Risk of Anaphylaxis Among New Users of GLP-1 Receptor Agonists: A Cohort Study.

OBJECTIVE: To assess risk of anaphylaxis among patients with type 2 diabetes mellitus who are initiating therapy with a glucagon-like peptide 1 receptor agonist (GLP-1 RA), with a focus on those starting lixisenatide therapy. RESEARCH DESIGN AND METHODS: A cohort study was conducted in three large, U.S. claims databases (2017-2021). Adult (aged ≥18 years) new users of a GLP-1 RA who had type 2 diabetes mellitus and ≥6 months enrollment in the database before GLP-1 RA initiation (start of follow-up) were included. GLP-1 RAs evaluated were lixisenatide, an insulin glargine/lixisenatide fixed-ratio combination (FRC), exenatide, liraglutide or insulin degludec/liraglutide FRC, dulaglutide, and semaglutide (injectable and oral). The first anaphylaxis event during follow-up was identified using a validated algorithm. Incidence rates (IRs) and 95% CIs were calculated within each medication cohort. The unadjusted IR ratio (IRR) comparing anaphylaxis rates in the lixisenatide cohort with all other GLP-1 RAs combined was analyzed post hoc. RESULTS: There were 696,089 new users with 456,612 person-years of exposure to GLP-1 RAs. Baseline demographics, comorbidities, and use of other prescription medications in the 6 months before the index date were similar across medication cohorts. IRs (95% CIs) per 10,000 person-years were 1.0 (0.0-5.6) for lixisenatide, 6.0 (3.6-9.4) for exenatide, 5.1 (3.7-7.0) for liraglutide, 3.9 (3.1-4.8) for dulaglutide, and 3.6 (2.6-4.9) for semaglutide. The IRR (95% CI) for the anaphylaxis rate for the lixisenatide cohort compared with the pooled other GLP-1 RA cohort was 0.24 (0.01-1.35). CONCLUSIONS: Anaphylaxis is rare with GLP-1 RAs. Lixisenatide is unlikely to confer higher risk of anaphylaxis than other GLP-1 RAs.

Suitability of Preference Methods Across the Medical Product Lifecycle: A Multicriteria Decision Analysis.

OBJECTIVES: This study aimed to understand the importance of criteria describing methods (eg, duration, costs, validity, and outcomes) according to decision makers for each decision point in the medical product lifecycle (MPLC) and to determine the suitability of a discrete choice experiment, swing weighting, probabilistic threshold technique, and best-worst scale cases 1 and 2 at each decision point in the MPLC. METHODS: Applying multicriteria decision analysis, an online survey was sent to MPLC decision makers (ie, industry, regulatory, and health technology assessment representatives). They ranked and weighted 19 methods criteria from an existing performance matrix about their respective decisions across the MPLC. All criteria were given a relative weight based on the ranking and rating in the survey after which an overall suitability score was calculated for each preference elicitation method per decision point. Sensitivity analyses were conducted to reflect uncertainty in the performance matrix. RESULTS: Fifty-nine industry, 29 regulatory, and 5 health technology assessment representatives completed the surveys. Overall, "estimating trade-offs between treatment characteristics" and "estimating weights for treatment characteristics" were highly important criteria throughout all MPLC decision points, whereas other criteria were most important only for specific MPLC stages. Swing weighting and probabilistic threshold technique received significantly higher suitability scores across decision points than other methods. Sensitivity analyses showed substantial impact of uncertainty in the performance matrix. CONCLUSION: Although discrete choice experiment is the most applied preference elicitation method, other methods should also be considered to address the needs of decision makers. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.

Incidence of diabetic ketoacidosis and its trends in patients with type 1 diabetes mellitus identified using a U.S. claims database, 2007-2019.

Diabetic ketoacidosis (DKA) is a common complication of type 1 diabetes mellitus (T1DM). We found that the incidence of DKA was 55.5 per 1000 person-years in US commercially insured patients with T1DM; age-sex-standardized incidence decreased at an average annual rate of 6.1% in 2018-2019 after a steady increase since 2011.

