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Record W4390268700 · doi:10.1177/15266028231219990

Prognosis and Prediction of Asymptomatic Intracranial Hemorrhage After Endovascular Thrombectomy: A Multi-Center Study

2023· article· en· W4390268700 on OpenAlexaboutno aff
Zhiming Kang, Guangzhi Liu, Dong Sun, Gang Zhou, Xiangbo Wu, Chuang Nie, Han Qiu, Bin Mei, Junjian Zhang

Bibliographic record

VenueJournal of Endovascular Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersZhongnan Hospital of Wuhan UniversityWuhan UniversityHealth Commission of Hubei Province
KeywordsMedicineAsymptomaticOdds ratioIntracerebral hemorrhageConfidence intervalLogistic regressionStroke (engine)OcclusionInternal medicineSurgerySubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

PURPOSE: The impact of asymptomatic intracranial hemorrhage (aICH) on functional outcomes after endovascular thrombectomy (EVT) remains unclear, and tools for forecasting this complication are lacking. We aim to evaluate the clinical relevance of aICH and establish a prediction model. METHODS: Data of patients who received EVT for acute anterior-circulation large vessel occlusion in 3 comprehensive hospitals were retrospectively analyzed. Asymptomatic intracranial hemorrhage was defined as any hemorrhage detected after EVT that did not fulfill the definition of symptomatic intracranial hemorrhage in the European Cooperative Acute Stroke Study. Logistic regression models were performed to assess the impact of aICH on 90-day functional outcomes and identify the predictors of aICH, which were then used to establish a prediction model. The discrimination, calibration, and clinical utility of the model were evaluated. RESULTS: This study included 460 patients, among whom 152 (33.0%) developed aICH after EVT. Asymptomatic intracranial hemorrhage was negatively associated with 90-day excellent outcomes (adjusted odds ratio [OR]: 0.414, 95% confidence interval [CI]: 0.230-0.745, p=0.003) and good outcome (adjusted OR: 0.603, 95% CI: 0.374-0.971, p=0.037), but not with mortality (adjusted OR: 1.110, 95% CI: 0.611-2.017, p=0.732) after adjusted for other predictors of functional outcome. Pre-stroke anticoagulant therapy (OR: 2.233, 95% CI: 1.073-4.647, p=0.032), Alberta stroke program early CT score (OR: 0.842, 95% CI: 0.754-0.939, p=0.002), site of occlusion (internal carotid artery occlusion as the reference; M1 segment of middle cerebral artery occlusion, OR: 2.827, 95% CI: 1.409-5.674, p=0.003; tandem occlusion, OR: 3.928, 95% CI: 1.752-8.806, p=0.001), intravenous thrombolysis (OR: 2.091, 95% CI: 1.362-3.209, p=0.001), and successful recanalization (OR: 0.383, 95% CI: 0.213-0.689, p=0.001) were identified as the predictors of aICH, which were incorporated into a nomogram model. The area under the receiver operating characteristic curve of the model was 0.707 (95% CI: 0.657-0.757), and the calibration plot demonstrated good consistency between actual observed and predicted probability of aICH. Decision curve analysis showed that patients might benefit from the model. CONCLUSION: Asymptomatic intracranial hemorrhage was negatively associated with favorable functional outcome after EVT. We established a nomogram model for predicting aICH, which requires external clinical validation.Clinical ImpactThe impact of asymptomatic intracranial hemorrhage after endovascular thrombectomy on mid-term functional outcome has been controversial. We found that asymptomatic intracranial hemorrhage may also decreased the likelihood of 90-day favourable functional outcome after endovascular thrombectomy, supporting the notion that asymptomatic intracranial hemorrhage at the acute stage may not be benign. Moreover, we established a prediction model for this complication, which may improve clinical evaluation and management of patients who would receive endovascular thrombectomy for large vessel occlusion.

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 score0.107
Threshold uncertainty score0.748

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 integrity0.0000.000
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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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