ArticleFrontiers in medicine2023
An early warning model for predicting major adverse kidney events within 30 days in sepsis patients.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From Mechanism to Therapy: Establishing an Integrated Framework for the Regulation and Intervention of Matrix Vesicles in Vascular Calcification.Cardiovascular drugs and therapy · 2026Review
- ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.Critical care (London, England) · 2025Article
- Association between serum glucose potassium ratio and short- and long-term all-cause mortality in patients with sepsis admitted to the intensive care unit: a retrospective analysis based on the MIMIC-IV database.Frontiers in endocrinology · 2025Article
- Leveraging artificial intelligence for early detection and prediction of acute kidney injury in clinical practice.Frontiers in physiology · 2025Article
- Association between triglyceride glucose-body mass index and acute kidney injury and renal replacement therapy in critically ill patients with sepsis: analysis of the MIMIC-IV database.Frontiers in endocrinology · 2025Article
- Non-linear association between lactate and 28 days mortality in elderly patients with sepsis across different SOFA score groups: results from the eICU Collaborative Research Database.Frontiers in medicine · 2025Article
- A retrospective study utilized MIMIC-IV database to explore the potential association between triglyceride-glucose index and mortality in critically ill patients with sepsis.Scientific reports · 2024Article
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5 authors.
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Abstract
Background: In sepsis patients, kidney damage is among the most dangerous complications, with a high mortality rate. In addition, major adverse kidney events within 30 days (MAKE30) served as a comprehensive and unbiased clinical outcome measure for sepsis patients due to the recent shift toward targeting patient-centered renal outcomes in clinical research. However, the underlying predictive model for the prediction of MAKE30 in sepsis patients has not been reported in any study. Methods: A cohort of 2,849 sepsis patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database was selected and subsequently allocated into a training set ( Results: The construction of the nomogram was based on several independent predictors (AUC above 0.6), including age, respiratory rate (RR), PaO2, lactate, and blood urea nitrogen (BUN). The predictive model demonstrated satisfactory discrimination for MAKE30, with an AUC of 0.740, 0.753, and 0.821 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the simple prediction model exhibited superior predictive value compared to the SOFA model in both the training (AUC = 0.710) and validation (AUC = 0.692) cohorts. The nomogram demonstrated satisfactory calibration and clinical utility as evidenced by the calibration curve and DCA. Additionally, the predictive model exhibited excellent accuracy in forecasting 30-day mortality (AUC = 0.737), PRD (AUC = 0.639), and new RRT (AUC = 0.846) within the training dataset. Additionally, the model displayed predictive power for 30-day mortality (AUC = 0.765), PRD (AUC = 0.667), and new RRT (AUC = 0.783) in the validation set. Conclusion: The proposed nomogram holds the potential to estimate the risk of MAKE30 promptly and efficiently in sepsis patients within the initial 24 h of admission, thereby equipping healthcare professionals with valuable insights to facilitate personalized interventions.
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