Evidence map›Paper›PMID 34476768›Full record

ArticlePharmaceutical medicine2021

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

Phuong Pham, Carmen Cheng, Eileen Wu, Ivone Kim, Rongmei Zhang, Yong Ma, Cindy M Kortepeter, Monica A Muñoz

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PubMed Full text at PMC Publisher
In one paragraph

Article in Pharmaceutical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Phuong PhamDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.
Carmen ChengOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA. Carmen.Cheng@fda.hhs.gov.ORCID 0000-0002-1151-7393
Eileen WuOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
Ivone KimOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
Rongmei ZhangOffice of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
Yong MaOffice of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
Cindy M KortepeterOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.ORCID 0000-0002-7774-1443
Monica A MuñozDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.ORCID 0000-0001-9754-1986

Funding

Intramural FDA HHS FD999999
6 · The paper itself

Abstract

introductionMissing 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.

objectivesThis 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.

methodsUsing 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.

resultsDuring 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.

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

Indexed as

Adverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsHumansNatural Language ProcessingPharmacovigilanceUnited StatesUnited States Food and Drug Administration

Identifiers

PMID34476768
PMCPMC9136956

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.