| 1. |
- Bergvall, Tomas, et al.
(author)
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vigiGrade : A Tool to Identify Well-Documented Individual Case Reports and Highlight Systematic Data Quality Issues
- 2014
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 37:1, s. 65-77
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Journal article (peer-reviewed)abstract
- Individual case safety reports of suspected harm from medicines are fundamental to post-marketing surveillance. Their value is directly proportional to the amount of clinically relevant information they include. To improve the quality of the data, communication between stakeholders is essential and can be facilitated by a simple score and visualisation of the results. The objective of this study was to propose a measure of completeness and identify predictors of well-documented reports, globally. The Uppsala Monitoring Centre has developed the vigiGrade completeness score to measure the amount of clinically relevant information in structured format, without reflecting whether the information establishes causality between the drug and adverse event. The vigiGrade completeness score (C) starts at 1 for reports with information on time-to-onset, age, sex, indication, outcome, report type, dose, country, primary reporter and comments. For each missing dimension, a penalty is detracted which varies with clinical relevance. We classified reports with C > 0.8 as well-documented and identified all such reports in the WHO global individual case safety report database, VigiBase, from 2007 to January 2012. We utilised odds ratios with statistical shrinkage to identify subgroups with unexpectedly high proportions of well-documented reports. Altogether, 430,000 (13 %) of the studied reports achieved C > 0.8 in VigiBase. For VigiBase as a whole, the median completeness was 0.41 with an interquartile range of 0.26-0.63. Two out of three well-documented reports come from Europe, and two out of three from physicians. Among the countries with more than 1,000 reports in total, the highest rate of well-documented reports is 65 % in Italy. Tunisia, Spain, Portugal, Croatia and Denmark each have rates above 50 %, and another 20 countries have rates above 30 %. On the whole, 24 % of the reports from physicians are well-documented compared with only 4 % for consumers/non-health professionals. Notably, Denmark and Norway have more than 50 % well-documented reports from consumers/non-health professionals and higher rates than for physicians. The rate of well-documented reports for the E2B format is 11 % compared with 22 % for the older INTDIS (International Drug Information System) format. However, for E2B reports entered via the WHO programme's e-reporting system VigiFlow, the rate is 29 %. Overall, only one report in eight provides the desired level of information, but much higher proportions are observed for individual countries. Physicians and e-reporting tools also generate greater proportions of well-documented reports overall. Reports from consumers/non-health professionals in specific regions have excellent quality, which illustrates their potential for the future. vigiGrade has already provided valuable information by highlighting data quality issues both in Italy and the USA.
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| 2. |
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| 3. |
- Caster, Ola, et al.
(author)
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Computing limits on medicine risks based on collections of individual case reports
- 2014
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In: Theoretical Biology and Medical Modelling. - : Springer Science and Business Media LLC. - 1742-4682. ; 11
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Journal article (peer-reviewed)abstract
- Background: Quantifying a medicine's risks for adverse effects is crucial in assessing its value as a therapeutic agent. Rare adverse effects are often not detected until after the medicine is marketed and used in large and heterogeneous patient populations, and risk quantification is even more difficult. While individual case reports of suspected harm from medicines are instrumental in the detection of previously unknown adverse effects, they are currently not used for risk quantification. The aim of this article is to demonstrate how and when limits on medicine risks can be computed from collections of individual case reports. Methods: We propose a model where drug exposures in the real world may be followed by adverse episodes, each containing one or several adverse effects. Any adverse episode can be reported at most once, and each report corresponds to a single adverse episode. Based on this model, we derive upper and lower limits for the per-exposure risk of an adverse effect for a given drug. Results: An upper limit for the per-exposure risk of the adverse effect Y for a given drug X is provided by the reporting ratio of X together with Y relative to all reports on X, under two assumptions: (i) the average number of adverse episodes following exposure to X is one or less; and (ii) adverse episodes that follow X and contain Y are more frequently reported than adverse episodes in general that follow X. Further, a lower risk limit is provided by dividing the number of reports on X together with Y by the total number of exposures to X, under the assumption that exposures to X that are followed by Y generate on average at most one report on X together with Y. Using real data, limits for the narcolepsy risk following Pandemrix vaccination and the risk of coeliac disease following antihypertensive treatment were computed and found to conform to reference risk values from epidemiological studies. Conclusions: Our framework enables quantification of medicine risks in situations where this is otherwise difficult or impossible. It has wide applicability, but should be particularly useful in structured benefit-risk assessments that include rare adverse effects.
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| 4. |
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| 5. |
- Caster, Ola, et al.
(author)
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Improved Statistical Signal Detection in Pharmacovigilance by Combining Multiple Strength-of-Evidence Aspects in vigiRank
- 2014
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 37:8, s. 617-628
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Journal article (peer-reviewed)abstract
- BackgroundDetection of unknown risks with marketed medicines is key to securing the optimal care of individual patients and to reducing the societal burden from adverse drug reactions. Large collections of individual case reports remain the primary source of information and require effective analytics to guide clinical assessors towards likely drug safety signals. Disproportionality analysis is based solely on aggregate numbers of reports and naively disregards report quality and content. However, these latter features are the very fundament of the ensuing clinical assessment.ObjectiveOur objective was to develop and evaluate a data-driven screening algorithm for emerging drug safety signals that accounts for report quality and content.MethodsvigiRank is a predictive model for emerging safety signals, here implemented with shrinkage logistic regression to identify predictive variables and estimate their respective contributions. The variables considered for inclusion capture different aspects of strength of evidence, including quality and clinical content of individual reports, as well as trends in time and geographic spread. A reference set of 264 positive controls (historical safety signals from 2003 to 2007) and 5,280 negative controls (pairs of drugs and adverse events not listed in the Summary of Product Characteristics of that drug in 2012) was used for model fitting and evaluation; the latter used fivefold cross-validation to protect against over-fitting. All analyses were performed on a reconstructed version of VigiBase® as of 31 December 2004, at around which time most safety signals in our reference set were emerging.ResultsThe following aspects of strength of evidence were selected for inclusion into vigiRank: the numbers of informative and recent reports, respectively; disproportional reporting; the number of reports with free-text descriptions of the case; and the geographic spread of reporting. vigiRank offered a statistically significant improvement in area under the receiver operating characteristics curve (AUC) over screening based on the Information Component (IC) and raw numbers of reports, respectively (0.775 vs. 0.736 and 0.707, cross-validated).ConclusionsAccounting for multiple aspects of strength of evidence has clear conceptual and empirical advantages over disproportionality analysis. vigiRank is a first-of-its-kind predictive model to factor in report quality and content in first-pass screening to better meet tomorrow’s post-marketing drug safety surveillance needs.
