Identification of Stevens-Johnson syndrome and toxic epidermal necrolysis in electronic health record databases

Pharmacoepidemiol Drug Saf. 2015 Jul;24(7):684-92. doi: 10.1002/pds.3778. Epub 2015 Apr 24.

Abstract

Background: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) carry a high mortality risk. While identifying clinical and genetic risk factors for these conditions has been hindered by their rarity, large electronic health databases hold promise for identifying large numbers of cases for study, especially with the introduction in 2008 of ICD-9 codes more specific for these conditions.

Objective: The objective of this study is to estimate the validity of ICD-9 codes for ascertaining SJS/TEN in 12 collaborating research units in the USA, covering almost 60 million lives.

Methods: From the electronic databases at each site, we ascertained potential cases of SJS/TEN using ICD-9 codes. At five sites, a subset of medical records was abstracted and standardized criteria applied by board-certified dermatologists to adjudicate diagnoses. Multivariate logistic regression was used to identify factors independently associated with validated SJS/TEN cases.

Results: A total of 56 591 potential cases of SJS/TEN were identified. A subset of 276 charts was selected for adjudication and 39 (of the 276) were confirmed as SJS/TEN. Patients with the ICD-9 codes introduced after 2008 were more likely to be confirmed as cases (OR 3.32; 95%CI 0.82, 13.47) than those identified in earlier years. Likelihood of case status increased with length of hospitalization. Applying the probability of case status to the 56 591 potential cases, we estimated 475-875 to be valid SJS/TEN cases.

Conclusion: Newer ICD-9 codes, along with length of hospitalization, identified patients with a high likelihood of SJS/TEN. This is important for identification of subjects for future pharmacogenomics studies.

Keywords: Stevens-Johnson syndrome; pharmacoepidemiology; toxic epidermal necrolysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Databases, Factual / statistics & numerical data*
  • Electronic Health Records / statistics & numerical data*
  • Feasibility Studies
  • Hospitalization / statistics & numerical data
  • Humans
  • International Classification of Diseases
  • Logistic Models
  • Pharmacoepidemiology
  • Stevens-Johnson Syndrome / diagnosis
  • Stevens-Johnson Syndrome / epidemiology*
  • United States / epidemiology