A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation
Abstract Background This study aimed to develop a nomogram for predicting persistent renal dysfunction in acute kidney injury (AKI) following lung transplantation (LTx). Method A total of 229 LTx patients were enrolled, and genotyping for 153 single nucleotide polymorphisms (SNPs) was performed. The...
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2024-12-01
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| Series: | BMC Nephrology |
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| Online Access: | https://doi.org/10.1186/s12882-024-03871-w |
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| author | Wenwen Du Xiaoxing Wang Dan Zhang Wenqian Chen Xianbo Zuo Pengmei Li |
| author_facet | Wenwen Du Xiaoxing Wang Dan Zhang Wenqian Chen Xianbo Zuo Pengmei Li |
| author_sort | Wenwen Du |
| collection | DOAJ |
| description | Abstract Background This study aimed to develop a nomogram for predicting persistent renal dysfunction in acute kidney injury (AKI) following lung transplantation (LTx). Method A 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. Results The 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. Conclusion This 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. |
| format | Article |
| id | doaj-art-b87d90fd659f431dbb4063ef306a5aaf |
| institution | OA Journals |
| issn | 1471-2369 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
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| series | BMC Nephrology |
| spelling | doaj-art-b87d90fd659f431dbb4063ef306a5aaf2025-08-20T02:01:29ZengBMCBMC Nephrology1471-23692024-12-0125111310.1186/s12882-024-03871-wA genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantationWenwen Du0Xiaoxing Wang1Dan Zhang2Wenqian Chen3Xianbo Zuo4Pengmei Li5Department of Pharmacy, Friendship HospitalDepartment of Pharmacy, Friendship HospitalDepartment of Pharmacy, Friendship HospitalDepartment of Pharmacy, Friendship HospitalDepartment of Dermatology, Department of Pharmacy, Friendship HospitalDepartment of Pharmacy, Friendship HospitalAbstract Background This study aimed to develop a nomogram for predicting persistent renal dysfunction in acute kidney injury (AKI) following lung transplantation (LTx). Method A 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. Results The 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. Conclusion This 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.https://doi.org/10.1186/s12882-024-03871-wPersistent acute kidney injuryLung transplantationTacrolimusPrediction model |
| spellingShingle | Wenwen Du Xiaoxing Wang Dan Zhang Wenqian Chen Xianbo Zuo Pengmei Li A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation BMC Nephrology Persistent acute kidney injury Lung transplantation Tacrolimus Prediction model |
| title | A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation |
| title_full | A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation |
| title_fullStr | A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation |
| title_full_unstemmed | A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation |
| title_short | A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation |
| title_sort | genotype guided prediction model for the incidence of persistent acute kidney injury following lung transplantation |
| topic | Persistent acute kidney injury Lung transplantation Tacrolimus Prediction model |
| url | https://doi.org/10.1186/s12882-024-03871-w |
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