Does follow-up of osteoporotic women treated with antiresorptive therapies improve effectiveness?
The use of biochemical markers of bone turnover has been advocated to improve follow-up of women receiving antiresorptive therapies for osteoporosis, but this strategy is not yet supported by trials showing it improves effectiveness of treatments. To explore the potential value of markers of bone turnover to monitor antiresorptive treatments of osteoporosis, we conducted a decision analysis using a decision tree and Markov modeling. We have compared two strategies: treatment of a woman without specific monitoring; and treatment of this woman with measurement of a serum marker of bone resorption after 3 months of treatment, with change of treatment if response to treatment assessed by this marker was not satisfactory. The base case is the treatment of a 60-year-old osteoporotic woman with a total hip T-score of -3, using a second generation bisphosphonate during 5 years. We found that follow-up produced slightly greater quality adjusted life years (QALYs) than no follow-up (8.1560 vs 8.1532, i.e. a one day difference). In a two-way sensitivity analysis, the follow-up option produced higher QALYs so long as adherence rate with follow-up was equal or superior to the proportion of women who adhered without follow-up. For example, if the proportion of women adherent to treatment was increased from 50% to 60% by follow-up, then the expected value of the follow-up branch was increased from 8.1560 QALYs to 8.1800 QALYs (i.e. a difference of 9 days). In addition, the higher the non-response rate, the greater the benefit from monitoring with a biochemical marker. In conclusion, our decision analysis model suggests that follow-up of osteoporotic women treated with a second generation bisphosphonate during a 5-year period using an early measurement of a serum marker of bone resorption may increase effectiveness of the treatment on quality of life, but the effect is very small. So, the use of follow-up measures of bone turnover may be based on patient and physician preferences.
Chapurlat
Roland D
RD
Prevention Sciences Group, Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA. roland.chapurlat@laposte.net
Cummings
S R
SR
D015415:Biomarkers / Q000097:blood; D016723:Bone Remodeling; D003663:Decision Trees; D004164:Diphosphonates / Q000627:therapeutic use*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008875:Middle Aged; D015663:Osteoporosis, Postmenopausal / Q000097:blood / Q000188:drug therapy* / Q000503:physiopathology; D010349:Patient Compliance; D019057:Quality-Adjusted Life Years; D016896:Treatment Outcome
D015415:Biomarkers; D004164:Diphosphonates