ArticleAging clinical and experimental research2022
Construction and validation of an early warning model for predicting the acute kidney injury in elderly patients with sepsis.
Article in Aging clinical and experimental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 20 citations in OpenAlex.
- The predictive value of early systemic inflammatory response index (SIRI), serum βRenal failure · 2026Article
- Risk factors and nomogram prediction model for prognosis in sepsis with acute kidney injury.BMC nephrology · 2026Article
- Association between prothrombin time activity and short-term outcomes in patients with infective endocarditis.BMC infectious diseases · 2026Article
- Prediction of hospital mortality in sepsis-associated acute kidney injury using a machine-learning approach: a multicenter study using SHAP interpretability analysis.Clinical kidney journal · 2026Article
- Development and validation of a nomogram for predicting acute kidney injury in elderly patients in intensive care unit.Renal failure · 2025Article
- Analysis of prognostic risk factors in critically ill elderly patients with sepsis-associated acute kidney injury.BMC nephrology · 2025Article
- Development of a machine learning-based prediction model for acute kidney injury associated with respiratory failure in the intensive care unit.Clinical and experimental medicine · 2025Article
- Enhancing mortality prediction in AIDS and disseminatedJournal of medical microbiology · 2025Article
- Unraveling immunosenescence in sepsis: from cellular mechanisms to therapeutics.Cell death & disease · 2025Review
- Albumin corrected anion gap and clinical outcomes in elderly patients with acute kidney injury caused or accompanied by sepsis: a MIMIC-IV retrospective study.European journal of medical research · 2025Article
- Article
- Heterogeneity in the definition of major adverse kidney events: a scoping review.Intensive care medicine · 2024Article
- Combination of Creatinine with Inflammatory Biomarkers (PCT, CRP, hsCRP) for Predicting Postoperative ICU Admissions for Elderly Patients.Advances in therapy · 2024Article
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
- Risk factors and prediction model for mortality in HIV/Talaromyces marneffei co-infection: A retrospective cohort study.Heliyon · 2024Article
- Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Septic Patients.Journal of inflammation research · 2024Article
- Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury.Frontiers in medicine · 2023Article
- An early warning model for predicting major adverse kidney events within 30 days in sepsis patients.Frontiers in medicine · 2023Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSepsis-induced acute kidney injury (S-AKI) is a significant complication and is associated with an increased risk of mortality, especially in elderly patients with sepsis. However, there are no reliable and robust predictive models to identify high-risk patients likely to develop S-AKI. We aimed to develop a nomogram to predict S-AKI in elderly sepsis patients and help physicians make personalized management within 24 h of admission.
methodsA total of 849 elderly sepsis patients from the First Affiliated Hospital of Xi'an Jiaotong University were identified and randomly divided into a training set (75%, n = 637) and a validation set (25%, n = 212). Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The corresponding nomogram was constructed based on those predictors. The calibration curve, receiver operating characteristics (ROC)curve, and decision curve analysis were performed to evaluate the nomogram. The secondary outcome was 30-day mortality and major adverse kidney events within 30 days (MAKE30). MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD).
resultsThe independent predictors for nomogram construction were mean arterial pressure (MAP), serum procalcitonin (PCT), and platelet (PLT), prothrombin time activity (PTA), albumin globulin ratio (AGR), and creatinine (Cr). The predictive model had satisfactory discrimination with an area under the curve (AUC) of 0.852-0.858 in the training and validation cohorts, respectively. The nomogram showed good calibration and clinical application according to the calibration curve and decision curve analysis. Furthermore, the prediction model had perfect predictive power for predicting 30-day mortality (AUC = 0.813) and MAKE30 (AUC = 0.823) in elderly sepsis patients.
conclusionThe proposed nomogram can quickly and effectively predict S-AKI risk in elderly sepsis patients within 24 h after admission, providing information for clinicians to make personalized interventions.
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