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Development of a Risk Prediction Model for Infection After Kidney Transplantation Transmitted from Bacterial
Mingxing Guo1, Chen Pan1, Ying Zhao1
1Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China.
Background:
The risk of transplant recipient infection is unknown when the preservation solution culture is positive.
Methods:
We developed a prediction model to evaluate the infection in kidney transplant recipients within microbial contaminated preservation solution. Univariate logistic regression was utilized to identify risk factors for infection. Both stepwise selection with Akaike information criterion (AIC) was used to identify variables for multivariate logistic regression. Selected variables were incorporated in the nomograms to predict the probability of infection for kidney transplant recipients with microbial contaminated preservation solution.
Results:
Age, preoperative creatinine, ESKAPE, PCT, hemofiltration, and sirolimus had a strongest association with infection risk, and a nomogram was established with an AUC value of 0.72 (95% confidence interval, 0.64-0.80) and Brier index 0.20 (95% confidence interval, 0.18-0.23). Finally, we found that when the infection probability was between 20% and 80%, the model oriented antibiotic strategy should have higher net benefits than the default strategy using decision curve analysis.
Conclusion:
Our study developed and validated a risk prediction model for evaluating the infection of microbial contaminated preservation solutions in kidney transplant recipients and demonstrated good net benefits when the total infection probability was between 20% and 80%.
Insights
A new prediction model helps assess infection risk in kidney transplant recipients with contaminated preservation solutions. This tool aids in optimizing antibiotic strategies, particularly when infection probability ranges from 20% to 80%.
Area of Science:
- Nephrology
- Transplant Surgery
- Infectious Diseases
Background:
- The risk of infection in kidney transplant recipients receiving organs with microbial contaminated preservation solutions remains unclear.
- Accurate risk assessment is crucial for managing post-transplant complications.
Purpose of the Study:
- To develop and validate a predictive model for identifying infection risk in kidney transplant recipients exposed to contaminated preservation solutions.
- To establish a nomogram for predicting infection probability and guiding antibiotic strategies.
Main Methods:
- Development of a prediction model using logistic regression and Akaike information criterion (AIC) for variable selection.
- Identification of risk factors including age, preoperative creatinine, ESKAPE pathogens, procalcitonin (PCT), hemofiltration, and sirolimus.
- Validation of the nomogram using AUC and Brier index, and assessment of clinical utility with decision curve analysis.
Main Results:
- The established nomogram demonstrated good predictive performance with an AUC of 0.72 (95% CI, 0.64-0.80) and Brier index of 0.20 (95% CI, 0.18-0.23).
- A model-oriented antibiotic strategy showed superior net benefits compared to the default strategy when infection probability was between 20% and 80%.
Conclusions:
- A validated risk prediction model effectively evaluates infection risk in kidney transplant recipients with contaminated preservation solutions.
- The model offers practical clinical benefits by guiding antibiotic use, especially within a specific probability range (20%-80%).
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