Publications by authors named "Juhaeri Juhaeri"

42 Publications

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

J Diabetes Complications 2021 07 20;35(7):107932. Epub 2021 Apr 20.

Epidemiology & Benefit-Risk Evaluation, Sanofi, 55 Corporate Drive, Bridgewater, NJ 08807, USA.

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.
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http://dx.doi.org/10.1016/j.jdiacomp.2021.107932DOI Listing
July 2021

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

Pharmacoepidemiol Drug Saf 2021 07 5;30(7):918-926. Epub 2021 May 5.

Sanofi, Paris, France.

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.
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http://dx.doi.org/10.1002/pds.5257DOI Listing
July 2021

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

Clin Ther 2021 05 20;43(5):852-858.e2. Epub 2021 Apr 20.

Sanofi, Bridgewater, New Jersey.

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.
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http://dx.doi.org/10.1016/j.clinthera.2021.03.014DOI Listing
May 2021

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

Pharmacoepidemiol Drug Saf 2021 10 4;30(10):1353-1359. Epub 2021 May 4.

Avigilan, LLC, Potomac, Maryland, USA.

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.
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http://dx.doi.org/10.1002/pds.5233DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453764PMC
October 2021

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

Pharmacoepidemiol Drug Saf 2021 05 3;30(5):610-618. Epub 2021 Feb 3.

Sanofi U.S. LLC, Bridgewater, New Jersey, USA.

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.
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http://dx.doi.org/10.1002/pds.5199DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049019PMC
May 2021

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

Ther Adv Drug Saf 2020 8;11:2042098620976951. Epub 2020 Dec 8.

Global Epidemiology & Benefit-Risk Evaluation, Sanofi, Chilly-Mazarin, France.

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: 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.
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http://dx.doi.org/10.1177/2042098620976951DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7727082PMC
December 2020

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

Diabetes Res Clin Pract 2020 Nov 17;169:108452. Epub 2020 Sep 17.

Epidemiology and Benefit Risk, Sanofi U.S., 55 Corporate Drive, Bridgewater, NJ 08807, USA.

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.
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http://dx.doi.org/10.1016/j.diabres.2020.108452DOI Listing
November 2020

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

BMC Med Inform Decis Mak 2020 06 19;20(1):114. Epub 2020 Jun 19.

Erasmus School of Health Policy & Management and Erasmus Choice Modelling Centre, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands.

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.
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http://dx.doi.org/10.1186/s12911-020-01142-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304129PMC
June 2020

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

Front Pharmacol 2019 3;10:1395. Epub 2019 Dec 3.

Clinical Pharmacology and Pharmacotherapy, KU Leuven, Leuven, Belgium.

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. 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. 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. 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.
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http://dx.doi.org/10.3389/fphar.2019.01395DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6902285PMC
December 2019

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

Front Pharmacol 2019 18;10:1009. Epub 2019 Sep 18.

Erasmus School of Health Policy & Management and Erasmus Choice Modelling Centre, Erasmus University, Rotterdam, Netherlands.

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. 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. 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). 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.
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http://dx.doi.org/10.3389/fphar.2019.01009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6759933PMC
September 2019

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

BMC Med Inform Decis Mak 2019 10 4;19(1):189. Epub 2019 Oct 4.

Erasmus School of Health Policy & Management (ESHPM) and Erasmus Choice Modelling Centre (ECMC), Erasmus University Rotterdam, P.O. Box 1738, 3000, DR, Rotterdam, The Netherlands.

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.
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http://dx.doi.org/10.1186/s12911-019-0875-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778383PMC
October 2019

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

Authors:
Juhaeri Juhaeri

Ther Adv Drug Saf 2019 26;10:2042098619871180. Epub 2019 Aug 26.

Sanofi, Bridgewater, 55 Corporate Drive, Bridgewater, NJ 08807, USA.

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.
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http://dx.doi.org/10.1177/2042098619871180DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712756PMC
August 2019

Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance.

Ther Adv Drug Saf 2019 5;10:2042098619864744. Epub 2019 Aug 5.

Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.

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http://dx.doi.org/10.1177/2042098619864744DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683315PMC
August 2019

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

Pharmacoepidemiol Drug Saf 2019 10 7;28(10):1309-1317. Epub 2019 Aug 7.

