ArticleBMC nephrology2024
A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation.
Article in BMC nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Predicting Acute Kidney Injury After Lung Transplantation: Development and Internal Validation of Perioperative Prediction Models.Clinical transplantation · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop a nomogram for predicting persistent renal dysfunction in acute kidney injury (AKI) following lung transplantation (LTx).
methodA total of 229 LTx patients were enrolled, and genotyping for 153 single nucleotide polymorphisms (SNPs) was performed. The cohort was randomly divided into training (n = 183) and validation (n = 46) sets in an 8:2 ratio. Statistically significant SNPs identified through pharmacogenomic analysis were combined with clinical factors to construct a comprehensive prediction model for persistent AKI using multivariate logistic regression analysis. Discrimination and calibration analyses were conducted to evaluate the performance of the model. Decision curve analysis was used to assess its clinical utility. Due to the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model.
resultsThe final nomogram comprised nine predictors, including body mass index, thrombin time, tacrolimus initial concentration, rs757210, rs1799884, rs6887695, rs1494558, rs2069762 and rs2275913. In the training set, the area under the receiver operating characteristic curve of the nomogram was 0.781 (95%CI: 0.715-0.846), while in the validation set it was 0.698 (95%CI: 0.542-0.855), indicating good model fit. As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Additionally, decision curve analysis confirmed its clinical applicability.
conclusionThis study presents a genotype-guided nomogram that can be used to assess the risk of persistent AKI following LTx and may assist in guiding personalized prevention strategies in clinical practice.
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