Sirolimus-based immunosuppression for transplant-associated thrombotic microangiopathy
Bibliographic record
Abstract
Thrombotic microangiopathy (TMA) is a well‐recognized complication of solid organ transplantation. Both calcineurin inhibitors cyclosporine [1] and tacrolimus [2] have been associated with TMA. Treatment strategies for TMA have included plasmapheresis [1,2], calcineurin inhibitor dose reduction [1,2], calcineurin inhibitor withdrawal [1,2], and conversion from one calcineurin inhibitor to the other [3]. However, calcineurin inhibitor dose reduction or discontinuation increases the risk of acute allograft rejection and recurrent TMA has been described in patients that were converted from cyclosporine to tacrolimus [4,5]. We present two cases of biopsy‐proven transplant‐associated TMA that were successfully treated by discontinuation of tacrolimus followed by the use of sirolimus, mycophenolate mofetil (MMF) and prednisone for the prevention of allograft rejection. A 42‐year‐old white male with end‐stage renal disease due to type I diabetes mellitus received a cadaveric renal transplant in June 2000. Both donor and recipient were sero‐negative for cytomegalovirus. Initial immunosuppression consisted of thymoglobulin 100 mg/day and methylprednisolone. The allograft functioned immediately and on post‐operative day 1 the serum creatinine had fallen from 597 to 247 μmol/l and the platelet count was 259 000/mm3. On post‐operative day 6, the serum creatinine reached a nadir of 103 μmol/l and tacrolimus was started at a dose of 5 mg p.o. twice a day (BID). On post‐operative day 8, the serum creatinine increased to 140 μmol/l, the platelet count was 182 000/mm3, and the tacrolimus level was 5.7 ng/ml. The following day, the serum creatinine increased to 282 μmol/l, the platelet count was 219 000/mm3, and a renal biopsy was performed. The biopsy revealed multiple thrombi in the glomerular capillaries without any interstitial lymphoid infiltrate, tubulitis, or endothelitis, confirming the diagnosis of TMA. The tacrolimus was discontinued and the patient continued to receive thymoglobulin and methylprednisolone. Plasmapheresis was initiated and continued for a total of seven treatments. On post‐operative day 10, the serum creatinine peaked at 359 μmol/l and then began to decline. During this period, the platelet count fell to a nadir of 93 000/mm3 on post‐operative day 13. The platelet count returned to within the reference range 4 days later, where it has remained to the present.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".