Dabigatran-Induced Acute Interstitial Nephritis: An Important Complication of Newer Oral Anticoagulation Agents
Bibliographic record
Abstract
Acute kidney injury (AKI) due to an acute interstitial nephritis (AIN) is common and can lead to increased morbidity and mortality. Medications such as antibiotics, nonsteroidal anti-inflammatory drugs (NSAIDs), proton pump inhibitors (PPI) and rifampin are common offending agents. Anticoagulant-associated AIN is more frequently reported with the use of warfarin; however, only few case reports have reported an association with the use of novel oral anticoagulants (NOACs). Herein, we report the case of a 59-year-old male who developed AKI after initiating dabigatran for the treatment of atrial fibrillation. Laboratory data demonstrated elevated blood urea nitrogen (BUN) of 115 mg/dL (baseline = 35 mg/dL) and serum creatinine (Cr) of 5.06 mg/dL (baseline = 1.3 mg/dL). Urinalysis revealed eosinophiluria. Renal biopsy disclosed diffuse tubulointerstitial nephritis and eosinophils and confirmed the diagnosis of AIN. At 1 week, renal function improved (BUN/Cr = 53/2.73 mg/dL) with steroid therapy and discontinuation of dabigatran. With an increasing use of NOACs, it is important to monitor renal function to diagnose AIN in a timely fashion. Early diagnosis and prompt treatment can mitigate serious renal damage induced by dabigatran.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".