Trends in Incidence of Intracerebral Hemorrhage and Association With Antithrombotic Drug Use in Denmark, 2005-2018
Bibliographic record
Abstract
Importance: Spontaneous (nontraumatic) intracerebral hemorrhage (ICH) is the most severe complication of antithrombotic drug use. Objectives: To estimate the strength of association between use of antithrombotic drugs and risk of ICH and to examine major changes in the incidence of ICH in the general population. Design, Setting, and Participants: This case-control study of patients with a first-ever ICH from January 1, 2005, to December 31, 2018, matched by age, sex, and calendar year with general population controls (1:40 ratio), assessed case and control patients 20 to 99 years of age in population-based nationwide registries in Denmark (population of 5.8 million). Exposures: Use of low-dose aspirin, clopidogrel, a vitamin K antagonist (VKA), or a direct oral anticoagulant (DOAC). Main Outcomes and Measures: Association of ICH with antithrombotic drug use, annual age- and sex-standardized incidence rate of ICH, and prevalence of treatment with antithrombotic drugs. Conditional logistic regression models estimated adjusted odds ratios (aORs) (95% CIs) for the association of antithrombotic drugs with ICH. Results: Among 16 765 cases with ICH (mean [SD] age, 72.8 [13.1] years; 8761 [52.3%] male), 7473 (44.6%) were exposed to antithrombotic medications at the time of ICH onset. The association with ICH was weakest for current use of low-dose aspirin (cases: 28.7%, controls: 22.6%; aOR, 1.51; 95% CI, 1.44-1.59) and clopidogrel (cases: 6.2%, controls: 3.4%; aOR, 1.65; 95% CI, 1.47-1.84) and strongest with current use of a VKA (cases: 12.0%, controls: 5.0%; aOR, 2.76; 95% CI, 2.58-2.96). The association with ICH was weaker for DOACs (cases: 3.0%, controls: 1.8%; aOR, 1.83; 95% CI, 1.61-2.07) than for VKAs. Compared with 2005, the prevalence of use of oral anticoagulants among general population controls in 2018 was higher (3.8% vs 11.1%), predominantly because of increased use of DOACs (DOACs: 0% vs 7.0%; VKA: 3.8% vs 4.2%). Antiplatelet drugs were used less frequently (24.7% vs 21.4%) because of decreased use of low-dose aspirin (24.3% vs 15.3%), whereas clopidogrel use increased (1.0% vs 6.8%). The age- and sex-standardized incidence rate of ICH decreased from 33 per 100 000 person-years in 2005 to 24 per 100 000 person-years in 2018 (P < .001 for trend). Conclusions and Relevance: In Denmark from 2005 to 2018, use of antithrombotic drugs, especially VKAs, was associated with ICH. Although use of oral anticoagulation increased substantially during the study period, the incidence rate of ICH decreased.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".