Examining the Characteristics of Colchicine-Induced Myelosuppression in Clinical Cases: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: The use of colchicine has been associated with varying degrees of myelosuppression. Despite expanded use in cardiovascular and inflammatory conditions, there remains clinician concern because of potential myelosuppressive side effects. A systematic review was conducted to explore the reported myelosuppressive events of colchicine. METHODS: A systematic review was conducted using the MeSH terms ("colchicine") AND ("myelosuppression," "bone*," "marrow," "suppression," "aplasia," "leukopenia/leucopenia," "lymphopenia," "neutropenia") on September 1, 2020, and was updated on November 30, 2021. The search was conducted in PubMed, ScienceDirect, Scopus, Embase, and Cochrane Library. The search included references published from 1978 to 2020 and was limited to English-language observational studies (ie, case reports, case series, case control studies, and cohort studies) or trial data. RESULTS: In total, 3233 articles were screened, with 30 studies of 47 patients with myelosuppression from colchicine identified. Most patients with myelosuppression had comorbidities, including renal impairment (21/47, 44.7%). Out of 47 patients, 15 (31.9%) and 13 (27.7%) were reported to be concurrently taking cytochrome P450 3A4 (CYP3A4) inhibitors and P-glycoprotein (P-gp) efflux transporter inhibitors, respectively. Patients with renal impairment accounted for the majority of overall patients taking these CYP3A4 and P-gp inhibitors (8/15, 53.3%, and 8/13, 61.5%, respectively). Out of 21 patients with renal impairment, 13 had worsening cytopenia during colchicine use. The presentations ranged from moderate anemia (grade 2) to severe thrombocytopenia, neutropenia, and leukopenia (grade 4). CONCLUSION: Colchicine has few reports of myelosuppression. The majority of patients with myelosuppression had preexisting renal impairment or concomitant CYP3A4 or P-gp inhibitor use. Caution should be taken in this subset of patients with increased monitoring.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".