Trial reportPloS one2019
Development of an electronic medical record-based algorithm to identify patients with Stevens-Johnson syndrome and toxic epidermal necrolysis in Japan.
Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Use of Electronic Health Records to Study Drug-Induced Hypersensitivity Reactions from 2000 to 2021: A Systematic Review.Immunology and allergy clinics of North America · 2022Pooled it
- Stevens-Johnson syndrome and toxic epidermal necrolysis: A systematic review of PubMed/MEDLINE case reports from 1980 to 2020.Frontiers in medicine · 2022Pooled it
- Clinical epidemiology and pharmacoepidemiology studies with real-world databases.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), severe drug reactions, are often misdiagnosed due to their rarity and lack of information on differential diagnosis. The objective of the study was to develop an electronic medical record (EMR)-based algorithm to identify patients with SJS/TEN for future application in database studies. From the EMRs of a university hospital, two dermatologists identified all 13 patients with SJS/TEN seen at the Department of Dermatology as the case group. Another 1472 patients who visited the Department of Dermatology were identified using the ICD-10 codes for diseases requiring differentiation from SJS/TEN. One hundred of these patients were then randomly sampled as controls. Based on clinical guidelines for SJS/TEN and the experience of the dermatologists, we tested 128 algorithms based on the use of ICD-10 codes, clinical courses for SJS/TEN, medical encounters for mucocutaneous lesions from SJS/TEN, and items to exclude paraneoplastic pemphigus. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic odds ratio (DOR) of each algorithm were calculated, and the optimal algorithm was defined as that with high PPV and maximal sensitivity and specificity. One algorithm, consisting of a combination of clinical course for SJS/TEN, medical encounters for mucocutaneous lesions from SJS/TEN, and items to exclude paraneoplastic pemphigus, but not ICD-10 codes, showed a sensitivity of 76.9%, specificity of 99.0%, PPV of 40.5%, NPV of 99.8%, and DOR of 330.00. We developed a potentially optimized algorithm for identifying SJS/TEN based on clinical practice records. The almost perfect specificity of this algorithm will prevent bias in estimating relative risks of SJS/TEN in database studies. Considering the small sample size, this algorithm should be further tested in different settings.
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