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Record W4384120170 · doi:10.1161/strokeaha.123.043285

Thrombectomy for M2 Occlusions: Predictors of Successful and Futile Recanalization

2023· article· en· W4384120170 on OpenAlexaboutno aff
Helge Kniep, Lukas Meyer, Gabriel Broocks, Matthias Bechstein, Christian Heitkamp, Laurens Winkelmeier, Tobias D. Faizy, Caspar Brekenfeld, Fabian Flottmann, Milani Deb‐Chatterji, Anna Alegiani, Uta Hanning, Götz Thomalla, Jens Fiehler, Susanne Gellißen, Joachim Röther, Bernd Eckert, Michael Braun, Gerhard F. Hamann, Eberhard Siebert, Christian H. Nolte, Sarah Zweynert, Georg Böhner, Jörg Berrouschot, Albrecht Bormann, Christoffer Kraemer, Hannes Leischner, Jörg Hattingen, Martina Petersen, Florian Stögbauer, Tobias Boeckh‐Behrens, Silke Wunderlich, Alexander Ludolph, Karl‐Heinz Henn, Christian Gerloff, Maximilian Schell, Arno Reich, Omid Nikoubashman, Franziska Dorn, Gabor C. Petzold, Jan Liman, Jan Hendrik Schäfer, Fee Keil, Klaus Gröschel, Timo Uphaus, Peter D. Schellinger, Jan Borggrefe, Steffen Tiedt, Lars Kellert, Christoph Trumm, Ulrike Ernemann, Sven Poli, Christian Riedel, Marielle Ernst

Bibliographic record

VenueStroke · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersUniversitätsmedizin der Johannes Gutenberg-Universität MainzRWTH Aachen UniversityUniversitätsmedizin Göttingen
KeywordsMedicineModified Rankin ScaleThrombolysisLogistic regressionOdds ratioStroke (engine)OcclusionSurgeryCerebral infarctionInternal medicineCardiologyIschemic strokeMyocardial infarctionIschemia

Abstract

fetched live from OpenAlex

BACKGROUND: Patient-specific factors associated with successful recanalization in mechanical thrombectomy (MT) have been evaluated for acute ischemic stroke with large vessel occlusion. However, MT for M2 occlusions is still a matter of debate, and predictors of successful and futile recanalization have not been assessed in detail. We sought to identify predictors of recanalization success in patients with M2 occlusions undergoing MT based on large-scale clinical data. METHODS: All patients prospectively enrolled in the German Stroke Registry (May, 2015 to December, 2021) were screened (N=13 082). Inclusion criteria for the complete case analysis were isolated M2 occlusions. Standard descriptive statistics and multivariable logistic regression analysis were used to identify factors associated with successful recanalization (Thrombolysis in Cerebral Infarction [TICI]≥2b), complete recanalization (TICI=3) and futile recanalization (TICI≥2b with 90-day modified Rankin Scale [mRS] score >2). RESULTS: One thousand two hundred ninety-four patients were included, thereof 439 (33.9%) with TICI=2b and 643 (49.7%) with TICI=3. Five hundred sixty-nine (44%) patients had good functional outcome (90-day mRS score ≤2). In multivariable logistic regression, general anesthesia (adjusted odds ratio [aOR], 1.47 [95% CI, 1.05–2.09]; P <0.05) was associated with higher probability of TICI≥2b while intraprocedural change from local to general anesthesia (aOR, 0.49 [0.26–0.95]; P <0.05) and higher pre-mRS (aOR, 0.75 [0.67–0.85]; P <0.001) lowered probability of successful recanalization. Futile recanalization was associated with higher age (aOR, 1.05 [1.04–1.07]; P <0.001), higher prestroke mRS (aOR, 3.12 [2.49–3.91]; P <0.001), higher NIHSS at admission (aOR, 1.11 [1.08–1.14]; P <0.001), diabetes (aOR, 1.96 [1.38–2.8]; P <0.001), higher number of passes (aOR, 1.29 [1.14–1.46]; P <0.001), and adverse events (aOR, 1.82 [1.2–2.74]; P <0.01). Higher Alberta Stroke Program Early CT Score (aOR, 0.85 [0.76–0.94]; P <0.01) and IV thrombolysis (aOR, 0.71 [0.52–0.97]; P <0.05) reduced risk of futile recanalization. CONCLUSIONS: In patients with M2 occlusions, successful recanalization was significantly associated with general anesthesia and low prestroke mRS, while intraprocedural change from conscious sedation to general anesthesia increased risk of unsuccessful recanalization, presumably caused by difficult anatomy and movement of patients in these cases. Futile recanalization was associated with severe prestroke mRS, comorbidity diabetes, number of passes and adverse events during treatment. IV thrombolysis reduced the risk of futile recanalization.

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)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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".

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Citations34
Published2023
Admission routes1
Has abstractyes

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