Evidence map›Paper›PMID 28401961›Full record

ArticleScientific reports2017

Development and validation of a prognostic model for predicting 30-day mortality risk in medical patients in emergency department (ED).

Duc T Ha, Tam Q Dang, Ngoc V Tran, Thao N T Pham, Nguyen D Nguyen, Tuan V Nguyen

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Duc T HaIntensive Care Unit, National Hospital of Can Tho, Vietnam.
Tam Q DangIntensive Care Unit, National Hospital of Can Tho, Vietnam.
Ngoc V TranDepartment of Internal Medicine, University of Medicine and Pharmacy in Ho Chi Minh City, Vietnam.
Thao N T PhamDepartment of Intensive Care Medicine, Emergency Medicine and Clinical Toxicology, University of Medicine and Pharmacy in Ho Chi Minh City, Vietnam.
Nguyen D NguyenTon Duc Thang University, Ho Chi Minh City, Vietnam.
Tuan V NguyenTon Duc Thang University, Ho Chi Minh City, Vietnam.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The primary aim of this prospective study is to develop and validate a new prognostic model for predicting the risk of mortality in Emergency Department (ED) patients. The study involved 1765 patients in the development cohort and 1728 in the validation cohort. The main outcome was mortality up to 30 days after admission. Potential risk factors included clinical characteristics, vital signs, and routine haematological and biochemistry tests. The Bayesian Model Averaging method within the Cox's regression model was used to identify independent risk factors for mortality. In the development cohort, the incidence of 30-day mortality was 9.8%, and the following factors were associated with a greater risk of mortality: male gender, increased respiratory rate and serum urea, decreased peripheral oxygen saturation and serum albumin, lower Glasgow Coma Score, and admission to intensive care unit. The area under the receiver operating characteristic curve for the model with the listed factors was 0.871 (95% CI, 0.844-0.898) in the development cohort and 0.783 (95% CI, 0.743-0.823) in the validation cohort. Calibration analysis found a close agreement between predicted and observed mortality risk. We conclude that the risk of mortality among ED patients could be accurately predicted by using common clinical signs and biochemical tests.

Indexed as

Emergency Service, HospitalModels, TheoreticalAgedAged, 80 and overBayes TheoremFemaleHumansMaleMiddle AgedMortalityPrognosisProspective StudiesRisk AssessmentRisk Factors

Identifiers

PMID28401961
PMCPMC5388874

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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.