Prior adherence to statins is a predictor of subsequent adherence and persistence to oral, but not parenteral, osteoporosis medications

Predicting the individualized risk of nonadherence to zoledronic acid among osteoporosis patients receiving the first infusion of zoledronic acid: development and validation of new predictive nomograms

Introduction:

Achieving optimal adherence to zoledronic acid (ZOL) among osteoporosis (OP) patients is a challenging task. Here, we aimed to develop and validate a precise and efficient prediction tool for ZOL nonadherence risk in OP patients.

Methods:

We prospectively collected and analyzed survey data from a clinical registry. A total of 1010 OP patients treated for the first time with ZOL in two separate hospitals were selected for nonadherence analysis. The evaluation included a 16-item ZOL Nonadherence Questionnaire and potential risk factors for ZOL nonadherence were assessed via univariate and multivariate analyses. We next developed and validated two distinct-stage nomograms. Discrimination, calibration, and clinical usefulness of the predicting models were assessed using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

Results:

The total nonadherence rate was 20.30% after the first ZOL infusion. To generate a model predicting ZOL nonadherence risk, six predictors of 16 items were retained. Model 2 (AUC, 0.8486; 95% confidence interval [CI], 0.8171–0.8801) exhibited considerably more discrimination in desirable functional outcomes, relative to Model 1 (AUC, 0.7644; 95% CI, 0.7265–0.8024). The calibration curves displayed good calibration. DCA revealed that a cutoff probability of 5–54% (Model 1) and 1–85% (Model 2) indicated that the models were clinically useful. External validation also exhibited good discrimination and calibration.

Conclusions:

This study developed and validated two novel, distinct-stage prediction nomograms that precisely estimate nonadherence risk among OP patients receiving the first infusion of ZOL. However, additional evaluation and external validation are necessary prior to widespread implementation.