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| 6. |
- Caster, Ola, et al.
(author)
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Large-scale regression-based pattern discovery : The example of screening the WHO global drug safety database
- 2010
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In: Statistical Analysis and Data Mining. - : Wiley. - 1932-1864 .- 1932-1872. ; 3:4, s. 197-208
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Journal article (peer-reviewed)abstract
- Most measures of interestingness for patterns of co-occurring events are based on data projections onto contingency tables for the events of primary interest. As an alternative, this article presents the first implementation of shrinkage logistic regression for large-scale pattern discovery, with an evaluation of its usefulness in real-world binary transaction data. Regression accounts for the impact of other covariates that may confound or otherwise distort associations. The application considered is international adverse drug reaction (ADR) surveillance, in which large collections of reports on suspected ADRs are screened for interesting reporting patterns worthy of clinical follow-up. Our results show that regression-based pattern discovery does offer practical advantages. Specifically it can eliminate false positives and false negatives due to other covariates. Furthermore, it identifies some established drug safety issues earlier than a measure based on contingency tables. While regression offers clear conceptual advantages, our results suggest that methods based on contingency tables will continue to play a key role in ADR surveillance, for two reasons: the failure of regression to identify some established drug safety concerns as early as the currently used measures, and the relative lack of transparency of the procedure to estimate the regression coefficients. This suggests shrinkage regression should be used in parallel to existing measures of interestingness in ADR surveillance and other large-scale pattern discovery applications.
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| 7. |
- Caster, Ola, et al.
(author)
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Quantitative Benefit-Risk Assessment Using Only Qualitative Information on Utilities
- 2012
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In: Medical decision making. - 0272-989X .- 1552-681X. ; 32:6, s. E1-E15
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Journal article (peer-reviewed)abstract
- Background: Utilities of pertinent clinical outcomes are crucial variables for assessing the benefits and risks of drugs, but numerical data on utilities may be unreliable or altogether missing. We propose a method to incorporate qualitative information into a probabilistic decision analysis framework for quantitative benefit-risk assessment. Objective: To investigate whether conclusive results can be obtained when the only source of discriminating information on utilities is widely agreed upon qualitative relations, for example, ''sepsis is worse than transient headache'' or ''alleviation of disease is better without than with complications.'' Method: We used the structure and probabilities of 3 published models that were originally evaluated based on the standard metric of quality-adjusted life years (QALYs): terfenadine versus chlorpheniramine for the treatment of allergic rhinitis, MCV4 vaccination against meningococcal disease, and alosetron for irritable bowel syndrome. For each model, we identified clinically straightforward qualitative relations among the outcomes. Using Monte Carlo simulations, the resulting utility distributions were then combined with the previously specified probabilities, and the rate of preference in terms of expected utility was determined for each alternative. Results: Our approach conclusively favored MCV4 vaccination, and it was concordant with the QALY assessments for the MCV4 and terfenadine versus chlorpheniramine case studies. For alosetron, we found a possible unfavorable benefit-risk balance for highly risk-averse patients not identified in the original analysis. Conclusion: Incorporation of widely agreed upon qualitative information into quantitative benefit-risk assessment can provide for conclusive results. The methods presented should prove useful in both population and individual-level assessments, especially when numerical utility data are missing or unreliable, and constraints on time or money preclude its collection.
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| 8. |
- Caster, Ola, et al.
(author)
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vigiRank for statistical signal detection in pharmacovigilance : First results from prospective real-world use
- 2017
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In: Pharmacoepidemiology and Drug Safety. - : Wiley. - 1053-8569 .- 1099-1557. ; 26:8, s. 1006-1010
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Journal article (peer-reviewed)abstract
- Purpose: vigiRank is a data-driven predictive model for emerging safety signals. In addition to disproportionate reporting patterns, it also accounts for the completeness, recency, and geographic spread of individual case reporting, as well as the availability of case narratives. Previous retrospective analysis suggested that vigiRank performed better than disproportionality analysis alone. The purpose of the present analysis was to evaluate its prospective performance. Methods: The evaluation of vigiRank was based on real-world signal detection in VigiBase. In May 2014, vigiRank scores were computed for pairs of new drugs and WHO Adverse Reaction Terminology critical terms with at most 30 reports from at least 2 countries. Initial manual assessments were performed in order of descending score, selecting a subset of drug-adverse drug reaction pairs for in-depth expert assessment. The primary performance metric was the proportion of initial assessments that were decided signals during in-depth assessment. As comparator, the historical performance for disproportionality-guided signal detection in VigiBase was computed from a corresponding cohort of drug-adverse drug reaction pairs assessed between 2009 and 2013. During this period, the requirement for initial manual assessment was a positive lower endpoint of the 95% credibility interval of the Information Component measure of disproportionality, observed for the first time. Results: 194 initial assessments suggested by vigiRank's ordering eventually resulted in 6 (3.1%) signals. Disproportionality analysis yielded 19 signals from 1592 initial assessments (1.2%; P <.05). Conclusions: Combining multiple strength-of-evidence aspects as in vigiRank significantly outperformed disproportionality analysis alone in real-world pharmacovigilance signal detection, for VigiBase.
