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Challenges in coding adverse events in clinical trials: a systematic review
Jeppe Bennekou Schroll1, Emma Maund, Peter C Gøtzsche
1Nordic Cochrane Centre, Rigshospitalet, Copenhagen, Denmark. js@cochrane.dk
Background:
Misclassification of adverse events in clinical trials can sometimes have serious consequences. Therefore, each of the many steps involved, from a patient's adverse experience to presentation in tables in publications, should be as standardised as possible, minimising the scope for interpretation. Adverse events are categorised by a predefined dictionary, e.g. MedDRA, which is updated biannually with many new categories. The objective of this paper is to study interobserver variation and other challenges of coding.
Methods:
Systematic review using PRISMA. We searched PubMed, EMBASE and The Cochrane Library. All studies were screened for eligibility by two authors.
Results:
Our search returned 520 unique studies of which 12 were included. Only one study investigated interobserver variation. It reported that 12% of the codes were evaluated differently by two coders. Independent physicians found that 8% of all the codes deviated from the original description. Other studies found that product summaries could be greatly affected by the choice of dictionary. With the introduction of MedDRA, it seems to have become harder to identify adverse events statistically because each code is divided in subgroups. To account for this, lumping techniques have been developed but are rarely used, and guidance on when to use them is vague. An additional challenge is that adverse events are censored if they already occurred in the run-in period of a trial. As there are more than 26 ways of determining whether an event has already occurred, this can lead to bias, particularly because data analysis is rarely performed blindly.
Conclusion:
There is a lack of evidence that coding of adverse events is a reliable, unbiased and reproducible process. The increase in categories has made detecting adverse events harder, potentially compromising safety. It is crucial that readers of medical publications are aware of these challenges. Comprehensive interobserver studies are needed.
Insights
Coding adverse events in clinical trials lacks reliability and reproducibility, potentially compromising patient safety due to increased categories and interpretation variability. More interobserver studies are crucial for accurate adverse event detection.
Area of Science:
- Clinical Trials
- Pharmacovigilance
- Medical Informatics
Background:
- Standardization of adverse event (AE) coding in clinical trials is crucial to minimize interpretation and ensure patient safety.
- Adverse events are categorized using predefined dictionaries like MedDRA, which are updated regularly, introducing new complexities.
- The objective of this study is to investigate interobserver variation and challenges in AE coding.
Purpose of the Study:
- To assess the reliability and reproducibility of adverse event coding in clinical trials.
- To identify challenges associated with the coding of adverse events, including interobserver variation and dictionary updates.
- To highlight the potential impact of coding inconsistencies on clinical trial outcomes and patient safety.
Main Methods:
- A systematic review of relevant literature was conducted using PRISMA guidelines.
- Searches were performed across major databases: PubMed, EMBASE, and The Cochrane Library.
- Eligibility screening of all retrieved studies was independently performed by two authors.
Main Results:
- Only 12 out of 520 unique studies met the inclusion criteria; only one examined interobserver variation, finding 12% code disagreement.
- Independent physicians noted 8% of codes deviated from original descriptions, and dictionary choice significantly impacts study summaries.
- Increased MedDRA categories complicate statistical detection of AEs; lumping techniques are underutilized, and AE censoring methods introduce potential bias.
Conclusions:
- Current evidence suggests AE coding is not consistently reliable, unbiased, or reproducible.
- The growing complexity of AE categories may hinder detection, potentially impacting patient safety.
- Awareness of these coding challenges is vital for readers of medical publications, and further interobserver studies are essential.
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