Development and validation of a predictive model algorithm to identify anaphylaxis in adults with type 2 diabetes in U.S. administrative claims data.

PURPOSE: To use medical record adjudication and predictive modeling methods to develop and validate an algorithm to identify anaphylaxis among adults with type 2 diabetes (T2D) in administrative claims. METHODS: A conventional screening algorithm that prioritized sensitivity to identify potential anaphylaxis cases was developed and consisted of diagnosis codes for anaphylaxis or relevant signs and symptoms. This algorithm was applied to adults with T2D in the HealthCore Integrated Research Database (HIRD) from 2016 to 2018. Clinical experts adjudicated anaphylaxis case status from redacted medical records. We used confirmed case status as an outcome for predictive models developed using lasso regression with 10-fold cross-validation to identify predictors and estimate the probability of confirmed anaphylaxis. RESULTS: Clinical adjudicators reviewed medical records with sufficient information from 272 adults identified by the anaphylaxis screening algorithm, which had an estimated Positive Predictive Value (PPV) of 65% (95% confidence interval [CI]: 60%-71%). The predictive model algorithm had a c-statistic of 0.95. The model's probability threshold of 0.60 excluded 89% (84/94) of false positives identified by the screening algorithm, with a PPV of 94% (95% CI: 91%-98%). The model excluded very few true positives (15 of 178), and identified 92% (95% CI: 87%-96%) of the cases selected by the screening algorithm. CONCLUSIONS: Predictive modeling techniques yielded an accurate algorithm with high PPV and sensitivity for identifying anaphylaxis in administrative claims. This algorithm could be considered in future safety studies using similar claims data to reduce potential outcome misclassification.

Evaluating the Risk of Digitalis Intoxication Associated With Concomitant Use of Dronedarone and Digoxin Using Real-World Data.

PURPOSE: Dronedarone may increase digoxin plasma levels through inhibition of P-glycoprotein. Using real-world data, we evaluated the risk of digitalis intoxication in concomitant users of dronedarone and digoxin compared digoxin-alone users. METHODS: We used the Clinformatics DataMart, a US claims database, to identify adult patients with atrial fibrillation (AF) or atrial flutter (AFL) who concomitantly used dronedarone and digoxin and those who used digoxin alone between July 2009 and March 2016. Digitalis intoxication during follow-up until March 2016 was ascertained using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Adjusted hazard ratios (HR) for digitalis intoxication in concomitant users versus users of digoxin alone were estimated, controlling for age, sex, cohort entry year, number of medical encounters for AF or AFL, history of congestive heart failure, diabetes, hypertension, stroke, myocardial infarction, renal failure, use of drugs interacting with digoxin, and digoxin dose. FINDINGS: Overall, 524 concomitant users and 32,459 users of digoxin alone were identified, among which 3 and 301 events of digitalis intoxication occurred during follow-up, respectively. Incidence rates were 17.25 and 9.17 cases per 1000 person-years, respectively. The adjusted HR for digitalis intoxication in concomitant users versus users of digoxin alone was 1.56 (95% CI, 0.50-4.88; P = 0.45). When digitalis intoxication was defined by ICD-9-CM and ICD-10-CM codes accompanied by laboratory testing for digoxin/digitoxin or hospitalization within 30 days, no events occurred in the concomitant users and 40 events occurred in the users of digoxin alone (incidence rate of 1.22 cases per 1000 person-years). IMPLICATIONS: Concomitant use of dronedarone and digoxin was uncommon in this study, and no significant increase in the risk of digitalis intoxication with concomitant use was found.

Risk of interstitial lung disease in patients treated for atrial fibrillation with dronedarone versus other antiarrhythmics.