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| 9. |
- Chandler, Rebecca E., et al.
(author)
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Current Safety Concerns with Human Papillomavirus Vaccine : A Cluster Analysis of Reports in VigiBase®
- 2017
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 40:1, s. 81-90
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Journal article (peer-reviewed)abstract
- INTRODUCTION: A number of safety signals-complex regional pain syndrome (CRPS), postural orthostatic tachycardia syndrome (POTS), and chronic fatigue syndrome (CFS)-have emerged with human papillomavirus (HPV) vaccines, which share a similar pattern of symptomatology. Previous signal evaluations and epidemiological studies have largely relied on traditional methodologies and signals have been considered individually.OBJECTIVE: The aim of this study was to explore global reporting patterns for HPV vaccine for subgroups of reports with similar adverse event (AE) profiles.METHODS: All individual case safety reports (reports) for HPV vaccines in VigiBase(®) until 1 January 2015 were identified. A statistical cluster analysis algorithm was used to identify natural groupings based on AE profiles in a data-driven exploratory analysis. Clinical assessment of the clusters was performed to identify clusters relevant to current safety concerns.RESULTS: Overall, 54 clusters containing at least five reports were identified. The four largest clusters included 71 % of the analysed HPV reports and described AEs included in the product label. Four smaller clusters were identified to include case reports relevant to ongoing safety concerns (total of 694 cases). In all four of these clusters, the most commonly reported AE terms were headache and dizziness and fatigue or syncope; three of these four AE terms were reported in >50 % of the reports included in the clusters. These clusters had a higher proportion of serious cases compared with HPV reports overall (44-89 % in the clusters compared with 24 %). Furthermore, only a minority of reports included in these clusters included AE terms of diagnoses to explain these symptoms. Using proportional reporting ratios, the combination of headache and dizziness with either fatigue or syncope was found to be more commonly reported in HPV vaccine reports compared with non-HPV vaccine reports for females aged 9-25 years. This disproportionality remained when results were stratified by age and when those countries reporting the signals of CRPS (Japan) and POTS (Denmark) were excluded.CONCLUSIONS: Cluster analysis reveals additional reports of AEs following HPV vaccination that are serious in nature and describe symptoms that overlap those reported in cases from the recent safety signals (POTS, CRPS, and CFS), but which do not report explicit diagnoses. While the causal association between HPV vaccination and these AEs remains uncertain, more extensive analyses of spontaneous reports can better identify the relevant case series for thorough signal evaluation.
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| 10. |
- Hauben, Manfred, et al.
(author)
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A decade of data mining and still counting
- 2010
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 33:7, s. 527-534
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Journal article (other academic/artistic)
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| 11. |
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| 12. |
- Hopstadius, Johan, et al.
(author)
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Impact of Stratification on Adverse Drug Reaction Surveillance
- 2008
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In: Drug Safety. - 0114-5916 .- 1179-1942. ; 31:11, s. 1035-1048
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Journal article (peer-reviewed)abstract
- BACKGROUND AND OBJECTIVES: Automated screening for excessive adverse drug reaction (ADR) reporting rates has proven useful as a tool to direct clinical review in large-scale drug safety signal detection. Some measures of disproportionality can be adjusted to eliminate any undue influence on the ADR reporting rate of covariates, such as patient age or country of origin, by using a weighted average of stratum-specific measures of disproportionality. Arguments have been made in favour of routine adjustment for a set of common potential confounders using stratification. The aim of this paper is to investigate the impact of using adjusted observed-to-expected ratios, as implemented for the Empirical Bayes Geometric Mean (EBGM) and the information component (IC) measures of disproportionality, for first-pass analysis of the WHO database. METHODS: A simulation study was carried out to investigate the impact of simultaneous adjustment for several potential confounders based on stratification. Comparison between crude and adjusted observed-to-expected ratios were made based on random allocation of reports to a set of strata with a realistic distribution of stratum sizes. In a separate study, differences between the crude IC value and IC values adjusted for (combinations of) patient sex, age group, reporting quarter and country of origin, with respect to their concordance with a literature comparison were analysed. Comparison was made to the impact on signal detection performance of a triage criterion requiring reports from at least two countries before a drug-ADR pair was highlighted for clinical review. RESULTS: The simulation study demonstrated a clear tendency of the adjusted observed-to-expected ratio to spurious (and considerable) underestimation relative to the crude one, in the presence of any very small strata in a stratified database. With carefully implemented stratification that did not yield any very small strata, this tendency could be avoided. Routine adjustment for potential confounders improved signal detection performance relative to the literature comparison, but the magnitude of the improvement was modest. The improvement from the triage criterion was more considerable. DISCUSSION AND CONCLUSIONS: Our results indicate that first-pass screening based on observed-to-expected ratios adjusted with stratification may lead to missed signals in ADR surveillance, unless very small strata are avoided. In addition, the improvement in signal detection performance due to routine adjustment for a set of common confounders appears to be smaller than previously assumed. Other approaches to improving signal detection performance such as the development of refined triage criteria may be more promising areas for future research.
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| 13. |
- Juhlin, Kristina, et al.
