Evidence map›Paper›PMID 31937265›Full record

ArticleBMC medical research methodology2020

A computational method to quantitatively measure pediatric drug safety using electronic medical records.

Gang Yu, Xian Zeng, Shaoqing Ni, Zheng Jia, Weihong Chen, Xudong Lu, Jiye An, Huilong Duan, Qiang Shu, Haomin Li

Abstract read
In one paragraph

Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Gang YuThe Children's Hospital of Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou, China.
Xian ZengCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Shaoqing NiThe Children's Hospital of Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou, China.
Zheng JiaCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Weihong ChenDepartment of Pharmacy, Shanxi Dayi Hospital, Taiyuan, China.
Xudong LuCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Jiye AnCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Huilong DuanCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Qiang ShuThe Children's Hospital of Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou, China.
Haomin LiThe Children's Hospital of Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou, China. hmli@zju.edu.cn.ORCID 0000-0002-6420-7719

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDrug safety in children is a major concern; however, there is still a lack of methods for quantitatively measuring, let alone to improving, drug safety in children under different clinical conditions. To assess pediatric drug safety under different clinical conditions, a computational method based on Electronic Medical Record (EMR) datasets was proposed.

methodsIn this study, a computational method was designed to extract the significant drug-diagnosis associations (based on a Bonferroni-adjusted hypergeometric P-value < 0.05) among drug and diagnosis co-occurrence in EMR datasets. This allows for differences between pediatric and adult drug use to be compared based on different EMR datasets. The drug-diagnosis associations were further used to generate drug clusters under specific clinical conditions using unsupervised clustering. A 5-layer quantitative pediatric drug safety level was proposed based on the drug safety statement of the pediatric labeling of each drug. Therefore, the drug safety levels under different pediatric clinical conditions were calculated. Two EMR datasets from a 1900-bed children's hospital and a 2000-bed general hospital were used to test this method.

resultsThe comparison between the children's hospital and the general hospital showed unique features of pediatric drug use and identified the drug treatment gap between children and adults. In total, 591 drugs were used in the children's hospital; 18 drug clusters that were associated with certain clinical conditions were generated based on our method; and the quantitative drug safety levels of each drug cluster (under different clinical conditions) were calculated, analyzed, and visualized.

conclusionWith this method, quantitative drug safety levels under certain clinical conditions in pediatric patients can be evaluated and compared. If there are longitudinal data, improvements can also be measured. This method has the potential to be used in many population-level, health data-based drug safety studies.

Indexed as

Pharmaceutical PreparationsChildComputational BiologyDrug-Related Side Effects and Adverse ReactionsElectronic Health RecordsFemaleHospitals, PediatricHumansMalePharmaceutical PreparationsDrug clusteringElectronic medical recordPediatric drug safetyQuantitative measurement

Identifiers

PMID31937265
PMCPMC6961323

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