First experiences with a tool to measure the level of clinical information present in adverse drug reaction reports

Oosterhuis, Ingrid; Rolfes, Leàn; Ekhart, Corine; Muller-Hansma, A H G; Härmark, Linda

doi:10.1080/14740338.2018.1400008

Cited by 13 publications

(27 citation statements)

References 9 publications

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“…Reviewers found that the tool was more difficult to use when multiple suspect products and relevant events were reported. This finding is consistent with the experience of other pharmacovigilance experts . More than one event was reported in 85% of cases, and more than one suspect product was reported in 8% of cases.…”

Section: Discussionsupporting

confidence: 90%

“…Because the provision of data in a structured data field does not necessarily translate to sufficiently complete information from a pharmacovigilance perspective, we conducted a manual review of a subset consumer reports to further assess quality differences between form types (GVR vs ConVR). We used the clinical documentation tool (ClinDoc) to compare the level of reporting of clinical information between the groups of reports . The tool assesses four domains: (1) adverse drug reaction (ADR) description; (2) chronology of the ADR; (3) suspect product description; and (4) patient characteristics.…”

Section: Methodsmentioning

confidence: 99%

“…We used the clinical documentation tool (ClinDoc) to compare the level of reporting of clinical information between the groups of reports. 11,12 The tool assesses four domains: (1) adverse drug reaction (ADR) description; (2) chronology of the ADR; (3) suspect product description; and (4) patient characteristics. Each domain consists of several subdomains (e.g., latency is a subdomain within the chronology domain).…”

Section: Manual Report Assessmentmentioning

confidence: 99%

“…This finding is consistent with the experience of other pharmacovigilance experts. 12 More than one event was reported in 85% of cases, and more than one suspect product was reported in 8% of cases. Because we adopted an inclusive approach for consistency that considered information as present if provided for any suspect product and any focal event, more information would be considered as present (e.g., if time to event onset was provided for one suspect product, but not the other, it was considered present).…”

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confidence: 99%

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Impact of a New Consumer Form on the Quantity and Quality of Adverse Event Reports Submitted to the United States Food and Drug Administration

et al. 2019

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Study Objective Consumers and healthcare professionals can voluntarily report adverse experiences associated with drug products to the United States Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS). Consumers and healthcare professionals used the same general voluntary reporting form (GVR) until mid‐2013, when a consumer voluntary reporting form (ConVR), written in plain language, was implemented. The objective of this study was to examine the effect of the ConVR on the quality and quantity of consumer reports submitted directly to FAERS. Design Descriptive; quasi‐experimental. Data Source FAERS database. Measurements and Main Results We identified all consumer and healthcare professional reports received directly by the FDA from January 1, 2011, through December 31, 2015. Report quality was defined by the completeness of 15 individual data fields and a structured tool measuring clinical documentation. An interrupted time series design was used to evaluate the impact on the quantity of consumer reports. Consumer reports submitted on the ConVR generally included more patient, product, and event data in the structured data fields than those submitted on the GVR. Fields with the greatest absolute percentage difference after the ConVR was introduced included race/ethnicity (+77.2%), product start and stop dates (+43% and +40.3%, respectively), dechallenge and rechallenge information (+19.1% and +29.4%, respectively), and medical history (+27%). Our structured assessment also classified more reports received on the ConVR as well documented relative to the GVR consumer reports (64.9% vs 37.8%, p<0.01). The time series model demonstrated an immediate increase of 499 consumer reports in the month following the ConVR's implementation (p<0.01). Conclusion Our findings suggest that the ConVR has contributed positively to both the quality and quantity of consumer reports in FAERS.

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Section: Discussionsupporting

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Section: Manual Report Assessmentmentioning

confidence: 99%

mentioning

confidence: 99%

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Impact of a New Consumer Form on the Quantity and Quality of Adverse Event Reports Submitted to the United States Food and Drug Administration

et al. 2019

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“…The study also found these app reports to be of a high average quality as measured by the vigiGrade completeness score, [ 12 ] with an average of 0.8 (range 0–1). For a report to be of most use for signal detection purposes, it needs to contain high-quality clinical information [ 13 ], which is needed for a good causality assessment. However, little is known concerning the characteristics, quality and the contribution to signal detection of spontaneous ADR reports submitted via an app compared with conventional routes.…”

Section: Introductionmentioning

confidence: 99%

Characteristics, Quality and Contribution to Signal Detection of Spontaneous Reports of Adverse Drug Reactions Via the WEB-RADR Mobile Application: A Descriptive Cross-Sectional Study

