Kidney Pathology Findings in Pediatric Patients with Kidney Injury and Inflammatory Bowel Disease: A Case Series
Bibliographic record
Abstract
Background: Patients with inflammatory bowel disease (IBD) are at increased risk of renal pathologies such as IgA Nephropathy (IgAN) and tubulointerstitial nephritis (TIN). Data describing the spectrum of renal pathology abnormalities and treatment approaches in pediatric IBD patients who develop kidney disease are limited. We aim to describe the spectrum of kidney pathology, treatments, and outcomes in pediatric IBD patients who underwent kidney biopsy at our centre. Methods: This is a single-centre retrospective case series conducted at a quaternary pediatric center in Toronto, Canada. We included pediatric patients diagnosed with IBD who underwent kidney biopsy between June 2018 and February 2024. Clinical data were obtained from chart review, and all biopsies were retrospectively reviewed by a renal pathologist. Results: 12 patients were included. 11 of 12 patients had Crohn’s disease, and 4 patients were female. Renal pathology diagnoses included TIN (33%) in four patients, focal segmental glomerulosclerosis (FSGS) in one, IgA nephropathy in one, and acute tubular necrosis in one. Five patients (42%) had normal or non-specific findings on biopsy. Indications for kidney biopsy included abnormal serum creatinine (n=10), nephrotic range proteinuria (n=1), and steroid-resistant nephrotic syndrome/SRNS(n=1). Kidney abnormalities were detected before IBD diagnosis in 2 patients, within one year of diagnosis in four, and 2-11 years post-diagnosis in six. TIN treatments included prednisone, cessation of suspected triggers such as ustekinumab and vedolizumab, and/or IBD therapy escalation. All patients with TIN did not have significant improvement in kidney function after treatment. The patient with IgA nephropathy had complete recovery of kidney function after steroid treatment. The patient with FSGS presented with SRNS and responded to Rituximab, while his IBD was independently treated successfully with infliximab. Conclusion: TIN was the most common renal pathology this pediatric patients with IBD cohort, but was associated with poor renal recovery despite steroids and cessation of potential triggers. A large proportion of biopsies showed no diagnostic abnormalities. Improved kidney function surveillance in pediatric IBD patients could be beneficial for earlier detection and management of diseases such as TIN.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".