MedElevator
Войти в исследования Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2026
Predictive models for graft rejection and infections in renal transplant patients based on TTV viral load.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Reyes Noelia Soledad, Poletta Fernando Adrián, Jara Raquel, Boccia Natalia, Díaz Carlos, Soler Pujol Gervasio, Hermida Eliana, Laham Gustavo, Echavarria Marcela
Аннотация
BACKGROUND: Graft rejection and opportunistic infections are among the main causes of graft dysfunction and loss in renal transplant recipients. Torque Teno Virus (TTV) is a highly prevalent, non-pathogenic virus that has been postulated as an immunosuppression biomarker in renal transplant patients. This study aimed to develop predictive models for these adverse events based on TTV viremia.
METHODS: From 130 patients, 871 plasmas were tested for TTV viral load. Patients' follow-up was documented up to 3 years post-transplantation. To develop predictive logistic regression models, ROC curve analyses were performed. The basal model analyzed only TTV viral loads while the enhanced model analyzed TTV viral load and patients' variables.
RESULTS: TTV prevalence by real time PCR was 81.2% pre-transplantation and 97.6% at month 3. The global rejection rate -clinical and subclinical rejection- was 36.6% and infections rate was 55.4%. The basal models showed areas under the curve (AUC) of 0.595 and 0.662 for rejection and infection prediction, respectively. In the enhanced models, AUCs increased to 0.781 and 0.765, respectively. Sensitivity and specificity with the enhanced models were 61.1% and 81.8% for graft rejection prediction; and 81.8% and 68.7% for infection prediction. NPV were 97.7% and 94.3% respectively. Internal validation of the enhanced models yielded comparable AUCs.
CONCLUSIONS: Enhanced models showed high probabilities to predict graft rejection and opportunistic infections in renal transplant patients. Incorporating these tools in the clinical setting can better identify patients at risk of developing adverse events. All statistical parameters improved with the enhanced model, achieving a very high NPV. Internal validation confirmed the robustness of these results.
METHODS: From 130 patients, 871 plasmas were tested for TTV viral load. Patients' follow-up was documented up to 3 years post-transplantation. To develop predictive logistic regression models, ROC curve analyses were performed. The basal model analyzed only TTV viral loads while the enhanced model analyzed TTV viral load and patients' variables.
RESULTS: TTV prevalence by real time PCR was 81.2% pre-transplantation and 97.6% at month 3. The global rejection rate -clinical and subclinical rejection- was 36.6% and infections rate was 55.4%. The basal models showed areas under the curve (AUC) of 0.595 and 0.662 for rejection and infection prediction, respectively. In the enhanced models, AUCs increased to 0.781 and 0.765, respectively. Sensitivity and specificity with the enhanced models were 61.1% and 81.8% for graft rejection prediction; and 81.8% and 68.7% for infection prediction. NPV were 97.7% and 94.3% respectively. Internal validation of the enhanced models yielded comparable AUCs.
CONCLUSIONS: Enhanced models showed high probabilities to predict graft rejection and opportunistic infections in renal transplant patients. Incorporating these tools in the clinical setting can better identify patients at risk of developing adverse events. All statistical parameters improved with the enhanced model, achieving a very high NPV. Internal validation confirmed the robustness of these results.