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Identification of patients with Churg-Strauss syndrome (CSS) using automated data
Leslie R Harrold1, Susan E Andrade, Mark Eisner
1Meyers Primary Care Institute, the University of Massachusetts Medical School and the Fallon Healthcare System, Worcester, MA 01655, USA. HarroldL@ummhc.org
Insights
Automated claims data can identify Churg-Strauss syndrome (CSS) in asthma patients. This method aids in studying risk factors for this rare condition.
Area of Science:
- Rheumatology
- Clinical Epidemiology
- Health Informatics
Background:
- Churg-Strauss syndrome (CSS) is a rare condition often diagnosed late.
- Identifying CSS cases in large populations is challenging.
- Asthma medication users are a potential cohort for CSS identification.
Purpose of the Study:
- To develop and validate algorithms using automated claims data to identify individuals with CSS.
- To assess the feasibility of using healthcare claims data for CSS case ascertainment.
- To facilitate future epidemiologic studies on CSS risk factors.
Main Methods:
- Retrospective study of asthma drug users across three HMOs (1994-2000).
- Development of 12 diagnostic/procedural code algorithms to flag potential CSS cases.
- Chart reviews by blinded reviewers, a rheumatologist, and clinical experts to confirm CSS based on ACR criteria.
Main Results:
- 185,604 asthma drug users identified; 350 selected for chart review.
- 15 cases classified as probable/definite CSS.
- Algorithms combining vasculitis codes with neurologic symptoms (40% PPV) or eosinophilia and vasculitis codes (80% PPV) showed the highest accuracy.
Conclusions:
- Automated claims data, particularly with specific code combinations, can effectively identify patients with CSS.
- This approach offers a valuable tool for epidemiological research into CSS.
- Improved case identification can lead to better understanding and management of CSS.
Purpose:
Our aim was to identify individuals with Churg-Strauss syndrome (CSS) among asthma drug users, based on patterns of diagnostic and procedural codes (termed 'algorithms') contained in automated claims data.
Methods:
A retrospective study was conducted among patients who had been dispensed asthma drugs at three HMOs. Individuals who received > or =3 dispensings of an asthma drug during any consecutive 12-month period beginning 1 January 1994 through 20 June 2000 were identified. Information on patient age, gender, enrollment status, asthma drugs dispensed, inpatient and outpatient diagnoses and procedures were obtained from the HMO automated databases. Twelve combinations of diagnostic and billing codes ('algorithms') were developed using the claims data to identify potential cases of CSS. Chart reviews blinded to drug exposure were performed using a standardized abstraction form. A rheumatologist reviewed abstracted information on all subjects, and those who met two or more American College of Rheumatology (ACR) criteria for CSS were further reviewed by two clinical experts. Cases were classified as unlikely, possible, or probable/definite CSS. Each clinical expert independently rated the cases; disagreements were resolved by consensus.
Results:
A total of 185 604 patients who had been dispensed asthma drugs were identified. Three hundred fifty subjects were selected for chart review, and 15 were classified as having 'probable/definite' CSS. The algorithms that were most successful in identifying patients with CSS were as follows: (1) two or more codes for vasculitis (13 confirmed cases from 129 reviewed; positive predictive value 10%); (2) codes for both vasculitis and neurologic symptoms (6 confirmed cases from 15 reviewed; positive predictive value 40%) and (3) codes for both eosinophilia and vasculitis (4 confirmed cases from 5 reviewed; positive predictive value 80%).
Conclusion:
Automated claims data can be used to identify patients with CSS. This approach can facilitate better epidemiologic study of the risk factors for the condition.
