Evidence map›Paper›PMID 37477961›Full record

ArticleJMIR formative research2023

A Neuro-Informatics Pipeline for Cerebrovascular Disease: Research Registry Development.

Thomas B H Potter, Sharmila Pratap, Juan Carlos Nicolas, Osman S Khan, Alan P Pan, Abdulaziz T Bako, Enshuo Hsu, Carnayla Johnson, Imory N Jefferson, Sofiat K Adegbindin and 6 more

Abstract read
In one paragraph

Article in JMIR formative research, 2023. 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

16 authors.

Thomas B H PotterDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0001-7884-4172
Sharmila PratapCenter for Health Data Science and Analytics, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0003-2441-8498
Juan Carlos NicolasCenter for Health Data Science and Analytics, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0003-3039-7339
Osman S KhanDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0002-4386-4015
Alan P PanCenter for Health Data Science and Analytics, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0002-8782-8024
Abdulaziz T BakoDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0002-1584-8114
Enshuo HsuCenter for Health Data Science and Analytics, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0002-8137-0171
Carnayla JohnsonDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0009-0004-9436-7483
Imory N JeffersonDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0003-0837-6275
Sofiat K AdegbindinDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0009-0009-1697-6570
Eman BaigDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0009-0007-5547-3007
Hannah R KellyDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0009-0003-4395-8673
Stephen L JonesCenter for Health Data Science and Analytics, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0002-1106-171X
Gavin W BritzDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0002-2358-3005
Jonika TannousDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0003-4022-9267
Farhaan S VahidyDepartment of Neurosurgery, Houston Methodist, Houston, TX, United States.ORCID https://orcid.org/0000-0002-3464-2111

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough stroke is well recognized as a critical disease, treatment options are often limited. Inpatient stroke encounters carry critical information regarding the mechanisms of stroke and patient outcomes; however, these data are typically formatted to support administrative functions instead of research. To support improvements in the care of patients with stroke, a substantive research data platform is needed.

objectiveTo advance a stroke-oriented learning health care system, we sought to establish a comprehensive research repository of stroke data using the Houston Methodist electronic health record (EHR) system.

methodsDedicated processes were developed to import EHR data of patients with primary acute ischemic stroke, intracerebral hemorrhage (ICH), transient ischemic attack, and subarachnoid hemorrhage under a review board-approved protocol. Relevant patients were identified from discharge diagnosis codes and assigned registry patient identification numbers. For identified patients, extract, transform, and load processes imported EHR data of primary cerebrovascular disease admissions and available data from any previous or subsequent admissions. Data were loaded into patient-focused SQL objects to enable cross-sectional and longitudinal analyses. Primary data domains (admission details, comorbidities, laboratory data, medications, imaging data, and discharge characteristics) were loaded into separate relational tables unified by patient and encounter identification numbers. Computed tomography, magnetic resonance, and angiography images were retrieved. Imaging data from patients with ICH were assessed for hemorrhage characteristics and cerebral small vessel disease markers. Patient information needed to interface with other local and national databases was retained. Prospective patient outreach was established, with patients contacted via telephone to assess functional outcomes 30, 90, 180, and 365 days after discharge. Dashboards were constructed to provide investigators with data summaries to support access.

resultsThe Registry of Neurological Endpoint Assessments among Patients with Ischemic and Hemorrhagic Stroke (REINAH) database was constructed as a series of relational category-specific SQL objects. Encounter summaries and dashboards were constructed to draw from these objects, providing visual data summaries for investigators seeking to build studies based on REINAH data. As of June 2022, the database contains 18,061 total patients, including 1809 (10.02%) with ICH, 13,444 (74.43%) with acute ischemic stroke, 1221 (6.76%) with subarachnoid hemorrhage, and 3165 (17.52%) with transient ischemic attack. Depending on the cohort, imaging data from computed tomography are available for 85.83% (1048/1221) to 98.4% (1780/1809) of patients, with magnetic resonance imaging available for 27.85% (340/1221) to 85.54% (11,500/13,444) of patients. Outcome assessment has successfully contacted 56.1% (240/428) of patients after ICH, with 71.3% (171/240) of responders providing consent for assessment. Responders reported a median modified Rankin Scale score of 3 at 90 days after discharge.

conclusionsA highly curated and clinically focused research platform for stroke data will establish a foundation for future research that may fundamentally improve poststroke patient care and outcomes.

Indexed as

acute ischemic strokecerebral amyloid angiopathyclinical outcomedatabasedata curationelectronic health recordintracerebral hemorrhagelearning health systemsubarachnoid hemorrhagetransient ischemic attack

Identifiers

PMID37477961
PMCPMC10403790

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Registered trials

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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.