ArticleNature communications2023
Predicting in-hospital outcomes of patients with acute kidney injury.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Association of nephrolithiasis history with adverse outcomes in cardiovascular-kidney-metabolic syndrome.Renal failure · 2026Article
- A tri-scale in silico framework integrating pharmacovigilance and mechanistic modeling suggests tepotinib-associated acute kidney injury risk.Renal failure · 2026Article
- Prognosis comparison in chronic kidney disease stages 4-5 patients with low parathyroid hormone level due to different causes.BMC nephrology · 2026Observational
- Challenges and Solutions in Deploying Systematized Nomenclature of Medicine-Clinical Terms in the Chinese Healthcare Context.Health care science · 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
- Acute kidney injury over the past decade: from definition evolution to pathogenesis insights and innovative therapeutic strategies.Cellular and molecular life sciences : CMLS · 2026Review
- The Impact of Nurse Staffing and Education on 30-Day Mortality Among Patients Hospitalized for Acute Kidney Injury.Research in nursing & health · 2026Article
- Optimization of Kidney Disease: Improving Global Outcomes Criteria for AKI for Pediatric Population.Kidney international reports · 2026Article
- Precision phenotyping of type 2 diabetes in chinese populations using a variational autoencoder-informed tree model.Nature communications · 2026Article
- Machine Learning-Based Prediction of Life-Threatening Complications During Hemodialysis in Hospitalized Patients With Poor General Conditions.Artificial organs · 2026Article
- Temporal Recurrent Neural Networks for Predicting Acute Kidney Injury Recovery by Time of Discharge.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- Thrombomodulin: a potential biomarker for sepsis-associated acute kidney injury resulting from bacterial infections.Frontiers in cellular and infection microbiology · 2026Observational
- Cilastatin a promising solution for acute kidney injury by targeting nephrotoxicity.Annals of medicine and surgery (2012) · 2026Article
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Nomogram for predicting acute kidney injury after primary PCI in STEMI patients: development and validation study.Renal failure · 2025Article
- Discontinuation of Renin-Angiotensin System Inhibitors during Acute Kidney Injury Episode and All-Cause Mortality: Target Trial Emulation Studies.Journal of the American Society of Nephrology : JASN · 2025Article
- Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy.Renal failure · 2025Article
- PANoptosis: A novel therapeutic target in kidney disease (Review).International journal of molecular medicine · 2025Review
- Control Tower in the hospital: a structure-process-outcome systemic review of telemedicine systems for interprofessional collaboration.NPJ digital medicine · 2025Article
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is prevalent and a leading cause of in-hospital death worldwide. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. We develop a deep learning model based on a nationwide multicenter cooperative network across China that includes 7,084,339 hospitalized patients, to dynamically predict the risk of in-hospital death (primary outcome) and dialysis (secondary outcome) for patients who developed AKI during hospitalization. A total of 137,084 eligible patients with AKI constitute the analysis set. In the derivation cohort, the area under the receiver operator curve (AUROC) for 24-h, 48-h, 72-h, and 7-day death are 95·05%, 94·23%, 93·53%, and 93·09%, respectively. For dialysis outcome, the AUROC of each time span are 88·32%, 83·31%, 83·20%, and 77·99%, respectively. The predictive performance is consistent in both internal and external validation cohorts. The model can predict important outcomes of patients with AKI, which could be helpful for the early management of AKI.
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