(author)
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A method for data-driven exploration to pinpoint key features in medical data and facilitate expert review
- 2017
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In: Pharmacoepidemiology and Drug Safety. - : Wiley. - 1053-8569 .- 1099-1557. ; 26:10, s. 1256-1265
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Journal article (peer-reviewed)abstract
- PurposeTo develop a method for data‐driven exploration in pharmacovigilance and illustrate its use by identifying the key features of individual case safety reports related to medication errors.MethodsWe propose vigiPoint, a method that contrasts the relative frequency of covariate values in a data subset of interest to those within one or more comparators, utilizing odds ratios with adaptive statistical shrinkage. Nested analyses identify higher order patterns, and permutation analysis is employed to protect against chance findings. For illustration, a total of 164 000 adverse event reports related to medication errors were characterized and contrasted to the other 7 833 000 reports in VigiBase, the WHO global database of individual case safety reports, as of May 2013. The initial scope included 2000 features, such as patient age groups, reporter qualifications, and countries of origin.ResultsvigiPoint highlighted 109 key features of medication error reports. The most prominent were that the vast majority of medication error reports were from the United States (89% compared with 49% for other reports in VigiBase); that the majority of reports were sent by consumers (53% vs 17% for other reports); that pharmacists (12% vs 5.3%) and lawyers (2.9% vs 1.5%) were overrepresented; and that there were more medication error reports than expected for patients aged 2‐11 years (10% vs 5.7%), particularly in Germany (16%).ConclusionsvigiPoint effectively identified key features of medication error reports in VigiBase. More generally, it reduces lead times for analysis and ensures reproducibility and transparency. An important next step is to evaluate its use in other data.
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| 14. |
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| 15. |
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| 16. |
- Juhlin, Kristina, et al.
(author)
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Outlier removal to uncover patterns in adverse drug reaction surveillance - a simple unmasking strategy
- 2013
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In: Pharmacoepidemiology and Drug Safety. - : Wiley. - 1053-8569 .- 1099-1557. ; 22:10, s. 1119-1129
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Journal article (peer-reviewed)abstract
- PurposeThis study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential outliers in two spontaneous reporting databases and evaluate the impact of outlier removal on disproportionality analysis. MethodsWe propose an algorithm that identifies influential outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China. ResultsFor WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia outlier removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an outlier. The overall increase in the number of SDRs for both datasets was 3%. ConclusionMasking by outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.
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| 17. |
- Juhlin, Kristina, et al.
(author)
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Using VigiBase to Identify Substandard Medicines : Detection Capacity and Key Prerequisites
- 2015
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 38:4, s. 373-382
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Journal article (peer-reviewed)abstract
- Background Substandard medicines, whether the result of intentional manipulation or lack of compliance with good manufacturing practice (GMP) or good distribution practice (GDP), pose a significant potential threat to patient safety. Spontaneous adverse drug reaction reporting systems can contribute to identification of quality problems that cause unwanted and/or harmful effects, and to identification of clusters of lack of efficacy. In 2011, the Uppsala Monitoring Centre (UMC) constructed a novel algorithm to identify reporting patterns suggestive of substandard medicines in spontaneous reporting, and applied it to VigiBase (R), the World Health Organization's global individual case safety report database. The algorithm identified some historical clusters related to substandard products, which were later able to be confirmed in the literature or by contact with national centres (NCs). As relevant and detailed information is often lacking in the VigiBase reports but might be available at the reporting NC, further evaluation of the algorithm was undertaken with involvement from NCs. Objective To evaluate the effectiveness of an algorithm that identifies clusters of potentially substandard medicines, when these are assessed directly at the NC concerned. Methods The algorithm identifies countries and time periods with disproportionately high reporting of product inadequacy. NCs with at least 20 clusters were eligible to participate in the study, and six NCs-those in the Republic of Korea, Malaysia, Singapore, South Africa, the UK and the USA-were selected, taking into account the geographical spread and prevalence of recent clusters. The clusters were systematically assessed at the NCs, following a standardized protocol, and then compiled centrally at the UMC. The clusters were classified as 'confirmed', 'potential' or 'unlikely' substandard products; or as 'confirmed not substandard' when confirmed by an investigation; or as 'indecisive' when the information available did not allow a sound assessment even at the NC. Results The assessment of a total of 147 clusters resulted in 8 confirmed, 12 potential and 51 unlikely substandard products, and a further 19 clusters were confirmed as not substandard. Reflecting the difficulty of evaluating suspected substandard products retrospectively when additional information from the primary reporter, as well as samples, are no longer available, 57 clusters were classified as indecisive. Conclusion While application of the algorithm to VigiBase allowed identification of some substandard medicines, some key prerequisites have been identified that need to be fulfilled at the national level for the algorithm to be useful in practice. Such key factors are fast handling and transfer of incoming reports into VigiBase, detailed information on the product and its distribution channels, the possibility of contacting primary reporters for further information, availability of samples of suspected products and laboratory capacity to analyse suspected products.
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| 18. |
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| 19. |
- Low, Yen S., et al.
(author)
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Cheminformatics-aided pharmacovigilance : application to Stevens-Johnson Syndrome
- 2016
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In: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press (OUP). - 1067-5027 .- 1527-974X. ; 23:5, s. 968-978
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Journal article (peer-reviewed)abstract
- Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.
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| 20. |
- Norén, G. Niklas, et al.
(author)
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A statistical methodology for drug–drug interaction surveillance
- 2008
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 27:16, s. 3057-3070
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Journal article (peer-reviewed)abstract
- Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug–ADR associations, but have yet to reach their full potential for drug–drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug–drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug–drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug–drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug–drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.
