ArticleRenal failure2024
Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury.
Article in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.Frontiers in medicine · 2025Pooled it
- Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled it
- Article
- Therapeutic Monitoring of Vancomycin and Factors Affecting Survival in ICU Patients with Infections.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.BMC medical informatics and decision making · 2026Article
- Risk factors and nomogram prediction model for prognosis in sepsis with acute kidney injury.BMC nephrology · 2026Article
- Multicenter study of the diagnostic value of erythrocyte morphology assessment by the EH-2090 for differentiation of glomerular and non-glomerular hematuria.BMC nephrology · 2026Article
- Current Status and Future Prospects of Research on Sepsis-Related Acute Kidney Injury.International journal of molecular sciences · 2026Review
- Integrative multimodal hybrid data fusion for mortality prediction.Scientific reports · 2026Article
- Development and validation of an interpretable machine learning-based model for predicting carbapenem-resistantFrontiers in cellular and infection microbiology · 2026Article
- Association between red blood cell distribution width to albumin ratio and prognosis in patients with sepsis-associated acute kidney injury: a retrospective cohort study.Frontiers in medicine · 2026Article
- Machine learning-based mortality prediction models for emergency department patients: a comparative analysis.Frontiers in medicine · 2026Article
- A machine learning model for predicting 28-day mortality in ICU patients with community-acquired pneumonia and acute kidney injury.Scientific reports · 2025Article
- IER3 drives the transition from sepsis-associated AKI to CKD by suppressing the mitochondrial translocation of PRDX5.Cellular and molecular life sciences : CMLS · 2025Article
- Artificial intelligence, machine learning, telemedicine, and digital transformation in nephrology and transplantation.Renal failure · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- The relationship between estimated pulse wave velocity and 28-day mortality in patients with sA-AKI: a retrospective cohort analysis of the MIMIC-IV database.Renal failure · 2025Article
- Clinical Characteristics of Adenovirus Pneumonia in Children.Pathogens (Basel, Switzerland) · 2025Article
- Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.Journal of medical Internet research · 2025Article
- Early body temperature trajectories and short term prognosis in sepsis associated acute kidney injury.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms.
methodsPatients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (
resultsA total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774-0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor prognosis.
conclusionsThe RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction.
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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 Socratic graph.