PURPOSE: To compare risks of interstitial lung disease (ILD) between patients treated with dronedarone versus other antiarrhythmics. METHODS: Parallel retrospective cohort studies were conducted in the United States Department of Defense Military Health System database (DoD) and the HealthCore Integrated Research Database (HIRD). Study patients were treated for atrial fibrillation (AF) with dronedarone, amiodarone, sotalol, or flecainide. Propensity score matching was employed to create analysis cohorts balanced on baseline variables considered potential confounders of treatment decisions. The study period of July 20, 2008 through September 30, 2014 included a 1-year baseline and minimum 6 months of follow-up, for patients with drugs dispensed between July 20, 2009 and March 31, 2014. Suspect ILD outcomes were reviewed by independent adjudicators. Cox proportional hazards regression compared risk of confirmed ILD between dronedarone and each comparator cohort. A sensitivity analysis examined the effect of broadening the outcome definition. RESULTS: A total 72 ILD cases (52 DoD; 20 HIRD) were confirmed among 27 892 patients. ILD risk was significantly higher among amiodarone than dronedarone initiators in DoD (HR = 2.5; 95% CI = 1.1-5.3, p = 0.02). No difference was detected in HIRD (HR = 1.0; 95% CI = 0.4-2.4). Corresponding risks in sotalol and flecainide exposure groups did not differ significantly from dronedarone in either database. CONCLUSIONS: ILD risk among AF patients initiated on dronedarone therapy was comparable to or lower than that of amiodarone initiators, and similar to that of new sotalol or flecainide users. This finding suggests that elevated ILD risk associated with amiodarone does not necessarily extend to dronedarone or other antiarrhythmic drugs.

Performance assessment of different machine learning approaches in predicting diabetic ketoacidosis in adults with type 1 diabetes using electronic health records data.

PURPOSE: To assess the performance of different machine learning (ML) approaches in identifying risk factors for diabetic ketoacidosis (DKA) and predicting DKA. METHODS: This study applied flexible ML (XGBoost, distributed random forest [DRF] and feedforward network) and conventional ML approaches (logistic regression and least absolute shrinkage and selection operator [LASSO]) to 3400 DKA cases and 11 780 controls nested in adults with type 1 diabetes identified from Optum® de-identified Electronic Health Record dataset (2007-2018). Area under the curve (AUC), accuracy, sensitivity and specificity were computed using fivefold cross validation, and their 95% confidence intervals (CI) were established using 1000 bootstrap samples. The importance of predictors was compared across these models. RESULTS: In the training set, XGBoost and feedforward network yielded higher AUC values (0.89 and 0.86, respectively) than logistic regression (0.83), LASSO (0.83) and DRF (0.81). However, the AUC values were similar (0.82) among these approaches in the test set (95% CI range, 0.80-0.84). While the accuracy values >0.8 and the specificity values >0.9 for all models, the sensitivity values were only 0.4. The differences in these metrics across these models were minimal in the test set. All approaches selected some known risk factors for DKA as the top 10 features. XGBoost and DRF included more laboratory measurements or vital signs compared with conventional ML approaches, while feedforward network included more social demographics. CONCLUSIONS: In our empirical study, all ML approaches demonstrated similar performance, and identified overlapping, but different, top 10 predictors. The difference in selected top predictors needs further research.

Structured benefit-risk evaluation for medicinal products: review of quantitative benefit-risk assessment findings in the literature.

