ArticleAmerican journal of translational research2022
Development and validation of AKI prediction model in postoperative critically ill patients: a multicenter cohort study.
Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Fluid balance, biomarkers of renal function and mortality in critically ill patients with AKI diagnosed before, or within 24 h of intensive care unit admission: a prospective study.Journal of nephrology · 2024Article
- Simulation of Vancomycin Exposure Using Trough and Peak Levels Achieves the Target Area under the Steady-State Concentration-Time Curve in ICU Patients.Antibiotics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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Abstract
backgroundAcute kidney injury (AKI) is a common complication, especially among postoperative critically ill patients. Early identification of AKI is essential for reducing mortality.
methodsMulticenter data were used to develop an AKI prediction model for critically ill postoperative patients. A total of 1731 patients admitted to intensive care units (ICUs) were divided into a development set (n=1196) and a validation set (n=535) according to the principle of 7:3 randomization. Multivariate logistic regression analysis was performed on the predictors identified by univariate analysis, and a nomogram was created based on the predictors. The area under the receiver operating characteristic curve (AUROC) was used to assess the discrimination of the model. Calibration curves were generated, and the Hosmer-Lemeshow (HL) goodness of fit test was carried out. Decision curve analysis (DCA) was performed to assess the net clinical benefit.
resultsThe final model included 7 predictors: age, emergency surgery, abnormal basal creatinine level (BCr), chronic kidney disease (CKD), use of nephrotoxic drugs, diuretic use, and the Sequential Organ Failure Assessment (SOFA) score. A nomogram was drawn based on the predictors. The AUROC of the model in the development set was 0.725 (95% confidence interval (CI): 0.696-0.754). In the validation set, the AUROC was 0.706 (95% CI: 0.656-0.744). The model showed good discrimination (>70%) in both sets, and the HL test indicated that the model fit was good (
conclusionThe novel prediction model can be used to identify high-risk postoperative patients and provide a scientific and effective basis for clinicians to identify AKI early with a nomogram.
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36105045PMC9452309What OpenQuestion holds
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