ArticleBMJ open2017
Predicting AKI in emergency admissions: an external validation study of the acute kidney injury prediction score (APS).
Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.BMJ open · 2017Pooled it
- The rate of acute kidney injury (AKI) alert detection by the attending physicians was associated with the prognosis of patients with AKI.Frontiers in public health · 2022Trial
- Development and Validation of Machine Learning Models to Identify Emergency Department Patients at Increased Risk of New or Progressive Acute Kidney Injury.Journal of the American College of Emergency Physicians open · 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
- InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.Nature communications · 2025Article
- Prognostic Biomarkers and AKI: Potential to Enhance the Identification of Post-Operative Patients at Risk of Loss of Renal Function.Research and reports in urology · 2024Review
- Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department.Scientific reports · 2022Article
- Integrating electronic health data records to develop and validate a predictive model of hospital-acquired acute kidney injury in non-critically ill patients.Clinical kidney journal · 2021Article
- External validation of the Madrid Acute Kidney Injury Prediction Score.Clinical kidney journal · 2021Article
- Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients.Journal of clinical medicine · 2021Article
- Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction.Kidney international reports · 2021Article
- Development and internal validation of a prediction model for hospital-acquired acute kidney injury.Clinical kidney journal · 2021Article
- Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study.Journal of medical Internet research · 2020Article
- Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor.Critical care (London, England) · 2019Observational
- A simple real-time model for predicting acute kidney injury in hospitalized patients in the US: A descriptive modeling study.PLoS medicine · 2019Observational
- Comment on Falconer et al. (2018) - the need for specific adverse drug reaction outcomes.British journal of clinical pharmacology · 2018Article
- Reply to Iuga and Genaidy 'Comment on Falconer et al. (2018) - the need for specific adverse drug reaction outcomes'.British journal of clinical pharmacology · 2018Article
- The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients.PloS one · 2018Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesHospital-acquired acute kidney injury (HA-AKI) is associated with a high risk of mortality. Prediction models or rules may identify those most at risk of HA-AKI. This study externally validated one of the few clinical prediction rules (CPRs) derived in a general medicine cohort using clinical information and data from an acute hospitals electronic system on admission: the acute kidney injury prediction score (APS). DESIGN, SETTING AND
participantsExternal validation in a single UK non-specialist acute hospital (2013-2015, 12 554 episodes); four cohorts: adult medical and general surgical populations, with and without a known preadmission baseline serum creatinine (SCr).
methodsPerformance assessed by discrimination using area under the receiver operating characteristic curves (AUCROC) and calibration.
resultsHA-AKI incidence within 7 days (kidney disease: improving global outcomes (KDIGO) change in SCr) was 8.1% (n=409) of medical patients with known baseline SCr, 6.6% (n=141) in those without a baseline, 4.9% (n=204) in surgical patients with baseline and 4% (n=49) in those without. Across the four cohorts AUCROC were: medical with known baseline 0.65 (95% CIs 0.62 to 0.67) and no baseline 0.71 (0.67 to 0.75), surgical with baseline 0.66 (0.62 to 0.70) and no baseline 0.68 (0.58 to 0.75). For calibration, in medicine and surgical cohorts with baseline SCr, Hosmer-Lemeshow p values were non-significant, suggesting acceptable calibration. In the medical cohort, at a cut-off of five points on the APS to predict HA-AKI, positive predictive value was 16% (13-18%) and negative predictive value 94% (93-94%). Of medical patients with HA-AKI, those with an APS ≥5 had a significantly increased risk of death (28% vs 18%, OR 1.8 (95% CI 1.1 to 2.9), p=0.015).
conclusionsOn external validation the APS on admission shows moderate discrimination and acceptable calibration to predict HA-AKI and may be useful as a severity marker when HA-AKI occurs. Harnessing linked data from primary care may be one way to achieve more accurate risk prediction.
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