The cost-effectiveness of immunohistochemistry.
OBJECTIVE
To evaluate the cost-effectiveness of immunohistochemistry.


METHODS
Using a theoretical decision analytic model, the cost-effectiveness of immunohistochemistry was evaluated in different scenarios depicting the beneficial use of immunohistochemistry. Data regarding the effectiveness of immunohistochemistry were obtained from the medical literature and costs were obtained from Allegheny General Hospital.


METHODS
The scenarios depicted both private practice and university patients from which anatomic pathology specimens were obtained. Immunohistochemistry was subsequently performed on these specimens.


METHODS
Cost, life expectancy, diagnostic certainty, ability to predict prognosis, and cost-effectiveness were evaluated.


RESULTS
In all scenarios, immunohistochemistry was cost-effective at very low efficacies. Assuming a per-antibody cost of $50 and use of a 5-antibody panel, immunohistochemistry was cost-effective if it resulted in a 1-year gain of population life expectancy in 1 or more of every 200 cases in which it was applied. Alternatively, if the gain in diagnostic certainty by using immunohistochemistry was 10% and the value placed on a percentage gain in diagnostic certainty was $1000, immunohistochemistry was cost-effective if it produced this increase in certainty in 1 of every 40 tests. If a life-year was valued at $50 000, immunohistochemistry was cost-effective if it resulted in a change in patient prognosis of 0.5 years in 1 of every 100 tests.


CONCLUSIONS
Using theoretical modeling, immunohistochemistry is extremely cost-effective. These data have implications in an era of managed care when providers attempt to trim laboratory services. Additional studies are needed to evaluate the actual practice efficacy of immunohistochemistry.
Department of Pathology and Laboratory Medicine, Medical College of Pennsylvania and Hahnemann University School of Medicine, Allegheny General Hospital, Pittsburgh, PA 15212-4772, USA. sraab@aherf.edu
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D000328:Adult; D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D003955:Diagnostic Tests, Routine / Q000191:economics; D005069:Evaluation Studies as Topic; D005260:Female; D017048:Health Care Costs; D006801:Humans; D007150:Immunohistochemistry / Q000191:economics*; D008297:Male; D008875:Middle Aged; D018803:Models, Economic; D011237:Predictive Value of Tests; D014481:United States