ArticleEuropean journal of clinical pharmacology2026
Exploring potential biomarkers and constructing prediction models of osimertinib based on a retrospective study.
Article in European journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
purposeThis study aimed to identify potential biomarkers for adverse reactions (ADRs) and efficacy of osimertinib, and to construct prediction models in patients with non-small cell lung cancer(NSCLC).
methodsNSCLC patients treated with osimertinib were retrospectively enrolled from January 2021 to July 2022. Their clinical characteristics, ADRs and prognostic outcomes (e.g., progression-free survival, objective response rate) were extracted from medical records. Univariate and multivariate analysis were performed on baseline data to identify potential biomarkers associated with ADRs and efficacy. Prediction models were constructed and validated using logistic regression. All analysis were conducted with SPSS 19.0 and Graphpad Prism 8.0.
resultsA total of 90 patients in training cohort and 74 patients in the validation cohort were enrolled. Univariate analysis revealed that drinking history, total bilirubin(TB), indirect bilirubin(I-Bil), and aspartate aminotransferase(AST) were potential biomarkers for ADRs. Multivariate analysis identified drinking history, TB, and AST as key predictors of severe ADRs. No variate was associated with efficacy. A prediction model for severe ADRs was established using logistic regression: y= -2.714×(drinking history) − 0.4×(TB) − 0.099×(AST) + 6.196 [Area Under the Curve (AUC) = 0.87; 95% confidence interval (CI): 0.752–0.995; P < 0.001; sensitivity: 90%; specificity: 82.5%; optimal Youden index: 0.725]. Validation of this model in 74 NSCLC patients showed a sensitivity of 83.3% and specificity of 98.2%.
conclusionThis study developed and validated a prediction model for severe osimertinib-related ADRs in NSCLC patients based on baseline characteristics. The model exhibits good predictive performance for severe osimertinib-related ADRs and may facilitate individualized osimertinib therapy in clinical practice.
Indexed as
Identifiers
41724848What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.