Risk factors for high‐dose methotrexate associated acute kidney injury in patients with hematological malignancies
Bibliographic record
Abstract
Abstract High dose methotrexate (HDMTX)‐induced acute kidney injury (AKI) is a well‐known adverse event in hemato‐oncology patients. Our purpose was to define factors and setup cut‐offs that may help better identify patients at‐risk for developing AKI following HDMTX. All consecutive patients who received MTX dose ≥1 g were retrospectively reviewed. We compared patients with or without renal toxicity. We used a logistic regression model to define baseline variables associated with AKI. Overall survival (OS) was estimated by the Kaplan‐Meier method employing log‐rank test. Between 2012 and 2017, 160 patients were included with a total of 265 courses. Indications included: primary central nervous system (CNS) lymphoma, CNS prophylaxis in other lymphoma types, acute lymphatic leukemia and others. Median age at diagnosis was 58 years (range, 18‐84), 54% were males, median MTX dose was 1941 mg/m 2 (range, 743‐5442) and AKI developed in 9% of drug administrations (n = 24). In univariate analysis: age > 40, LDH > 380 units/L, eGFR < 112 mL/min, albumin <3.6 mg/dL at baseline and Charlson comorbidity index (CCI) were associated with AKI. In multivariable analysis, only LDH > 380 units/L (OR = 4.1, 95% confidence interval [CI] 1.04‐20.9, P = .04) and albumin levels <3.6 g/dL (OR = 4.17, 95% CI 1.04‐6.5, P = .04) remained significant. In patients with AKI, median drug elimination was longer (8 days vs 5 days). In 80% of cases, the creatinine levels returned to normal within 1 month. Yet, the median survival of patients who developed AKI was 37 months, compared to 145 months in patients without AKI (Log rank = 0.015). In conclusion, LDH > 380 units/L and albumin <3.6 g/dL were the strongest factors associated with AKI in patients receiving HDMTX. Although the rise in creatinine levels was almost uniformly reversible, AKI was associated with increased mortality rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".