ArticleCritical care (London, England)2019
Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care.
Article in Critical care (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 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
181 citing papers in PubMed, 1 synthesis or guideline pooled it, 351 citations in OpenAlex.
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis.BMC surgery · 2023Trial
- Using machine learning methods to predict 28-day mortality in patients with hepatic encephalopathy.BMC gastroenterology · 2023Trial
- Predicting 90-day mortality in patients with HBV-ACLF: A comparison of machine learning models and logistic regression.Medicine · 2026Article
- Explainable artificial intelligence in ICU admission prediction for methanol poisoning patients: a comparative study of deep and machine learning models.International journal of emergency medicine · 2026Article
- The High-resolution Urban Meteorology for Impacts Dataset for Atlanta Metropolitan Region (HUMID-Atlanta).Scientific data · 2026Article
- Lactate-albumin ratio predicts in-hospital mortality in critically Ill patients with congestive heart failure and diabetes.BMC medical informatics and decision making · 2026Article
- AI-driven integration of Framingham Heart Study data with machine learning, deep learning, and explainable AI for enhanced pharmaceutical marketing.Scientific reports · 2026Article
- Association of triglyceride-glucose index fluctuation with in-hospital all-cause mortality in critically ill patients: a multidatabase retrospective study.Scientific reports · 2026Observational
- Prognostic value of the fibrosis-4 index for predicting in-hospital mortality in sepsis patients: evidence from MIMIC-IV and eICU databases.Scientific reports · 2026Article
- Development and validation of a machine learning model for predicting early death in metastatic pancreatic ductal adenocarcinoma: a study based on the SEER database.Translational cancer research · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database.PloS one · 2026Article
- Development and External Validation of Machine Learning Model to Predict Live Birth Following Assisted Reproductive Technology in Women with Ovarian Endometriomas: A Decision-Support Tool.Clinical epidemiology · 2026Article
- Article
- Machine learning model for predicting the risk of AKI in early hemodynamically stable sepsis patients: a study based on the MIMIC IV database.Frontiers in medicine · 2026Article
- Machine learning models for predicting renal injury in patients with gout.Renal failure · 2025Article
- Prediction of all-cause in-hospital mortality after ICU admission and 1-year mortality after discharge of patients with acute kidney injury.Renal failure · 2025Article
- Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.Scientific reports · 2025Article
- Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.BMC medical informatics and decision making · 2025Article
121 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesExcess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volume-responsive (VR) and volume-unresponsive (VU) AKI.
methodsAKI patients with urine output < 0.5 ml/kg/h for the first 6 h after ICU admission and fluid intake > 5 l in the following 6 h in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-III)) were considered. Patients who received diuretics and renal replacement on day 1 were excluded. Two predictive models, using either machine learning extreme gradient boosting (XGBoost) or logistic regression, were developed to predict urine output > 0.65 ml/kg/h during 18 h succeeding the initial 6 h for assessing oliguria. Established models were assessed by using out-of-sample validation. The whole sample was split into training and testing samples by the ratio of 3:1. MAIN
resultsOf the 6682 patients included in the analysis, 2456 (36.8%) patients were volume responsive with an increase in urine output after receiving > 5 l fluid. Urinary creatinine, blood urea nitrogen (BUN), age, and albumin were the important predictors of VR. The machine learning XGBoost model outperformed the traditional logistic regression model in differentiating between the VR and VU groups (AU-ROC, 0.860; 95% CI, 0.842 to 0.878 vs. 0.728; 95% CI 0.703 to 0.753, respectively).
conclusionsThe XGBoost model was able to differentiate between patients who would and would not respond to fluid intake in urine output better than a traditional logistic regression model. This result suggests that machine learning techniques have the potential to improve the development and validation of predictive modeling in critical care research.
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.