Mini-Sentinel's systematic reviews of validated methods for identifying health outcomes using administrative data: summary of findings and suggestions for future research

The validity of findings from surveillance activities, which use administrative and claims data to link exposures to adverse events, depends in part on the validity of algorithms to identify health outcomes using these data. This review provides a high level overview of the findings of 19 systematic reviews of studies, which have examined the validity of algorithms to identify health outcomes using these data. The author categorized outcomes on the basis of the strength of evidence supporting valid algorithms to identify acute or incident events and suggested priorities for future validation studies.

The 19 reviews were evaluated, and key findings and suggestions for future research were summarized by a single reviewer. Outcomes with algorithms that consistently identified acute events or incident conditions with positive predictive values of greater than 70% across multiple studies and populations are described as low priority for future algorithm validation studies.

Algorithms to identify cerebrovascular accidents, transient ischemic attacks, congestive heart failure, deep vein thrombosis, pulmonary embolism, angioedema, and total hip arthroplasty revision performed well across multiple studies and are considered low priority for future validation studies. Other outcomes were generally thought to require additional validation studies or algorithm refinement to be confident in algorithms. Few studies examined the validity of International Classification of Diseases, 10th Revision, codes.

Users of these reviews need to consider the generalizability of findings to their study populations. For some outcomes with poorly performing codes, it may always be necessary to validate cases.