Observational studyJournal of the American Heart Association2020
Novel Blood Cytokine-Based Model for Predicting Severe Acute Kidney Injury and Poor Outcomes After Cardiac Surgery.
Observational study in Journal of the American Heart Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Machine learning for the prediction of acute kidney injury post cardiac surgery: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- sncRNA levels predict SNORD105B is a novel biomarker of chronic kidney disease risk and SGLT2 inhibitor response in type 2 diabetes.Molecular therapy. Nucleic acids · 2026Article
- Article
- Single-cell immune landscape of acute kidney injury after cardiac surgery with cardiopulmonary bypass.Renal failure · 2025Article
- When the Heart Hurts the Kidneys: From Flow to Translational Future.Seminars in nephrology · 2025Review
- Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach.JMIR formative research · 2025Article
- Vasoplegic Syndrome Following Bypass: A Comprehensive Review of Pathophysiology and Proposed Treatments.Cureus · 2025Review
- Acute kidney injury following fatty liver ischemia-reperfusion injury: indirect protection by hepatic ferroptosis inhibition.Frontiers in physiology · 2025Article
- Growth Differentiation Factor 15 as a Biomarker for Risk Stratification in the Cardiothoracic Surgery Intensive Care Unit.Biomolecules · 2024Article
- An Integrated Approach Based on Clinical Data Combined with Metabolites and Biomarkers for the Assessment of Post-Operative Complications after Cardiac Surgery.Journal of clinical medicine · 2024Article
- Article
- Prediction of Pelvic Organ Prolapse Postsurgical Outcome Using Biomaterial-Induced Blood Cytokine Levels: Machine Learning Approach.JMIR perioperative medicine · 2023Article
- Development and Validation of a Machine Learning Predictive Model for Cardiac Surgery-Associated Acute Kidney Injury.Journal of clinical medicine · 2023Article
- Diagnosis, pathophysiology and preventive strategies for cardiac surgery-associated acute kidney injury: a narrative review.European journal of medical research · 2023Review
- Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury.Advances in kidney disease and health · 2023Review
- Risk factors for severe acute kidney injury post complication after total arch replacement combined with frozen elephant trunk, in acute type A aortic dissection.Cardiovascular diagnosis and therapy · 2022Article
- Narrative review of the systemic inflammatory reaction to cardiac surgery and cardiopulmonary bypass.Artificial organs · 2022Review
- Predicting mortality in acute kidney injury patients undergoing continuous renal replacement therapy using a visualization model: A retrospective study.Frontiers in physiology · 2022Article
- Machine learning for the prediction of acute kidney injury in patients after cardiac surgery.Frontiers in surgery · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Alterations in serum creatinine levels delay the identification of severe cardiac surgery-associated acute kidney injury. To provide timely diagnosis, novel predictive tools should be investigated. Methods and Results This prospective observational study consists of a screening cohort (n=204) and a validation cohort (n=198) from 2 centers from our hospital. Thirty-two inflammatory cytokines were measured via a multiplex cytokine assay. Least absolute shrinkage and selection operator regression was conducted to select the cytokine signatures of severe cardiac surgery-associated acute kidney injury. Afterwards, the significant candidates including interferon-γ, interleukin-16, and MIP-1α (macrophage inflammatory protein-1 alpha) were integrated into the logistic regression model to construct a predictive model. The predictive accuracy of the model was evaluated in these 2 cohorts. The cytokine-based model yielded decent performance in both the screening (C-statistic: 0.87, Brier 0.10) and validation cohorts (C-statistic: 0.86, Brier 0.11). Decision curve analysis revealed that the cytokine-based model had a superior net benefit over both the clinical factor-based model and the established plasma biomarker-based model for predicting severe acute kidney injury. In addition, elevated concentrations of each cytokine were associated with longer mechanical ventilation times, intensive care unit stays, and hospital stays. They strongly predicted the risk of composite events (defined as treatment with renal replacement therapy and/or in-hospital death) (OR of the fourth versus the first quartile [95% CI]: interferon-γ, 27.78 [3.61-213.84], interleukin-16, 38.07 [4.98-291.07], and MIP-1α, 9.13 [2.84-29.33]). Conclusions Our study developed and validated a promising blood cytokine-based model for predicting severe acute kidney injury after cardiac surgery and identified prognostic biomarkers for assisting in outcome risk stratification.
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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.