Personalizing immunosuppressive therapy: predictors of drug switches in Malaysian kidney transplant recipients
Bibliographic record
Abstract
Objective: Tacrolimus-mycophenolic acid (MPA)-prednisolone immunosuppression remains the first-line management of kidney transplantation. Despite this, a switch to low-dose tacrolimus in combination with an mTOR inhibitor may be inevitable in some patients due to various factors. This study aims to identify the reasons and factors influencing the switch of tacrolimus-MPA to other combination immunosuppressive agents among kidney transplant recipients (KTRs). Methods: This retrospective observational cohort study included adult KTRs between year 2011-2019 at the two main kidney transplant centers in Malaysia. Demographic data, clinical, laboratory and medication information were collected. Multiple logistic regression was used to determine factors associated with the initial switch of tacrolimus-MPA immunosuppressive therapy. Results: From the 257 KTRs studied, 81 KTRs had their immunosuppressive agents switched from tacrolimus-MPA-prednisolone immunosuppressive regimen, with majority (96.3%, n = 78) switching to everolimus, an mTOR inhibitor in combination with low-dose tacrolimus. The average time switch was 125.8 ± 100.9 days. The main reasons for the initial switch include unresolved transaminitis (n = 15, 18.5%), cytomegalovirus (CMV) infection (n = 13, 16.0%) and BK virus (BKV) infection (n = 10, 12.3%). In the multiple logistic analysis, Malay ethnicity (P < 0.001), KTRs without post-transplant hypertension (P = 0.004) and KTRs with BKV infection (P < 0.001) were predictors for the initial switch of tacrolimus-MPA-prednisolone immunosuppressive therapy. Conclusion: Early identification of factors associated with the switch may prepare healthcare professionals for KTRs risk stratification, allowing ample time for appropriate optimization of tacrolimus-MPA-prednisolone immunosuppressive therapy based on individual patient's needs. This can possibly be a cost-effective alternative to switching to mTOR inhibitors for improved transplant outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".