Prediction of long-term renal transplant allograft function from day 3 post-transplant Tc-99m MAG3 scintigraphy

Stanley Ngai1, Timothy Demetriades

  • 1Department of Radiology, Princess Alexandra Hospital, Brisbane, Queensland, Australia. sngai01@hotmail.com

Clinical Nuclear Medicine
|January 23, 2008
PubMed

Insights

Day 3 post-transplant perfusion index and early serum creatinine levels can predict long-term kidney transplant outcomes. These markers show a strong relationship with later serum creatinine levels, aiding in predicting allograft survival.

Area of Science:

  • Nephrology
  • Transplant Surgery
  • Nuclear Medicine

Background:

  • Long-term renal allograft survival is crucial after transplantation.
  • Serum creatinine (sCr) levels at 1 year post-transplant are predictive of outcomes.
  • Tc-99m MAG3 scans with Hilson's perfusion index (PI) are routinely used on day 3 post-transplant.

Purpose of the Study:

  • To evaluate the predictive power of day 3 post-transplant Hilson's PI for long-term sCr levels.
  • To assess early sCr levels (1 or 3 months) for predicting 12-month sCr levels.
  • To explore markers for predicting long-term renal allograft survival.

Main Methods:

  • Retrospective analysis of 108 renal transplant patients (April 2001-March 2002).
  • Recorded sCr levels at 1, 3, 12 months, and 5 years post-transplantation.
  • Compared sCr levels with day 3 post-transplant Hilson's PI.

Main Results:

  • Hilson's PI showed 72-74% accuracy and 90% sensitivity in predicting sCr levels up to 12 months.
  • Significant correlation (R=0.32-0.39, P=0.00002-0.003) between Hilson's PI and sCr, but with low specificity.
  • Early sCr levels (1 or 3 months) strongly predicted 12-month sCr levels (R=0.68-0.85, P<0.000001).

Conclusions:

  • A strong relationship exists between day 3 Hilson's PI and serial sCr levels post-transplantation.
  • Early sCr levels (1 and 3 months) are strongly related to 12-month sCr levels.
  • A composite index integrating these markers warrants investigation for predicting long-term renal allograft survival.
Abstract