ArticleRenal failure2024
Development and validation of a predictive model for acute kidney injury in patients with ureterolithiasis.
Article in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Establishing a clinical tool to predict acute kidney injury in pulmonary tuberculosis: insights from a large-scale retrospective study.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- A predictive model for PICC-related thrombosis in sepsis patients using XGBoost algorithm.Scientific reports · 2026Article
- Article
- Preoperative donor urinary UDP-Glc as an independent risk factor for delayed graft function.Frontiers in immunology · 2025Observational
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Authors and funding
8 authors.
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Abstract
objectivesThis study aims to identify risk factors for acute kidney injury (AKI) in patients with ureterolithiasis and to develop a predictive model for early AKI detection in this population.
methodsA retrospective analysis was conducted on data from 1,016 patients with ureterolithiasis who presented to our outpatient emergency department between January 2021 and December 2022. Using multifactorial logistic regression, we identified independent risk factors for AKI and constructed a nomogram to predict AKI risk. The predictive model's efficacy was assessed through the area under the ROC curve, calibration curves, Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA).
resultsAKI was diagnosed in 18.7% of the patients. Independent risk factors identified included age, fever, diabetes, hyperuricemia, bilateral calculi, functional solitary kidney, self-medication, and prehospital delay. The nomogram demonstrated excellent discriminatory capabilities, with AUCs of 0.818 (95% CI, 0.775-0.861) for the modeling set and 0.782 (95% CI, 0.708-0.856) for the validation set. Both calibration curve and HL test results confirmed strong concordance between the model's predictions and actual observations. DCA highlighted the model's significant clinical utility.
conclusionsThe predictive model developed in this study provides clinicians with a valuable tool for early identification and management of patients at high risk for AKI, thereby potentially enhancing patient outcomes.
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