scholarly journals The impact of database restriction on pharmacovigilance signal detection of selected cancer therapies

2017 ◽  
Vol 8 (5) ◽  
pp. 145-156 ◽  
Author(s):  
Manfred Hauben ◽  
Eric Hung ◽  
Jennifer Wood ◽  
Amit Soitkar ◽  
Daniel Reshef
Keyword(s):  
Adverse Event ◽  
Signal Detection ◽  
Safety Information ◽  
Adverse Event Reporting System ◽  
Package Insert ◽  
The United States ◽  
Drug Event ◽  
Reference Set ◽  
Oncology Drug ◽  
The Impact

Background: The aim of this study was to investigate whether database restriction can improve oncology drug pharmacovigilance signal detection performance. Methods: We used spontaneous adverse event (AE) reports in the United States (US) Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Positive control (PC) drug medical concept (DMC) pairs were selected from safety information not included in the product’s first label but subsequently added as label changes. These medical concepts (MCs) were mapped to the Medical Dictionary for Regulatory Activities (MedDRA) preferred terms (PTs) used in FAERS to code AEs. Negative controls (NC) were MCs with circumscribed PTs not included in the corresponding US package insert (USPI). We calculated shrinkage-adjusted observed-to-expected (O/E) reporting frequencies for the aforementioned drug–PT pairs. We also formulated an adjudication framework to calculate performance at the MC level. Performance metrics [sensitivity, specificity, positive and negative predictive value (PPV, NPV), signal/noise (S/N), F and Matthews correlation coefficient (MCC)] were calculated for each analysis and compared. Results: The PC reference set consisted of 11 drugs, 487 PTs, 27 MCs, 37 drug–MC combinations and 638 drug–event combinations (DECs). The NC reference set consisted of 11 drugs, 9 PTs, 5 MCs, 40 drug–MC combinations and 67 DECs. Most drug–event pairs were not highlighted by either analysis. A small percentage of signals of disproportionate reporting were lost, more noise than signal, with no gains. Specificity and PPV improved whereas sensitivity, NPV, F and MCC decreased, but all changes were small relative to the decrease in sensitivity. The overall S/N improved. Conclusion: This oncology drug restricted analysis improved the S/N ratio, removing proportionately more noise than signal, but with significant credible signal loss. Without broader experience and a calculus of costs and utilities of correct versus incorrect classifications in oncology pharmacovigilance such restricted analyses should be optional rather than a default analysis.

Understanding vaccine safety information from the Vaccine Adverse Event Reporting System

2004 ◽  
Vol 23 (4) ◽  
pp. 287-294 ◽  
Author(s):  
FREDERICK VARRICCHIO ◽  
JOHN ISKANDER ◽  
FRANK DESTEFANO ◽  
ROBERT BALL ◽  
ROBERT PLESS ◽  
...  
Keyword(s):  
Adverse Event ◽  
Reporting System ◽  
Safety Information ◽  
Adverse Event Reporting System ◽  
Vaccine Safety ◽  
Adverse Event Reporting ◽  
Event Reporting

scholarly journals Real World Risk of Splenic Rupture with G-CSF/GM-CSF Therapy: A Pharmacovigilance Assessment Using FDA Adverse Event Reporting System (FAERS) Database

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-33
Author(s):  
Anahat Kaur ◽  
Shuai Wang ◽  
Apoorva Jayarangaiah ◽  
Mariuxi Malone ◽  
Arlene Yu ◽  
...  
Keyword(s):  
Adverse Event ◽  
Reporting System ◽  
Splenic Rupture ◽  
Adverse Event Reporting System ◽  
The United States ◽  
Colony Stimulating Factor ◽  
Publicly Traded ◽  
Gm Csf ◽  
Life Threatening ◽  
Stimulating Factor

