Do package inserts reflect symptoms experienced in practice?: assessment using an automated phone pharmacovigilance system with varenicline and zolpidem in a primary care setting.
ABSTRACT While the US FDA maintains a voluntary reporting system, postmarketing adverse drug events (ADEs) are underreported, and this case report-based system does not allow accurate determination of incidence.
The aim of the study was to assess the usefulness of an automated phone pharmacovigilance system for ambulatory patients by comparing systematically collected, patient-reported symptoms to reflect possible ADEs with those reported on the package inserts of two drugs with postmarketing safety concerns, varenicline and zolpidem.
English-speaking adults who received a prescription for zolpidem (n = 370) or varenicline (n = 107) from a primary care physician at one of 11 participating clinics, and who participated in the pharmacovigilance system during 2008-2010, were included in the study. Patients were called approximately 4 weeks following their visit and asked to complete a standard script that asked about adherence and pre-specified symptoms.
The main outcome measures were elicited rates of pre-specified symptoms or possible ADEs.
Compared with the package insert, patients taking zolpidem were significantly (p < 0.001) more likely to report fatigue (9.0% vs 1.0%), itching (4.5% vs 1.0%) and muscle aches (5.6% vs 1.0%). Elicited rates of depression and hallucination were similar to those reported in the package insert. Patients taking varenicline were significantly more likely to report confusion (1.7% vs 0.1%), depression (3.4% vs 0.1%), fatigue (6.0% vs 1.0%), hallucinations (1.7% vs 0.1%), muscle aches (6.0% vs 1.0%) and sexual dysfunction (4.3% vs 0.1%).
Automated phone pharmacovigilance can provide estimates of possible ADEs in clinical practice. In the case of varenicline, these data support some of the safety concerns that have come to light postmarketing, while others such as depression and hallucination related to zolpidem were not detected. These data highlight the potential value of, and innovative ways of collecting, information about possible ADEs directly from patients.
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ABSTRACT: To improve and learn from patient outcomes, particularly under new care models such as Accountable Care Organizations and Patient-Centered Medical Homes, requires establishing systems for follow-up and feedback. To provide post-visit feedback to physicians on patient outcomes following acute care visits. A three-phase cross-sectional study [live follow-up call three weeks after acute care visits (baseline), one week post-visit live call, and one week post-visit interactive voice response system (IVRS) call] with three patient cohorts was conducted. A family medicine clinic and an HIV clinic participated in all three phases, and a cerebral palsy clinic participated in the first two phases. Patients answered questions about symptom improvement, medication problems, and interactions with the healthcare system. A total of 616 patients were included: 142 from Phase 1, 352 from Phase 2 and 122 from Phase 3. Primary outcomes included: problem resolution, provider satisfaction with the system, and comparison of IVRS with live calls made by research staff. During both live follow-up phases, at least 96 % of patients who were reached completed the call compared to only 48 % for the IVRS phase. At baseline, 98 of 113 (88 %) patients reported improvement, as well as 167 of 196 (85 %) in the live one-week follow-up. In the one-week IVRS phase, 25 of 39 (64 %) reported improvement. In all phases, the majority of patients in both the improved and unimproved groups had not contacted their provider or another provider. While 63 % of providers stated they wanted to receive patient feedback, they varied in the extent to which they used the feedback reports. Many patients who do not improve as expected do not take action to further address unresolved problems. Systematic follow-up/feedback mechanisms can potentially identify and connect such patients to needed care.Journal of General Internal Medicine 03/2014; 29(8). DOI:10.1007/s11606-014-2783-3 · 3.42 Impact Factor