Oosterhuis

Taavola

Tregunno³

et al. 2018

Drug Saf

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IntroductionSpontaneous reporting of suspected adverse drug reactions is key for efficient post-marketing safety surveillance. To increase usability and accessibility of reporting tools, the Web-Recognising Adverse Drug Reactions (WEB-RADR) consortium developed a smartphone application (app) based on a simplified reporting form.ObjectiveThe objective of this study was to evaluate the characteristics, quality and contribution to signals of reports submitted via the WEB-RADR app.MethodsThe app was launched in the UK, the Netherlands and Croatia between July 2015 and May 2016. Spontaneous reports submitted until September 2016 with a single reporter were included. For each country, app reports and reports received through conventional means in the same time period were compared to identify characteristic features. A random subset of reports was assessed for clinical quality and completeness. The contribution to signal detection was assessed by a descriptive analysis.ResultsHigher proportions of app reports were submitted by patients in the UK (28 vs. 18%) and Croatia (32 vs. 7%); both p < 0.01. In the Netherlands, the difference was small (60 vs. 57%; p = 0.5). The proportion of female patients and the median patient ages in app reports submitted by patients were similar to the reference. The proportion of reports of at least moderate quality was high in both samples (app: 78–85%, reference: 78–98%), for all countries. App reports contributed to detecting eight potential safety signals at the national level, four of which were eventually signalled.ConclusionThe WEB-RADR app offers a new route of spontaneous reporting that shows promise in attracting reports from patients and that could become an important tool in the future. Patient demographics are similar to conventional routes, report quality is sufficient despite a simplified reporting form, and app reports show potential in contributing to signal detection.Electronic supplementary materialThe online version of this article (10.1007/s40264-018-0679-6) contains supplementary material, which is available to authorized users.

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Development of a multivariate prediction model to identify individual case safety reports which require clinical review

Gosselt

Bazelmans

Lieber

et al. 2022

Pharmacoepidemiology and Drug

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Background The number of Individual Case Safety Reports (ICSRs) in pharmacovigilance databases are rapidly increasing world‐wide. The majority of ICSRs at the Netherlands Pharmacovigilance Centre Lareb is reviewed manually to identify potential signal triggering reports (PSTR) or ICSRs which need further clinical assessment for other reasons. Objectives To develop a prediction model to identify ICSRs that require clinical review, including PSTRs. Secondly, to identify the most important features of these reports. Methods All ICSRs (n = 30 424) received by Lareb between October 1, 2017 and February 26, 2021 were included. ICSRs originating from marketing authorisation holders and ICSRs reported on vaccines were excluded. The outcome was defined as PSTR (yes/no), where PSTR ‘yes’ was defined as an ICSR discussed at a signal detection meeting. Nineteen features were included, concerning structured information on: patients, adverse drug reactions (ADR) or drugs. Data were divided into a training (70%) and test set (30%) using a stratified split to maintain the PSTR/no PSTR ratio. Logistic regression, elastic net logistic regression and eXtreme Gradient Boosting models were trained and tuned on a training set. Random down‐sampling of negative controls was applied on the training set to adjust for the imbalanced dataset. Final models were evaluated on the test set. Model performances were assessed using the area under the curve (AUC) with 95% confidence interval of a receiver operating characteristic (ROC), and specificity and precision were assessed at a threshold for perfect sensitivity (100%, to not miss any PSTRs). Feature importance plots were inspected and a selection of features was used to re‐train and test model performances with fewer features. Results 1439 (4.7%) of reports were PSTR. All three models performed equally with a highest AUC of 0.75 (0.73–0.77). Despite moderate model performances, specificity (5%) and precision (5%) were low. Most important features were: ‘absence of ADR in the Summary of product characteristics’, ‘ADR reported as serious’, ‘ADR labelled as an important medical event’, ‘ADR reported by physician’ and ‘positive rechallenge’. Model performances were similar when using only nine of the most important features. Conclusions We developed a prediction model with moderate performances to identify PSTRs with nine commonly available features. Optimisation of the model using more ICSR information (e.g., free text fields) to increase model precision is required before implementation.

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First experiences with a tool to measure the level of clinical information present in adverse drug reaction reports

Abstract: This is the first tool to give insight in the level of relevant clinical information present in ADR reports. It can be used internationally to compare reports coming from different reporting methods and different types of reporters in pharmacovigilance.

Cited by 13 publications

References 9 publications

Impact of a New Consumer Form on the Quantity and Quality of Adverse Event Reports Submitted to the United States Food and Drug Administration

Impact of a New Consumer Form on the Quantity and Quality of Adverse Event Reports Submitted to the United States Food and Drug Administration

Characteristics, Quality and Contribution to Signal Detection of Spontaneous Reports of Adverse Drug Reactions Via the WEB-RADR Mobile Application: A Descriptive Cross-Sectional Study

Development of a multivariate prediction model to identify individual case safety reports which require clinical review

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