ArticleFrontiers in neurology
Natural language processing (NLP)-assisted assessment of neurologic severity and functional outcomes in factor Xa inhibitor-related intracranial hemorrhages.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Factor Xa inhibitors (FXai) are associated with a risk of serious bleeding, including intracranial hemorrhage (ICH), which can lead to long-term disability. Functional status, often measured using the modified Rankin Scale (mRS), is a key outcome after ICH but is rarely recorded in structured electronic medical record (EMR) fields. This study used natural language processing (NLP) in the retrospective derivation of mRS scores from unstructured clinical notes and evaluate clinical correlates of functional outcomes in a national cohort of US veterans with FXai-related ICH. Methods: This retrospective cohort study used data from the Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI) from March 2014 to May 2022. Adults (≥18 years) hospitalized for direct or indirect FXai-related ICH and treated with reversal or replacement agents were included. Functional outcomes were assessed using mRS scores derived via NLP of unstructured clinical notes. Glasgow Coma Scale (GCS) scores were extracted using an algorithm to support model adjustment and NLP reliability. Results: The cohort included 269 patients (mean age 75.7 years; 96.7% male). An NLP algorithm was used to retrospectively derive mRS scores, and GCS scores were extracted using an algorithm to support NLP accuracy and model adjustment. Favorable functional status (mRS 0-3) was observed in 36.1% at baseline, increasing to 58.3% at 90 days. Higher baseline mRS and Charlson Comorbidity Index (CCI) were independently associated with worse functional outcomes (mRS ≥ 4) at discharge and 90 days ( Discussion: Retrospective derivation of mRS scores from clinical documentation is feasible using NLP. This approach demonstrates functional outcome assessment in large-scale EMR-based studies when direct measurement is unavailable. Because long-term recovery after ICH is often defined by functional status rather than survival alone, incorporating mRS into retrospective analyses may improve the evaluation of interventions for FXai-related ICH.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.