Evidence map›Paper›PMID 28469101›Full record

ArticleChinese medical journal2017

Retrospective Analysis of Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis in 88 Chinese Patients.

Li Wang, Xue-Ling Mei

Abstract read
In one paragraph

Article in Chinese medical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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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3 · Its place in the literature

Who cites it

13 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

2 authors.

Li WangDepartment of Dermatology, Allergy and Clinical Immunology Centre, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Xue-Ling MeiDepartment of Dermatology, Allergy and Clinical Immunology Centre, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are life-threatening diseases with high mortality rates. This study was designed to analyze the pathogenic factors, clinical manifestations, complications, treatment, and prognosis of SJS/TEN and to explore the differences between surviving and deceased patients.

methodsSJS/TEN patients admitted to Beijing Friendship Hospital from January 2006 to December 2015 were included in the study. Patients' data were retrospectively analyzed. Comparative studies were performed on the survival group and the deceased group, and Fisher's exact probability test was used for statistical analysis.

resultsAmong the 88 patients included, 40 (45.5%) were male with a mean age of 45 ± 18 years. Forty-eight (54.5%) had SJS, 34 (38.6%) had SJS/TEN, and 6 (6.8%) had TEN. Fifty-three (60.2%) cases were caused by medications, mainly antibiotics (n = 24) followed by traditional Chinese medicines (n = 7). Forty-two cases (47.7%) developed visceral damage. Eighty-two patients improved or recovered and were discharged from hospital, and six patients died. Comparative studies on the survival group and the deceased group showed that the presence of malignant tumor ( χ2 = 27.969,P < 0.001), connective tissue diseases ( χ2 = 9.187, P= 0.002), previous abnormal liver/kidney functions ( χ2 = 6.006, P= 0.014), heart rate >100 times/min ( χ2 = 6.347, P= 0.012), detached skin area >20% ( χ2 = 5.594, P= 0.018), concurrent mucosal involvement at the mouth, eyes, and external genitals ( χ2 = 4.945, P= 0.026), subsequent accompanying liver/kidney damage ( χ2 = 11.839, P= 0.001, and χ2 = 36.302,P < 0.001, respectively), and SCORTEN score >2 ( χ2 = 37.148,P < 0.001) increased the risk of death.

conclusionsSJS/TEN is mainly caused by medications, and nearly half of patients develop visceral damage. Multiple factors increase the mortality risk.

Indexed as

AdultAnti-Bacterial AgentsConnective Tissue DiseasesEyeFemaleGenitaliaHumansKidneyLiverMaleMiddle AgedMouthRetrospective StudiesSkinStevens-Johnson SyndromeAnti-Bacterial Agents

Identifiers

PMID28469101
PMCPMC5421176

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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.