Epidemiology and Benefit Risk Evaluation, Sanofi, Bridgewater, NJ, USA.

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.
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http://dx.doi.org/10.1002/pds.4857DOI Listing
October 2019

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

Patient 2019 10;12(5):513-526

Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Herestraat 49, Box 521, 3000, Leuven, Belgium.

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.
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http://dx.doi.org/10.1007/s40271-019-00367-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697755PMC
October 2019

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

Drug Discov Today 2019 07 8;24(7):1324-1331. Epub 2019 May 8.

Erasmus School of Health Policy & Management, Erasmus University Rotterdam, P.O. Box 1738, Rotterdam, 3000 DR the Netherlands; Erasmus Choice Modelling Centre, Erasmus University Rotterdam, P.O. Box 1738, Rotterdam, 3000 DR the Netherlands. Electronic address:

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.
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http://dx.doi.org/10.1016/j.drudis.2019.05.001DOI Listing
July 2019

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

J Clin Endocrinol Metab 2019 06;104(6):1989-1998

Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

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.
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http://dx.doi.org/10.1210/jc.2018-01402DOI Listing
June 2019

Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis.

J Med Internet Res 2018 11 20;20(11):e10466. Epub 2018 Nov 20.

Epidemiology and Benefit Risk Evaluation, Sanofi, Bridgewater, NJ, United States.

Background: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs).

Objective: This study aimed (1) to assess the consistency of SDRs detected from patients' medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems.

Methods: Messages posted on patients' forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided.

Results: The comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase.

Conclusions: The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients' medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals.
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http://dx.doi.org/10.2196/10466DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6280030PMC
November 2018

Factors and situations influencing the value of patient preference studies along the medical product lifecycle: a literature review.

Drug Discov Today 2019 01 26;24(1):57-68. Epub 2018 Sep 26.

Clinical Pharmacology and Pharmacotherapy, University of Leuven, Herestraat 49 Box 521, 3000 Leuven, Belgium.

Industry, regulators, health technology assessment (HTA) bodies, and payers are exploring the use of patient preferences in their decision-making processes. In general, experience in conducting and assessing patient preference studies is limited. Here, we performed a systematic literature search and review to identify factors and situations influencing the value of patient preference studies, as well as applications throughout the medical product lifecyle. Factors and situations identified in 113 publications related to the organization, design, and conduct of studies, and to communication and use of results. Although current use of patient preferences is limited, we identified possible applications in discovery, clinical development, marketing authorization, HTA, and postmarketing phases.
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http://dx.doi.org/10.1016/j.drudis.2018.09.015DOI Listing
January 2019

Assessing the Risk for Peripheral Neuropathy in Patients Treated With Dronedarone Compared With That in Other Antiarrhythmics.

Clin Ther 2018 03 28;40(3):450-455.e1. Epub 2018 Feb 28.

Sanofi, Bridgewater, New Jersey.

Purpose: There are few data on the risk for peripheral neuropathy associated with dronedarone, a newer antiarrhythmic medicine. The objective of this study was to assess whether dronedarone is potentially associated with an increased risk for peripheral neuropathy compared with other antiarrhythmics, including amiodarone, sotalol, flecainide, and propafenone.

Methods: The MarketScan database was used for identifying patients who were at least 18 years of age, had atrial fibrillation or flutter, and had not been diagnosed with peripheral neuropathy in the 180-day period prior to or on the date of the first prescription of an antiarrhythmic between July 20, 2009, and December 31, 2011. Peripheral neuropathy that occurred during the treatment period for a study drug was ascertained using ICD-9-CM diagnostic codes. For each antiarrhythmic, the incidence rate of peripheral neuropathy was calculated. The adjusted hazard ratio (aHR) for peripheral neuropathy for dronedarone compared with another antiarrhythmic was obtained, with control for age, sex, diabetes mellitus status, and the presence of other comorbidities.