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| 21. |
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| 22. |
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| 23. |
- Norén, G. Niklas, et al.
(author)
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Empirical Performance of the Calibrated Self-Controlled Cohort Analysis Within Temporal Pattern Discovery : Lessons for Developing a Risk Identification and Analysis System
- 2013
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 36, s. s107-S121
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Journal article (peer-reviewed)abstract
- Background Observational healthcare data offer the potential to identify adverse drug reactions that may be missed by spontaneous reporting. The self-controlled cohort analysis within the Temporal Pattern Discovery framework compares the observed-to-expected ratio of medical outcomes during post-exposure surveillance periods with those during a set of distinct pre-exposure control periods in the same patients. It utilizes an external control group to account for systematic differences between the different time periods, thus combining within- and between-patient confounder adjustment in a single measure. Objectives To evaluate the performance of the calibrated self-controlled cohort analysis within Temporal Pattern Discovery as a tool for risk identification in observational healthcare data. Research Design Different implementations of the calibrated self-controlled cohort analysis were applied to 399 drug-outcome pairs (165 positive and 234 negative test cases across 4 health outcomes of interest) in 5 real observational databases (four with administrative claims and one with electronic health records). Measures Performance was evaluated on real data through sensitivity/specificity, the area under receiver operator characteristics curve (AUC), and bias. Results The calibrated self-controlled cohort analysis achieved good predictive accuracy across the outcomes and databases under study. The optimal design based on this reference set uses a 360 days surveillance period and a single control period 180 days prior to new prescriptions. It achieved an average AUC of 0.75 and AUC >0.70 in all but one scenario. A design with three separate control periods performed better for the electronic health records database and for acute renal failure across all data sets. The estimates for negative test cases were generally unbiased, but a minor negative bias of up to 0.2 on the RR-scale was observed with the configurations using multiple control periods, for acute liver injury and upper gastrointestinal bleeding. Conclusions The calibrated self-controlled cohort analysis within Temporal Pattern Discovery shows promise as a tool for risk identification; it performs well at discriminating positive from negative test cases. The optimal parameter configuration may vary with the data set and medical outcome of interest.
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| 24. |
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| 25. |
- Norén, G. Niklas, et al.
(author)
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Modern methods of pharmacovigilance : detecting adverse effects of drugs
- 2009
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In: Clinical medicine (London). - 1470-2118 .- 1473-4893. ; 9:5, s. 486-489
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Journal article (peer-reviewed)abstract
- Medicines improve health and the chances of survival in a wide variety of conditions. At the same time, no substance with pharmacological effects is without hazard. Adverse drug reactions (ADRs) can be associated with the intended pharmacological effect of the medicine (eg bleeding from warfarin), mediated by other mechanisms (eg anticholinergic effects of tricyclic antidepressants) or can be altogether unexpected (eg hypersensitivity reactions to abacavir). Some ADRs can be identified early in the development of a medicine, but knowledge of the adverse effects profile is provisional when the medicine is first marketed and usually changes over time. Premarketing clinical trials include too few patients and are too short to detect every outcome that will affect public health and individual patient safety. In addition, clinical trials are carried out in controlled settings that differ from real-world practice. This reduces their power to detect ADRs, for example those that are due to drug-drug interactions or that affect only susceptible subgroups (eg phocomelia due to thalidomide). Safety needs to be evaluated continuously throughout the life-cycle of a medicinal product.1,2 A key challenge is to identify emerging problems as early as possible, without generating false alarms.
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| 26. |
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| 27. |
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| 28. |
- Norén, G. Niklas, et al.
(author)
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Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery
- 2013
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In: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 22:1, s. 57-69
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Journal article (peer-reviewed)abstract
- Large observational data sets are a great asset to better understand the effects of medicines in clinical practice and, ultimately, improve patient care. For an empirical pattern in observational data to be of practical relevance, it should represent a substantial deviation from the null model. For the purpose of identifying such deviations, statistical significance tests are inadequate, as they do not on their own distinguish the magnitude of an effect from its data support. The observed-to-expected (OE) ratio on the other hand directly measures strength of association and is an intuitive basis to identify a range of patterns related to event rates, including pairwise associations, higher order interactions and temporal associations between events over time. It is sensitive to random fluctuations for rare events with low expected counts but statistical shrinkage can protect against spurious associations. Shrinkage OE ratios provide a simple but powerful framework for large-scale pattern discovery. In this article, we outline a range of patterns that are naturally viewed in terms of OE ratios and propose a straightforward and effective statistical shrinkage transformation that can be applied to any such ratio. The proposed approach retains emphasis on the practical relevance and transparency of highlighted patterns, while protecting against spurious associations.
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| 29. |
- Norén, G. Niklas, 1977-
(author)
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Statistical methods for knowledge discovery in adverse drug reaction surveillance
- 2007
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Doctoral thesis (other academic/artistic)abstract
- Collections of individual case safety reports are the main resource for early discovery of unknown adverse reactions to drugs once they have been introduced to the general public. The data sets involved are complex and based on voluntary submission of reports, but contain pieces of very important information. The aim of this thesis is to propose computationally feasible statistical methods for large-scale knowledge discovery in these data sets. The main contributions are a duplicate detection method that can reliably identify pairs of unexpectedly similar reports and a new measure for highlighting suspected drug-drug interaction. Specifically, we extend the hit-miss model for database record matching with a hit-miss mixture model for scoring numerical record fields and a new method to compensate for strong record field correlations. The extended hit-miss model is implemented for the WHO database and demonstrated to be useful in real world duplicate detection, despite the noisy and incomplete information on individual case safety reports. The Information Component measure of disproportionality has been in routine use since 1998 to screen the WHO database for excessive adverse drug reaction reporting rates. Here, it is further refined. We introduce improved credibility intervals for rare events, post-stratification adjustment for suspected confounders and an extension to higher order associations that allows for simple but robust screening for potential risk factors. A new approach to identifying reporting patterns indicative of drug-drug interaction is also proposed. Finally, we describe how imprecision estimates specific to each prediction of a Bayes classifier may be obtained with the Bayesian bootstrap. Such case-based imprecision estimates allow for better prediction when different types of errors have different associated loss, with a possible application in combining quantitative and clinical filters to highlight drug-ADR pairs for clinical review.
