III Clinical Database CareVue subset used as temporal validation, and the eICU-CRD and SICdb used as external validation. Lasso regression and recursive feature elimination were used for feature selection. Six machine learning (ML) algorithms, including k-nearest neighbors, logistic regression, naive bayes, random forest (RF), support vector machine, and decision tree, were utilized to establish the prediction model. Model performance was assessed using ROC curves, calibration curves and DCA analysis. SHapley Additive exPlanations (SHAP) method was used for the interpretation of the models. Results The MIMIC-IV, MIMIC-III subset, eICU-CRD and SICdb included 9193, 2178, 10332, and 1701 patients with SA-AKI. 12 variables were selected for model construction, including weight, liver disease, mechanical ventilation, systolic blood pressure, hemoglobin, glucose, blood urea nitrogen, creatinine, chloride, anion gap, and urine output. A RF model achieved the best performance in both internal, temporal and external validation (AUC is 0.779, 0.758 and 0.713, respectively). A user-friendly platform was built to early predict SA-AKI progression for clinician use. Conclusion ML could be a useful tool for predicting AKI progression in septic patients. We developed an RF model to predict the risk of SA-AKI progression, which may provide a reference for early identification and prompt intervention of high-risk group.
Reference Key
openalex_W7166292242 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xiaodong You, Yuehua Chen, J B Chen, Xinyi Mao, Yifei Wang, Zhongman Zhang, Z Z Zhou, Wei Li, Yong Mei, Zhu Y, Peipei Huang, Xufeng Chen
Journal clinical kidney journal
Year 2026
DOI
10.1093/ckj/sfag216
URL
https://doi.org/10.1093/ckj/sfag216
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.