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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, AuthorCount:3

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Objective: Rhabdomyolysis is a rare and potentially serious adverse drug reaction (ADR) to antipsychotic medicines. The aim of this study was to investigate the clinical circumstances surrounding the diagnosis of rhabdomyolysis in children and adolescents treated with antipsychotic medicines. We also critically reviewed individual case safety reports (ICSRs) of suspected ADRs to evaluate how clinically useful they can be in a case series analysis. Methods: This was a descriptive and an exploratory study. Published case reports and ICSRs from the World Health Organization (WHO) Global ICSR database, VigiBase, reported with rhabdomyolysis and antipsychotic medicines for patients <= 17 years years of age were described. Reporting patterns of ICSRs with rhabdomyolysis and antipsychotic medicines were explored in VigiBase for children and adolescents and for adults. The VigiBase ICSRs were also systematically evaluated regarding the report content. Results: Of the 26 evaluated reports, 6 co-reported neuroleptic malignant syndrome (NMS) and 20 reports concerned rhabdomyolysis in the absence of NMS. The reported suspected antipsychotic medicines for these 20 reports were olanzapine, risperidone, haloperidol, paliperidone, quetiapine, clozapine, cyamemazine, and aripiprazole. In VigiBase, rhabdomyolysis (in the absence of NMS) was reported more frequently with olanzapine relative to all reports for children and adolescents with antipsychotic medicines. In the range of events that preceded rhabdomyolysis, muscle pains and abdominal pain were commonly recorded to have started during the week prior to the diagnosis. Other preceding symptoms were general weakness and dark urine. Onset of rhabdomyolysis for most patients occurred at any time within 2 months of starting antipsychotic treatment, in several cases triggered by changes to the patient's drug therapy or known risk factors of rhabdomyolysis. It was found that ICSRs can contribute with additional information, but that

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., AuthorCount:4

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There is great interest in using electronic patient records for early signal detection. However, the potential opportunities need to be balanced agaisnt the potential problems.

Background In screening the Intercontinental Medical Statistics (IMS) Health Disease Analyzer database of GP records from the UK, an increased registration of pneumonia subsequent to the prescription of some antipsychotic medicines was identified. Aim To investigate the temporal pattern between antipsychotic prescriptions and pneumonia with respect to age, type of pneumonia and other chest infections, and antipsychotic class. Design of study Self-controlled cohort analysis. Setting Electronic health records from the UK IMS Health Disease Analyzer database. Method Three groups of pneumonia-related International Classification of Diseases (ICD)-10 terms and prescriptions of atypical and conventional antipsychotic medicines were studied. Separate analyses were carried out for patients aged >= 65 years. The observed rate of pneumonia terms registered in different time periods in connection to antipsychotic prescriptions was contrasted to the overall rate of pneumonia terms relative to prescriptions of other drugs in the same dataset. Results In patients aged >= 65 years, an increased registration of a group of terms defined as 'acute chest infections', after atypical antipsychotic prescriptions, was identified. The corresponding increase after conventional antipsychotic prescriptions was much smaller. Bronchopneumonia had a striking increase after both atypical and conventional antipsychotic prescriptions, and was commonly recorded with fatal outcome. Few registrations of hypostatic. pneumonia were noted. Patients aged <65 years did not have a higher rate of acute chest infections after receiving antipsychotic prescriptions. Conclusion The consistent pattern of an increased rate of chest infections after atypical antipsychotic prescriptions in older people seen in this outpatient study, together with the higher risk shown in a previous study on hospitalised patients, suggests a causal relationship. This is of importance since bronchopneumonia seems highly linke

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.

AIMS: In a systematic screening of the World Health Organization Adverse Drug Reaction database, VigiBase, in July 2008, a measure of association used to detect interactions (Omega) highlighted azithromycin with the individual statins atorvastatin, lovastatin and simvastatin and rhabdomyolysis. The aim was to examine all reports including rhabdomyolysis-azithromycin and statins in VigiBase to assess if the data were suggestive of an interaction. METHODS: The individual case reports in VigiBase and the original files were reviewed. In order to investigate the reporting over time for rhabdomyolysis with azithromycin and statins to VigiBase, Omega values were generated retrospectively. RESULTS: The reporting over time showed that rhabdomyolysis under concomitant use of azithromycin and statins was reported more often than expected from 2000 and onwards in Vigibase. After exclusion of possible duplicates and follow-up reports, 53 cases from five countries remained. Rhabdomyolysis occurred shortly after initiation of azithromycin in 23% of cases. In 11 patients an interaction had been suggested by the reporter. With the exception of one patient, the statin doses reported were within the recommended daily doses. CONCLUSIONS: Case reports in VigiBase are suggestive that interactions between azithromycin and statins resulting in rhabdomyolysis may occur. This analysis showed the potential of the newly developed disproportionality measure, Omega, which can help to identify drug interactions in VigiBase in the future. The results also showed that reviewing spontaneous reports can add information to drug interactions not established previously.

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Part of urn:nbn:se:su:diva-6764

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.