The frequency of acute kidney injury in patients with chronic hepatitis C virus infection treated with sofosbuvir‐based regimens
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend withholding sofosbuvir (SOF) in patients with an estimated glomerular filtration rate (eGFR) of less than 30 mL/min. AIM: To assess the risk of acute kidney injury (AKI) in patients with no renal contraindications for SOF-based treatment. METHODS: This multicenter retrospective observational study included all consecutive patients that were treated with SOF-based or telaprevir/boceprevir (TVR/BOC)-based regimens at two tertiary university centers in North America. AKI was defined as an increase of ≥0.3 mg/dL (≥26.5 μmol/L) in serum creatinine level. Multivariable logistic regression analysis was used to identify risk factors for the occurrence of AKI. RESULTS: In total, 426 patients were included and treated with a SOF-based regimen (n=233, 54.7%) or TVR/BOC-based regimen (n=193, 45.3%). Among patients treated with a TVR/BOC-based regimen 34 (18%) of 193 patients experienced AKI compared to 26 (11%) of 233 patients treated with SOF-based regimens (P=.056). Multivariable logistic regression analysis showed that the presence of ascites (OR: 4.44, 95%CI: 1.46-13.54, P=.009) and the use of NSAIDs (OR: 4.47, 95%CI: 1.32-15.19, P=.016) were associated with a risk of AKI during SOF-based antiviral therapy. Creatinine levels returned to normal at end of follow-up in 23 (88%) of the 26 patients who experienced AKI with a SOF-based regimen and had a creatinine level available during follow-up. CONCLUSIONS: Although the risk for AKI was lower than for patients treated with TVR/BOC-based regimens, AKI was seen during 11% of SOF-based regimens and was mostly reversible. Patients with ascites and patients using NSAIDs have an increased risk for AKI during SOF-based antiviral therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".