Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

Abstract

BackgroundOther studies have assessed nonadherence to proton pump inhibitors (PPIs), but none has developed a screening test for its detection.ObjectivesTo construct and internally validate a predictive model for nonadherence to PPIs.MethodsThis prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count). Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP) of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC), was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android).ResultsThe points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83–0.91], p < 0.001). The test yielded a sensitivity of 0.80 (95% CI [0.70–0.87]) and a specificity of 0.82 (95% CI [0.76–0.87]). The three parameters were very similar in the bootstrap validation.ConclusionsA points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.

Ongoing activities to influence the prescribing of proton pump inhibitors within the Scottish National Health Service: their effect and implications

“…In addition, there can be differences between prescribing and dispensing data. We also could not measure adherence rates, which can be a concern among patients on PPIs (83). In addition, we could not measure any improvements in longterm outcomes from reduced prescribing of high dose PPIs with the administrative data sets we used.…”

A systematic review of predictive models for recurrence and mortality in patients with tongue cancer

“…Additionally, although it is simple, it is too laborious for clinical practice due to time constraints as calculations must be made with a ruler, and the limited amount of time spent with each patient in the clinic prevents its use. These two problems could be overcome by integrating the model into a mobile application (Mares‐García et al, ; Paredes‐Aracil et al, ).…”