Findings: The study population included 106,933 patients treated with dronedarone (n = 12,989), amiodarone (n = 45,173), sotalol (n = 22,036), flecainide (n = 14,244), or propafenone (n = 12,491). The incidence rates (per 1000 person-years) of peripheral neuropathy were 1.33 for dronedarone, 2.38 for amiodarone, 1.20 for sotalol, 1.08 for flecainide, and 1.97 for propafenone. The aHRs for peripheral neuropathy for dronedarone relative to other drugs ranged from 0.53 (95% CI, 0.21-1.34) compared with propafenone, to 0.94 (95% CI, 0.38-2.30) compared with sotalol. A new-user analysis showed similar results.

Implications: The risks for peripheral neuropathy were not significantly different between dronedarone and other antiarrhythmics.
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http://dx.doi.org/10.1016/j.clinthera.2018.01.015DOI Listing
March 2018

Trends in Hospital Admission for Diabetic Ketoacidosis in Adults With Type 1 and Type 2 Diabetes in England, 1998-2013: A Retrospective Cohort Study.

Diabetes Care 2018 09 31;41(9):1870-1877. Epub 2018 Jan 31.

Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC

Objective: This study determined trends in hospital admission for diabetic ketoacidosis (DKA) in adults with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) from 1998 to 2013 in England.

Research Design And Methods: The study population included 23,246 adults with T1DM and 241,441 adults with T2DM from the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES). All hospital admissions for DKA as the primary diagnosis from 1998 to 2013 were identified. Trends in hospital admission for DKA in incidence, length of hospital stay, 30-day all-cause readmission rate, and 30-day and 1-year all-cause mortality rates were determined using joinpoint regression, negative binomial regression, and logistic regression models.

Results: For T1DM, the incidence of hospital admission for DKA increased between 1998 and 2007 and remained static until 2013. The incidence in 2013 was higher than that in 1998 (incidence rate ratio 1.53 [95% CI 1.09-2.16]). For T2DM, the incidence increased 4.24% (2.82-5.69) annually between 1998 and 2013. The length of hospital stay decreased over time for both diabetes types ( ≤ 0.0004). Adults with T1DM were more likely to be discharged within 2 days compared with adults with T2DM (odds ratio [OR] 1.28 [1.07-1.53]). The 30-day readmission rate was higher in T1DM than in T2DM (OR 1.61 [1.04-2.50]) but remained unchanged for both diabetes types over time. Trends in 30-day and 1-year all-cause mortality rates were also stable, with no difference by diabetes type.

Conclusions: In the previous two decades in England, hospitalization for DKA increased in adults with T1DM and in those with T2DM, and associated health care performance did not improve except decreased length of hospital stay.
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http://dx.doi.org/10.2337/dc17-1583DOI Listing
September 2018

HbA variability and hypoglycemia hospitalization in adults with type 1 and type 2 diabetes: A nested case-control study.

J Diabetes Complications 2018 02 23;32(2):203-209. Epub 2017 Oct 23.

Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA. Electronic address:

Aims: To determine association between HbA variability and hypoglycemia requiring hospitalization (HH) in adults with type 1 diabetes (T1D) and type 2 diabetes (T2D).

Methods: Using nested case-control design in electronic health record data in England, one case with first or recurrent HH was matched to one control who had not experienced HH in incident T1D and T2D adults. HbA variability was determined by standard deviation of ≥3 HbA results. Conditional logistic models were applied to determine association of HbA variability with first and recurrent HH.

Results: In T1D, every 1.0% increase in HbA variability was associated with 90% higher first HH risk (95% CI, 1.25-2.89) and 392% higher recurrent HH risk (95% CI, 1.17-20.61). In T2D, a 1.0% increase in HbA variability was associated with 556% higher first HH risk (95% CI, 3.88-11.08) and 573% higher recurrent HH risk (95% CI,1.59-28.51). In T2D for first HH, the association was the strongest in non-insulin non-sulfonylurea users (P<0.0001); for recurrent HH, the association was stronger in insulin users than sulfonylurea users (P=0.07). The HbA variability-HH association was stronger in more recent years in T2D (P≤0.004).

Conclusions: HbA variability is a strong predictor for HH in T1D and T2D.
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http://dx.doi.org/10.1016/j.jdiacomp.2017.10.008DOI Listing
February 2018

Incidence and Trends in Hypoglycemia Hospitalization in Adults With Type 1 and Type 2 Diabetes in England, 1998-2013: A Retrospective Cohort Study.

