Acute and Chronic Kidney Dysfunction Associated with Anaplastic Lymphoma Kinase (ALK) Inhibitors
Bibliographic record
Abstract
Background: Anaplastic Lymphoma Kinase (ALK) inhibitors have marked activity against ALK-positive non-small cell lung cancers (NSCLC). Kidney adverse effects of ALK inhibitors have been increasingly reported, though some may relate to impairment of creatinine secretion. Methods: We performed a retrospective observational study of patients who received ALK inhibitors for NSCLC between 2010-2022. The primary outcome was incidence of acute kidney injury (AKI) within 90 days of ALK start, and chronic kidney disease (CKD) during treatment. AKI and CKD were defined via KDIGO criteria, using the creatinine-based CKD-EPI equation. We performed logistic regression for AKI risk factors and used Kaplan-Meier analysis to assess overall survival (OS) by AKI status. Spline curves were generated to estimate eGFR means over time. Results: Among 149 Stage IIIB/IV NSCLC patients, median age was 60 years; 56% were female; 269 different TKIs were initiated: Alectinib (n=118), Ceritinib (n=30), Brigatinib (n=27), Crizotinib (n=68) and Lorlatinib (n=26). There were a total of 22 (15%) AKI events in the 90 days after initiating ALK inhibitors with 5 patients requiring treatment change due to kidney function. Figure 1 shows eGFR changes after ALK inhibitor initiation and termination. A total of 25(17%) patients developed CKD, 9 leading to treatment change. The mean eGFR for patients on Alectinib and Ceritinib was generally lower than Crizotinib. Age, hypertension, and diabetes were associated with AKI [adjusted OR (95%CI): 1.04 (1.00,1.08), 3.74 (1.50,9.54), 4.20 (1.55,11.2)]. OS did not differ by AKI status (Figure 1c). Conclusions: There was a substantial number of AKI/CKD events observed, with a minority resulting in treatment change. Mean creatinine-based eGFR declined in the first 3 months post-ALK inhibitor start, but most patients had mild CKD during treatment, with eGFR recovering post-drug cessation. AKI did not impact OS. Our findings suggest that most patients may continue ALK TKI therapy despite kidney function changes.
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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) | 0.001 | 0.001 |
| 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 | 0.000 | 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".