Venous thromboembolism in myelodysplastic syndrome patients receiving lenalidomide: results from postmarketing surveillance and data mining techniques.
ABSTRACT Multiple myeloma treatment with lenalidomide-based regimens is associated with risk of venous thromboembolism (VTE), particularly during concomitant use with erythropoiesis-stimulating agents (ESAs). The risk of VTE in myelodysplastic syndrome (MDS) patients treated with lenalidomide is not well characterized and the background risk in untreated patients is not known. This study set out to determine the reporting rate of VTE in MDS patients on lenalidomide in the two years of postmarketing experience in the US, and to investigate whether there is a disproportional signal of VTE in MDS patients on lenalidomide by screening the US FDA Adverse Event Reporting System (AERS) safety database.
The MDS population exposed to lenalidomide was obtained from RevAssist, the company's proprietary restrictive distribution programme. VTE reports were identified from the company's postmarketing surveillance safety database. The FDA AERS database was used for disproportionality analysis, and signal scores computed using three algorithms: multi-item gamma Poisson shrinker (MGPS), proportional reporting ratio (PRR), and reporting odds ratios (ROR).
A total of 7764 MDS patients were prescribed lenalidomide during the first two years of commercial use in the US. VTE representing deep vein thrombosis and pulmonary embolism was reported in 41 patients, a reporting rate of 0.53%. The computed signal scores did not exceed the statistical threshold for identification of a significant disproportional signal for VTE in MDS reports involving use of lenalidomide without concomitant use of ESAs. However, a disproportional signal of VTE was detected in MDS reports where lenalidomide was concurrently used with ESAs.
The VTE reporting rate for MDS patients receiving lenalidomide during the first two years of postmarketing exposure was low (0.53%). Disproportionality analysis demonstrated a statistically meaningful association of VTE with lenalidomide concomitantly used with ESAs in MDS patients, but the association was not statistically significant when lenalidomide was used in the absence of ESAs.
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ABSTRACT: Various data mining algorithms (DMAs) that perform disporportionality analysis on spontaneous reporting system (SRS) data are being heavily promoted to improve drug safety surveillance. The incremental value of DMAs is ultimately related to their ability to detect truly unexpected associations that would have escaped traditional surveillance and/or their ability to identify the same associations as traditional methods but with greater scientific efficiency. As to the former potential benefit, in the course of evaluating DMAs, we have observed what we call 'surprise reactions'. These adverse reactions may be discounted in manual review of adverse drug reaction (ADR) lists because they are less clinically dramatic, less characteristic of drug effects in general, less serious than the classical type B hypersensitivity reactions or may have subtle pharmacological explanations. Thus these reactions may only become recognised when post hoc explanations are sought based on more refined pharmacological knowledge of the formulation. The objective of this study was to explore notions of 'unexpectedness' as relates to signal detection and data mining by introducing the concept of 'surprise reactions' and to determine if the latter associations, often first reported in the literature, represent a type of ADR amenable to detection with the assistance of adjunctive statistical calculations on SRS data. Using commonly cited thresholds, the multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratios (PRRs) were applied to reports in the US FDA Adverse Event Reporting System (AERS) database of well documented 'surprise reactions' compiled by the authors. There were 34 relevant surprise reactions involving 29 separate drugs in 17 different drug classes. Using PRRs (PRR >2, chi(2) >4, N >2), 12 drug-event combinations were signalled before the first ADR citation appeared in MEDLINE, four occurred concurrently and 11 after. With empirical Bayes geometric mean (EBGM) analysis (EBGM >2, N >0), 12 signals occurred before, three concurrently and 11 after publication of the first literature citation. With EB(05) (EB(05)> or =2, N >0), six occurred before, two concurrently and 14 after MEDLINE citation. Pharmacovigilance is rather unique in terms of the number and variety of events under surveillance. Some events may be more appropriate targets for statistical approaches than others. The experience of many organisations is that most statistical disproportionalities represent known associations but our findings suggest there could be events that may be discounted on manual review of adverse event lists, which may be usefully highlighted via DMAs. Identification of surprise reactions may serve as an important niche for DMAs.Drug Safety 02/2007; 30(2):143-55. · 3.41 Impact Factor
- JAMA The Journal of the American Medical Association 01/2007; 296(21):2558-60. · 29.98 Impact Factor
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ABSTRACT: A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human reviewers in identifying associations worthy of further investigation (i.e., signals) embedded within a database consisting largely of background 'noise' containing reports of no substantial public health significance. Data mining algorithms are, therefore, being developed, tested and/or used by health authorities, pharmaceutical companies and academic researchers. After a focused review of postapproval drug safety signal detection, the authors explain how the currently used algorithms work and address key questions related to their validation, comparative performance, deployment in naturalistic pharmacovigilance settings, limitations and potential for misuse. Suggestions for further research and development are offered.Expert Opinion on Drug Safety 10/2005; 4(5):929-48. · 2.62 Impact Factor