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Record W4388532627 · doi:10.1161/strokeaha.123.043194

Antithrombotic and Statin Prescription After Intracerebral Hemorrhage in the Get With The Guidelines-Stroke Registry

2023· article· en· W4388532627 on OpenAlexaff
Santosh B. Murthy, Cenai Zhang, Shreyansh Shah, Lee H. Schwamm, Gregg C. Fonarow, Eric E. Smith, Deepak L. Bhatt, Wendy Ziai, Hooman Kamel, Kevin N. Sheth

Bibliographic record

VenueStroke · 2023
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Calgary
FundersDaiichi Sankyo EuropeGenentechNational Institutes of HealthEli Lilly and CompanyAstraZenecaCSL BehringNational Center for Advancing Translational SciencesAmerican Stroke AssociationAlexion PharmaceuticalsBiogenNational Heart, Lung, and Blood InstitutePfizerAmgenDuke Clinical Research InstituteNational Institute of Neurological Disorders and StrokeNovo NordiskBelvoir Media GroupAmerican Heart Association
KeywordsMedicineAntithromboticIntracerebral hemorrhageAtrial fibrillationStroke (engine)Medical prescriptionStatinInternal medicineCohortIntensive care medicineSubarachnoid hemorrhagePharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Survivors of intracerebral hemorrhage (ICH) face an increased risk of ischemic cardiovascular events. Current ICH guidelines do not provide definitive recommendations regarding the use of antithrombotic and statin therapies. We, therefore, sought to study practice patterns and factors associated with the use of such medications after ICH. METHODS: This was a cross-sectional study of patients with ICH in the Get With The Guidelines-Stroke registry, between 2011 and 2021. Patients transferred to another hospital, those who died during hospitalization, and those with missing information on discharge medications were excluded. The study exposure was the proportion of patients who were prescribed antithrombotic or statin medications. We first ascertained the proportion of patients prescribed antithrombotic and lipid-lowering medications at discharge overall and across strata defined by pre-ICH use and history of previous ischemic vascular disease or atrial fibrillation. We then studied factors associated with the discharge prescription of these medications after ICH, using multiple logistic regressions. RESULTS: In the final cohort, 50 416 (10.4%) of 486 586 patients with ICH were prescribed antiplatelet medications, 173 322 (35.1%) of 493 491 patients with ICH were prescribed statins, and 27 085 (5.4%) of 486 585 patients with ICH were prescribed anticoagulation therapy at discharge. The proportion of patients with antiplatelet therapy was 16.6% with pre-ICH use and 15.6% in those with previous ischemic vascular disease. Statins were prescribed to 41.1% and 43.7% of patients on previous lipid-lowering therapy and ischemic vascular disease, respectively. Anticoagulation therapy was restarted in 11.1% of patients. In logistic regression analysis, factors associated with higher use of antithrombotic or statin therapies after ICH were younger age, male sex, pre-ICH medication use, previous ischemic vascular disease, atrial fibrillation, lower admission National Institutes of Health Stroke Scale, longer length of stay, and favorable discharge outcome. CONCLUSIONS: Few patients with ICH are prescribed antithrombotic or statin therapies at hospital discharge. Given the emerging association between ICH and future major cardiovascular events, trials examining the net benefit of antiplatelet and lipid-lowering therapy after ICH are warranted.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.304
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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