Delays in pulmonary decline in eteplirsen-treated patients with Duchenne muscular dystrophy.

INTRODUCTION/AIMS: Pulmonary decline is a major issue in patients with Duchenne muscular dystrophy (DMD). Eteplirsen is a United States-approved treatment for patients with DMD and exon 51 skip-amenable mutations. Previous analyses have shown that eteplirsen is associated with a statistically significant attenuation of pulmonary decline. In this study we evaluate the effect of eteplirsen treatment from newly available data sources on pulmonary function over time in patients with DMD. METHODS: We used a post hoc pooled analysis to compare the percentage of predicted forced vital capacity (FVC%p) and projected time with pulmonary function milestones in patients with DMD and exon 51 skip-amenable mutations receiving eteplirsen (Studies 204 and 301) or standard of care (SoC; Cooperative International Neuromuscular Research Group Duchenne Natural History Study). A mixed model for repeated-measures framework was applied to evaluate the impact of eteplirsen. RESULTS: An average annual rate of FVC%p decline for eteplirsen-treated patients was estimated to be 3.47%, a statistically significant attenuation from the 5.95% rate of decline estimated in SoC patients (P = .0001). Using linear extrapolations of the model-estimated decline in FVC%p, the attenuation in FVC%p decline for eteplirsen-treated patients corresponded to a delay of 5.72 years in time to needing continuous ventilation, 3.31 years in time to needing nighttime ventilation, and 2.11 years in time to needing a cough assist device compared with SoC patients. DISCUSSION: The attenuation of FVC%p decline suggests that eteplirsen-treated patients had statistically significant and clinically meaningful attenuations in pulmonary decline compared with SoC patients.

Detection of spironolactone-associated hyperkalaemia following the Randomized Aldactone Evaluation Study (RALES).

INTRODUCTION: A population-based analysis has suggested that the publication of the RALES (Randomized Aldactone Evaluation Study) in late 1999 was associated with both the wider use of spironolactone to treat heart failure and a corresponding increase in hyperkalaemia-associated morbidity and mortality in patients also being treated with ACE inhibitors. OBJECTIVES: To gain further insight into the reporting of spironolactone-associated hyperkalaemia in an independent dataset by analysing the spontaneous reporting experience in relation to the publication of RALES, and to determine whether the implementation of a commonly used data mining algorithm (DMA) might have directed the attention of safety reviewers to the spironolactone/hyperkalaemia association in advance of epidemiological findings. METHODS: We calculated the reporting rate of spironolactone-associated hyperkalaemia per 1,000 reports per year from 1970 through to the end of 2005 by identifying relevant cases in the US FDA Adverse Event Reporting System. We did this for reports of spironolactone-associated hyperkalaemia (where spironolactone was listed as a suspect drug) and according to whether the reports listed an ACE inhibitor as a co-suspect or concomitant medication. A further statistical analysis of the overall reporting of spironolactone (suspect drug)-associated hyperkalaemia was also performed. We also performed 3-dimensional (3-D; drug-drug-event) disproportionality analyses using a DMA known as the multi-item gamma-Poisson shrinker, which allows the calculation and display of a 3-D disproportionality metric known as the 'interaction signal score' (INTSS). This metric is a measure of the strength of a higher order reporting relationship of a triplet (i.e. drug-drug-event) association above and beyond what would be expected from the largest disproportionalities associated with the individual 2-way associations. RESULTS: Visual inspection of a graph of the reporting frequency of spironolactone (suspect drug)-associated hyperkalaemia per 1,000 reports was highly suggestive of a change point. The t-test on the arcsine-transformed data showed a significant difference in reporting of spironolactone-hyperkalaemia combination through 1999 compared with 2000 onwards (p < 0.001). When examining the reporting time trends according to the presence or absence of an ACE inhibitor, the change point seemed to be mostly attributable to an increase in the number of spironolactone (suspect drug)-associated hyperkalaemia reports with ACE inhibitors listed as a co-suspect drug. No obvious change points in INTSSs for spironolactone-ACE inhibitor-hyperkalaemia reports were observed. DISCUSSION: Although we could not pinpoint the relative contribution of many possible artifacts in the reporting process, as well as increased drug exposure, increased adverse event incidence and/or a change in patient monitoring practices, to our findings, we observed a notable change in reporting frequency of spironolactone-associated hyperkalaemia in temporal proximity to the publication of RALES. Evidence of this was provided by a trend analysis depicted in a simple graph that was supported by statistical analysis. The observed trend was in large part due to increased reporting of spironolactone-associated hyperkalaemia with reported co-medication with ACE inhibitors. CONCLUSION: These findings are consistent with those originally reported in an epidemiological analysis. In this retrospective exercise, a simple graph was more illuminating than more complex data mining analyses.