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| 30. |
- Norén, G. Niklas, et al.
(author)
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Temporal pattern discovery in longitudinal electronic patient records
- 2010
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In: Data mining and knowledge discovery. - : Springer Science and Business Media LLC. - 1384-5810 .- 1573-756X. ; 20:3, s. 361-387
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Journal article (peer-reviewed)abstract
- Large collections of electronic patient records provide a vast but still underutilised source of information on the real world use of medicines. They are maintained primarily for the purpose of patient administration, but contain a broad range of clinical information highly relevant for data analysis. While they are a standard resource for epidemiological confirmatory studies, their use in the context of exploratory data analysis is still limited. In this paper, we present a framework for open-ended pattern discovery in large patient records repositories. At the core is a graphical statistical approach to summarising and visualising the temporal association between the prescription of a drug and the occurrence of a medical event. The graphical overview contrasts the observed and expected number of occurrences of the medical event in different time periods both before and after the prescription of interest. In order to effectively screen for important temporal relationships, we introduce a new measure of temporal association, which contrasts the observed-to-expected ratio in a time period immediately after the prescription to the observed-to-expected ratio in a control period 2 years earlier. An important feature of both the observed-to-expected graph and the measure of temporal association is a statistical shrinkage towards the null hypothesis of no association, which provides protection against highlighting spurious associations. We demonstrate the usefulness of the proposed pattern discovery methodology by a set of examples from a collection of over two million patient records in the United Kingdom. The identified patterns include temporal relationships between drug prescriptions and medical events suggestive of persistent and transient risks of adverse events, possible beneficial effects of drugs, periodic co-occurrence, and systematic tendencies of patients to switch from one medication to another.
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| 31. |
- Norén, G. Niklas, et al.
(author)
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Zoo or savannah? Choice of training ground for evidence-based pharmacovigilance
- 2014
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 37:9, s. 655-659
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Journal article (peer-reviewed)abstract
- Pharmacovigilance seeks to detect and describe adverse drug reactions early. Ideally, we would like to see objective evidence that a chosen signal detection approach can be expected to be effective. The development and evaluation of evidence-based methods require benchmarks for signal detection performance, and recent years have seen unprecedented efforts to build such reference sets. Here, we argue that evaluation should be made against emerging and not established adverse drug reactions, and we present real-world examples that illustrate the relevance of this to pharmacovigilance methods development for both individual case reports and longitudinal health records. The establishment of broader reference sets of emerging safety signals must be made a top priority to achieve more effective pharmacovigilance methods development and evaluation.
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| 32. |
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| 33. |
- Star, Kristina, et al.
(author)
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Suspected Adverse Drug Reactions Reported For Children Worldwide : An Exploratory Study Using VigiBase
- 2011
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 34:5, s. 415-428
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Journal article (peer-reviewed)abstract
- BackgroundAs a first step towards implementing routine screening of safety issues specifically related to children at the Uppsala Monitoring Centre, this study was performed to explore reporting patterns of adverse reactions in children. ObjectiveThe first aim of this study was to characterize and contrast child reports against adult reports in an overall drug and adverse reaction review. The second aim was to highlight increases in reporting of specific adverse reactions during recent years subdivided by age group. Study DesignThis was an exploratory study of internationally compiled individual case safety reports (ICSRs). SettingReports were extracted from the WHO global ICSR database, VigiBase, up until 5 February 2010. The reports in VigiBase originate from 97 countries and the likelihood that a medicine caused the adverse effect may vary from case to case. Suspected duplicate and vaccine reports were excluded from the analysis, as were reports with age not specified. The Medical Dictionary for Regulatory Activities (MedDRA (R)) and the WHO Anatomical Therapeutic Chemical (ATC) classification were used to group adverse reactions and drugs. PatientsIn the general review, reports from 1968 to 5 February 2010 were divided into child (aged 0-17 years) and adult (>= 18 years) age groups. To highlight increases in reporting rates of specific adverse reactions during recent years, reports from 2005 to February 2010 were compared with reports from 1995 to 1999. The ten adverse reactions with the greatest difference in the proportion of reports between the two time periods were reviewed. In the latter analysis, the reports were subdivided into age groups: neonates 27 days; infants 28 days-23 months; children 2-11 years; and adolescents 12-17 years. ResultsA total of 3 472 183 reports were included in the study, of which 7.7% (268 145) were reports for children (0-17 years). Fifty-three percent of the child reports were for males, whilst 39% of reports in the adult group were for males. The proportion of reports involving children among Asian reports was 14% and was 15% among reports from Africa and Latin America, including the Caribbean. Among reports from North America, Oceania and Europe, 7% of the reports involved children. For the ATC drug classification groups, the largest difference in percentage units between the child and adult groups was seen for the anti-infective (33 vs 15%), respiratory (11 vs 5%) and dermatological (12 vs 7%) drug groups. Skin reactions were most commonly reported for the children; these were recorded in 35% of all reports for children and 23% of all reports for adults. Medication error-related terms in the younger age groups were reported with an increased frequency during recent years. This was particularly noticeable for the infants aged 28 days-23 months, recorded with accidental overdose and drug toxicity. Reactions reported in suspected connection to medicines used for attention-deficit hyperactivity disorders (ADHD) completely dominated the 2- to 11-year age group and were also common for the adolescents. This study presents variations in the reporting pattern in different age groups in VigiBase which, in some cases, could be due to susceptibilities to specific drug-related problems in certain age groups. Other likely explanations might be common drug usage and childhood diseases in these age groups. ConclusionsReports in VigiBase received internationally for more than 40 years reflect real concerns for children taking medicines. The study highlights adverse reactions with an increased reporting during recent years, particularly those connected to the introduction of ADHD medicines in the child population. To enhance patient safety, medication errors indicating administration and dosing difficulties of drugs, especially in the younger age groups, require further attention.
