Benefits and strengths of the disproportionality analysis for identification of adverse drug reactions in a pharmacovigilance database

Montastruc, Jean‐Louis; Sommet, Agnès; Bagheri, Haleh; Lapeyre‐Mestre, Maryse

doi:10.1111/j.1365-2125.2011.04037.x

Cited by 367 publications

(404 citation statements)

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“…Proportional Reporting Ratio (PRR) represents a direct measure of the strength of a safety signal. A PRR is the ratio of the proportion of all reported cases of the event of interest among people exposed to a particular drug compared with the corresponding proportion among people exposed to all or several other drugs [6]. The breakdown of PRR can be seen in the equation: = ( / + )/( / + ) This algorithm helps with avoiding biases caused by varying details in reports.…”

Section: Proposed Methodologymentioning

confidence: 99%

Pharmacovigilance, signal detection using statistical data mining methods

Sriramakrishnan¹,

Selvam²,

Mariappan³

et al. 2018

IJET

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Pharmacovigilance programmes monitor and help safeguarding the use of medicines which is grave to the success of public health programmes. Identifying new possible risks and developing risk minimization action plans to prevent or ease these risks is at the heart of all pharmacovigilance activities throughout the product lifecycle. In this paper we examine the use of data mining algorithms to identify signals from adverse events reported. The capabilities include screening, data mining and frequency tabulation for potential signals, including signal estimation using established statistical signal detection methods. We have standard processes, algorithms and follow current requirements for signal detection and risk management activities.

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Section: Proposed Methodologymentioning

confidence: 99%

Pharmacovigilance, signal detection using statistical data mining methods

Sriramakrishnan¹,

Selvam²,

Mariappan³

et al. 2018

IJET

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“…It is only the convergence of proofs which allows final conclusions and decisions in pharmacovigilance. Thus, the notion of 'levels of evidence', widely used for evaluating drug efficacy, cannot be actually applied in the field of ADRs; all methods are of interest in the evaluation of ADRs [16].…”

Section: Pharmacovigilance Tools: An Overviewmentioning

confidence: 99%

“…Some benefits and strengths of using DMAs are undisputed, since they are quick and inexpensive analyses routinely performed by regulators and researchers for drug safety evaluation [16]. Apart from the hypothesisgenerating purpose of signal detection, other important application of this method are (a) validation of a pharmacological hypothesis about the mechanism of occurrence of ADRs [58]; (b) characterization of the safety profile of drugs [59].…”

Section: Dmas: Current Debatementioning

confidence: 99%

Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)

Poluzzi¹,

Raschi²,

Piccinni³

et al. 2012

Data Mining Applications in Engineering and Medicine

174

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Background: Acute kidney injury (AKI) is associated with signi cant increases in short-and long-term morbidity and mortality. Drug-induced AKI is a major concern in the present healthcare system. Our spontaneous reporting system (SRS) analysis assessed links between AKIs, along with patients' age, as healthcare-associated risks and administered anti-infectives. We also generated anti-infectives-related AKI-onset pro les. Method: We calculated adjusted reporting odds ratios (RORs) for reports of anti-infectives-related AKIs (per Medical Dictionary for Regulatory Activities) in the Japanese Adverse Drug Event Report database and evaluated associations between anti-infectives and age by association rule mining. We evaluated time-to-onset data and hazard types using the Weibull parameter. Results: Among 534,688 reports (submission period: April 2004-June 2018), there were 21,727 AKI events. Anti-infective treatments including glycopeptide antibacterials, uoroquinolones, third-generation cephalosporins, triazole derivatives, and carbapenems were associated with 596, 494, 341, 315, and 313 AKI incidences, respectively. Adjusted RORs of anti-infectives-related AKIs increased among older patients and were higher in anti-infective combination therapies [anti-infectives, ≥ 2; ROR, 2.75 (2.56-2.95)] than in monotherapies [ROR, 1.52 (1.45-1.61)]. In association rule mining, the number of anti-infectives and age were associated with anti-infectives-related AKI lift values (as consequent). Moreover, 48.1% of AKIs occurred within 5 days (median, 5.0 days) of anti-infective therapy initiation. Conclusion: Thus, adjusted RORs derived from our new SRS analysis indicate potential AKI risks linked to age and number of administered anti-infectives.

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“…This is an important methodological question when considering the special section in this month's issue of the Journal featuring a study of disproportionality analysis in assessing spontaneous reports [9]. Here, in two related commentaries we draw attention to the debate surrounding the increasing use of disproportionality analysis, and emphasize for our readers, the pros and cons of such methods [10,11].The evaluation of relative or absolute risks from the numerous spontaneous reporting databanks has so far been hindered by the lack of information on number of people exposed to the drug, thus making it almost impossible to determine accurately if the excess of reports of a specific adverse event with a particular drug (relative to other similar drugs) was due to greater numbers of patients taking the drug or a genuine reflection of a higher potential incidence for harm.…”

mentioning

confidence: 99%