ArticleAnnals of translational medicine2021
Development and validation of a risk stratification model for predicting the mortality of acute kidney injury in critical care patients.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.BMC medical informatics and decision making · 2024Pooled it
- Article
- Association between hemoglobin/red blood cell distribution width ratio and acute kidney injury in sepsis and heart failure patients.PloS one · 2026Article
- Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.Journal of the American Society of Nephrology : JASN · 2025Article
- Impact of prognostic scores on acute kidney injury assessment in the postoperative period of myocardial revascularization.Revista da Escola de Enfermagem da U S P · 2025Article
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
- Observational
- Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.BMC medical informatics and decision making · 2024Article
- Article
- Development and validation of a nomogram to predict mortality of patients with DIC in ICU.Frontiers in medicine · 2024Article
- A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.BMC medical informatics and decision making · 2023Article
- Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.Renal failure · 2022Article
- Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.Computational and structural biotechnology journal · 2022Article
- Red Cell Distribution Width to Platelet Ratio Is Associated with Increasing In-Hospital Mortality in Critically Ill Patients with Acute Kidney Injury.Disease markers · 2022Article
- Predicting mortality in acute kidney injury patients undergoing continuous renal replacement therapy using a visualization model: A retrospective study.Frontiers in physiology · 2022Article
- Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury.Frontiers in medicine · 2022Article
- Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis.Scientific reports · 2021Article
- Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury.Frontiers in endocrinology · 2021Article
- Prediction of acute kidney injury in intensive care unit patients based on interpretable machine learning.Digital healthArticle
- Comparative Analysis of Machine Learning Models for Prediction of Acute Liver Injury in Sepsis Patients.Journal of emergencies, trauma, and shockArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop and validate a model for mortality risk stratification of intensive care unit (ICU) patients with acute kidney injury (AKI) using the machine learning technique.
methodsEligible data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Calibration, discrimination, and risk classification for mortality prediction were evaluated using conventional scoring systems and the new algorithm. A 10-fold cross-validation was performed. The predictive models were externally validated using the eICU database and also patients treated at the Second People's Hospital of Shenzhen between January 2015 to October 2018.
resultsFor the new model, the areas under the receiver operating characteristic curves (AUROCs) for mortality during hospitalization and at 28 and 90 days after discharge were 0.91, 0.87, and 0.87, respectively, which were higher than for the Simplified Acute Physiology Score (SAPS II) and Sequential Organ Failure Assessment (SOFA). For external validation, the AUROC was 0.82 for in-hospital mortality, higher than SOFA, SAPS II, and Acute Physiology and Chronic Health Evaluation (APACHE) IV in the eICU database, but for the 28- and 90-day mortality, the new model had AUROCs (0.79 and 0.80, respectively) similar to that of SAPS II in the SZ2 database. The reclassification indexes were superior for the new model compared with the conventional scoring systems.
conclusionsThe new risk stratification model shows high performance in predicting mortality in ICU patients with AKI.
Indexed as
Identifiers
What OpenQuestion holds
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.