Observational studyRenal failure2022
The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury.
Observational study in Renal failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk prediction models for acute kidney injury in patients with acute pancreatitis: a systematic review.BMC gastroenterology · 2026Pooled it
- Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.Frontiers in medicine · 2025Pooled it
- Association between lactate-to-albumin ratio and 28-day all-cause mortality in critically ill patients with chronic kidney disease: A retrospective cohort study from the Medical Information Mart for Intensive Care IV database.The Journal of international medical research · 2026Article
- Development of a Clinical Prediction Model for Acute Kidney Injury Among In-Hospital Cardiac Arrest Patients During Intensive Care Unit Hospitalization.Reviews in cardiovascular medicine · 2026Article
- Article
- An interpretable machine-learning model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury.Frontiers in medicine · 2026Article
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- Hemoglobin variability predicts mortality in acute kidney injury patients undergoing continuous renal replacement therapy: a multi-database cohort study.Renal failure · 2025Article
- Relationship between intra-bladder pressure and acute kidney injury in patients with acute pancreatitis: interpretable machine learning approach.BMC nephrology · 2025Article
- Comparison of Intensive Care Scoring Systems in Predicting Overall Mortality of Sepsis.Diagnostics (Basel, Switzerland) · 2025Article
- Development and assessment of a mortality risk prediction nomogram model for pneumocystis disease in ICU within 28 days.Scientific reports · 2025Article
- Personalized ICU mortality assessment by interpretable machine learning algorithms in patients with sepsis combined lung cancer: a population-based study and an external validation cohort.Frontiers in oncology · 2025Article
- Prognostic Scores for Acute Kidney Injury in Critically Ill Patients.Nursing reports (Pavia, Italy) · 2024Article
- Association between white blood cell-to-haemoglobin ratio and 30 day mortality in heart failure in intensive care unit.ESC heart failure · 2024Article
- Association of serum ferritin and all-cause mortality in AKI patients: a retrospective cohort study.Frontiers in medicine · 2024Article
- Urine output for predicting in-hospital mortality of intensive care patients with cardiogenic shock.Heliyon · 2023Article
- Association and prediction of red blood cell distribution width to albumin ratio in all-cause mortality of acute kidney injury in critically ill patients.Frontiers in medicine · 2023Article
- Effect of sex on the association between arterial partial pressure of oxygen and in-hospital mortality in ICU patients with cardiogenic shock: a retrospective cohort study.Annals of translational medicine · 2022Article
- Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches.BMC medical informatics and decision making · 2022Article
- No sex differences in the incidence, risk factors and clinical impact of acute kidney injury in critically ill patients with sepsis.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo compare the performance of the Oxford Acute Severity of Illness Score (OASIS), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Simplified Acute Physiology Score II (SAPS II), and the Sequential Organ Failure Assessment (SOFA) score in predicting 28-day mortality in acute kidney injury (AKI) patients.
methodsData were extracted from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 2954 patients with complete clinical data were included in this study. Receiver operating characteristic (ROC) curves were used to analyze and evaluate the predictive effects of the four scoring systems on the 28-day mortality risk of AKI patients and each subgroup. The best cutoff value was identified by the highest combined sensitivity and specificity using Youden's index.
resultsAmong the four scoring systems, the area under the curve (AUC) of OASIS was the highest. The comparison of AUC values of different scoring systems showed that there were no significant differences among OASIS, APACHE II, and SAPS II, which were better than SOFA. Moreover, logistic analysis revealed that OASIS was an independent risk factor for 28-day mortality in AKI patients. OASIS also had good predictive ability for the 28-day mortality of each subgroup of AKI patients.
conclusionOASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of AKI patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute.
trial registrationThis study was registered at www.chictr.org.cn (registration number Chi CTR-ONC-11001875). Registered on 14 December 2011.
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