Economic burden of hypoglycemia with basal insulin in type 2 diabetes.

OBJECTIVES: To assess the impact of hypoglycemia and potential underlying factors of economic burden in patients with type 2 diabetes (T2D) who are initiating basal insulin therapy. STUDY DESIGN: This retrospective cohort study combined commercial insurance and Medicare Advantage data from the Clinformatics Data Mart. METHODS: Adults with T2D on oral antidiabetes drugs initiating basal insulin (n = 18,918) were assessed at baseline (12 months prior to insulin initiation) and follow-up (1 and 2 years). The population was stratified by whether or not patients experienced hypoglycemia during year 1 after insulin initiation. Outcomes included hypoglycemia rate, complications, comorbidities, and adjusted economic burden (primary). RESULTS: There were 1683 (8.9%) patients in the hypoglycemia group and 17,235 (91.1%) in the no-hypoglycemia group. During year 1, the estimated rate of hypoglycemia events was 0.412 per member per year. Baseline hypoglycemia was the strongest predictor of subsequent hypoglycemia. The hypoglycemia group was older, with a significantly greater clinical and economic burden at baseline; these differences persisted during follow-up. In the hypoglycemia group, for every 100 members per year, 463 hypoglycemia episodes were recorded, with a mean cost per episode of $986. Hypoglycemia-related medical expenses accounted for 12.6% ($4563/$36,272) of total healthcare expenditure, with hypoglycemia-related hospitalizations accounting for 19.7% ($2602/$13,191) of total hospitalization expenditure. CONCLUSIONS: Compared with patients with no hypoglycemia-related claims in year 1 after basal insulin initiation, patients with a hypoglycemia-related claim had a greater burden of complications and comorbidity associated with significantly higher healthcare utilization and cost at baseline; these persisted during follow-up.

Safety and efficacy of insulin glargine 300 u/mL compared with other basal insulin therapies in patients with type 2 diabetes mellitus: a network meta-analysis.

OBJECTIVE: To compare the efficacy and safety of a concentrated formulation of insulin glargine (Gla-300) with other basal insulin therapies in patients with type 2 diabetes mellitus (T2DM). DESIGN: This was a network meta-analysis (NMA) of randomised clinical trials of basal insulin therapy in T2DM identified via a systematic literature review of Cochrane library databases, MEDLINE and MEDLINE In-Process, EMBASE and PsycINFO. OUTCOME MEASURES: Changes in HbA1c (%) and body weight, and rates of nocturnal and documented symptomatic hypoglycaemia were assessed. RESULTS: 41 studies were included; 25 studies comprised the main analysis population: patients on basal insulin-supported oral therapy (BOT). Change in glycated haemoglobin (HbA1c) was comparable between Gla-300 and detemir (difference: -0.08; 95% credible interval (CrI): -0.40 to 0.24), neutral protamine Hagedorn (NPH; 0.01; -0.28 to 0.32), degludec (-0.12; -0.42 to 0.20) and premixed insulin (0.26; -0.04 to 0.58). Change in body weight was comparable between Gla-300 and detemir (0.69; -0.31 to 1.71), NPH (-0.76; -1.75 to 0.21) and degludec (-0.63; -1.63 to 0.35), but significantly lower compared with premixed insulin (-1.83; -2.85 to -0.75). Gla-300 was associated with a significantly lower nocturnal hypoglycaemia rate versus NPH (risk ratio: 0.18; 95% CrI: 0.05 to 0.55) and premixed insulin (0.36; 0.14 to 0.94); no significant differences were noted in Gla-300 versus detemir (0.52; 0.19 to 1.36) and degludec (0.66; 0.28 to 1.50). Differences in documented symptomatic hypoglycaemia rates of Gla-300 versus detemir (0.63; 0.19 to 2.00), NPH (0.66; 0.27 to 1.49) and degludec (0.55; 0.23 to 1.34) were not significant. Extensive sensitivity analyses supported the robustness of these findings. CONCLUSIONS: NMA comparisons are useful in the absence of direct randomised controlled data. This NMA suggests that Gla-300 is also associated with a significantly lower risk of nocturnal hypoglycaemia compared with NPH and premixed insulin, with glycaemic control comparable to available basal insulin comparators.

Data mining in pharmacovigilance: the need for a balanced perspective.