Diabetes Care 2017 12 17;40(12):1651-1660. Epub 2017 Jul 17.

Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC

Objective: To determine trends in hospitalization for hypoglycemia in adults with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) in England.

Research Design And Methods: Adults with T1DM or T2DM were identified from 398 of the 684 practices within the Clinical Practice Research Datalink, for which linkage to the Hospital Episode Statistics was possible. Hypoglycemia as the primary reason for hospitalization between 1998 and 2013 was extracted. Trends were estimated using joinpoint regression models for adults with T1DM, young and middle-aged adults with T2DM (18-64 years), and elderly adults with T2DM (≥65 years), respectively.

Results: Among 23,246 adults with T1DM, 1,591 hypoglycemia hospitalizations occurred during 121,262 person-years. Among 241,441 adults with T2DM, 3,738 hypoglycemia hospitalizations occurred during 1,344,818 person-years. In adults with T1DM, the incidence increased 3.74% (95% CI 1.70-5.83) annually from 1998 to 2013. In young and middle-aged adults with T2DM, the annual incidence increase was 4.12% (0.61-7.75) from 1998 to 2013. In elderly adults with T2DM, the incidence increased 8.59% (5.76-11.50) annually from 1998 to 2009, and decreased 8.05% (-14.48 to -1.13) annually from 2009 to 2013, but the incidence was still higher in 2013 than 1998 (adjusted rate ratio 3.01 [1.76-5.14]). Trends in HbA level did not parallel trends of hypoglycemia hospitalization for both diabetes types. A possible reason for declined hypoglycemia trend in 2009-2013 in elderly adults with T2DM may be continuously decreased sulfonylurea use after 2009, which was not seen in young and middle-aged adults with T2DM.

Conclusions: Hypoglycemia requiring hospitalization has been an increasing burden in adults with T1DM and T2DM in England in the previous two decades, with the exception of the decline in elderly adults with T2DM starting in 2009.
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http://dx.doi.org/10.2337/dc16-2680DOI Listing
December 2017

Dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes.

J Diabetes Complications 2017 Aug 20;31(8):1340-1347. Epub 2017 Apr 20.

Department of Nutrition, University of North Carolina, Chapel Hill, NC, USA; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA. Electronic address:

Aims: To determine the association between dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes.

Methods: Type 1 adolescents from a randomized trial wore a blinded continuous glucose monitoring (CGM) system at baseline for one week in free-living conditions. Dietary intake was calculated as the average from two 24-h dietary recalls. Non-severe hypoglycemia was defined as having blood glucose <70mg/dL for ≥10min but not requiring external assistance, categorized as daytime and nocturnal (11PM-7AM). Data were analyzed using logistic regression models.

Results: Among 98 participants with 14,277h of CGM data, 70 had daytime hypoglycemia, 66 had nocturnal hypoglycemia, 55 had both, and 17 had neither. Soluble fiber and protein intake were positively associated with both daytime and nocturnal hypoglycemia. Glycemic index, monounsaturated fat, and polyunsaturated fat were negatively associated with daytime hypoglycemia only. Adjusting for total daily insulin dose per kilogram eliminated all associations.

Conclusions: Dietary intake was differentially associated with daytime and nocturnal hypoglycemia. Over 80% of type 1 adolescents had hypoglycemia in a week, which may be attributed to the mismatch between optimal insulin dose needed for each meal and actually delivered insulin dose without considering quality of carbohydrate and nutrients beyond carbohydrate.

Clinical Trial Registration: ClinicalTrials.gov identifier: NCT01286350.
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http://dx.doi.org/10.1016/j.jdiacomp.2017.04.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5526710PMC
August 2017

Validation of New Signal Detection Methods for Web Query Log Data Compared to Signal Detection Algorithms Used With FAERS.

Drug Saf 2017 05;40(5):399-408

Pharmacoepidemiology, Global Safety Sciences, Sanofi, 55 Corporate Dr., Bridgewater, NJ, 08807, USA.

Introduction: Post-marketing drug surveillance is largely based on signals found in spontaneous reports from patients and healthcare providers. Rare adverse drug reactions and adverse events (AEs) that may develop after long-term exposure to a drug or from drug interactions may be missed. The US FDA and others have proposed that web-based data could be mined as a resource to detect latent signals associated with adverse drug reactions.