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| 34. |
- Strandell, Johanna, et al.
(author)
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Key Elements in Adverse Drug Interaction Safety Signals
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Other publication (other academic/artistic)abstract
- Background: Effective surveillance of adverse drug interactions (a problematic drug combination resulting in an adverse drug reaction (ADR)) in large collections of individual case safety reports (ICSRs) requires a combination of expert clinical assessments and efficient algorithms. To date, most methods proposed for adverse drug interaction surveillance focus on disproportionality analysis, although a recent study proposed that the reported clinical and pharmacological information is also useful for systematic screening of adverse drug interactions. Objective: The purpose of this study is to identify and describe key elements in adverse drug interaction safety signals. Methods: Altogether 137 case reports from three previously published safety signals of suspected adverse drug interaction were re-evaluated using an operational algorithm for causality analysis of drug interactions; the Drug Interaction Probability Scale (DIPS). Reports in the WHO Global ICSR Database, VigiBase, and their corresponding original files were analysed, examining whether the DIPS elements were registered. The retrieved case information was specified as being listed in the structured fields, free text and, in total. In addition, information not covered by DIPS, such as explicit notifications of a suspected drug interaction by the reporter or the pharmacovigilance centre was also registered. Results: As expected from the data used in this analysis, the most frequently fulfilled DIPS elements were: objective evidence (such as ADR) of a drug interaction (137 cases; 100%). Other frequent elements were (ranked order) plausible time to onset (53 cases; 38%), and resolution of the ADR after terminating the drug inducing the interaction (10 cases; 7%). Ten cases (7%) fulfilled both a plausible time to onset and resolution of the ADR after stopping the drug. Positive rechallenge was only reported in 3 cases (2%). For 32 cases additional information was reported in free text in the original files that were not available in VigiBase. A suspected drug interaction was noted by the reporter in 47 original cases (35%) and more than 80% of these were assessed as a possible or probable drug interaction according to the DIPS classification. Among cases without notes of suspected interactions were 58 original reports (64%) assessed as possible (56 cases) or probable (2 cases). Conclusions: A plausible time to onset pattern and resolution of the ADR after withdrawal of the drug inducing the interaction frequently strengthened the suspected causality of a drug interaction. Particularly strong cases were those containing both these key elements. Since this information is often available in structured format, it could potentially be used to automatically highlight strong cases in firstpass screening. Finally, this analysis also demonstrated the importance of free text where particularly relevant clinically details such as timeliness, severity, resolution of the reaction after withdrawal of the drug inducing the interaction, possible alternative causes, and dosage changes are available.
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| 35. |
- Strandell, Johanna, et al.
(author)
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Key Elements in Adverse Drug Interaction Safety Signals An Assessment of Individual Case Safety Reports
- 2013
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In: Drug Safety. - : Adis. - 0114-5916 .- 1179-1942. ; 36:1, s. 63-70
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Journal article (peer-reviewed)abstract
- Background A large proportion of potential drug interactions are known from pre-authorization studies, but adverse drug reactions (ADRs) due to interactions (adverse drug interactions) are often first detected through astute observation in clinical practice. Individual case safety reports (ICSRs) are collected from broad patient populations and allow for the identification of groups of similar reports. Systematic screening for adverse drug interactions in ICSRs will require an understanding of which information on these reports can be suggestive of adverse drug interactions. less thanbrgreater than less thanbrgreater thanObjective The aim of the study was to identify what reported information may support the identification of drug interaction safety signals in collections of ICSRs. less thanbrgreater than less thanbrgreater thanMethods Three previously published safety signals of suspected adverse drug interactions were re-evaluated. To this end, 137 reports related to these signals were retrieved from the WHO Global ICSR Database, VigiBase (TM), and corresponding original reports were obtained from national pharmacovigilance centres. Criteria from an operational score for causality analysis of drug interactions of clinical cases, the Drug Interaction Probability Scale (DIPS), were applied to each of these reports with the aim of identifying what supportive information tends to be available in ICSRs. For three DIPS elements (plausible time course, resolution of the ADR after terminating the drug inducing the interaction without changes in affected drug therapy (positive dechallenge) and alternative causes of the reaction) we also compared the amount of information in VigiBase (TM) and in original reports, and in free text and structured data. less thanbrgreater than less thanbrgreater thanResults Commonly fulfilled DIPS elements on reports supporting an adverse drug interaction signal were plausible time course (50 reports; 36 %) and positive dechallenge (8 reports; 6 %). Alternative causes for the observed adverse reaction were observed in 72 (53 %) reports. We found limited differences between VigiBase (TM) and original reports for the structured data, although a substantial amount of additional information was available in free text in original reports. less thanbrgreater than less thanbrgreater thanConclusions Information on plausible time courses and resolution of the adverse reaction upon withdrawal of the drug suspected to have induced the interaction may be a useful element in identifying suspected adverse drug interactions from ICSRs. Of these, plausible time course is by far the most commonly reported element in the three signals studied here. Our analysis also demonstrated the importance of sharing and analysing information available in free text where relevant clinical details are often available, such as those mentioned above, along with severity and dosage changes.
