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Record W3161171477 · doi:10.1161/strokeaha.120.032487

Predicting 90-Day Outcome After Thrombectomy: Baseline-Adjusted 24-Hour NIHSS Is More Powerful Than NIHSS Score Change

2021· article· en· W3161171477 on OpenAlexaboutno aff
Eva Mistry, Sharon D. Yeatts, Adam de Havenon, Tapan Mehta, Niraj Arora, Felipe De Los Rios La Rosa, Amy Starosciak, James E. Siegler, Akshitkumar M. Mistry, Shadi Yaghi, Pooja Khatri

Bibliographic record

VenueStroke · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMedicineStroke (engine)Baseline (sea)Internal medicineCardiology

Abstract

fetched live from OpenAlex

Background and Purpose: The National Institutes of Health Stroke Scale (NIHSS) measured at an early time point is an appealing surrogate marker for long-term functional outcome of stroke patients treated with endovascular therapy. However, definitions and analytical methods for an early NIHSS-based outcome measure that optimize power and precision in clinical studies are not well-established. Methods: In this post-hoc analysis of our prospective observational study that enrolled endovascular therapy-treated patients at 12 comprehensive stroke centers across the US, we compared the ability of 24-hour NIHSS, ΔNIHSS (baseline minus 24-hour NIHSS), and percentage change (NIHSS×100/baseline NIHSS), analyzed as continuous and dichotomous measures, to predict 90-day modified Rankin Scale (mRS) using logistic regression (adjusted for age, baseline NIHSS, glucose, hypertension, Alberta Stroke Program Early CT Score, time to recanalization, recanalization status, and intravenous thrombolysis) and Spearman ρ. Results: Of 485 patients in the BEST (Blood Pressure After Endovascular Stroke Therapy) cohort, 446 (92%) with 90-day follow-up data were included. An absolute 24-hour NIHSS, adjusted for baseline in multivariable modeling, had the highest predictive power of all definitions evaluated (aR 2 0.368 and adjusted odds ratio 0.79 [0.75–0.84], P <0.001 for mRS score 0–2; aR 2 0.444 and adjusted odds ratio 0.84 [0.8–0.86] for ordinal mRS). For predicting mRS score of 0–2 with a cut point, the second most efficient approach, the optimal threshold for 24-hour NIHSS score was ≤7 (sensitivity 80.1%, specificity 80.4%; adjusted odds ratio 12.5 [7.14–20], P <0.001), followed by percent change in NIHSS (sensitivity 79%, specificity 58.5%; adjusted odds ratio 4.55 [2.85–7.69], P <0.001). Conclusions: Twenty-four–hour NIHSS, adjusted for baseline, was the strongest predictor of both dichotomous and ordinal 90-day mRS outcomes for endovascular therapy-treated patients. A dichotomous 24-hour NIHSS score of ≤7 was the second-best predictor. Although ΔNIHSS, continuous and dichotomized at ≥4, predicted 90-day outcomes, absolute 24-hour NIHSS definitions performed better.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.295
Teacher spread0.250 · 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.

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

Citations82
Published2021
Admission routes1
Has abstractyes

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