Kevin Chen1, Grahya Guntur2, Tapati Stalam3, Nkechi Okonkwo4, Cinthia Drachenberg4, Naeem Goussous5, David Bruno5, Valeria Mas5, Jonathan Bromberg6, Abdolreza Haririan3. 1. University of Maryland Medical Center, Baltimore, Maryland. Electronic address: KTChen@som.umaryland.edu. 2. University of Maryland Medical Center, Baltimore, Maryland. Electronic address: Grahya.Guntur@som.umaryland.edu. 3. Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland. 4. Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland. 5. Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland. 6. Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland.
Abstract
BACKGROUND: Deceased-donor kidney quality pretransplantation is considered critical for future graft function. Assessment of donor kidney quality considers clinical and histologic variables. Several models that incorporate a variety of these factors have been proposed to predict long-term graft survival. METHODS: We compared the performance metrics of 4 scoring systems models---the Maryland Aggregate Pathology Index, Banff, Remuzzi, and Leuven---for predicting renal allograft survival. In this retrospective cohort study, we analyzed 173 renal allografts that underwent preoperative baseline biopsy. Donor demographics and donor baseline histopathology data were collected and correlated with graft survival posttransplant. RESULTS: Among the 4 scoring systems, none were significantly associated with posttransplant graft survival or early graft function. The Maryland Aggregate Pathology Index scoring system had better predictive capacity in receiver operating characteristic curve analysis; however, the utility as a predictor of graft survival was only slightly better than chance. Baseline histologic features were individually analyzed, and it was found that none were associated with graft survival in this cohort. Among donor demographics, none were significantly associated with graft survival. CONCLUSIONS: In our study none of the 4 previously proposed predictive models were associated with graft survival after transplantation. Further studies are needed to define new models with stronger predictive value for graft outcome that could help minimize organ discards.
BACKGROUND: Deceased-donor kidney quality pretransplantation is considered critical for future graft function. Assessment of donor kidney quality considers clinical and histologic variables. Several models that incorporate a variety of these factors have been proposed to predict long-term graft survival. METHODS: We compared the performance metrics of 4 scoring systems models---the Maryland Aggregate Pathology Index, Banff, Remuzzi, and Leuven---for predicting renal allograft survival. In this retrospective cohort study, we analyzed 173 renal allografts that underwent preoperative baseline biopsy. Donor demographics and donor baseline histopathology data were collected and correlated with graft survival posttransplant. RESULTS: Among the 4 scoring systems, none were significantly associated with posttransplant graft survival or early graft function. The Maryland Aggregate Pathology Index scoring system had better predictive capacity in receiver operating characteristic curve analysis; however, the utility as a predictor of graft survival was only slightly better than chance. Baseline histologic features were individually analyzed, and it was found that none were associated with graft survival in this cohort. Among donor demographics, none were significantly associated with graft survival. CONCLUSIONS: In our study none of the 4 previously proposed predictive models were associated with graft survival after transplantation. Further studies are needed to define new models with stronger predictive value for graft outcome that could help minimize organ discards.
Authors: F Villanego; L A Vigara; J M Cazorla; J Naranjo; L Atienza; A M Garcia; M E Montero; M C Minguez; T Garcia; A Mazuecos Journal: Transpl Int Date: 2022-06-06 Impact factor: 3.842