Publicação
Torque teno virus as a biomarker for infection risk in kidney transplant recipients : a machine learning-enabled cohort study
| datacite.subject.fos | Ciências Médicas::Ciências da Saúde | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Querido, Sara | |
| dc.contributor.author | Ramalhete, Luís | |
| dc.contributor.author | Gomes, Perpétua | |
| dc.contributor.author | Weigert, André | |
| dc.date.accessioned | 2026-05-28T15:06:16Z | |
| dc.date.available | 2026-05-28T15:06:16Z | |
| dc.date.issued | 2025-09 | |
| dc.description.abstract | Background: Torque Teno Virus (TTV) viremia has been proposed as a marker for infection risk in kidney transplant (KT) recipients. This study aimed to evaluate the prognostic value of TTV levels for predicting infections post-KT. Methods: A cohort of 82 KT patients was analyzed. TTV loads were measured before KT and at the time of cutoff analysis (mean time since KT: 20.2 ± 10.3 months). Infections were tracked within six months following the time of cutoff analysis. Univariable analyses and a supervised machine learning approach (logistic regression with leave-one-out cross-validation) were conducted to rigorously assess TTV’s predictive ability for post-transplant infection. Results: Seventy-two patients (87.8%) had detectable TTV before KT. Of these, 30.5% developed infections, predominantly viral. TTV loads increased significantly from 3.35 ± 1.67 log10 cp/mL before KT to 4.53 ± 1.93 log10 cp/mL at the time of cutoff analysis. Infected patients had significantly higher TTV loads (5.39 ± 1.68 log10 vs. 4.16 ± 1.94 log10 cp/mL, p = 0.0057). The optimal TTV threshold for predicting infection at the time of cutoff analysis was 5.16 log10 cp/mL, with 60% sensitivity and 81% specificity. Machine learning models improved performance, with sensitivity and specificity 0.805 and 0.735, respectively. Conclusions: TTV viremia may serve as a biomarker for infection risk, particularly when used with other clinical variables. The identified TTV threshold of 5.16 log10 cp/mL offers a practical tool for clinical decision-making, particularly when integrated with a machine learning model. Further studies with larger cohorts are needed to validate these findings and refine clinical applications. | eng |
| dc.identifier.citation | Querido S, Ramalhete L, Gomes P, Weigert A. Torque Teno Virus as a Biomarker for Infection Risk in Kidney Transplant Recipients: A Machine Learning-Enabled Cohort Study. Infectious Disease Reports. 2025; 17(5):107. https://doi.org/10.3390/idr17050107 | |
| dc.identifier.doi | 10.3390/idr17050107 | |
| dc.identifier.issn | 2036-7449 | |
| dc.identifier.uri | http://hdl.handle.net/10400.26/63391 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation.hasversion | https://doi.org/10.3390/idr17050107 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | TTV | |
| dc.subject | kidney transplantation | |
| dc.subject | machine learning | |
| dc.subject | infection | |
| dc.subject | immunosuppression | |
| dc.title | Torque teno virus as a biomarker for infection risk in kidney transplant recipients : a machine learning-enabled cohort study | eng |
| dc.type | contribution to journal | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 5 | |
| oaire.citation.startPage | 107 | |
| oaire.citation.title | Infectious Disease Reports | |
| oaire.citation.volume | 17 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |