Towards Automating Adverse Event Review: A Prediction Model for Case Report Utility

Muñoz, Mónica; Pan, Gerald J. Dal; Wei, Yu‐Jung; Delcher, Chris; Xiao, Hong; Kortepeter, Cindy; Winterstein, Almut G.

doi:10.1007/s40264-019-00897-0

Cited by 19 publications

(28 citation statements)

References 19 publications

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“…• Supports better ability to predict safety profiles of medicines under development and to contextualise emerging data on developed products, leading to anticipation of future benefit-risk profile. format and content for the reporting of one or several suspected adverse events to a medicinal product that occur actionable high-quality cases potentially affecting benefit-risk (Figure 2A) that most impacts the overall safety signal detection capability [15].…”

Section: Improvements In Data Availability and Capacity To Use For Safetymentioning

confidence: 99%

“…This does not mean that further method improvement is not possible (and necessary) in the way ICSRs are currently analysed. Several directions are possible: advances in the ability to highlight drug/vaccine-event pairs or individual cases for clinical analysis, clustering of related reports or concepts, and highlighting of duplicates (all areas of active research [15,[17][18][19][20][21]); or development of more sophisticated analyses to group in a single patient at a specific point of time [88]. Intelligent automation: combined used of tools and techniques with novel business process improvements which together mimic or exceed human performance (http://www.…”

Section: Improvements In Data Availability and Capacity To Use For Safetymentioning

confidence: 99%

“…Several dimensions should be considered (A). The first is the actual pharmacovigilance utility of an ICSR, that is, the potential to really impact the current understanding of the benefitrisk profile of a product [15]. For example, spontaneous reports cannot be used to estimate relative incidence of AEs [11].…”

Section: Real-world Datamentioning

confidence: 99%

See 2 more Smart Citations

Safety of medicines and vaccines – building next generation capability

Bate

Stegmann

2021

Trends in Pharmacological Sciences

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Section: Improvements In Data Availability and Capacity To Use For Safetymentioning

confidence: 99%

Section: Improvements In Data Availability and Capacity To Use For Safetymentioning

confidence: 99%

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Safety of medicines and vaccines – building next generation capability

Bate

Stegmann

2021

Trends in Pharmacological Sciences

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“…Among the candidate papers, we have three representatives of this kind of research. Muñoz et al developed and validated a prediction model for adverse event reporting [ 14 ], Groenhof et al demonstrated how the mining of EHR data can be used to estimate patients’ smoking status accurately [ 15 ], and Zhang et al present a study on real-time artificial intelligence prediction for major adverse cardiac events [ 16 ].…”

Section: Findings and Trends: Clinical Information Systems Research 2020mentioning

confidence: 99%

Clinical Information Systems Research in the Pandemic Year 2020

Hackl

Hoerbst

2021

Yearb Med Inform

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Summary Objective: In this synopsis, we give an overview of recent research and propose a selection of best papers published in 2020 in the field of Clinical Information Systems (CIS). Method: As CIS section editors, we annually apply a systematic process to retrieve articles for the International Medical Informatics Association Yearbook of Medical Informatics. For seven years now, we use the same query to find relevant publications in the CIS field. Each year we retrieve more than 2,400 papers which we categorize in a multi-pass review to distill a preselection of 15 candidate papers. External reviewers and yearbook editors then assess the selected candidate papers. Based on the review results, the IMIA Yearbook editorial board chooses up to four best publications for the section at a selection meeting. To get an overview of the content of the retrieved articles, we use text mining and term co-occurrence mapping techniques. Results: We carried out the query in mid-January 2021 and retrieved a deduplicated result set of 2,787 articles from 1,135 different journals. We nominated 15 papers as candidates and finally selected four of them as the best papers in the CIS section. As in the previous years, the content analysis of the articles revealed the broad spectrum of topics covered by CIS research. Thus, this year we could observe a significant impact of COVID-19 on CIS research. Conclusions: The trends in CIS research, as seen in recent years, continue to be observable. What was very visible was the impact of the Corona Virus Disease 2019 (COVID-19) pandemic, which has affected not only our lives but also CIS.

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“…Such a prediction model was previously described by Muñoz et al, where pharmacovigilance utility (PVU) is predicted, what the authors consider PSTR of ICSRs. 22 The best performing model used logistic regression and had an area under the curve (AUC) of 0.71, meaning that the model could correctly categorise 71% of ICSRs with PVU and those without. In the model 'positive de-challenge', 'reason for use of drugs provided' and a 'reported designated medical event' (a serious and rare medical event, list maintained by the European Medicines Agency [EMA]) were the strongest predictors (features) for PVU.…”

Section: Introductionmentioning

confidence: 99%

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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Towards Automating Adverse Event Review: A Prediction Model for Case Report Utility

Cited by 19 publications

References 19 publications

Safety of medicines and vaccines – building next generation capability

Safety of medicines and vaccines – building next generation capability

Clinical Information Systems Research in the Pandemic Year 2020

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

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