An alternative model for assessing mortality risk in Stevens Johnson syndrome/toxic epidermal necrolysis using a random forests classifier: A pilot study.

ABCD SCORTEN score Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) (SJS/TEN) machine learning mortality risk random forest (bagging) and machine learning

Journal

Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047

Informations de publication

Date de publication:
2022
Historique:
received: 03 05 2022
accepted: 23 11 2022
entrez: 26 12 2022
pubmed: 27 12 2022
medline: 27 12 2022
Statut: epublish

Résumé

Mortality risk prediction is an important part of the clinical assessment in the Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) patient. The SCORTEN and ABCD-10 scoring systems have been used as predictive clinical tools for assessing this risk. However, some of the metrics required in calculating these scores, such as the total body surface area (TBSA) involvement, are difficult to calculate. In addition, TBSA involvement is calculated in a variety of ways and is observer dependent and subjective. The goal of this study was to develop an alternative method to predict mortality in patients with SJS/TEN. Data was split into training and test datasets and preprocessed. Models were trained using five-fold cross validation. Out of several possible candidates, a random forests model was evaluated as being the most robust in predictive power for this dataset. Upon feature selection, a final random forests model was developed which was used for comparison against SCORTEN. The differences in both accuracy ( This new alternative can make the mortality prediction process more efficient, along with providing a seamless implementation of the patient laboratory tests directly into the model from existing electronic health record (EHR) systems. Once the model was developed, a web application was built to deploy the model which integrates with the Epic EHR system on the Fast Healthcare Interoperability Resources (FHIR) Application Programming Interface (API); this only requires the patient medical record number and a date of the lab tests as parameters. This model ultimately allows clinicians to calculate patient mortality risk with only a few clicks. Further studies are needed for validation of this tool.

Identifiants

pubmed: 36569158
doi: 10.3389/fmed.2022.935408
pmc: PMC9772610
doi:

Types de publication

Journal Article

Langues

eng

Pagination

935408

Informations de copyright

Copyright © 2022 Shareef, Kwan, Lau, Tahboub and Saeed.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Omar Shareef (O)

Harvard College, Cambridge, MA, United States.
Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, United States.

James T Kwan (JT)

Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, United States.
Tufts University School of Medicine, Boston, MA, United States.

Sarina Lau (S)

Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, United States.
Department of Biology, Simmons University, Boston, MA, United States.

Mohammad Ali Tahboub (MA)

Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, United States.

Hajirah N Saeed (HN)

Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, United States.
Department of Ophthalmology, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, IL, United States.

Classifications MeSH