ArticleNature communications2025
Development and validation of a real-time prediction model for acute kidney injury in hospitalized patients.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for sepsis-associated acute kidney injury: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Article
- Article
- Lactate in acute kidney injury: pathobiology, risk stratification, and clinical interpretation.Renal failure · 2026Review
- Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning.Renal failure · 2026Article
- Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.Nature communications · 2026Article
- Impact of an Artificial Intelligence-Powered Clinical Decision Support System for Acute Kidney Injury Prevention in the Intensive Care Unit: Single-Center Uncontrolled Before-and-After Implementation Study.JMIR formative research · 2026Article
- Challenges and Solutions in Deploying Systematized Nomenclature of Medicine-Clinical Terms in the Chinese Healthcare Context.Health care science · 2026Article
- CBC-derived inflammatory indices predict adverse outcomes in geriatric emergency patients with acute infectious diarrhea: a MIMIC-IV analysis.Scientific reports · 2026Article
- Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets.iScience · 2026Article
- Development and external validation of an interpretable early prediction model for acute kidney injury using TabPFN and routine admission data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Deep learning models for acute kidney injury prediction: multi-center external validation and evaluation under simulated continuous monitoring conditions.NPJ digital medicine · 2026Article
- Risk Factors and Prediction of Acute Kidney Injury in Hospitalized Urology Patients: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Prediction of acute kidney injury in patients with acute pesticide poisoning using the PKIP score.Scientific reports · 2026Article
- Forecasting ICU Acute Kidney Injury with Actionable Lead Time Using Interpretable Machine Learning: Development and Multi-Center Validation.Journal of clinical medicine · 2026Article
- Predicting Acute Kidney Injury with Nephrotoxic Burden in Noncritical Patients: An Internal and External Validation Study.Kidney360 · 2026Article
- Biomarker-driven risk stratification and early intervention in acute kidney injury: a comprehensive review from early warning to clinical response.Frontiers in medicine · 2026Review
- Observational
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.Renal failure · 2025Article
Corrections and comments
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. Here we show a simple, real-time, interpretable AKI prediction model for general hospitalized patients developed from a large tertiary hospital in China, which has been validated across five independent, geographically distinct, different tiered hospitals. The model containing 20 readily available variables demonstrates consistent, high levels of predictive discrimination in validation cohort, with AUCs for serum creatinine-based AKI and severe AKI within 48 h ranging from 0.74-0.85 and 0.83-0.90 for transported models and from 0.81-0.90 and 0.88-0.95 for refitted models, respectively. With optimal probability cutoffs, the refitted model could predict AKI at a median of 72 (24-198) hours in advance in internal validation, and 54-90 h in advance in external validation. Broad application of the model in the future may provide an effective, convenient and cost-effective approach for AKI prevention.
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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.