Cost analysis of prostate cancer detection including the prostate health index (phi) View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Romain Mathieu, Christel Castelli, Tarek Fardoun, Benoit Peyronnet, Shahrokh F. Shariat, Karim Bensalah, Sébastien Vincendeau

ABSTRACT

OBJECTIVE: To assess the economic impact of introducing the prostate health index (phi) for prostate cancer (PCa) detection. METHODS: A total of 177 patients who presented in an academic institution with a tPSA between 2 and 10 ng/ml and underwent prostate biopsies within the 3 months were enrolled. With phi and tPSA thresholds of 43 and 4 ng/ml, respectively, probability for each branch of a decision tree model for PCa diagnosis and corresponding mean cost were estimated with "Monte Carlo" simulations. A sensitivity analysis was performed. RESULTS: With a similar sensitivity, phi strategy increased positive predictive value by 13.9 points and negative predictive value by 31.6 points in comparison to tPSA strategy. Mean costs per patient with tPSA and phi strategies were €514 and €528, respectively, for a phi test price at 50€. One-way sensitivity analysis showed that phi strategy was less expensive (508€/patient) than tPSA strategy with a phi test price below 30€. In multi-criteria sensitivity analysis, PPV and the rates of positive phi and tPSA were the parameters with the largest impact on the final cost as opposed to the cost of the biopsy or imaging which have less influence. With an expected rate of positive phi test < 60%, tPSA strategy was more expensive than phi strategy. CONCLUSIONS: The introduction of phi index in PCa detection would result in a significant clinical benefit compared to tPSA strategy. In our economic model, the phi strategy was equivalent or slightly more expensive than the current tPSA strategy. More... »

PAGES

481-487

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00345-018-2362-z

DOI

http://dx.doi.org/10.1007/s00345-018-2362-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105335171

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29980838


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