ArticlePloS one2017
Approaches to Predicting Outcomes in Patients with Acute Kidney Injury.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Artificial intelligence for predicting paediatric acute kidney injury: a systematic review and meta-analysis.Clinical kidney journal · 2026Article
- Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe AKI.Clinical journal of the American Society of Nephrology : CJASN · 2025Article
- When to start renal replacement therapy in acute kidney injury: What are we waiting for?Journal of intensive medicine · 2024Review
- Using artificial intelligence to predict mortality in AKI patients: a systematic review/meta-analysis.Clinical kidney journal · 2024Article
- Endothelium-related biomarkers enhanced prediction of kidney support therapy in critically ill patients with non-oliguric acute kidney injury.Scientific reports · 2024Observational
- The use of diagnostic tools for pediatric AKI: applying the current evidence to the bedside.Pediatric nephrology (Berlin, Germany) · 2021Review
- Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.Clinical kidney journal · 2021Article
- Determinants of Outcomes of Acute Kidney Injury: Clinical Predictors and Beyond.Journal of clinical medicine · 2021Review
- Electronic health records accurately predict renal replacement therapy in acute kidney injury.BMC nephrology · 2019Observational
- Phenotyping of Acute Kidney Injury: Beyond Serum Creatinine.Seminars in nephrology · 2018Review
- A sequential machine learning framework for ICU and hospital length of stay prediction from admission-time data.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into training and validation cohorts. Outcomes of interest were death within 7 days, dialysis within 7 days, and length of stay. Data elements eligible for model-building included lab values, medications and dosages, procedures, and demographics. We assessed model discrimination using the area under the receiver operator characteristic curve and r-squared values. 2241 individuals were available for analysis. Both modeling techniques created viable models with very good discrimination ability, with AUCs exceeding 0.85 for dialysis and 0.8 for death prediction. Model performance was similar across model building strategies, though the strategy employing more advanced variable selection was more parsimonious. Very good to excellent prediction of outcome events is feasible in patients with AKI. More advanced techniques may lead to more parsimonious models, which may facilitate adoption in other settings.
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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.