Methods: Recently, a web-based search query method called a query log reaction score (QLRS) was developed to detect whether AEs associated with certain drugs could be found from search engine query data. In this study, we compare the performance of two other algorithms, the proportional query ratio (PQR) and the proportional query rate ratio (Q-PRR) against that of two reference signal-detection algorithms (SDAs) commonly used with the FDA AE Reporting System (FAERS) database.

Results: In summary, the web query methods have moderate sensitivity (80%) in detecting signals in web query data compared with reference SDAs in FAERS when the web query data are filtered, but the query metrics generate many false-positives and have low specificity compared with reference SDAs in FAERS.

Conclusion: Future research is needed to find better refinements of query data and/or the metrics to improve the specificity of these web query log algorithms.
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http://dx.doi.org/10.1007/s40264-017-0507-4DOI Listing
May 2017

The US Food and Drug Administration's Risk Evaluation and Mitigation Strategy (REMS) Program - Current Status and Future Direction.

Clin Ther 2016 Dec 30;38(12):2526-2532. Epub 2016 Nov 30.

Global Phamacovigilance and Epidemiology, Sanofi, Bridgewater, New Jersey.

The US Food and Drug Administration (FDA) Amendments Act of 2007 granted the FDA new authorities to enhance drug safety by requiring application holders to submit a proposed Risk Evaluation and Mitigation Strategy (REMS). A REMS is a required risk management plan that uses tools beyond the package insert. REMS elements may include a medication guide and patient package insert for patients and a communication plan focused on health care professionals. Elements to assure safe use (ETASUs) are put in place to mitigate a specific known serious risk when other less restrictive elements of a REMS are not sufficient to mitigate such risk. An implementation system is required for an REMS that includes the ETASUs. With approximately eight years of experience with REMS programs, many health care settings have created systems to manage REMS and also to integrate REMS into their practice settings. At the same time, there are issues associated with the development and implementation of REMS. In 2011, FDA created the REMS Integration Initiative to develop guidance on how to apply statutory criteria to determine when a REMS is required, to improve standardization and assessment of REMS, and to improve integration of REMS into the existing healthcare system. A key component of the REMS Integration Initiative is stakeholder outreach to better understand how existing REMS programs are working and to identify opportunities for improvement. This review attempts to share our company's experience with the REMS program, and to provide updates on FDA's efforts to improve REMS communication, to standardize REMS process, to reduce REMS program burdens and to build a common REMS platform.
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http://dx.doi.org/10.1016/j.clinthera.2016.11.007DOI Listing
December 2016

Intranasal triamcinolone use during pregnancy and the risk of adverse pregnancy outcomes.

J Allergy Clin Immunol 2016 07 1;138(1):97-104.e7. Epub 2016 Apr 1.

Sanofi, R&D, Global Pharmacovigilance and Epidemiology, Bridgewater, NJ.

Background: Intranasal corticosteroid use during pregnancy has increased over the past decade.

Objective: We aim to estimate the safety of intranasal triamcinolone use during pregnancy, which was introduced for over-the-counter use in October 2013.

Methods: We designed a population-based prospective cohort study. From a cohort of 289,723 pregnancies in Montreal, Quebec, Canada, from 1998-2008, intranasal triamcinolone-exposed, other intranasal corticosteroid-exposed, and nonexposed women during the first trimester were studied for major congenital malformations (overall and organ specific) and spontaneous abortions and during the second/third trimesters for small-for-gestational age (SGA) newborns. The first trimester is the time window of interest for malformations and spontaneous abortion (organogenesis), and the second/third trimesters are the time windows of interest for SGA (fetal growth). Logistic regression model-based generalized estimating equations were used.

Results: Adjusting for potential confounders, use of intranasal triamcinolone during the first trimester of pregnancy was not significantly associated with the risk of overall congenital malformations (odds ratio [OR], 0.88; 95% CI, 0.60-1.28; 31 exposed cases) compared with nonexposure; however, it was associated with the risk of respiratory defects (OR, 2.71; 95% CI, 1.11-6.64; 5 exposed cases). Pregnancy exposure to intranasal triamcinolone was not significantly associated with the risk of spontaneous abortion (OR, 1.04; 95% CI, 0.76-1.43; 50 exposed cases). No association was found between second- or third-trimester exposure to intranasal triamcinolone and the risk of SGA (OR, 1.06; 95% CI, 0.79-1.43; 50 exposed cases).

