ArticleClinical and experimental medicine2025
A study on the factors influencing mortality risk in sepsis-induced acute kidney injury based on analysis of the MIMIC database.
Article in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled 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 albumin-based nutritional inflammation indices and all-cause mortality in patients with severe pneumonia.BMC infectious diseases · 2026Article
- Acute Kidney Injury in Hospitalized Cancer Patients: Single-Centre Real-Life Analysis of Incidence and Clinical Impact.Journal of clinical medicine · 2026Article
- Presepsin as a Diagnostic and Prognostic Biomarker of Sepsis-Associated Acute Kidney Injury: A Scoping Review of Clinical Evidence.Journal of clinical medicine · 2025Review
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5 authors.
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Abstract
Sepsis-induced acute kidney injury (SA-AKI) significantly increases mortality and healthcare burdens. Identifying key mortality risk factors is crucial for improving patient outcomes. This study aims to identify the primary factors affecting mortality in SA-AKI patients using the MIMIC-III database. A retrospective analysis was conducted on 4868 SA-AKI patients from the MIMIC-III database. Clinical data from the first 24 h of ICU admission were analyzed using logistic regression to identify mortality predictors. Key mortality predictors included advanced age (OR = 1.015, 95% CI: 1.006-1.024), severe AKI stages (OR = 1.470, 95% CI: 1.285-1.676), low serum albumin (OR = 0.606, 95% CI: 0.506-0.722), delayed antibiotics (OR = 1.001, 95% CI: 1.000-1.002), high AST (OR = 1.035, 95% CI: 1.027-1.083), and bilirubin (OR = 1.055, 95% CI: 1.037-1.083). The area under the curve (AUC) of the combined predictors for mortality risk was 0.796, indicating high predictive accuracy. Conclusions: Early intervention and monitoring of identified risk factors such as age, AKI stage, albumin levels, and antibiotic timeliness can enhance survival rates in SA-AKI patients.
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