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Biomarkers and immune monitoring

Tuesday September 24, 2024 - 10:40 to 12:10

Room: Emirgan 1

321.7 Construct and validate a mortality prediction model for severe pulmonary infection post-kidney transplantation

Puxun Tian, People's Republic of China

The First Affiliated Hospital of Xi'an Jiaotong University

Abstract

Construct and validate a mortality prediction model for severe pulmonary infection post-kidney transplantation

Puxun Tian1, Tian Wei1, Ge Deng1, Zejiaxin Niu1, Meng Dou1, Bingxuan Zheng1.

1Department of Kidney Transplantation, The First Affiliated Hospital of Xi’an Jiaotong University, Xi'an, People's Republic of China

Introduction: Postoperative pulmonary infections are a critical clinical prognostic factor that affects both patient and graft survival, with high mortality and substantial economic burden. This study aims to analyze the clinical features of pulmonary infections post-kidney transplantation, evaluate risk factors, and develop novel prediction models utilizing least absolute shrinkage and selection operator LASSO-Logistic regression to reduce mortality from severe pulmonary infections and improve patient prognosis.
Method: This study analyzed patients with post-kidney transplantation pulmonary infections treated at the First Affiliated Hospital of Xi'an Jiaotong University from January 2021 to December 2023. Baseline and clinical data to examine infection prevalence, manifestations, pathogens, and outcomes were collected. Dividing patients into training and testing cohorts (7:3 ratio), analyzed postoperative mortality risk factors and developed a LASSO-Logistic regression prediction model, verifying its discrimination, calibration, and clinical efficacy.
Results: In this study, a total of 373 patients were analyzed, of whom 249 experienced severe infections. Infections predominantly occurred within the first year post-transplant, with 41.0% in the first year, peaking at 17.4% within 1-6 months. Pathogen detection was positive in 90.1% of cases, identifying 118 different bacteria, with viruses and bacteria being the most common. The study observed a 9.38% mortality rate, higher in severe cases. There were 261 patients in the training set and 112 patients in the testing set. The final LASSO-logistic regression model included 4 risk factors: age, neutrophil count, CD8+T cell count, and history of rejection and/or DGF. LASSO-Logistic regression had a satisfying discrimination and calibration in both training [area under the curve (AUC)=0.877, 95% CI: 0.7970-0.9565] and testing cohorts (AUC=0.746, 95% CI: 0.511-0.980). The Bootstrap method showed that the models had good discrimination, calibration, and clinical application efficiency.  
Conclusion: Pulmonary infections are most common in the first year post-transplant, with rapid disease progression and a variety of pathogens. The LASSO-logistic regression model can predict the risk of mortality in patients with pulmonary infection after kidney transplantation.

References:

[1] Pulmonary Infection; Mortality risk; Prediction model; LASSO-Logistic regression.

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