A favorable benefit-risk profile remains an essential requirement for marketing authorization of medicinal drugs and devices. Furthermore, prior subjective, implicit and inconsistent ad hoc benefit-risk assessment methods have rightly evolved towards more systematic, explicit or "structured" approaches. Contemporary structured benefit-risk evaluation aims at providing an objective assessment of the benefit-risk profile of medicinal products and a higher transparency for decision making purposes. The use of a descriptive framework should be the preferred starting point for a structured benefit-risk assessment. In support of more precise assessments, quantitative and semi-quantitative methodologies have been developed and utilized to complement descriptive or qualitative frameworks in order to facilitate the structured evaluation of the benefit-risk profile of medicinal products. In addition, quantitative structured benefit-risk analysis allows integration of patient preference data. Collecting patient perspectives throughout the medical product development process has become increasingly important and key to the regulatory decision-making process. Both industry and regulatory authorities increasingly rely on descriptive structured benefit-risk evaluation and frameworks in drug, vaccine and device evaluation and comparison. Although varied qualitative methods are more commonplace, quantitative approaches have recently been emphasized. However, it is unclear how frequently these quantitative frameworks have been used by pharmaceutical companies to support submission dossiers for drug approvals or to respond to the health authorities' requests. The objective of this study has been to identify and review, for the first time, currently available, published, structured, quantitative benefit-risk evaluations which may have informed health care professionals and/or payor as well as contributed to decision making purposes in the regulatory setting for drug, vaccine and/or device approval. PLAIN LANGUAGE SUMMARY: Quantitative evaluation of the benefit-risk balance for medicinal products The review of the benefits and the risks associated with a medicinal product is called benefit-risk assessment. One of the conditions for a medicinal product to receive marketing authorization is to demonstrate a positive benefit-risk balance in which the benefits outweigh the risks. In order to enhance the transparency and consistency in the assessment of benefit-risk balance, frameworks and quantitative methods have been developed for decision making purposes and regulatory approvals of medicinal products. This article considers published quantitative benefit-risk evaluations which may have informed health care professionals and/or payor as well as contributed to decision making purposes in the regulatory setting for drug, vaccine and/or device approval.

Age- and sex-specific incidence of non-traumatic lower limb amputation in patients with type 2 diabetes mellitus in a U.S. claims database.

AIM: To estimate age- and sex-specific incidence rates (IRs) of non-traumatic lower limb amputations (LLA) in patients with type 2 diabetes mellitus (T2DM) using a claims database from the United States (US). METHODS: Patients with T2DM 18 years and older were identified using the Truven Health MarketScan database from January 1, 2007 to September 30, 2018. The overall and age- and sex-specific IRs of all non-traumatic LLA, minor LLA (amputation at or below the ankle), and major LLA (amputation above ankle) were calculated. RESULTS: Among the 6,117,981 patients with T2DM, 14,627 LLA events occurred (minor LLA; 72.8%; major LLA: 27.2%). The IRs (95% CI) of all LLA, minor LLA, and major LLA per 1000 person-years or PY were 0.86 (0.85, 0.88), 0.63 (0.62, 0.64), and 0.23 (0.23, 0.24), respectively. The IR (95% CI) of all LLA per 1000 PY in males was higher compared to females [1.24 (1.22, 1.26) vs. 0.46 (0.45, 0.48)]. The incidence of all LLA increased with an increasing age (highest IR in age-group of ≥80 years). CONCLUSIONS: This study identified males and older patients with T2DM at higher risk of developing LLA in the US, warranting further exploration of risk factors of LLA in these subgroups.

Appraising patient preference methods for decision-making in the medical product lifecycle: an empirical comparison.

BACKGROUND: Incorporating patient preference (PP) information into decision-making has become increasingly important to many stakeholders. However, there is little guidance on which patient preference assessment methods, including preference exploration (qualitative) and elicitation (quantitative) methods, are most suitable for decision-making at different stages in the medical product lifecycle (MPLC). This study aimed to use an empirical approach to assess which attributes of PP assessment methods are most important, and to identify which methods are most suitable, for decision-makers' needs during different stages in the MPLC. METHODS: A four-step cumulative approach was taken: 1) Identify important criteria to appraise methods through a Q-methodology exercise, 2) Determine numerical weights to ascertain the relative importance of each criterion through an analytical hierarchy process, 3) Assess the performance of 33 PP methods by applying these weights, consulting international health preference research experts and review of literature, and 4) Compare and rank the methods within taxonomy groups reflecting their similar techniques to identify the most promising methods. RESULTS: The Q-methodology exercise was completed by 54 stakeholders with PP study experience, and the analytical hierarchy process was completed by 85 stakeholders with PP study experience. Additionally, 17 health preference research experts were consulted to assess the performance of the PP methods. Thirteen promising preference exploration and elicitation methods were identified as likely to meet decision-makers' needs. Additionally, eight other methods that decision-makers might consider were identified, although they appeared appropriate only for some stages of the MPLC. CONCLUSIONS: This transparent, weighted approach to the comparison of methods supports decision-makers and researchers in selecting PP methods most appropriate for a given application.