Conclusions: Maternal exposure to intranasal triamcinolone during pregnancy was not associated with the risk of SGA/spontaneous abortions/overall malformations. However, it has been shown to increase the risk of respiratory system defects. Chance finding cannot be ruled out.
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http://dx.doi.org/10.1016/j.jaci.2016.01.021DOI Listing
July 2016

Recommendations for benefit-risk assessment methodologies and visual representations.

Pharmacoepidemiol Drug Saf 2016 Mar 22;25(3):251-62. Epub 2016 Jan 22.

MerckSerono SA, Geneva, Switzerland.

Purpose: The purpose of this study is to draw on the practical experience from the PROTECT BR case studies and make recommendations regarding the application of a number of methodologies and visual representations for benefit-risk assessment.

Methods: Eight case studies based on the benefit-risk balance of real medicines were used to test various methodologies that had been identified from the literature as having potential applications in benefit-risk assessment. Recommendations were drawn up based on the results of the case studies.

Results: A general pathway through the case studies was evident, with various classes of methodologies having roles to play at different stages. Descriptive and quantitative frameworks were widely used throughout to structure problems, with other methods such as metrics, estimation techniques and elicitation techniques providing ways to incorporate technical or numerical data from various sources. Similarly, tree diagrams and effects tables were universally adopted, with other visualisations available to suit specific methodologies or tasks as required. Every assessment was found to follow five broad stages: (i) Planning, (ii) Evidence gathering and data preparation, (iii) Analysis, (iv) Exploration and (v) Conclusion and dissemination.

Conclusions: Adopting formal, structured approaches to benefit-risk assessment was feasible in real-world problems and facilitated clear, transparent decision-making. Prior to this work, no extensive practical application and appraisal of methodologies had been conducted using real-world case examples, leaving users with limited knowledge of their usefulness in the real world. The practical guidance provided here takes us one step closer to a harmonised approach to benefit-risk assessment from multiple perspectives.
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http://dx.doi.org/10.1002/pds.3958DOI Listing
March 2016

Benefit-Risk Assessment, Communication, and Evaluation (BRACE) throughout the life cycle of therapeutic products: overall perspective and role of the pharmacoepidemiologist.

Pharmacoepidemiol Drug Saf 2015 Dec 12;24(12):1233-40. Epub 2015 Oct 12.

Eli Lilly and Company, Indianapolis, IN, USA.

Purpose: Optimizing a therapeutic product's benefit-risk profile is an on-going process throughout the product's life cycle. Different, yet related, benefit-risk assessment strategies and frameworks are being developed by various regulatory agencies, industry groups, and stakeholders. This paper summarizes current best practices and discusses the role of the pharmacoepidemiologist in these activities, taking a life-cycle approach to integrated Benefit-Risk Assessment, Communication, and Evaluation (BRACE).

Methods: A review of the medical and regulatory literature was performed for the following steps involved in therapeutic benefit-risk optimization: benefit-risk evidence generation; data integration and analysis; decision making; regulatory and policy decision making; benefit-risk communication and risk minimization; and evaluation. Feedback from International Society for Pharmacoepidemiology members was solicited on the role of the pharmacoepidemiologist. The case example of natalizumab is provided to illustrate the cyclic nature of the benefit-risk optimization process.

Results: No single, globally adopted benefit-risk assessment process exists. The BRACE heuristic offers a way to clarify research needs and to promote best practices in a cyclic and integrated manner and highlight the critical importance of cross-disciplinary input. Its approach focuses on the integration of BRACE activities for risk minimization and optimization of the benefit-risk profile.

Conclusion: The activities defined in the BRACE heuristic contribute to the optimization of the benefit-risk profile of therapeutic products in the clinical world at both the patient and population health level. With interdisciplinary collaboration, pharmacoepidemiologists are well suited for bringing in methodology expertise, relevant research, and public health perspectives into the BRACE process.
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http://dx.doi.org/10.1002/pds.3859DOI Listing
December 2015
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