Best Practices for Improving the Quality of Individual Case Safety Reports in Pharmacovigilance

Kumar, Rakesh; Pranay, Kumar; Kalaiselvan, Vivekanandan; Kaur, Ishwinder; Singh, Gyanendra

doi:10.1177/2168479016634766

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Moreover, LMICs face qualitative challenges due to poorly documented reports [ 39 ]. Needless to say, the comprehensiveness of a report plays a critical role in pharmacovigilance in order to aid in the detection of signals [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, not all respondents were able to provide information to thoroughly describe the direct impact of the apps. Further investigation would be warranted on outcomes, such as the time from event onset to data submission on VigiBase, and the report quality appraised through various measurements [ 39 , 40 , 42 ] between those received from ADR reporting apps and those via conventional routes.…”

Section: Discussionmentioning

confidence: 99%

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Smartphone-based mobile applications for adverse drug reactions reporting: global status and country experience

Fukushima

Iessa

Balakrishnan

et al. 2022

BMC Med Inform Decis Mak

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Background Smartphone technology can support paperless reporting of adverse drug reactions (ADRs). The aims of this study were to systematically assess smartphone ADR-reporting applications, understand their qualitative and quantitative impact on ADR reporting, and garner key lessons from owners and developers. Methods This study had three components: (1) An assessment of ADR-reporting apps, (2) an online survey on the impact of app implementation on ADR reporting and the experiences of app developers and owners, and (3) a search of VigiBase, the World Health Organization global database of individual case safety reports (ICSRs), to observe trends in the number of ADR reports targeting countries where the apps were implemented. Results Twenty-two apps were included. Eight out of the 22 apps were for countries in the WHO African region. Features observed included E2B data elements (E stands for efficacy) and functions supporting reporting and user engagement. Seventeen app developers and owners answered to the survey and reported overall positive experiences with app features, and post-launch increases in the total number of ICSRs. User type and user environment were cited as factors influencing app use: Respondents said younger people and/or those with an inclination to use technology were more likely to use apps compared to older or more technology-averse people, while respondents in countries with limited internet connectivity reported persistent difficulties in app use. Conclusions Smartphone apps for reporting ADRs offer added value compared to conventional reporting tools. Reporting tools should be selected based on interface features and factors that may influence app usage.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Smartphone-based mobile applications for adverse drug reactions reporting: global status and country experience

Fukushima

Iessa

Balakrishnan

et al. 2022

BMC Med Inform Decis Mak

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“…We developed a report completeness measure based on work by The Uppsala Monitoring Centre and the Pharmacovigilance Programme of India to quantify the amount of information available in an ADE report. 18 , 19 The features used are displayed in Table 2 . Time-to-onset is defined as the time from treatment initiation to the suspected ADE.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of four machine learning models for signal detection

Dauner,

Leal,

Adam

et al. 2023

Therapeutic Advances in Drug Safety

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Background: Logistic regression-based signal detection algorithms have benefits over disproportionality analysis due to their ability to handle potential confounders and masking factors. Feature exploration and developing alternative machine learning algorithms can further strengthen signal detection. Objectives: Our objective was to compare the signal detection performance of logistic regression, gradient-boosted trees, random forest and support vector machine models utilizing Food and Drug Administration adverse event reporting system data. Design: Cross-sectional study. Methods: The quarterly data extract files from 1 October 2017 through 31 December 2020 were downloaded. Due to an imbalanced outcome, two training sets were used: one stratified on the outcome variable and another using Synthetic Minority Oversampling Technique (SMOTE). A crude model and a model with tuned hyperparameters were developed for each algorithm. Model performance was compared against a reference set using accuracy, precision, F1 score, recall, the receiver operating characteristic area under the curve (ROCAUC), and the precision-recall curve area under the curve (PRCAUC). Results: Models trained on the balanced training set had higher accuracy, F1 score and recall compared to models trained on the SMOTE training set. When using the balanced training set, logistic regression, gradient-boosted trees, random forest and support vector machine models obtained similar performance evaluation metrics. The gradient-boosted trees hyperparameter tuned model had the highest ROCAUC (0.646) and the random forest crude model had the highest PRCAUC (0.839) when using the balanced training set. Conclusion: All models trained on the balanced training set performed similarly. Logistic regression models had higher accuracy, precision and recall. Logistic regression, random forest and gradient-boosted trees hyperparameter tuned models had a PRCAUC ⩾ 0.8. All models had an ROCAUC ⩾ 0.5. Including both disproportionality analysis results and additional case report information in models resulted in higher performance evaluation metrics than disproportionality analysis alone.