Design, Conduct, and Use of Patient Preference Studies in the Medical Product Life Cycle: A Multi-Method Study.

Objectives: To investigate stakeholder perspectives on how patient preference studies (PPS) should be designed and conducted to allow for inclusion of patient preferences in decision-making along the medical product life cycle (MPLC), and how patient preferences can be used in such decision-making. Methods: Two literature reviews and semi-structured interviews (n = 143) with healthcare stakeholders in Europe and the US were conducted; results of these informed the design of focus group guides. Eight focus groups were conducted with European patients, industry representatives and regulators, and with US regulators and European/Canadian health technology assessment (HTA) representatives. Focus groups were analyzed thematically using NVivo. Results: Stakeholder perspectives on how PPS should be designed and conducted were as follows: 1) study design should be informed by the research questions and patient population; 2) preferred treatment attributes and levels, as well as trade-offs among attributes and levels should be investigated; 3) the patient sample and method should match the MPLC phase; 4) different stakeholders should collaborate; and 5) results from PPS should be shared with relevant stakeholders. The value of patient preferences in decision-making was found to increase with the level of patient preference sensitivity of decisions on medical products. Stakeholders mentioned that patient preferences are hardly used in current decision-making. Potential applications for patient preferences across industry, regulatory and HTA processes were identified. Four applications seemed most promising for systematic integration of patient preferences: 1) benefit-risk assessment by industry and regulators at the marketing-authorization phase; 2) assessment of major contribution to patient care by European regulators; 3) cost-effectiveness analysis; and 4) multi criteria decision analysis in HTA. Conclusions: The value of patient preferences for decision-making depends on the level of collaboration across stakeholders; the match between the research question, MPLC phase, sample, and preference method used in PPS; and the sensitivity of the decision regarding a medical product to patient preferences. Promising applications for patient preferences should be further explored with stakeholders to optimize their inclusion in decision-making.

Factors and Situations Affecting the Value of Patient Preference Studies: Semi-Structured Interviews in Europe and the US.

Objectives: Patient preference information (PPI) is gaining recognition among the pharmaceutical industry, regulatory authorities, and health technology assessment (HTA) bodies/payers for use in assessments and decision-making along the medical product lifecycle (MPLC). This study aimed to identify factors and situations that influence the value of patient preference studies (PPS) in decision-making along the MPLC according to different stakeholders. Methods: Semi-structured interviews (n = 143) were conducted with six different stakeholder groups (physicians, academics, industry representatives, regulators, HTA/payer representatives, and a combined group of patients, caregivers, and patient representatives) from seven European countries (the United Kingdom, Sweden, Italy, Romania, Germany, France, and the Netherlands) and the United States. Framework analysis was performed using NVivo 11 software. Results: Fifteen factors affecting the value of PPS in the MPLC were identified. These are related to: study organization (expertise, financial resources, study duration, ethics and good practices, patient centeredness), study design (examining patient and/or other preferences, ensuring representativeness, matching method to research question, matching method to MPLC stage, validity and reliability, cognitive burden, patient education, attribute development), and study conduct (patients' ability/willingness to participate and preference heterogeneity). Three types of situations affecting the use of PPS results were identified (stakeholder acceptance, market situations, and clinical situations). Conclusion: The factors and situation types affecting the value of PPS, as identified in this study, need to be considered when designing and conducting PPS in order to promote the integration of PPI into decision-making along the MPLC.

Opportunities and challenges for the inclusion of patient preferences in the medical product life cycle: a systematic review.

