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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.

The risk of acute liver injury associated with the use of antibiotics--evaluating robustness of results in the pharmacoepidemiological research on outcomes of therapeutics by a European consortium (PROTECT) project.

PURPOSE: To examine the robustness of findings of case-control studies on the association between acute liver injury (ALI) and antibiotic use in the following different situations: (i) Replication of a protocol in different databases, with different data types, as well as replication in the same database, but performed by a different research team. (ii) Varying algorithms to identify cases, with and without manual case validation. (iii) Different exposure windows for time at risk. METHODS: Five case-control studies in four different databases were performed with a common study protocol as starting point to harmonize study outcome definitions, exposure definitions and statistical analyses. RESULTS: All five studies showed an increased risk of ALI associated with antibiotic use ranging from OR 2.6 (95% CI 1.3-5.4) to 7.7 (95% CI 2.0-29.3). Comparable trends could be observed in the five studies: (i) without manual validation the use of the narrowest definition for ALI showed higher risk estimates, (ii) narrow and broad algorithm definitions followed by manual validation of cases resulted in similar risk estimates, and (iii) the use of a larger window (30 days vs 14 days) to define time at risk led to a decrease in risk estimates. CONCLUSIONS: Reproduction of a study using a predefined protocol in different database settings is feasible, although assumptions had to be made and amendments in the protocol were inevitable. Despite differences, the strength of association was comparable between the studies. In addition, the impact of varying outcome definitions and time windows showed similar trends within the data sources.

Recommendations for benefit-risk assessment methodologies and visual representations.

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.

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

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.

Benefit-risk assessment in a post-market setting: a case study integrating real-life experience into benefit-risk methodology.

PURPOSE: Difficulties may be encountered when undertaking a benefit-risk assessment for an older product with well-established use but with a benefit-risk balance that may have changed over time. This case study investigates this specific situation by applying a formal benefit-risk framework to assess the benefit-risk balance of warfarin for primary prevention of patients with atrial fibrillation. METHODS: We used the qualitative framework BRAT as the starting point of the benefit-risk analysis, bringing together the relevant available evidence. We explored the use of a quantitative method (stochastic multi-criteria acceptability analysis) to demonstrate how uncertainties and preferences on multiple criteria can be integrated into a single measure to reduce cognitive burden and increase transparency in decision making. RESULTS: Our benefit-risk model found that warfarin is favourable compared with placebo for the primary prevention of stroke in patients with atrial fibrillation. This favourable benefit-risk balance is fairly robust to differences in preferences. The probability of a favourable benefit-risk for warfarin against placebo is high (0.99) in our model despite the high uncertainty of randomised clinical trial data. In this case study, we identified major challenges related to the identification of relevant benefit-risk criteria and taking into account the diversity and quality of evidence available to inform the benefit-risk assessment. CONCLUSION: The main challenges in applying formal methods for medical benefit-risk assessment for a marketed drug are related to outcome definitions and data availability. Data exist from many different sources (both randomised clinical trials and observational studies), and the variability in the studies is large.

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.

Predicting the risk of end-stage renal disease in the population-based setting: a retrospective case-control study.

BACKGROUND: Previous studies of predictors of end-stage renal disease (ESRD) have limitations: (1) some focused on patients with clinically recognized chronic kidney disease (CKD); (2) others identified population-based patients who developed ESRD, but lacked earlier baseline clinical measures to predict ESRD. Our study was designed to address these limitations and to identify the strength and precision of characteristics that might predict ESRD pragmatically for decision-makers--as measured by the onset of renal replacement therapy (RRT). METHODS: We conducted a population-based, retrospective case-control study of patients who developed ESRD and started RRT. We conducted the study in a health maintenance organization, Kaiser Permanente Northwest (KPNW). The case-control study was nested within the adult population of KPNW members who were enrolled during 1999, the baseline period. Cases and their matched controls were identified from January 2000 through December 2004. We evaluated baseline clinical characteristics measured during routine care by calculating the adjusted odds ratios and their 95% confidence intervals after controlling for matching characteristics: age, sex, and year. RESULTS: The rate of RRT in the cohort from which we sampled was 58 per 100,000 person-years (95% CI, 53 to 64). After excluding patients with missing data, we analyzed 350 cases and 2,114 controls. We identified the following characteristics that predicted ESRD with odds ratios ≥ 2.0: eGFR<60 mL/min/1.73 m(2) (OR = 20.5; 95% CI, 11.2 to 37.3), positive test for proteinuria (OR = 5.0; 95% CI, 3.5 to 7.1), hypertension (OR = 4.5; 95% CI, 2.5 to 8.0), gout/positive test for uric acid (OR = 2.5; 95% CI, 1.8 to 3.5), peripheral vascular disease (OR = 2.2; 95% CI, 1.4 to 3.6), congestive heart failure (OR = 2.1; 95% CI, 1.4 to 3.3), and diabetes (OR = 2.1; 95% CI, 1.5 to 2.9). CONCLUSIONS: The clinical characteristics needed to predict ESRD--for example, to develop a population-based, prognostic risk score--were often documented during routine care years before patients developed ESRD and required RRT.

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.

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.

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.

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.