ArticleJournal of medical Internet research2024
Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 33 citations in OpenAlex.
- Article
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- Prognostic Value of Blood Urea Nitrogen for Acute Kidney Injury and Mortality in Vasculitis: A Large Cohort Study Using Multivariate Joint Model and Machine Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Prognostic value of blood urea nitrogen to albumin ratio in critically ill cirrhotic patients with ascites.Scientific reports · 2026Article
- Explainable machine learning model for predicting traumatic brain injury-induced coagulopathy in elderly patients: A multicenter cohort study.Neurosurgical review · 2026Article
- Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study.JMIR medical informatics · 2026Article
- Age-Specific Prognostic Models for Sepsis-Associated Acute Kidney Injury: A Multicenter Cohort Study.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Simultaneous assessment of stress hyperglycemia ratio and glucose variability to predict all-cause mortality in sepsis patients across different glucose metabolic states: an observational cohort study with interpretable machine learning approach.International journal of surgery (London, England) · 2026Observational
- Machine learning early risk assessment model for acute kidney injury in critically ill children: a retrospective cohort study.Frontiers in pediatrics · 2026Article
- Nutritional indicator-based prediction model for esophagogastric variceal bleeding in cirrhosis using XGBoost and SHAP.Frontiers in nutrition · 2026Article
- Association between hemoglobin/red blood cell distribution width ratio and acute kidney injury in sepsis and heart failure patients.PloS one · 2026Article
- Fast multimodal imaging combined with machine learning identifying taurine as a potential marker for breast cancer margin assessment.NPJ digital medicine · 2025Article
- Obesity and mortality in KDIGO stage 3 acute kidney injury: a machine learning-driven retrospective cohort study.BMC nephrology · 2025Article
- Machine learning model development and validation using SHAP: predicting 28-day mortality risk in pulmonary fibrosis patients.BMC medical informatics and decision making · 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
- Admission hematocrit and fluctuating blood urea nitrogen levels predict the efficacy of blood purification treatment in severe acute pancreatitis patients.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes.
objectiveWe aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps.
methodsData on elderly patients with AKI (n=3542) and AKD (n=2661) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used to develop 2 models for predicting the AKD risk and in-hospital mortality, respectively. Data collected from Xiangya Hospital of Central South University were for external validation. A bootstrap method was used for internal validation to obtain relatively stable results. We extracted the indicators within 24 hours of the first diagnosis of AKI and the fluctuation range of some indicators, namely delta (day 3 after AKI minus day 1), as features. Six machine learning algorithms were used for modeling; the area under the receiver operating characteristic curve (AUROC), decision curve analysis, and calibration curve for evaluating; Shapley additive explanation (SHAP) analysis for visually interpreting; and the Heroku platform for deploying the best-performing models as web-based apps.
resultsFor the model of predicting the risk of AKD in elderly patients with AKI during hospitalization, the Light Gradient Boosting Machine (LightGBM) showed the best overall performance in the training (AUROC=0.844, 95% CI 0.831-0.857), internal validation (AUROC=0.853, 95% CI 0.841-0.865), and external (AUROC=0.755, 95% CI 0.699-0.811) cohorts. In addition, LightGBM performed well for the AKD prognostic prediction in the training (AUROC=0.861, 95% CI 0.843-0.878), internal validation (AUROC=0.868, 95% CI 0.851-0.885), and external (AUROC=0.746, 95% CI 0.673-0.820) cohorts. The models deployed as online prediction apps allowed users to predict and provide feedback to submit new data for model iteration. In the importance ranking and correlation visualization of the model's top 10 influencing factors conducted based on the SHAP value, partial dependence plots revealed the optimal cutoff of some interventionable indicators. The top 5 factors predicting the risk of AKD were creatinine on day 3, sepsis, delta blood urea nitrogen (BUN), diastolic blood pressure (DBP), and heart rate, while the top 5 factors determining in-hospital mortality were age, BUN on day 1, vasopressor use, BUN on day 3, and partial pressure of carbon dioxide (PaCO
conclusionsWe developed and validated 2 online apps for predicting the risk of AKD and its prognostic mortality in elderly patients, respectively. The top 10 factors that influenced the AKD risk and mortality during hospitalization were identified and explained visually, which might provide useful applications for intelligent management and suggestions for future prospective research.
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