Evidence map›Paper›PMID 35146139›Full record

ArticleeNeurologicalSci2022

Demographic and institutional predictors of stroke hospitalization mortality among adults in the United States.

Gabriel Neves, Travis Cole, Jeannie Lee, Tulio Bueso, Chip Shaw, Victor Montalvan

Abstract read
In one paragraph

Article in eNeurologicalSci, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Gabriel NevesDepartment of Neurology, Texas Tech University Medical Sciences Center, Lubbock, TX, USA.
Travis ColeGraduate School of Biomedical Sciences, Texas Tech University Medical Sciences Center, Lubbock, TX, USA.
Jeannie LeeDepartment of Neurology, Texas Tech University Medical Sciences Center, Lubbock, TX, USA.
Tulio BuesoDepartment of Neurology, Texas Tech University Medical Sciences Center, Lubbock, TX, USA.
Chip ShawGraduate School of Biomedical Sciences, Texas Tech University Medical Sciences Center, Lubbock, TX, USA.
Victor MontalvanDepartment of Neurology, Texas Tech University Medical Sciences Center, Lubbock, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionStroke remains a primary source of functional disability and inpatient mortality in the United States (US). Recent evidence reveals declining mortality associated with stroke hospitalizations in the US. However, data updating trends in inpatient mortality is lacking. This study aims to provide a renewed inpatient stroke mortality rate in a national sample and identify common predictors of inpatient stroke mortality.

methodsIn this cross-sectional study, we analyzed data from a nationwide database between 2010 and 2017. We included patient encounters for both ischemic (ICD9 433-434, ICD10 I630-I639) and hemorrhagic stroke (ICD9 430-432, ICD10 I600-I629). We performed an annual comparison of in-hospital stroke mortality rates, and a cross-sectional analytic approach of multiple variables identified common predictors of inpatient stroke mortality.

resultsBetween 2010 and 2017, we identified 518,185 total stroke admissions (86.6% ischemic stroke and 13.4% hemorrhagic strokes). Stroke admissions steadily increased during the studied period, whereas we observed a steady decline in in-hospital mortality during the same time. The inpatient stroke mortality rate gradually declined from 4.8% in 2010 (95% CI 4.6-5.1) to 2.1% in 2017 (95% CI 2.0-2.1). Predictors of higher odds of dying from ischemic stroke were female (OR 1.059, 95% CI 1.015-1.105,

conclusionThis study identified a continuous declining rate in in-hospital mortality due to stroke in the United States, and it also identified demographic and hospital predictors of inpatient stroke mortality.

Indexed as

Hemorrhagic strokeInpatient mortalityIschemic strokeMortalityStroke

Identifiers

PMID35146139
PMCPMC8802002

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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.