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Record W2943496042 · doi:10.1177/2048872619827471

Incidence, predictors and prognostic impact of intracranial bleeding within the first year after an acute coronary syndrome in patients treated with percutaneous coronary intervention

2019· article· en· W2943496042 on OpenAlexaff
Sergio Raposeiras‐Roubín, Emad Abu‐Assi, Berenice Caneiro Queija, Rafael Cobas Paz, Fabrizio D’Ascenzo, Josè P.S. Henriques, Jorge Saucedo, José Ramón González‐Juanatey, Stephen B. Wilton, Wouter J. Kikkert, Iván J. Núñez‐Gil, Albert Ariza‐Solé, Xiantao Song, Dimitrios Alexopoulos, Christoph Liebetrau, Tetsuma Kawaji, Claudio Moretti, Zenon Huczek, Shaoping Nie, Toshiharu Fujii, Luís Cláudio Lemos Correia, Masa‐aki Kawashiri, M Cespon Fernandez, Isabel Muñoz‐Pousa, E Lopez Rodriguez, María Castiñeira-Busto, Cristina Barreiro Pardal, José María García‐Acuña, Danielle A. Southern, Belén Terol, Alberto Garay, Dongfeng Zhang, Yalei Chen, Ioanna Xanthopoulou, Neriman Osman, Helge Möllmann, Hiroki Shiomi, Fiorenzo Gaita, Michał Kowara, Krzysztof J. Filipiak‬, Wang Xiao, Yan Yan, Jingyao Fan, Yuji Ikari, Takuya Nakahayshi, Kenji Sakata, Masakazu Yamagishi, Saško Kedev, Andrés Íñiguez

Bibliographic record

VenueEuropean Heart Journal Acute Cardiovascular Care · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsMedicineAcute coronary syndromePercutaneous coronary interventionIncidence (geometry)Internal medicineCardiologyIntracranial bleedingMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: The rate of intracranial haemorrhage after an acute coronary syndrome has been studied in detail in the era of thrombolysis; however, in the contemporary era of percutaneous coronary intervention, most of the data have been derived from clinical trials. With this background, we aim to analyse the incidence, timing, predictors and prognostic impact of post-discharge intracranial haemorrhage in patients with acute coronary syndrome undergoing percutaneous coronary intervention. METHODS: We analysed data from the BleeMACS registry (patients discharged for acute coronary syndrome and undergoing percutaneous coronary intervention from Europe, Asia and America, 2003-2014). Analyses were conducted using a competing risk framework. Uni and multivariate predictors of intracranial haemorrhage were assessed using the Fine-Gray proportional hazards regression analysis. The endpoint was 1-year post-discharge intracranial haemorrhage. RESULTS: Of 11,136 patients, 30 presented with intracranial haemorrhage during the first year (0.27%). The median time to intracranial haemorrhage was 150 days (interquartile range 55.7-319.5). The fatality rate of intracranial haemorrhage was very high (30%). After multivariate analysis, only age (subhazard ratio 1.05, 95% confidence interval 1.01-1.07) and prior stroke/transient ischaemic attack (hazard ratio 3.29, 95% confidence interval 1.36-8.00) were independently associated with a higher risk of intracranial haemorrhage. Hypertension showed a trend to associate with higher intracranial haemorrhage rate. The combination of older age (⩾75 years), prior stroke/transient ischaemic attack, and/or hypertension allowed us to identify most of the patients with intracranial haemorrhage (86.7%). The annual rate of intracranial haemorrhage was 0.1% in patients with no risk factors, 0.2% in those with one factor, 0.6% in those with two factors and 1.3% in those with three factors. CONCLUSION: The incidence of intracranial haemorrhage in the first year after an acute coronary syndrome treated with percutaneous coronary intervention is low. Advanced age, previous stroke/transient ischaemic attack, and hypertension are the main predictors of increased intracranial haemorrhage risk.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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 teacher head, 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

Citations9
Published2019
Admission routes1
Has abstractyes

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