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Peer-reviewedCC BY 4.0Frontiers in Medicine

Risk factors for invasive fungal infections after kidney transplantation and the construction of a predictive model

  • Lulu Tang1,2
  • Jiqiu Wen3,4,5
  • Hongdi Xiong2
  • Chunying Lv2
  • Jianhui Dong3,4,5
  • Ning Wen3,4,5
  • Guomei Huang2
  • Zhiwen Jiang1
  • Haiju Chen2
  • Cairong Yan2
  • Shuangshuang Li2
  • Yuyang Jiang2
3 more authors
  • Yi Wu2
  • Cunwei Cao1,6,7
  • Xuyong Sun3,4,5
7 affiliations
  1. 1Department of Dermatology and Venereology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
  2. 2Department of Dermatology and Venereology, the Second Affiliated Hospital of Guangxi Medical University, Nanning, China
  3. 3Institute of Transplant Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
  4. 4Guangxi Clinical Research Center for Organ Transplantation, Nanning, China
  5. 5Guangxi Key Laboratory of Organ Donation and Transplantation, Nanning, China
  6. 6Guangxi Scientific and Technological Innovation Cooperation Base of Mycosis Prevention and Control, Nanning, China
  7. 7Guangxi Key Laboratory of Mycosis Prevention and Treatment, Nanning, China
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Abstract

Background: Post-transplant infection is one of the main complications in the early stage of kidney transplantation. However, there is a lack of risk prediction tools to identify those at high risk of infection.

Objective: To investigate the risk factors for invasive fungal infections (IFIs) after kidney transplantation in a single center in Guangxi region and to construct a predictive model.

Methods: A total of 997 patients who underwent kidney transplantation were selected as the research subjects. Patients with IFIs after kidney transplantation were classified into the IFIs group (n = 125), while the remaining patients formed the control group (n = 872). The clinical data of the two groups were compared, and the risk factors for IFIs after kidney transplantation were analyzed using multivariate logistic stepwise forward regression. The 997 samples were randomly divided into a training set and a validation set in an 7:3 ratio. The training set was used to build the model, while the validation set was used to evaluate the model performance.

Results: Heart failure (OR = 4.235), low EOS% level (OR = 0.825), high Cr level (OR = 1.003), and low Mg + level (OR = 0.198) are independent risk factors influencing the occurrence of IFIs after kidney transplantation (all P < 0.05). Based on these four risk factors, a nomogram prediction model was constructed. The AUC of the training set was 0.654 (95% CI: 0.591–0.717), with a specificity of 59.3% and a sensitivity of 65.3%. The AUC of the validation set was 0.677(95% CI: 0.583–0.770), with a specificity of 59.0% and a sensitivity of 76.6%.

Conclusion: Heart failure, low EOS%, high Cr level, and low Mg level are closely related to the occurrence of IFIs after kidney transplantation. The prediction model based on these risk factors has certain predictive value.

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Cite

BibTeXRIS
BibTeX
@article{tang2026risk,
  title = {{Risk factors for invasive fungal infections after kidney transplantation and the construction of a predictive model}},
  author = {Lulu Tang and Jiqiu Wen and Hongdi Xiong and Chunying Lv and Jianhui Dong and Ning Wen and Guomei Huang and Zhiwen Jiang and Haiju Chen and Cairong Yan and Shuangshuang Li and Yuyang Jiang and Yi Wu and Cunwei Cao and Xuyong Sun},
  journal = {Frontiers in Medicine},
  year = {2026},
  month = oct,
  doi = {10.3389/fmed.2026.1850921},
  url = {https://doi.org/10.3389/fmed.2026.1850921},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Risk factors for invasive fungal infections after kidney transplantation and the construction of a predictive model
AU  - Lulu Tang
AU  - Jiqiu Wen
AU  - Hongdi Xiong
AU  - Chunying Lv
AU  - Jianhui Dong
AU  - Ning Wen
AU  - Guomei Huang
AU  - Zhiwen Jiang
AU  - Haiju Chen
AU  - Cairong Yan
AU  - Shuangshuang Li
AU  - Yuyang Jiang
AU  - Yi Wu
AU  - Cunwei Cao
AU  - Xuyong Sun
PY  - 2026
DA  - 2026/10/12
JO  - Frontiers in Medicine
DO  - 10.3389/fmed.2026.1850921
UR  - https://doi.org/10.3389/fmed.2026.1850921
AB  - Background: Post-transplant infection is one of the main complications in the early stage of kidney transplantation. However, there is a lack of risk prediction tools to identify those at high risk of infection.  Objective: To investigate the risk factors for invasive fungal infections (IFIs) after kidney transplantation in a single center in Guangxi region and to construct a predictive model.  Methods: A total of 997 patients who underwent kidney transplantation were selected as the research subjects. Patients with IFIs after kidney transplantation were classified into the IFIs group (n = 125), while the remaining patients formed the control group (n = 872). The clinical data of the two groups were compared, and the risk factors for IFIs after kidney transplantation were analyzed using multivariate logistic stepwise forward regression. The 997 samples were randomly divided into a training set and a validation set in an 7:3 ratio. The training set was used to build the model, while the validation set was used to evaluate the model performance.  Results: Heart failure (OR = 4.235), low EOS% level (OR = 0.825), high Cr level (OR = 1.003), and low Mg + level (OR = 0.198) are independent risk factors influencing the occurrence of IFIs after kidney transplantation (all P < 0.05). Based on these four risk factors, a nomogram prediction model was constructed. The AUC of the training set was 0.654 (95% CI: 0.591–0.717), with a specificity of 59.3% and a sensitivity of 65.3%. The AUC of the validation set was 0.677(95% CI: 0.583–0.770), with a specificity of 59.0% and a sensitivity of 76.6%.  Conclusion: Heart failure, low EOS%, high Cr level, and low Mg level are closely related to the occurrence of IFIs after kidney transplantation. The prediction model based on these risk factors has certain predictive value.
SN  - 2296-858X
ER  -