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Predictive model for delayed graft function based on easily available pre-renal transplant variables

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Abstract

Identification of pre-transplant factors influencing delayed graft function (DGF) could have an important clinical impact. This could allow clinicians to early identify dialyzed chronic kidney disease (CKD) patients eligible for special transplant programs, preventive therapeutic strategies and specific post-transplant immunosuppressive treatments. To achieve these objectives, we retrospectively analyzed main demographic and clinical features, follow-up events and outcomes registered in a large dedicated dataset including 2,755 patients compiled collaboratively by four Italian renal/transplant units. The years of transplant ranged from 1984 to 2012. Statistical analysis clearly demonstrated that some recipients’ characteristics at the time of transplantation (age and body weight) and dialysis-related variables (modality and duration) were significantly associated with DGF development (p ≤ 0.001). The area under the receiver-operating characteristic (ROC) curve of the final model based on the four identified variables predicting DGF was 0.63 (95 % CI 0.61, 0.65). Additionally, deciles of the score were significantly associated with the incidence of DGF (p value for trend <0.001). Therefore, in conclusion, in our study we identified a pre-operative predictive model for DGF, based on inexpensive and easily available variables, potentially useful in routine clinical practice in most of the Italian and European dialysis units.

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Acknowledgments

The authors thank Dr. Simona Granata and Dr. Paola Tomei (Renal Unit, Department of Medicine, University-Hospital of Verona, Verona, Italy) for their collaboration in collecting the clinical data.

Conflict of interest

None.

Author information

Authors and Affiliations

  1. Renal Unit, Department of Medicine, University-Hospital of Verona, Piazzale A. Stefani 1, 37126, Verona, Italy

    Gianluigi Zaza & Antonio Lupo

  2. Division of Nephrology and Dialysis, Columbus-Gemelli Hospital Catholic University, School of Medicine Rome, 00168, Rome, Italy

    Pietro Manuel Ferraro & Giovanni Gambaro

  3. Section of Dermatology and Venereology, University-Hospital of Verona, 37126, Verona, Italy

    Gianpaolo Tessari & Giampiero Girolomoni

  4. Section of Nephrology, Azienda Ospedaliera “Spedali Civili” Brescia, 25123, Brescia, Italy

    Silvio Sandrini

  5. Institute of Nephrology, Dialysis and Renal Transplantation, S. Orsola University Hospital, 40138, Bologna, Italy

    Maria Piera Scolari & Irene Capelli

  6. Nephrology Unit, Careggi University Hospital, 50124, Florence, Italy

    Enrico Minetti

  7. Renal, Dialysis and Transplant Unit, Department of Emergency and Transplantation, University of Bari, 70124, Bari, Italy

    Loreto Gesualdo

  8. First Surgical Clinic, Kidney Transplantation Center, University Hospital of Verona, 37126, Verona, Italy

    Luigino Boschiero

Authors
  1. Gianluigi Zaza
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  2. Pietro Manuel Ferraro
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  3. Gianpaolo Tessari
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  4. Silvio Sandrini
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  5. Maria Piera Scolari
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  6. Irene Capelli
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  7. Enrico Minetti
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  8. Loreto Gesualdo
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  9. Giampiero Girolomoni
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  10. Giovanni Gambaro
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  11. Antonio Lupo
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  12. Luigino Boschiero
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Corresponding author

Correspondence to Gianluigi Zaza.

Additional information

Dr. Gianluigi Zaza and Dr. Pietro Manuel Ferraro equally contributed to the present study.

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Zaza, G., Ferraro, P.M., Tessari, G. et al. Predictive model for delayed graft function based on easily available pre-renal transplant variables. Intern Emerg Med 10, 135–141 (2015). https://doi.org/10.1007/s11739-014-1119-y

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  • DOI: https://doi.org/10.1007/s11739-014-1119-y

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