A systematic review of validated methods for identifying systemic lupus erythematosus (SLE) using administrative or claims data.

PURPOSE: To examine the validity of billing, procedural, or diagnosis code, or pharmacy claim-based algorithms used to identify patients with systemic lupus erythematosus (SLE) in administrative and claims databases. METHODS: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to SLE. We also searched the reference lists of included studies. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria. The two reviewers independently extracted data regarding participant and algorithm characteristics and assessed a study's methodologic rigor using a pre-defined approach. RESULTS: Twelve studies included validation statistics for the identification of SLE in administrative and claims databases. Seven of these studies used the ICD-9 code of 710.0 in selected populations of patients seen by a rheumatologist or patients who had experienced the complication of SLE-associated nephritis, other kidney disease, or pregnancy. The other studies looked at limited data in general populations. The algorithm in the selected populations had a positive predictive value (PPV) in the range of 70-90% and of the limited data in general populations it was in the range of 50-60%. CONCLUSIONS: Few studies use rigorous methods to validate an algorithm for the identification of SLE in general populations. Algorithms including ICD-9 code of 710.0 in physician billing and hospitalization records have a PPV of approximately 60%. A requirement that the code is obtained from a record based on treatment by a rheumatologist increases the PPV of the algorithm but limits the generalizability in the general population.

Mini-Sentinel's systematic reviews of validated methods for identifying health outcomes using administrative and claims data: methods and lessons learned.

PURPOSE: To overview the methods used in the Mini-Sentinel systematic reviews of validation studies of algorithms to identify health outcomes in administrative and claims data and to describe lessons learned in the development of search strategies, including their ability to identify articles from previous systematic reviews which used different search strategies. METHODS: Literature searches were conducted using PubMed and the citation database of the Iowa Drug Information Service. Embase was searched for some outcomes. The searches were based on a strategy developed by the Observational Medical Outcomes Partnership (OMOP) researchers. All citations were reviewed by two investigators. Exclusion criteria were applied at abstract and full-text review stages to ultimately identify algorithm validation studies that used data sources from the USA or Canada, as the results of these studies were considered most likely to generalize to Mini-Sentinel data. Nonvalidated algorithms were reviewed if fewer than five algorithm validation studies were identified. RESULTS: The results of this project are described in the separate articles and reports written on algorithms to identify each outcome of interest. CONCLUSIONS: The Mini-Sentinel systematic reviews of algorithms to identify health outcomes in administrative and claims data are expected to be relatively complete, despite some limitations. Algorithm validation studies are inconsistently indexed in PubMed, creating challenges in conducting systematic reviews of these studies. Google Scholar searches, which can perform text word searches of electronically available articles, are suggested as a strategy to identify studies that are not captured through searches of standard citation databases.

A systematic review of validated methods for identifying transfusion-related sepsis using administrative and claims data.

PURPOSE: To systematically review algorithms to identify transfusion-related sepsis or septicemia in administrative data, with a focus on studies that have examined the validity of the algorithms. METHODS: A literature search was conducted using PubMed, the database of the Iowa Drug Information Service (IDIS/Web), and Embase. A Google Scholar search was conducted because of difficulty identifying relevant studies. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada, because these data sources were most likely to reflect the coding practices of Mini-Sentinel data sources. RESULTS: No studies that were identified that used administrative data to identify sepsis or septicemia related to transfusion of blood products. Thus, four studies that studied the validity of algorithms to identify sepsis and two that studied algorithms to identify allogeneic blood transfusion are described in this review. Two studies found acceptable positive predictive values of 80% and 89% for algorithms to identify sepsis in hospitalized patients. One study reported a negative predictive value of 80% in hospitalized patients, and another, a sensitivity of 75%. One study of veterans receiving surgery reported much worse performance characteristics. Two studies reported near-perfect specificity of codes for allogeneic red blood cell transfusion, but sensitivity ranged from 21% to 83%. CONCLUSIONS: There is no information to assess the validity of algorithms to identify transfusion-related sepsis or septicemia. Codes to identify sepsis performed well in most studies. Algorithms to identify transfusions need further research that includes a broader range of transfusion types.