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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., (Copyright © 2015 John Wiley & Sons, Ltd.)

Introduction/Objective: The objective of this study was to develop an algorithm to identify Kaiser Permanente Colorado (KPCO) members with a history of cancer. Background: Tumor registries are used with high precision to identify incident cancer, but are not designed to capture prevalent cancer within a population. We sought to identify a cohort of adults with no history of cancer; thus, we could not rely solely on the tumor registry. Methods: We included all KPCO members between the ages of 40-75 years who were continuously enrolled during 2013 (N=201,787). Data from the tumor registry, chemotherapy files, and inpatient and outpatient claims were used to create an algorithm to identify members with a high likelihood of cancer. We validated the algorithm using chart review and calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for occurrence of cancer. Findings: The final version of the algorithm achieved a sensitivity of 100 percent and specificity of 84.6 percent for identifying cancer. If we relied on the tumor registry alone, 47 percent of those with a history of cancer would have been missed. Discussion: Using the tumor registry alone to identify a cohort of patients with prior cancer is not sufficient. In the final version of the algorithm, the sensitivity and PPV were improved when a diagnosis code for cancer was required to accompany oncology visits or chemotherapy administration. Conclusion: Electronic medical record (EMR) data can be used effectively in combination with data from the tumor registry to identify health plan members with a history of cancer. [ABSTRACT FROM AUTHOR]