Data mining is receiving considerable attention as a tool for pharmacovigilance and is generating many perspectives on its uses. This paper presents four concepts that have appeared in various professional venues and represent potential sources of misunderstanding and/or entail extended discussions: (i) data mining algorithms are unvalidated; (ii) data mining algorithms allow data miners to objectively screen spontaneous report data; (iii) mathematically more complex Bayesian algorithms are superior to frequentist algorithms; and (iv) data mining algorithms are not just for hypothesis generation. Key points for a balanced perspective are that: (i) validation exercises have been done but lack a gold standard for comparison and are complicated by numerous nuances and pitfalls in the deployment of data mining algorithms. Their performance is likely to be highly situation dependent; (ii) the subjective nature of data mining is often underappreciated; (iii) simpler data mining models can be supplemented with 'clinical shrinkage', preserving sensitivity; and (iv) applications of data mining beyond hypothesis generation are risky, given the limitations of the data. These extended applications tend to 'creep', not pounce, into the public domain, leading to potential overconfidence in their results. Most importantly, in the enthusiasm generated by the promise of data mining tools, users must keep in mind the limitations of the data and the importance of clinical judgment and context, regardless of statistical arithmetic. In conclusion, we agree that contemporary data mining algorithms are promising additions to the pharmacovigilance toolkit, but the level of verification required should be commensurate with the nature and extent of the claimed applications.

Perspectives on the use of data mining in pharmaco-vigilance.

In the last 5 years, regulatory agencies and drug monitoring centres have been developing computerised data-mining methods to better identify reporting relationships in spontaneous reporting databases that could signal possible adverse drug reactions. At present, there are no guidelines or standards for the use of these methods in routine pharmaco-vigilance. In 2003, a group of statisticians, pharmaco-epidemiologists and pharmaco-vigilance professionals from the pharmaceutical industry and the US FDA formed the Pharmaceutical Research and Manufacturers of America-FDA Collaborative Working Group on Safety Evaluation Tools to review best practices for the use of these methods.In this paper, we provide an overview of: (i) the statistical and operational attributes of several currently used methods and their strengths and limitations; (ii) information about the characteristics of various postmarketing safety databases with which these tools can be deployed; (iii) analytical considerations for using safety data-mining methods and interpreting the results; and (iv) points to consider in integration of safety data mining with traditional pharmaco-vigilance methods. Perspectives from both the FDA and the industry are provided. Data mining is a potentially useful adjunct to traditional pharmaco-vigilance methods. The results of data mining should be viewed as hypothesis generating and should be evaluated in the context of other relevant data. The availability of a publicly accessible global safety database, which is updated on a frequent basis, would further enhance detection and communication about safety issues.

Comparative effectiveness without head-to-head trials: a method for matching-adjusted indirect comparisons applied to psoriasis treatment with adalimumab or etanercept.

The absence of head-to-head trials is a common challenge in comparative effectiveness research and health technology assessment. Indirect cross-trial treatment comparisons are possible, but can be biased by cross-trial differences in patient characteristics. Using only published aggregate data, adjustment for such biases may be impossible. Although individual patient data (IPD) would permit adjustment, they are rarely available for all trials. However, many researchers have the opportunity to access IPD for trials of one treatment, a new drug for example, but only aggregate data for trials of comparator treatments. We propose a method that leverages all available data in this setting by adjusting average patient characteristics in trials with IPD to match those reported for trials without IPD. Treatment outcomes, including continuous, categorical and censored time-to-event outcomes, can then be compared across balanced trial populations. The proposed method is illustrated by a comparison of adalimumab and etanercept for the treatment of psoriasis. IPD from trials of adalimumab versus placebo (n = 1025) were re-weighted to match the average baseline characteristics reported for a trial of etanercept versus placebo (n = 330). Re-weighting was based on the estimated propensity of enrolment in the adalimumab versus etanercept trials. Before matching, patients in the adalimumab trials had lower mean age, greater prevalence of psoriatic arthritis, less prior use of systemic treatment or phototherapy, and a smaller mean percentage of body surface area affected than patients in the etanercept trial. After matching, these and all other available baseline characteristics were well balanced across trials. Symptom improvements of ≥75% and ≥90% (as measured by the Psoriasis Area and Severity Index [PASI] score at week 12) were experienced by an additional 17.2% and 14.8% of adalimumab-treated patients compared with the matched etanercept-treated patients (respectively, both p < 0.001). Mean percentage PASI score improvements from baseline were also greater for adalimumab than for etanercept at weeks 4, 8 and 12 (all p < 0.05). Matching adjustment ensured that this indirect comparison was not biased by differences in mean baseline characteristics across trials, supporting the conclusion that adalimumab was associated with significantly greater symptom reduction than etanercept for the treatment of moderate to severe psoriasis.

Illusions of objectivity and a recommendation for reporting data mining results.