BACKGROUND: The inclusion of patient preferences (PP) in the medical product life cycle is a topic of growing interest to stakeholders such as academics, Health Technology Assessment (HTA) bodies, reimbursement agencies, industry, patients, physicians and regulators. This review aimed to understand the potential roles, reasons for using PP and the expectations, concerns and requirements associated with PP in industry processes, regulatory benefit-risk assessment (BRA) and marketing authorization (MA), and HTA and reimbursement decision-making. METHODS: A systematic review of peer-reviewed and grey literature published between January 2011 and March 2018 was performed. Consulted databases were EconLit, Embase, Guidelines International Network, PsycINFO and PubMed. A two-step strategy was used to select literature. Literature was analyzed using NVivo (QSR international). RESULTS: From 1015 initially identified documents, 72 were included. Most were written from an academic perspective (61%) and focused on PP in BRA/MA and/or HTA/reimbursement (73%). Using PP to improve understanding of patients' valuations of treatment outcomes, patients' benefit-risk trade-offs and preference heterogeneity were roles identified in all three decision-making contexts. Reasons for using PP relate to the unique insights and position of patients and the positive effect of including PP on the quality of the decision-making process. Concerns shared across decision-making contexts included methodological questions concerning the validity, reliability and cognitive burden of preference methods. In order to use PP, general, operational and quality requirements were identified, including recognition of the importance of PP and ensuring patient understanding in PP studies. CONCLUSIONS: Despite the array of opportunities and added value of using PP throughout the different steps of the MPLC identified in this review, their inclusion in decision-making is hampered by methodological challenges and lack of specific guidance on how to tackle these challenges when undertaking PP studies. To support the development of such guidance, more best practice PP studies and PP studies investigating the methodological issues identified in this review are critically needed.

Benefit-risk evaluation: the past, present and future.

In the last two decades there has been a shift in the approach to evaluating the benefit-risk (BR) profiles of medicinal products from an unstructured, subjective, and inconsistent, to a more structured and objective, process. This article describes that shift from a historical perspective; the past, the present, and the future, and highlights key events that played critical roles in changing the field.

Comparison of text processing methods in social media-based signal detection.

PURPOSE: Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co-occurrence-based ML methods are rather basic, as they identify simultaneous appearance of drugs and clinical events in a single post. In contrast, statistical learning methods involve more complex NLP and identify drugs, events, and associations between them. We aimed to compare the ability of co-occurrence and NLP to identify AEs and signals of disproportionate reporting (SDR) in patient-generated SM. We also examined the performance of lift in SM-based signal detection (SD). METHODS: Our examination was performed in a corpus of SM posts crawled from open online patient forums and communities, using the spontaneously reported VigiBase data as reference data set. RESULTS: We found that co-occurrence and NLP produce AEs, which are 57% and 93% consistent with VigiBase AEs, respectively. Among the SDRs identified both in SM and in VigiBase, up to 55.3% were identified earlier in co-occurrence, and up to 32.1% were identified earlier in NLP-processed SM. Using lift in SM SD provided performance similar to frequentist methods, both in co-occurrence and in NLP-processed AEs. CONCLUSION: Our results indicate that using SM as a data source complementary to traditional pharmacovigilance sources should be considered further. Various levels of SM processing may be considered, depending on the preferred policies and tolerance for false-positive to false-negative balance in routine pharmacovigilance processes.

Patient Preferences in the Medical Product Life Cycle: What do Stakeholders Think? Semi-Structured Qualitative Interviews in Europe and the USA.