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| 36. |
- Strandell, Johanna, et al.
(author)
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The Development and Evaluation of Triage Algorithms for Early Discovery of Adverse Drug Interactions
- 2013
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In: Drug Safety. - : Adis. - 0114-5916 .- 1179-1942. ; 36:5, s. 371-388
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Journal article (peer-reviewed)abstract
- Background Around 20 % of all adverse drug reactions (ADRs) are due to drug interactions. Some of these will only be detected in the postmarketing setting. Effective screening in large collections of individual case safety reports (ICSRs) requires automated triages to identify signals of adverse drug interactions. Research so far has focused on statistical measures, but clinical information and pharmacological characteristics are essential in the clinical assessment and may be of great value in first-pass filtering of potential adverse drug interaction signals. less thanbrgreater than less thanbrgreater thanObjective The aim of this study was to develop triages for adverse drug interaction surveillance, and to evaluate these prospectively relative to clinical assessment. less thanbrgreater than less thanbrgreater thanMethods A broad set of variables were considered for inclusion in the triages, including cytochrome P450 (CYP) activity, explicit suspicions of drug interactions as noted by the reporter, dose and treatment overlap, and a measure of interaction disproportionality. Their unique contributions in predicting signals of adverse drug interactions were determined through logistic regression. This was based on the reporting in the WHO global ICSR database, VigiBase (TM), for a set of known adverse drug interactions and corresponding negative controls. Three triages were developed, each producing an estimated probability that a given drug-drug-ADR triplet constitutes an adverse drug interaction signal. The triages were evaluated against two separate benchmarks derived from expert clinical assessment: adverse drug interactions known in the literature and prospective adverse drug interaction signals. For reference, the triages were compared with disproportionality analysis alone using the same benchmarks. less thanbrgreater than less thanbrgreater thanResults The following were identified as valuable predictors of adverse drug interaction signals: plausible CYP metabolism; notes of suspected interaction by the reporter; and reports of unexpected therapeutic response, altered therapeutic effect with dose information and altered therapeutic effect when only two drugs had been used. The new triages identified reporting patterns corresponding to both prospective signals of adverse drug interactions and already established ones. They perform better than disproportionality analysis alone relative to both benchmarks. less thanbrgreater than less thanbrgreater thanConclusions A range of predictors for adverse drug interaction signals have been identified. They substantially improve signal detection capacity compared with disproportionality analysis alone. The value of incorporating clinical and pharmacological information in first-pass screening is clear.
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| 37. |
- Tregunno, Philip Michael, et al.
(author)
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Performance of Probabilistic Method to Detect Duplicate Individual Case Safety Reports
- 2014
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In: Drug Safety. - : Springer Science and Business Media LLC. - 0114-5916 .- 1179-1942. ; 37:4, s. 249-258
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Journal article (peer-reviewed)abstract
- Individual case reports of suspected harm from medicines are fundamental for signal detection in postmarketing surveillance. Their effective analysis requires reliable data and one challenge is report duplication. These are multiple unlinked records describing the same suspected adverse drug reaction (ADR) in a particular patient. They distort statistical screening and can mislead clinical assessment. Many organisations rely on rule-based detection, but probabilistic record matching is an alternative. The aim of this study was to evaluate probabilistic record matching for duplicate detection, and to characterise the main sources of duplicate reports within each data set. vigiMatch (TM), a published probabilistic record matching algorithm, was applied to the WHO global individual case safety reports database, VigiBase(A (R)), for reports submitted between 2000 and 2010. Reported drugs, ADRs, patient age, sex, country of origin, and date of onset were considered in the matching. Suspected duplicates for the UK, Denmark, and Spain were reviewed and classified by the respective national centre. This included evaluation to determine whether confirmed duplicates had already been identified by in-house, rule-based screening. Furthermore, each confirmed duplicate was classified with respect to the likely source of duplication. For each country, the proportions of suspected duplicates classified as confirmed duplicates, likely duplicates, otherwise related, and unrelated were obtained. The proportions of confirmed or likely duplicates that were not previously known by the national organisation were determined, and variations in the rates of suspected duplicates across subsets of reports were characterised. Overall, 2.5 % of the reports with sufficient information to be evaluated by vigiMatch were classified as suspected duplicates. The rates for the three countries considered in this study were 1.4 % (UK), 1.0 % (Denmark), and 0.7 % (Spain). Higher rates of suspected duplicates were observed for literature reports (11 %) and reports with fatal outcome (5 %), whereas a lower rate was observed for reports from consumers and non-health professionals (0.5 %). The predictive value for confirmed or likely duplicates among reports flagged as suspected duplicates by vigiMatch ranged from 86 % for the UK, to 64 % for Denmark and 33 % for Spain. The proportions of confirmed duplicates that were previously unknown to national centres ranged from 89 % for Spain, to 60 % for the UK and 38 % for Denmark, despite in-house duplicate detection processes in routine use. The proportion of unrelated cases among suspected duplicates were below 10 % for each national centre in the study. Probabilistic record matching, as implemented in vigiMatch, achieved good predictive value for confirmed or likely duplicates in each data source. Most of the false positives corresponded to otherwise related reports; less than 10 % were altogether unrelated. A substantial proportion of the correctly identified duplicates had not previously been detected by national centre activity. On one hand, vigiMatch highlighted duplicates that had been missed by rule-based methods, and on the other hand its lower total number of suspected duplicates to review improved the accuracy of manual review.
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