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“…Variables used in existing methods for the assessment of spontaneous report completeness (vigiGrade [ 12 ]; modified vigiGrade [ 17 – 19 ]), clinical quality scoring (ClinDoc [ 15 ]), and utility (FDA pharmacovigilance utility scoring [ 13 ]) were reviewed, evaluated and agreed upon by the consensus of a group of eight safety experts with a diversity of experiences who routinely conduct safety assessments. In addition, variables commonly used by safety staff, but not included in the aforementioned methodologies, were also considered.…”

Section: Methodsmentioning

confidence: 99%

“…The vigiGrade scoring system, developed to highlight quality issues in ICSR datasets by scoring the completeness of case reports, is currently the most widely used of these tools (predominantly in the field of pharmaceutical regulatory science), and both the vigiGrade and modified vigiGrade methods have previously been used successfully in several countries [ 12 , 17 – 19 ]. However, a previous attempt to apply vigiGrade for prioritisation within a marketing authorisation holder (MAH) database failed to improve quantitative signal detection [ 20 ], suggesting that ‘completeness’ does not necessarily imply ‘utility’ (i.e.…”

Section: Introductionmentioning

confidence: 99%

Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility

Kara,

Powell,

Mahaux

et al. 2023

Drug Saf

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Introduction Spontaneous reporting of adverse events has increased steadily over the past decades, and although this trend has contributed to improving post-marketing surveillance pharmacovigilance activities, the consequent amount of data generated is challenging to manually review during assessment, with each individual report requiring review by pharmacovigilance experts. This highlights a clear need for alternative or complementary methodologies to help prioritise review. Objective Here, we aimed to develop and test an automated methodology, the Clinical Utility Score for Prioritisation (CUSP), to assist pharmacovigilance experts in prioritising clinical assessment of safety data to improve the rapidity of case series review when case volumes are large. Methods The CUSP method was tested on a reference dataset of individual case safety reports (ICSRs) associated to five drug-event pairs that led to labelling changes. The selected drug-event pairs were of varying characteristics across the portfolio of GSK’s products. Results The mean CUSP score for ‘key cases’ and ‘cases of low utility’ was 19.7 (median: 21; range: 7–27) and 17.3 (median: 19; range: 4–27), respectively. CUSP distribution for ‘key cases’ were skewed toward the higher range of scores compared with ‘all cases’. The overall performance across each individual drug-event pair varied considerably, showing higher predictive power for ‘key cases’ for three of the drug-event pairs (average CUSP between these three: 22.8; range: 22.5–23.0) and lesser power for the remaining two (average CUSP between these two: 17.6; range: 14.5–20.7). Conclusion Although several tools have been developed to assess ICSR completeness and regulatory utility, this is the first attempt to successfully develop an automated clinical utility scoring system that can support the prioritisation of ICSRs for clinical review. Supplementary Information The online version contains supplementary material available at 10.1007/s40264-023-01327-y.

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Best Practices for Improving the Quality of Individual Case Safety Reports in Pharmacovigilance

Abstract: The higher quality ICSRs aids in more effective identification of new drug safety alerts and provides evidence-based information for regulating the drug safety.

Cited by 6 publications

References 9 publications

Smartphone-based mobile applications for adverse drug reactions reporting: global status and country experience

Smartphone-based mobile applications for adverse drug reactions reporting: global status and country experience

Evaluation of four machine learning models for signal detection

Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility

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