Introduction Splenic rupture is a rare but serious adverse event associated with the use of Granulocyte-Colony Stimulating Factor (G-CSF) and Granulocyte Macrophage-Colony Stimulating Factor (GM-CSF). Instances of spontaneous splenic rupture following administration of these hematopoietic growth factors have been sporadically documented in case reports. We aimed to conduct a more comprehensive study to generate signal for splenic rupture with G-CSF/GM-CSF therapy using disproportionality analysis. Methods The United States Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS) database, a pharmacovigilance database, was used to extract data. All reported splenic rupture cases between January 1, 1991 and December 31,2019 for G-CSF or GM-CSF were collected by using the search terms "Pegfilgrastim", "Filgrastim", "Lenograstim", "Filgrastim-sndz", "Sargramostim" and "Pegfilgrastim-jmdb". We used reporting odds ratio (ROR) for proportionality analysis. ROR was calculated by SPSS 26 and considered significant with p value <0.05 when lower limit of 95% Confidence Interval (CI) of a ROR signal exceeded 1.0. Results Total of 58,725 reports of adverse reactions linked to G-CSF and GM-CSF were extracted from FAERS database. Out of these, splenic rupture was included as adverse event in 100 cases (Table 1). The median age of diagnosis was 54 years (interquartile range 46-62). Gender was reported in 92 cases, out of which 47 were male and 45 females. Final outcomes of 100 patients with growth factor associated splenic rupture showed that 31 were hospitalized, 30 had life-threatening illness and 18 died. ROR was calculated separately for Pegfilgrastim 16.03 (95% CI 11.90-21.59), Filgrastim (49.79; 36.32-68.26), Lenograstim (133.82; 55.39-323.32), Filgrastim-sndz (87.87; 28.21-273.71), Filgrastim+Pegfilgrastim (35.01; 11.26-108.85), Filgrastim+Sargramostim (895.20; 214.83-3730.27) and for all G-CSF/GM-CSF drugs taken together (28.13; 22.92-34.52). All calculated RORs were significant (Table 2). Conclusion This study demonstrates disproportionately elevated signals of developing splenic rupture in patient receiving G-CSF/GM-CSF therapy as compared to those receiving other drugs based on the FAERS database. Physicians should be aware of this rare adverse event as it could potentially lead to life-threatening outcomes. Disclosures Kumar: Amgen:Current equity holder in publicly-traded company;Bristol-Myers Squibb:Current equity holder in publicly-traded company;AstraZeneca:Current equity holder in publicly-traded company;Aveo Pharma:Current equity holder in publicly-traded company;Viking Therapeutics:Current equity holder in publicly-traded company;Acadia Pharma:Current equity holder in publicly-traded company;Iovance Biosciences:Current equity holder in publicly-traded company;AIKIDO:Current equity holder in publicly-traded company;CRISPR Therapeutics:Current equity holder in publicly-traded company;PTC Therapeutics:Current equity holder in publicly-traded company;ELiLilly:Current equity holder in publicly-traded company;Bio Path Holdings:Current equity holder in publicly-traded company;Northwest Bio:Current equity holder in publicly-traded company;Vertex Pharmaceuticals:Current equity holder in publicly-traded company;Precision Biosciences:Current equity holder in publicly-traded company;Cara Therapeutics:Current equity holder in publicly-traded company;IDEXX Laboratories:Current equity holder in publicly-traded company;Editas Medicine:Current equity holder in publicly-traded company;ChemBio Diagnostics:Current equity holder in publicly-traded company;Beyond Spring:Current equity holder in publicly-traded company;Poseida Therapeutics:Current equity holder in publicly-traded company;Spectrum Pharmaceuticals Inc.:Current equity holder in publicly-traded company;Cardiff Oncology:Current equity holder in publicly-traded company;Contrafect Corp:Current equity holder in publicly-traded company;Globus Medicine Inc.:Current equity holder in publicly-traded company;Johnson and Johnson:Current equity holder in publicly-traded company;AbbVie:Current equity holder in publicly-traded company;Five Prime Therapeutics:Current equity holder in publicly-traded company;ADMA Biologics:Current equity holder in publicly-traded company;CVS Health:Current equity holder in publicly-traded company;AGNEUS:Current equity holder in publicly-traded company;Biotelemetry Inc.:Current equity holder in publicly-traded company;Pfizer:Current equity holder in publicly-traded company.

