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      Patient-Reported Outcome Measures in Safety Event Reporting: PROSPER Consortium Guidance

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          Abstract

          The Patient-Reported Outcomes Safety Event Reporting (PROSPER) Consortium was convened to improve safety reporting by better incorporating the perspective of the patient. PROSPER comprises industry, regulatory authority, academic, private sector and patient representatives who are interested in the area of patient-reported outcomes of adverse events (PRO-AEs). It has developed guidance on PRO-AE data, including the benefits of wider use and approaches for data capture and analysis. Patient-reported outcomes (PROs) encompass the full range of self-reporting, rather than only patient reports collected by clinicians using validated instruments. In recent years, PROs have become increasingly important across the spectrum of healthcare and life sciences. Patient-centred models of care are integrating shared decision making and PROs at the point of care; comparative effectiveness research seeks to include patients as participatory stakeholders; and industry is expanding its involvement with patients and patient groups as part of the drug development process and safety monitoring. Additionally, recent pharmacovigilance legislation from regulatory authorities in the EU and the USA calls for the inclusion of patient-reported information in benefit–risk assessment of pharmaceutical products. For patients, technological advancements have made it easier to be an active participant in one’s healthcare. Simplified internet search capabilities, electronic and personal health records, digital mobile devices, and PRO-enabled patient online communities are just a few examples of tools that allow patients to gain increased knowledge about conditions, symptoms, treatment options and side effects. Despite these changes and increased attention on the perceived value of PROs, their full potential has yet to be realised in pharmacovigilance. Current safety reporting and risk assessment processes remain heavily dependent on healthcare professionals, though there are known limitations such as under-reporting and discordant perspectives between patient reports and clinician perceptions of adverse outcomes. PROSPER seeks to support the wider use of PRO-AEs. The scope of this guidance document, which was completed between July 2011 and March 2013, considered a host of domains related to PRO-AEs, including definitions and suitable taxonomies, the range of datasets that could be used, data collection mechanisms, and suitable analytical methodologies. PROSPER offers an innovative framework to differentiate patient populations. This framework considers populations that are prespecified (such as those in clinical trials, prospective observational studies and some registries) and non-prespecified populations (such as those in claims databases, PRO-enabled online patient networks, and social websites in general). While the main focus of this guidance is on post-approval PRO-AEs from both prespecified and non-prespecified population groups, PROSPER has also considered pre-approval, prespecified populations. The ultimate aim of this guidance is to ensure that the patient ‘voice’ and perspective feed appropriately into collection of safety data. The guidance also covers a minimum core dataset for use by industry or regulators to structure PRO-AEs (accessible in the online appendix) and how data, once collected, might be evaluated to better inform on the safe and effective use of medicinal products. Structured collection of such patient data can be considered both a means to an end (improving patient safety) as well as an end in itself (expressing the patient viewpoint). The members of the PROSPER Consortium therefore direct this PRO-AE guidance to multiple stakeholders in drug safety, including industry, regulators, prescribers and patients. The use of this document across the entirety of the drug development life cycle will help to better define the benefit–risk profile of new and existing medicines. Because of the clinical relevance of ‘real-world’ data, PROs have the potential to contribute important new knowledge about the benefits and risks of medicinal products, communicated through the voice of the patient.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s40264-013-0113-z) contains supplementary material, which is available to authorized users.

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          Most cited references37

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          In recent years quality of life instruments have been featured as primary outcomes in many randomized trials. One of the challenges facing the investigator using such measures is determining the significance of any differences observed, and communicating that significance to clinicians who will be applying the trial results. We have developed an approach to elucidating the significance of changes in score in quality of life instruments by comparing them to global ratings of change. Using this approach we have established a plausible range within which the minimal clinically important difference (MCID) falls. In three studies in which instruments measuring dyspnea, fatigue, and emotional function in patients with chronic heart and lung disease were applied the MCID was represented by mean change in score of approximately 0.5 per item, when responses were presented on a seven point Likert scale. Furthermore, we have established ranges for changes in questionnaire scores that correspond to moderate and large changes in the domains of interest. This information will be useful in interpreting questionnaire scores, both in individuals and in groups of patients participating in controlled trials, and in the planning of new trials.
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            Under-reporting of adverse drug reactions : a systematic review.

            The purpose of this review was to estimate the extent of under-reporting of adverse drug reactions (ADRs) to spontaneous reporting systems and to investigate whether there are differences between different types of ADRs. A systematic literature search was carried out to identify studies providing a numerical estimate of under-reporting. Studies were included regardless of the methodology used or the setting, e.g. hospital versus general practice. Estimates of under-reporting were either extracted directly from the published study or calculated from the study data. These were expressed as the percentage of ADRs detected from intensive data collection that were not reported to the relevant local, regional or national spontaneous reporting systems. The median under-reporting rate was calculated across all studies and within subcategories of studies using different methods or settings. In total, 37 studies using a wide variety of surveillance methods were identified from 12 countries. These generated 43 numerical estimates of under-reporting. The median under-reporting rate across the 37 studies was 94% (interquartile range 82-98%). There was no significant difference in the median under-reporting rates calculated for general practice and hospital-based studies. Five of the ten general practice studies provided evidence of a higher median under-reporting rate for all ADRs compared with more serious or severe ADRs (95% and 80%, respectively). In comparison, for five of the eight hospital-based studies the median under-reporting rate for more serious or severe ADRs remained high (95%). The median under-reporting rate was lower for 19 studies investigating specific serious/severe ADR-drug combinations but was still high at 85%. This systematic review provides evidence of significant and widespread under-reporting of ADRs to spontaneous reporting systems including serious or severe ADRs. Further work is required to assess the impact of under-reporting on public health decisions and the effects of initiatives to improve reporting such as internet reporting, pharmacist/nurse reporting and direct patient reporting as well as improved education and training of healthcare professionals.
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              Is Open Access

              Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking

              A new class of patient-driven health care services is emerging to supplement and extend traditional health care delivery models and empower patient self-care. Patient-driven health care can be characterized as having an increased level of information flow, transparency, customization, collaboration and patient choice and responsibility-taking, as well as quantitative, predictive and preventive aspects. The potential exists to both improve traditional health care systems and expand the concept of health care though new services. This paper examines three categories of novel health services: health social networks, consumer personalized medicine and quantified self-tracking.
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                Author and article information

                Contributors
                +44-14-80300300 , +44-14-80497970 , Swapu.Banerjee@popewoodhead.com
                Journal
                Drug Saf
                Drug Saf
                Drug Safety
                Springer International Publishing (Cham )
                0114-5916
                1179-1942
                5 October 2013
                5 October 2013
                2013
                : 36
                : 1129-1149
                Affiliations
                [ ]Pope Woodhead and Associates Ltd, The Old Grammar School, 1 Ramsey Road, St. Ives, Cambridgeshire, PE27 5BZ UK
                [ ]PatientsLikeMe, Boston, USA
                [ ]WHO/Uppsala Monitoring Centre, Uppsala, Sweden
                [ ]AbbVie, Chicago, USA
                [ ]University of Namur, Namur, Belgium
                [ ]MD Anderson Cancer Center, University of Texas, Austin, USA
                [ ]Duke University Medical Center, Durham, NC USA
                [ ]Department of Surgery, ULH NHS Trust, Grantham District Hospital, Lincolnshire, NG31 8DG UK
                Article
                113
                10.1007/s40264-013-0113-z
                3834161
                24092596
                d094c2b4-d580-462c-850d-f23a958e8f8e
                © Springer International Publishing Switzerland 2013
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                © Springer International Publishing Switzerland 2013

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