Clinicopathological Characteristics of Drug-Induced Acute Interstitial Nephritis and Role of Steroids in Management: A Single-Center Observational Study
Bibliographic record
Abstract
Background: Acute interstitial nephritis (AIN) is a potentially reversible, but underdiagnosed cause of acute kidney injury. The role of steroids in the management of drug-induced AIN is debatable. There is scarcity of data on clinical characteristics of drug-induced AIN, management approach and effect of steroids on renal outcomes from the low-middle-income countries. Methods: We performed a retrospective study of patients with biopsy-proven AIN over a period of 12 years. The main outcomes were recovery of renal function (early (? 3 weeks) or late (> 3 weeks)) and hemodialysis (HD) dependence at 12 weeks. Results: A total of 48 (4.9%) AIN cases were found among 978 renal biopsies. Mean age was 47.6 ± 12.1 years and 56.3% were males. The offending agent could be identified in half of the patients (non-steroidal anti-inflammatory drugs (NSAIDs) most commonly followed by antibiotics, diuretics and proton pump inhibitor (PPI)). Three quarters of patients presented with estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m 2 (n = 36), out of which 58.3% (n = 21) required HD. Thirty-nine (81.3%) patients ended up receiving steroids. Mean dose of prednisone was 0.8 ± 0.2 mg/kg per day. Early use of steroids (? 11 days) was significantly associated with earlier (? 21 days) onset of recovery (P = 0.003) as compared to late use of steroids (> 11 days). Conclusion: Our data showed the benefit of earlier use of steroids in achieving rapid and complete renal recovery in drug-induced AIN in a low-middle-income country with frequent use of over-the-counter drugs. Late steroid use (? 3 weeks) was not associated with any further recovery at an additional risk of exposing patients to undue adverse effects. World J Nephrol Urol. 2022;11(1):24-30 doi: https://doi.org/10.14740/wjnu427
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".