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. 2021 Sep;35(5):307-316.
doi: 10.1007/s40290-021-00398-5. Epub 2021 Sep 2.

Leveraging Case Narratives to Enhance Patient Age Ascertainment from Adverse Event Reports

Phuong Pham  1   2 , Carmen Cheng  3 , Eileen Wu  2 , Ivone Kim  2 , Rongmei Zhang  4 , Yong Ma  4 , Cindy M Kortepeter  2 , Monica A Muñoz  1   2
Affiliations

Leveraging Case Narratives to Enhance Patient Age Ascertainment from Adverse Event Reports

Phuong Pham et al. Pharmaceut Med. 2021 Sep.

Abstract

Introduction: Missing age presents a significant challenge when evaluating individual case safety reports (ICSRs) in the FDA Adverse Event Reporting System (FAERS). When age is missing in an ICSR's structured field, it may be in the report's free-text narrative.

Objectives: This study aimed to evaluate the performance and assess the potential impact of a rule-based natural language processing (NLP) tool that utilizes a text string search to identify patients' numerical age from unstructured narratives.

Methods: Using FAERS ICSRs from 2002 to 2018, we evaluated the annual proportion of ICSRs with age missing in the structured field before and after NLP application. Reviewers manually identified patients' age from ICSR narratives (gold standard) from a random sample of 1500 ICSRs. The gold standard was compared to the NLP-identified age.

Results: During the study period, the percentage of ICSRs missing age in the structured field increased from 21.9 to 43.8%. The NLP tool performed well among the random sample: sensitivity 98.5%, specificity 92.9%, positive predictive value (PPV) 94.9%, and F-measure 96.7%. It also performed well for the subset of ICSRs missing age in the structured field; when applied to these cases, NLP identified age for an additional one million ICSRs (10% of the total number of ICSRs from 2002 to 2018) and decreased the percentage of ICSRs missing age to 27% overall.

Conclusions: NLP has potential utility to extract patients' age from ICSR narratives. Use of this tool would enhance pharmacovigilance and research using FAERS data.

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Conflict of interest statement

Declarations

Conflict of interest Phuong Pham, Carmen Cheng, Eileen Wu, Ivone Kim, Rongmei Zhang, Yong Ma, Cindy M. Kortepeter, and Monica A. Muñoz have no conflicts of interest.

Figures

Fig. 1
Fig. 1
Percentage of FAERS ICSRs with missing age in the structured field by report type and overall, from 2002 to 2018. FAERS FDA Adverse Event Reporting System, ICSR individual case safety report
Fig. 2
Fig. 2
Percentage of FAERS ICSRs with missing age before and after NLP implementation. FAERS FDA Adverse Event Reporting System, ICSR individual case safety report, NLP natural language processing
See this image and copyright information in PMC

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