Maximum Plasma Concentration of Lenvatinib Is Useful for Predicting Thrombocytopenia in Patients Treated for Hepatocellular Carcinoma
Bibliographic record
Abstract
Background: Although lenvatinib treatment has a favorable efficacy for unresectable hepatocellular carcinoma (HCC), it is associated with adverse events (AEs) that must be closely monitored and managed. Thrombocytopenia is one of the major AEs. The aim of this study was to clarify whether thrombocytopenia can be predicted by the plasma concentration of lenvatinib. Methods: This was a single-center retrospective observational study. Twenty-three patients with unresectable HCC and pharmacokinetics data at the initial lenvatinib administration between May 2018 and September 2020 at Oita University Hospital were enrolled. The AEs during the 4 weeks after the initiation of treatment were evaluated, and the correlations between the thrombocytopenia and the plasma concentration of lenvatinib were examined. Spearman’s correlation was used to evaluate the correlation between two continuous variables. Results: The rate of platelet count decrease correlated with the maximum plasma concentration (C max ) (r = 0.65, P = 0.001), whereas it did not with the minimum plasma concentration (C min ) (r = 0.29, P = 0.206). After stepwise multiple linear regression analysis, the starting dose of lenvatinib and the serum albumin concentration were identified as independent explanatory variables. Next, a formula for predicting the C max using these two variables was created. The predicted C max was strongly correlated with the C max (r = 0.87, P < 0.0001) and the rate of platelet count decrease (r = 0.67, P = 0.001). Conclusions: This study identified the usefulness of the drug C max to predict the rate of platelet count decrease within 4 weeks after the initiation of treatment. Although it is difficult to measure the plasma concentration of lenvatinib in community hospitals, the predicted C max is useful for predicting the rate of platelet count decrease with this treatment. World J Oncol. 2021;12(5):165-172 doi: https://doi.org/10.14740/wjon1399
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".