BACKGROUND: Patient preferences (PP), which are investigated in PP studies using qualitative or quantitative methods, are a growing area of interest to the following stakeholders involved in the medical product lifecycle: academics, health technology assessment bodies, payers, industry, patients, physicians, and regulators. However, the use of PP in decisions along the medical product lifecycle remains limited. As the adoption of PP heavily relies on these stakeholders, knowledge of their perceptions of PP is critical. OBJECTIVE: This study aimed to characterize stakeholders' attitudes, needs, and concerns with respect to PP in decision making along the medical product lifecycle. METHODS: Semi-structured interviews (n = 143) were conducted with academics (n = 24), health technology assessment/payer representatives (n = 24), industry representatives (n = 24), patients, caregivers and patient representatives (n = 24), physicians (n = 24), and regulators (n = 23) from seven European countries and the USA. Interviews were conducted between April and August 2017. The framework method was used to organize the data and identify themes and key findings in each interviewed stakeholder group. RESULTS: Interviewees reported being unfamiliar (43%), moderately familiar (42%), or very familiar (15%) with preference methods and studies. Interviewees across stakeholder groups generally supported the idea of using PP in the medical product lifecycle but expressed mixed opinions about the feasibility and impact of using PP in decision making. Interviewees from all stakeholder groups stressed the importance of increasing stakeholders' understanding of the concept of PP and preference methods and ensuring patients' understanding of the questions asked in PP studies. Key concerns and needs in each interviewed stakeholder group were as follows: (1) academics: investigating the validity, reliability, reproducibility, and generalizability of preference methods; (2) health technology assessment/payer representatives: developing quality criteria for evaluating PP studies and gaining insights into how to weigh them in reimbursement/payer decision making; (3) industry representatives: obtaining guidance on PP studies and recognition on the importance of PP from decision makers; (4) patients, caregivers, and patient representatives: providing an incentive and adequate information towards patients when participating in PP studies; (5) physicians: avoiding bias as a result of commercial agendas in PP studies and clarifying how to deal with subjective and emotional elements when measuring PP; and (6) regulators: avoiding the misuse of PP study results to overrule the traditional efficacy and safety criteria used for marketing authorization and obtaining robust PP study results. CONCLUSIONS: Despite the interest all interviewed stakeholder groups reported in PP, the effective use of PP in decision making across the medical product lifecycle is currently hampered by a lack of standardization and consensus on how to both measure and use PP.

Methods for exploring and eliciting patient preferences in the medical product lifecycle: a literature review.

Preference studies are becoming increasingly important within the medical product decision-making context. Currently, there is limited understanding of the range of methods to gain insights into patient preferences. We developed a compendium and taxonomy of preference exploration (qualitative) and elicitation (quantitative) methods by conducting a systematic literature review to identify these methods. This review was followed by analyzing prior preference method reviews, to cross-validate our results, and consulting intercontinental experts, to confirm our outcomes. This resulted in the identification of 32 unique preference methods. The developed compendium and taxonomy can serve as an important resource for assessing these methods and helping to determine which are most appropriate for different research questions at varying points in the medical product lifecycle.

Proximal HbA1C Level and First Hypoglycemia Hospitalization in Adults With Incident Type 2 Diabetes.

CONTEXT: Hemoglobin A1C (HbA1C) is an important predictor of severe hypoglycemia. OBJECTIVE: To determine the association of proximal HbA1C level with first hypoglycemia hospitalization (HH) in adults with incident type 2 diabetes (T2D). DESIGN, SETTING, AND PARTICIPANTS: A nested case-control study was designed using linked data from the Clinical Practice Research Datalink and Hospital Episode Statistics in England in 1997 to 2014. The first hypoglycemia event as primary diagnosis for hospitalization after T2D diagnosis was identified. Proximal HbA1C was measured within 90 days before the first HH. MAIN OUTCOME MEASURE: OR for developing HH. RESULTS: The association of proximal HbA1C level with first HH was similar between HbA1C levels of 6.0% (OR, 1.54; 95% CI, 1.12 to 2.11) and 9.0% [1.48 (1.01 to 2.17)] compared with the reference HbA1C level of 7.0%. For proximal HbA1C level of 4.0% to 6.5%, every additional 0.5% increase in HbA1C was associated with lower first HH risk, with ORs (95% CI) ranging between 0.37 (0.20 to 0.67) and 0.86 (0.76 to 0.98). For proximal HbA1C level of 8.0% to 11.5%, every additional 0.5% increase in HbA1C was associated with higher first HH risk, with ORs (95% CI) ranging between 1.16 (1.04 to 1.29) and 1.34 (1.18 to 1.52). The U-shaped association between proximal HbA1C level and first HH did not exist among current sulfonylurea users but persisted among current insulin users (Pinteraction = 0.002). Among current noninsulin nonsulfonylurea users who had a first HH, 78% took insulin or sulfonylureas before the HH. CONCLUSIONS: Having either poor or near-normal HbA1C was associated with a higher risk of first HH within 3 months in T2D.