OBJECTIVE: Data mining algorithms (DMAs) are being applied to spontaneous reporting system (SRS) databases in the hope of obtaining timely insights into post-licensure safety data. Some DMAs have been characterized as "objective" screening tools. However, there are numerous available modifiable configuration parameters to choose from, including choice of vendor, that may affect results. Our objective is to compare the data mining results on pre-selected drug-event combinations (DECs) between two commonly used software programs using similar protocols. METHODS: Two DMAs, using three thresholds, were retrospectively applied to the USFDA safety database through Q2 2005 to a set of eight pre-selected DECs. RESULTS: Differences between the two vendors were found for the number of cases associated with a signal of disproportionate reporting (SDR), first year of SDRs, and the magnitude of the SDR scores for the selected DECs. These were deemed to be potentially significant for 45.8% (11/24) of the data points. CONCLUSION: The observed differences between vendors could partially be explained by their differing methods of data cleaning and transformation as well as by the specific features of individual algorithms. The choices of vendors and available data mining configurations maximize the exploratory capacity of data mining, but they also raise questions about the claimed objectivity of data mining results and can make data mining exercises susceptible to confirmation bias given the exploratory nature of data mining in pharmacovigilance. When reporting results, the vendor and all data mining configuration details should be specified.

A comparison of pioglitazone and rosiglitazone for hospitalization for acute myocardial infarction in type 2 diabetes.

BACKGROUND: Recent studies have raised concerns about potential increased cardiovascular (CV) risk in type 2 diabetes patients treated with some peroxisome proliferator-activated receptor gamma (PPAR-gamma) agonists. OBJECTIVE: To ascertain the risk of hospitalization for acute myocardial infarction (AMI) in type 2 diabetes patients treated with pioglitazone relative to rosiglitazone. METHODOLOGY: Using data covering 2003-2006 from a large health care insurer in the US, a retrospective cohort study was conducted in patients who initiated treatment with pioglitazone or rosiglitazone. The hazard ratio (HR) of incident hospitalization for AMI after initiation of treatment with these drugs was estimated from multivariate Cox's proportional hazards survival analysis; similarly, the HR was ascertained for hospitalization for the composite endpoint of AMI or coronary revascularization (CR). RESULTS: A total of 29 911 eligible patients were identified in the database; 14 807 in the pioglitazone and 15 104 in the rosiglitazone group. Baseline demographics, medical history, and dispensed medications were generally well balanced between groups. The unadjusted HR for hospitalization for AMI was 0.82, 95%CI: 0.67-1.01. After adjustment for baseline covariates the HR was 0.78, 95%CI: 0.63-0.96. The adjusted HR for the composite of AMI or CR was 0.85, 95%CI: 0.75-0.98. CONCLUSION: This retrospective cohort study showed that pioglitazone, in comparison with rosiglitazone, is associated with a 22% relative risk reduction of hospitalization for AMI in patients with type 2 diabetes.

Reports of hyperkalemia after publication of RALES--a pharmacovigilance study.

PURPOSE: A population-based study and anecdotal reports have indicated that the publication of the Randomized Aldactone Evaluation Study (RALES) was associated with not merely a broader use of spironolactone in the treatment of heart failure, but also with a coinciding sharp increase in hyperkalemia-associated morbidity/mortality in patients also being treated with ACE-inhibitors. Data mining algorithms (DMAs) are being applied to spontaneous reporting system (SRS) databases in hopes of obtaining early warnings/additional insights into post-licensure safety data. We applied two DMAs (i.e. multi-item gamma Poisson shrinker [MGPS] and proportional reporting ratios [PRRs]) to spontaneous reporting system (SRS) data to determine if these DMAs could have provided an earlier indication of a possible hyperkalemia safety issue. METHODS: MGPS and PRRs were retrospectively applied to US FDA-AERS, an SRS database. Year-by-year analysis and analysis of increasing cumulative time intervals were performed on cases in which both spironolactone and hyperkalemia and possibly related cardiac events had been reported. RESULTS: Neither of the DMAs initially provided a compelling signal of disproportionate reporting (SDR) for hyperkalemia after publication of RALES. However, using events consistent with clinical sequelae of hyperkalemia (e.g,. sudden death), SDRs were identified with PRRs. CONCLUSIONS: The quality and usefulness of data mining analysis is highly situation dependent and may vary with the knowledge and experience of the drug safety reviewer. Our analysis suggests that contemporary DMAs may have significant limitations in detecting increased frequency of labeled events in real-life prospective pharmacovigilance. There is a paucity of research in this area and we recommend further research for new approaches to detecting increased frequency of labeled events.

The role of data mining in pharmacovigilance.

A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human reviewers in identifying associations worthy of further investigation (i.e., signals) embedded within a database consisting largely of background 'noise' containing reports of no substantial public health significance. Data mining algorithms are, therefore, being developed, tested and/or used by health authorities, pharmaceutical companies and academic researchers. After a focused review of postapproval drug safety signal detection, the authors explain how the currently used algorithms work and address key questions related to their validation, comparative performance, deployment in naturalistic pharmacovigilance settings, limitations and potential for misuse. Suggestions for further research and development are offered.