Machine Learning-Based Evaluation of Combined EBV and CMV Serostatus as Predictors of Post-Transplant Lymphoproliferative Disorder

基于机器学习的EBV和CMV血清状态联合检测作为移植后淋巴增生性疾病预测指标的评估

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Abstract

Post-transplant lymphoproliferative disorder (PTLD) is a major complication of solid organ transplantation (SOT), with the greatest risk in Epstein-Barr virus (EBV) donor-positive/recipient-negative (D+/R-) pairs. The contribution of cytomegalovirus (CMV) serostatus is less well defined. We conducted a population-based study of 47,333 abdominal SOT recipients in the United States (1995-2015) using linked SRTR data. Donor-recipient EBV/CMV serostatus was evaluated as a compound variable. The primary outcome was PTLD incidence, with secondary analyses assessing predictors of PTLD and impact on survival. Overall, 716 patients (1.5%) developed PTLD at a median of 6.1 years (IQR 2.9-9.7) after transplant. EBV D+/R- recipients had the highest incidence (3.2%), and those with compound [EBV D+/R-, CMV D-/R-] serostatus had more than double the PTLD risk compared with [EBV D+/R-, CMV D+/R-] (5.3% vs. 2.5%, p < 0.001). Logistic regression and random forest models consistently identified [EBV D+/R-, CMV D-/R-] serostatus, age, and race as leading predictors, though discrimination was modest (test AUC ∼0.61). In a matched survival analysis, PTLD was not associated with increased all-cause mortality (aHR ∼1.0). Our findings demonstrate that combined EBV/CMV serostatus improves PTLD risk prediction compared with EBV alone and emphasize the need for targeted preventive strategies.

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