Drug-Induced Thrombocytopenia in the Critically Ill
Bibliographic record
Abstract
BACKGROUND: Drugs are suspected when obvious causes of intensive care unit (ICU)-acquired thrombocytopenia have been excluded. It has been estimated that 10% to 25% of cases may be drug induced. OBJECTIVES: The objectives of this study were to evaluate the risk of thrombocytopenia associated with drug classes commonly used in the ICU. METHODS: Data concerning patients admitted for more than 48 hours between 1997 and 2011 were extracted from a research-purpose database. Patients with thrombocytopenia within the first 72 hours of admission and with diagnoses or interventions considered strongly associated with thrombocytopenia were excluded. Drug exposures were compared and adjusted for confounders using conditional logistic regression. RESULTS: A total of 238 cases were identified after exclusions. Each case was matched according to sex, age, admission year, and admission unit with 1 control. In univariate analysis, quinolones (odds ratio [OR] = 1.56; 95% CI = 1.01-2.40) and extended spectrum β-lactams (OR = 1.71; 95% CI = 1.00-2.93) were significantly associated with an increased risk of thrombocytopenia. After adjusting for confounders, exposure to quinolones was the only drug class with a statistically significant increase in risk of thrombocytopenia (OR = 1.697; 95% CI = 1.002-2.873; P = 0.049). CONCLUSION: In this study of ICU-acquired thrombocytopenia, we found no association between the exposures to several antibiotic classes, anticonvulsants, antiplatelet agents, nonsteroidal anti-inflammatory agents, and heparins and thrombocytopenia. As linezolid was not studied, no conclusions can be drawn concerning this agent. The statistically significant association between quinolones and thrombocytopenia warrants further investigation.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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, 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".