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. 2024 Dec 18;25(1):458.
doi: 10.1186/s12882-024-03871-w.

A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation

Wenwen Du  1 , Xiaoxing Wang  1 , Dan Zhang  1 , Wenqian Chen  1 , Xianbo Zuo  2 , Pengmei Li  3
Affiliations

A genotype-guided prediction model for the incidence of persistent acute kidney injury following lung transplantation

Wenwen Du et al. BMC Nephrol. .

Erratum in

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.

Keywords: Lung transplantation; Persistent acute kidney injury; Prediction model; Tacrolimus.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: All procedures in this study were in accordance with the 1964 Helsinki declaration and its amendments, and was approved by the Ethics Committee of China-Japan Friendship Hospital in June, 2022 (No. 2022-KY-056-1). A waiver for informed consent was granted by the Ethics Committee of China-Japan Friendship Hospital. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Manhattan plot of candidate SNPs. Dots indicate the positions of candidate SNPs. The dotted red line is the bonferroni-corrected significance threshold
Fig. 2
Fig. 2
ROC curve analysis for A the training set and the validation set. B and C depicted the ROC curve analysis which was validated by 500 iterations of bootstrap internal sampling in the training set and validation set, respectively
Fig. 3
Fig. 3
Calibration curves for model prediction of persistent AKI in A the training set and B the validation set. The dashed line represented the ideal model, the blue line represented the calibration of the model constructed, and the red line represented the calibration after conduction 500 iterations of bootstrap internal sampling
Fig. 4
Fig. 4
Decision curve analysis for A the training set and B the validation set of the model sampled by Bootstrap 500 times
Fig. 5
Fig. 5
Comparison of the performance of the nomogram and single predictors in predicting persistent AKI using ROC curves in A the training set and B the validation set and comparison of the performance of the nomogram (Mod A) and SNPs alone (ModB), clinical predictors alone (ModC) in predicting persistent AKI using ROC curves in C the training set and D the validation set
Fig. 6
Fig. 6
Predictive nomogram for persistent AKI after LTx
See this image and copyright information in PMC

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References

    1. Wajda-Pokrontka M, Nadziakiewicz P, Krauchuk A, et al. Incidence and Perioperative Risk Factors of Acute Kidney Injury Among Lung Transplant Recipients. Transplant Proc. 2022;54(4):1120–3. - PubMed
    1. Botros M, Jackson K, Singh P, et al. Insights into early postoperative acute kidney injury following lung transplantation. Clin Transplant. 2022;36(4): e14568. - PubMed
    1. Xue J, Wang L, Chen CM, et al. Acute kidney injury influences mortality in lung transplantation. Ren Fail. 2014;36(4):541–5. - PubMed
    1. Du WW, Wang XX, Zhang D, et al. Retrospective analysis on incidence and risk factors of early onset acute kidney injury after lung transplantation and its association with mortality. Ren Fail. 2021;43(1):535–42. - PMC - PubMed
    1. Jing L, Chen W, Zhao L, et al. Acute kidney injury following adult lung transplantation. Chin Med J (Engl). 2021;135(2):172–80. - PMC - PubMed