The Impact of Regorafenib on Cardiac Function in Metastatic Colorectal Cancer Patients a Retrospective Cohort Study
Bibliographic record
Abstract
Introduction: Metastatic colorectal cancer (mCRC) poses significant clinical challenges, necessitating the exploration of novel treatment modalities. Regorafenib, a multi-kinase inhibitor, has emerged as a promising therapeutic option for refractory mCRC. However, concerns regarding its potential cardiotoxic effects warrant comprehensive evaluation of its impact on cardiac function parameters in this patient population. Methods: The purpose of this retrospective cohort research was to find out how regorafenib affected cardiac function measures in patients with metastatic colorectal cancer (mCRC) at the Pakistan Institute of Medical Sciences (PIMS) in Islamabad. 78 adult patients in all, having histologically proven mCRC, who were treated with regorafenib between January 2023 and March 2024 were included in the analysis. Baseline characteristics, treatment details, cardiac function parameters, and incidence of cardiac events were retrospectively analyzed. Statistical analyses were performed to assess changes in cardiac function parameters and identify predictors of cardiac toxicity associated with regorafenib therapy. Results: Following regorafenib therapy, there was a significant decrease in left ventricular ejection fraction (LVEF), alterations in diastolic function indices, and an incidence of clinically significant cardiac events (12%), including heart failure, arrhythmias, and myocardial infarction. Subgroup analyses identified older age, male sex, pre-existing hypertension, longer treatment duration, and higher cumulative doses of regorafenib as potential predictors of cardiac toxicity. Conclusion: This study underscores the potential cardiotoxic effects of regorafenib in mCRC patients and highlights the importance of personalized cardiac monitoring and risk management strategies during treatment. Further research is warranted to validate these findings and inform evidence-based approaches to optimize the cardiovascular safety of regorafenib in clinical practice.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".