Evidence map›Paper›PMID 29915256›Full record

ArticleScientific reports2018

Geriatric influenza death (GID) score: a new tool for predicting mortality in older people with influenza in the emergency department.

Jui-Yuan Chung, Chien-Chin Hsu, Jiann-Hwa Chen, Wei-Lung Chen, Hung-Jung Lin, How-Ran Guo, Chien-Cheng Huang

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

7 authors.

Jui-Yuan ChungDepartment of Emergency Medicine, Cathay General Hospital, Taipei, Taiwan.
Chien-Chin HsuDepartment of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan.
Jiann-Hwa ChenDepartment of Emergency Medicine, Cathay General Hospital, Taipei, Taiwan.
Wei-Lung ChenDepartment of Emergency Medicine, Cathay General Hospital, Taipei, Taiwan.
Hung-Jung LinDepartment of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan.
How-Ran GuoDepartment of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan. hrguo@mail.ncku.edu.tw.
Chien-Cheng HuangDepartment of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan. chienchenghuang@yahoo.com.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although influenza may cause death in the geriatric population, the best method for predicting mortality in this population is still unclear. We retrospectively recruited older people (≥65 yr) with influenza visiting the emergency department (ED) of a medical center between January 1, 2010, and December 31, 2015. We performed univariate and multivariate logistic regression to identify independent mortality predictors and then developed a prediction score. Four hundred nine older ED patients with a nearly equal sex ratio were recruited. Five independent mortality predictors were identified: severe coma (Glasgow Coma Scale score ≤8), past histories of cancer and coronary artery disease, elevated C-reactive protein levels (>10 mg/dl), and bandemia (>10% band cells). We divided the patients into three mortality risk and disposition groups: (1) low risk (1.1%; 95% confidence interval [CI], 0.5-3.0%); (2) moderate risk (16.7%; 95% CI, 9.3-28.0%); and (3) high risk (40%; 95% CI, 19.8-64.2%). The area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness of fit of the GID score were 0.86 and 0.578, respectively. The GID score is an efficient and simple tool for predicting mortality in older ED patients with influenza. Further studies are warranted to validate its use.

Indexed as

Emergency Service, HospitalGeriatricsAgedAged, 80 and overArea Under CurveFemaleHumansInfluenza, HumanLogistic ModelsMaleMultivariate Analysis

Identifiers

PMID29915256
PMCPMC6006148

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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.