Evidence map›Paper›PMID 39825740›Full record

Observational studyAnnals of neurology2025

Quantitative Pupillometry Predicts Neurologic Deterioration in Patients with Large Middle Cerebral Artery Stroke.

Yili Du, Jack E Pohlmann, Stefanos Chatzidakis, Benjamin Brush, Leigh Ann Malinger, Rebecca A Stafford, Anna M Cervantes-Arslanian, Emelia J Benjamin, Emily J Gilmore, Josée Dupuis and 4 more

Abstract readObservational Study
In one paragraph

Observational study in Annals of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Yili DuDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.ORCID 0000-0001-8444-5477
Jack E PohlmannDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Stefanos ChatzidakisDepartment of Neurology, Brigham and Women's Hospital, Boston, MA, USA.ORCID 0000-0002-4652-397X
Benjamin BrushDepartment of Neurology, NYU Langone Medical Center, New York, NY, USA.
Leigh Ann MalingerDepartment of Neurology, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
Rebecca A StaffordDepartment of Neurology, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
Anna M Cervantes-ArslanianDepartment of Neurology, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
Emelia J BenjaminDepartment of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
Emily J GilmoreDepartment of Neurology, Yale School of Medicine, New Haven, CT, USA.
Josée DupuisDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
David M GreerDepartment of Neurology, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.ORCID 0000-0002-2026-8333
Stelios M SmirnakisDepartment of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
Shariq MohammedDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Charlene J OngDepartment of Neurology, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.

Funding

Dynamic Risk Prediction of Life-Threatening Mass Effect After Ischemic StrokeK23NS116033 · NINDS · BOSTON MEDICAL CENTER · PI ONG, CHARLENE J · 2021 to 2025
$1.0M
NINDS NIH HHS K23NS11603NINDS NIH HHS K23 NS116033
6 · The paper itself

Abstract

objectiveThis study assesses whether longitudinal quantitative pupillometry predicts neurological deterioration after large middle cerebral artery (MCA) stroke and determines how early changes are detectable.

methodsThis prospective, single-center observational cohort study included patients with large MCA stroke admitted to Boston Medical Center's intensive care unit (2019-2024). Associations between time-to-neurologic deterioration and quantitative pupillometry, including Neurological Pupil Index (NPi), were assessed using Cox proportional hazards models with time-dependent covariates adjusted for age, sex, and Alberta Stroke Program Early CT Score. Models using dilation velocity were compared with partial likelihood ratio tests. Pupillometric changes over 2-h intervals in the 12 h preceding deterioration were analyzed with linear mixed-effects modeling and Tukey's test. Matched referents (age, sex, stroke side, follow-up duration) were used for comparison. Optimal thresholds were identified using the Youden Index.

resultsAmong 71 patients (mean age 66.5 years; 59.2% women), 32 (45.1%) experienced deterioration. A 1-unit decrease in NPi was associated with a higher hazard of deterioration (hazard ratio 2.46; 95% confidence interval 1.68-3.61). Dilation velocity improved model performance compared to NPi alone. NPi was significantly lower at 0-2 h (3.81 vs. 4.38, p = 0.001) and 2-4 h (3.71 vs. 4.38, p < 0.001) before deterioration compared to 10-12 h prior. Optimal thresholds were 4.01 for NPi, 0.49 mm/s for dilation velocity, and -0.15 change in NPi over 12 h.

interpretationQuantitative pupillometry predicts neurological deterioration in MCA stroke, with declines detectable up to 12 h prior. Dilation velocity shows promise as a novel biomarker. ANN NEUROL 2025;97:930-941.

Indexed as

Infarction, Middle Cerebral ArteryPupilAgedCohort StudiesDisease ProgressionFemaleHumansMaleMiddle AgedPredictive Value of TestsProspective Studies

Identifiers

PMID39825740
PMCPMC12011534

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.