Observational studyRenal failure2025
A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.
Observational study in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Article
- Development of a machine learning-based prediction model for hypothyroidism-associated delirium in elderly hypothyroid patients in the intensive care unit.BMC geriatrics · 2026Article
- Current Status and Future Prospects of Research on Sepsis-Related Acute Kidney Injury.International journal of molecular sciences · 2026Review
- Research advances on acute kidney injury and brain dysfunction.Frontiers in nephrology · 2026Review
- Association of Cumulative Average Uric Acid to HDL Cholesterol Ratio with the Risk of Rapidly Declining Renal Function: A Retrospective Community-Based Cohort Study in Shanghai Undertaken on Elderly Subjects with Hypertension and Type 2 Diabetes Mellitus Patients.Risk management and healthcare policy · 2026Article
- Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.European journal of medical research · 2025Article
- Toward better outcomes in pediatric septic shock: the role of practical risk models.Pediatric nephrology (Berlin, Germany) · 2025Article
- Article
- Early prediction of incident delirium in traumatic brain injury: a multicenter validated and interpretable machine learning approach.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis-associated acute kidney injury (SA-AKI) patients in the ICU often suffer from sepsis-associated delirium (SAD), which is linked to unfavorable outcomes. This research aimed to develop a machine learning-based model for early SAD prediction in SA-AKI patients. Data was sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). Various models, including logistic regression, extreme gradient boosting (XGBoost), random forest, k-nearest neighbors, support vector machine, decision tree, and naive Bayes, were constructed and evaluated. The XGBoost model emerged as the best, with an internal validation AUROC of 0.775 and an external validation AUROC of 0.687. Unlike traditional delirium assessments, this model enables earlier SAD prediction and is suitable for patients who are hard to assess conventionally.
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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.