Novel Statistical Tools for Monitoring the Safety of Marketed Drugs

Almenoff, June; Pattishall, Edward N.; Gibbs, Trevor; DuMouchel, William; Evans, Stephen; Yuen, Nancy A.

doi:10.1038/sj.clpt.6100258

Cited by 216 publications

(172 citation statements)

References 54 publications

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“…The sum of the numbers of DKA reports for each SGLT2i may exceed the number of DKA reports for any SGLT2i because reports can list more than one SGLT2i. The information component (IC) is a safety signal logarithmic measure of disproportionality and was calculated as described in [7]. The PRR is analogous to relative risk in a cohort study, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…In pharmacovigilance assessment, disproportionality emerges when a specific adverse event is associated with a given drug, whereas in the absence of disproportionality the distributions of reported adverse events are similar across drugs [7]. The FAERS database contains adverse events filed to the FDA from the first quarter (Q1) of 2004 to the third quarter (Q3) of 2016, in a total of 8,554,531 reports.…”

Section: Methodsmentioning

confidence: 99%

“…We first ran a query on AERSMine spanning the entire FAERS database (see electronic supplementary material [ESM] Methods) to obtain the number of cases, indications and concomitant medications, and to calculate the rates and measures of disproportionality (proportional reporting ratios [PRRs] and safety signals [information components] [7]). After gathering this overall picture, we mined the FAERS database from 2014 Q1 to 2016 Q3 using a custom procedure developed to collect details of each single case.…”

Section: Methodsmentioning

confidence: 99%

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SGLT2 inhibitors and diabetic ketoacidosis: data from the FDA Adverse Event Reporting System

2017

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Aims/hypothesis Sodium-glucose co-transporter-2 inhibitors (SGLT2i) are indicated for the treatment of type 2 diabetes and may also improve glucose control in type 1 diabetes. In 2015, regulatory agencies warned that SGLT2i may favour diabetic ketoacidosis (DKA). We provide a detailed analysis of DKA reports in which an SGLT2i was listed among suspect or concomitant drugs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). Methods We first analysed the entire public FAERS up to September (third quarter [Q3]) 2016 to extract the number of reports, background indications and concomitant medications, and to calculate proportional reporting ratios (PRRs) and safety signals. We then mined single FAERS files from the first quarter (Q1) of 2014 to 2016 Q3 to obtain detailed information on DKA reports. Results The FAERS database contains >2500 DKA reports in which SGLT2i are listed as suspect or concomitant drugs. The PRR of DKA in reports including vs those not including an SGLT2i and having a diabetes indication was 7.9 (95% CI 7.5, 8.4) and was higher for type 1 diabetes. Several concomitant conditions were less prevalent in DKA reports with SGLT2i vs DKA reports filed for other drugs. A detailed analysis of 2397 DKA reports for SGLT2i from 2014 Q1 to 2016 Q3 revealed a predominance of women, an extremely wide range of age and body weight, and a highly variable duration of SGLT2i treatment before onset of DKA. In 37 individuals (1.54%), DKA was fatal. Conclusions/interpretation Based on the profile of these reports, SGLT2i-associated DKA may not be limited to any particular demographic or comorbid subpopulation and can occur at any duration of SGLT2i use.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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SGLT2 inhibitors and diabetic ketoacidosis: data from the FDA Adverse Event Reporting System

2017

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“…Data mining methods [12,13] have been extensively used in routine signal detection [14] of single drugadverse drug reaction (ADR) combinations in pharmacovigilance. Measures of disproportionality have also been proposed in order to detect drug interactions [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Rhabdomyolysis a result of azithromycin and statins: an unrecognized interaction

Strandell

Bate

Hägg

et al. 2009

Brit J Clinical Pharma

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WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Rhabdomyolysis is a serious but rare adverse effect of statins. • The mechanism of rhabdomyolysis with statins is poorly defined, but the occurrence is known to increase with dose/ concentration. • Some macrolides are known to interact with statins; however, azithromycin has not been described to interact with statins with the exception of two literature case reports. WHAT THIS STUDY ADDS • A case series in the World Health Organization Adverse Drug Reaction (WHO‐ADR) database was suggestive of a possible drug interaction between statins and azithromycin with rhabdomyolysis. • A disproportionality measure, Omega, was shown to identify previously not recognized suspected drug interactions within the WHO‐ADR dataset. 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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“…The PRR (or, rather, one standard error below the PRR itself-and it is not clear if that would have been a better measure here) was a contributor to the strength of the evidence. Seabroke et al [10] developed this further, using a more complex system of prioritization, and chose a cut-off of 10 for an alternative to the PRRthe empirical Bayes geometric mean [11]-but a cut-off of 3 for the RR from formal studies.…”

Section: What Can We Learn From This?mentioning

confidence: 99%

What Is the Plural of a ‘Yellow’ Anecdote?

Evans

2015

Drug Saf

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Novel Statistical Tools for Monitoring the Safety of Marketed Drugs

Cited by 216 publications

References 54 publications

SGLT2 inhibitors and diabetic ketoacidosis: data from the FDA Adverse Event Reporting System

SGLT2 inhibitors and diabetic ketoacidosis: data from the FDA Adverse Event Reporting System

Rhabdomyolysis a result of azithromycin and statins: an unrecognized interaction

What Is the Plural of a ‘Yellow’ Anecdote?

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