Improved Awareness of Tuberculosis Infection in Advanced Stage Chronic Renal Disease Could Reduce Cases of Active TB: Lessons from Four Challenging Cases
Bibliographic record
Abstract
Patients with chronic kidney disease (CKD) have an increased risk of developing tuberculosis (TB) compared to those with normal renal function. The reasons for this are well described, but include impaired cellular immunity, a high incidence of co-morbid conditions as well as the concomitant use of immunosuppressive medications. Ethnicity as well as socio-economic factors also prevail. Expert guidelines recommend TB chemoprophylaxis in renal transplant recipients deemed at high risk – invariably those from ethnic minorities or recent arrivals from areas with high endemic rates of TB. However, in most renal centres within the UK including our own, high risk patients with advanced CKD (stages 4 and 5) are not routinely screened for latent TB infection (LTBI) prior to transplantation, thus contributing to missed opportunities for preventing TB disease. We report four challenging cases of patients diagnosed with TB disease on a background of advanced CKD, who all presented within an 18-month period whilst under the care of a large tertiary renal centre. Two of the cases were patients with well-functioning renal transplants and two were on renal replacement therapy. We describe our experience and share practical considerations on how to manage TB in CKD including the role of therapeutic drug monitoring in peritoneal dialysis. These cases highlight the need for a better awareness of the possibility of TB reactivation and underscore the arguments in favour of screening programmes for LTBI in patients with advanced CKD.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".