Evidence map›Paper›PMID 36696219›Full record

Observational studyJournal of thrombosis and haemostasis : JTH2023

The relative risk of bleeding after medical hospitalization: the medical inpatient thrombosis and hemorrhage study.

Mansour Gergi, Katherine Wilkinson, Insu Koh, Jordan Munger, Hanny Al-Samkari, Nicholas L Smith, Nicholas S Roetker, Timothy B Plante, Mary Cushman, Allen B Repp and 2 more

Open access · bronzeAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Journal of thrombosis and haemostasis : JTH, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
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

7 citing papers in PubMed, 6 citations in OpenAlex.

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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

12 authors at 4 institutions in 1 country.

Mansour GergiDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA; University of Vermont Medical Center, Burlington, Vermont, USA.
Katherine WilkinsonDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA.
Insu KohDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA.
Jordan MungerDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA.
Hanny Al-SamkariDepartment of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Nicholas L SmithDepartment of Epidemiology, University of Washington, Seattle, Washington, USA; Department of Veterans Affairs Office of Research and Development, Seattle Epidemiologic Research and Information Center, Seattle, Washington, USA; Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, USA.
Nicholas S RoetkerChronic Disease Research Group, Hennepin Healthcare Research Institute, Minneapolis, Minnesota, USA.
Timothy B PlanteDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA; University of Vermont Medical Center, Burlington, Vermont, USA.
Mary CushmanDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA; University of Vermont Medical Center, Burlington, Vermont, USA; Department of Pathology and Laboratory Medicine, University of Vermont Medical Center, Burlington, Vermont, USA.
Allen B ReppDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA; University of Vermont Medical Center, Burlington, Vermont, USA.
Chris E HolmesDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA; University of Vermont Medical Center, Burlington, Vermont, USA.
Neil A ZakaiDepartment of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA; University of Vermont Medical Center, Burlington, Vermont, USA; Department of Pathology and Laboratory Medicine, University of Vermont Medical Center, Burlington, Vermont, USA. Electronic address: Neil.Zakai@med.uvm.edu.
University of Vermont · USHarvard University · USHennepin Healthcare Research Institute · USKaiser Permanente Washington Health Research Institute · US

Funding

Thrombosis and Bleeding Risk Assessment in Medical InpatientsR01HL141290 · NHLBI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Deirdra R Terrell, Neil Adrian Zakai · 2019 to 2026
$3.5M
Antiangiogenic Therapy to Reduce Bleeding and Improve Health-Related Quality of Life in Hereditary Hemorrhagic TelangiectasiaK23HL159313 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Hanny T Al-Samkari · 2022 to 2026
$999k
NHLBI NIH HHS K23 HL159313NHLBI NIH HHS R01 HL141290
6 · The paper itself

Abstract

backgroundClinically relevant bleeding risk in discharged medical patients is underestimated and leads to rehospitalization, morbidity, and mortality. Studies assessing this risk are lacking.

objectiveThe aim of this study was to develop and validate a computable phenotype for clinically relevant bleeding using electronic health record (EHR) data and quantify the relative and absolute risks of this bleeding after medical hospitalization.

methodsWe conducted an observational cohort study of people receiving their primary care at sites affiliated with an academic medical center in northwest Vermont, United States. We developed a computable phenotype using EHR data (diagnosis codes, procedure codes, laboratory, and transfusion data) and validated it by manual chart review. Cox proportional hazard models with hospitalization modeled as a time-varying covariate were used to estimate clinically relevant bleeding risk.

resultsThe computable phenotype had a positive predictive value of 80% and a negative predictive value of 99%. The bleeding rate in individuals with no medical hospitalizations in the past 3 months was 2.9 per 1000 person-years versus 98.9 per 1000 person-years in those who were discharged in the past 3 months. This translates into a hazard ratio (95% CI) of clinically relevant bleeding of 22.9 (18.9, 27.7), 13.0 (10.0, 16.9), and 6.8 (4.7, 9.8) over the first, second, and third months after discharge, respectively.

conclusionWe developed and validated a computable phenotype for clinically relevant bleeding and determined its relative and absolute risk in the 3 months after medical hospitalization discharge. The high rates of bleeding observed underscore the clinical importance of capturing and further studying bleeding after medical discharge.

Indexed as

InpatientsThrombosisCohort StudiesHemorrhageHospitalizationHumansRiskUnited Statescomputable phenotypeelectronic health recordinformaticsmedical hospitalizationpostdischarge bleedingpostdischarge hemorrhage

Identifiers

PMID36696219
PMCPMC10044469
OpenAlexW4317940555

What OpenQuestion holds

Textmetadata
LicenceTDM
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.