Evidence map›Paper›PMID 31910506›Full record

ArticleClinical and experimental emergency medicine2019

Predictors of poor prognosis in patients with heat stroke.

Jae-Kwon Chun, Sangchun Choi, Hyuk-Hoon Kim, Hee Won Yang, Chang Seong Kim

Open access · diamondAbstract read
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Article in Clinical and experimental emergency medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.3field-weighted citation impact, top 38% 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

8 citing papers in PubMed, 9 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

5 authors at 1 institution in 1 country.

Jae-Kwon ChunDepartment of Emergency Medicine, Ajou University School of Medicine, Suwon, Korea.
Sangchun ChoiDepartment of Emergency Medicine, Ajou University School of Medicine, Suwon, Korea.
Hyuk-Hoon KimDepartment of Emergency Medicine, Ajou University School of Medicine, Suwon, Korea.
Hee Won YangDepartment of Emergency Medicine, Ajou University School of Medicine, Suwon, Korea.
Chang Seong KimAjou University Graduate School of Medicine, Suwon, Korea.
Ajou University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe predictors of poor prognosis in heat stroke (HS) remain unknown. This study investigated the predictive factors of poor prognosis in patients with HS.

methodsData were obtained and analyzed from the health records of patients diagnosed with heat illness at Ajou university hospital between January 2008 and December 2017. Univariate and multivariate analyses were performed to identify the independent predictors of poor prognosis.

resultsThirty-six patients (median age, 54.5 years; 33 men) were included in the study. Poor prognosis was identified in 27.8% of the study population (10 patients). The levels of S100B protein, troponin I, creatinine, alanine aminotransferase, and serum lactate were statistically significant in the univariate analysis. Multiple regression analysis revealed that poor prognosis was significantly associated with an increased S100B protein level (odds ratio, 177.37; 95% confidence interval, 2.59 to 12,143.80; P=0.016). The S100B protein cut-off level for predicting poor prognosis was 0.610 μg/L (area under the curve, 0.906; 95% confidence interval, 0.00 to 1.00), with 86% sensitivity and 86% specificity.

conclusionAn increased S100B protein level on emergency department admission is an independent prognostic factor of poor prognosis in patients with HS. Elevation of the S100B protein level represents a potential target for specific and prompt therapies in these patients.

Indexed as

BiomarkersHeat strokePrognosisS100B protein

Identifiers

PMID31910506
PMCPMC6952628
OpenAlexW2998167913

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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.