National survey on the effect of oncology drug shortages in clinical practice: A Hematology Oncology Pharmacy Association (HOPA) survey.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13609-e13609
Author(s):  
Sarah Hudson-Disalle ◽  
David L. DeRemer ◽  
Larry W Buie ◽  
Mark Hamm ◽  
Jeffrey Pilz ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Supportive Care ◽  
Cancer Patients ◽  
Medication Errors ◽  
The United States ◽  
Adverse Outcomes ◽  
Common Disease ◽  
Drug Shortages ◽  
Oncology Drug ◽  
The Impact

e13609 Background: Drug shortages are a clear and growing challenge. Prominent shortages included oncology medications and supportive care products essential for the care of cancer patients. Oncology drug shortages often result in disruptions in the timing of chemotherapy treatments, alterations in the dose or regimen administered, or even missed doses when alternative agents are unavailable. The purpose of this survey was to characterize the impact of oncology drug shortages across the United States, including the experiences of health care organizations, resource implications, and the impact on patient safety, patient care, and clinical trials. Methods: A 34-item online survey was distributed to HOPA membership of the Hematology Oncology Pharmacy Association to gather information on shortages of oncology drugs (i.e., all drugs essential in the care of cancer patients, including supportive care agents. Results: Sixty-eight organizations completed the survey; almost all completed by pharmacists, and analysis completed. Sixty-three percent of institutions reported one or more drugs shortages a month, with a 34.33% increase in 2019 from 2018. Sixty four percent of responded had incurred increased costs from oncology drugs shortages, with 7% noting reimbursement issues when switched to brand name therapies due to shortages. Treatment delays, reduced doses or alternative regimens were reported by 74.63% of respondents. The most common disease states which causes a dose delay of treatment included Acute Lymphocytic Leukemia, Lymphoma and Multiple Myeloma with dose reductions noted in 36.36%, 36.36 and 15.91%. The top five oncology drugs on shortage included epirubicin, flutamide, decitabine, mechlorethamine, dactinomycin with the top 5 supportive care drugs on shortage being noted as hydrocortisone, bivalirudin, promethazine, mycophenolate sodium and scopolamine. Respondents noted medication errors related to oncology drug shortages at 4.48%, with noted errors including incorrect conversion from iv to oral etoposide and incorrect EMR drug builds. Oncology Drug shortages impacted clinical trials in 13.4% of respondents in which 54.55% of respondents noting patients not being enrolled in clinical trials. Conclusions: A survey of US oncology pharmacists and technicians indicated that oncology drug shortages occurred frequently in 2020. Shortages led to delays in chemotherapy and changes in treatment or omission, complicated clinical research and increased the risk of medication errors and adverse outcomes.

Safety Signal Detection in the Drug Development Process

2015 ◽  
pp. 64-89
Author(s):  
Ramin B. Arani ◽  
Antoni F.Z. Wisniewski
Keyword(s):  
Adverse Event ◽  
Drug Development ◽  
Signal Detection ◽  
Safety Information ◽  
Safety Signal ◽  
Drug Development Process ◽  
Safety And Efficacy ◽  
Strength Of Association ◽  
Efficacy Profile ◽  
Complicated Task

Drug development is a complex set of inter-linked processes in which the cumulative understanding of a drug's safety and efficacy profile is shaped during different learning phases. Often, drugs are approved based on limited safety information, for example in highly at risk or rare disease populations. Therefore, post approval, regulatory organizations have mandated proactive surveillance strategies that include the collection of reported adverse events experienced by exposed populations, some of whom may have been on treatment for extended periods of time. Analyzing these accumulating adverse event reports to understand their clinical significance, given the limitations imposed by the methods of data collection, is a complicated task. The aim of this chapter is to provide the readers with a general understanding of safety signal detection and assessment, followed by a description of statistical methods (both classical and Bayesian) typically utilized for quantifying the strength of association between a drug and an adverse event.

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