Establishment, optimization and practice of an automatic central nervous system adverse reactions monitoring module based on hospital information system data

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Author: LI Haiyan ^1, ²  GUO Daihong ¹ ZHU Man ¹ GAO Ao ¹ LU Jingchuan ^1, ² FU An ¹ LI Chao ¹ LI Peng ¹ ZHAO Anqi ¹

Affiliation: 1. Department of Pharmacy, Medical Supplies Center of PLA General Hospital, Beijing 100853, China 2. College of Pharmacy, Chongqing Medical University, Chongqing 400016, China

Keywords: Central nervous system Adverse drug reaction Imipenem/cilastatin Text classification technology Real world study

DOI: 10.12173/j.issn.1005-0698.202401024

Reference: LI Haiyan, GUO Daihong, ZHU Man, GAO Ao, LU Jingchuan, FU An, LI Chao, LI Peng,ZHAO Anqi.Establishment, optimization and practice of an automatic central nervous system adverse reactions monitoring module based on hospital information system data[J].Yaowu Liuxingbingxue Zazhi,2024, 33(9):971-977.DOI: 10.12173/j.issn.1005-0698.202401024.[Article in Chinese]  Copied

Abstract
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Abstract

Objective To construct a module for drug-induced central nervous system adverse reactions (CNS-ADR) within the Clinical Adverse Drug Event Active Monitoring and Intelligent Assessment Alert System-II (ADE-ASAS-II), and to conduct a large- scale, real-world active monitoring and evaluation of CNS-ADR specifically related to imipenem/cilastatin.

Methods Based on literature review, spontaneous report evaluation, and initial word set of CNS-ADR related descriptions in electronic medical records, text recognition technology was used to construct and optimize the condition settings of the CNS-ADR automatic monitoring module. Hospitalized patients using imipenem/cilastatin were retrospectively monitored from 2017 to 2021, and the positive patients which had CNS-ADR were statistically described in terms of the demographic characteristics, CNS symptoms, and hospital departments.

Results Based on a repeated testing optimization using 1 185 manually monitored results, the best setting for the determined module includes 62 sets of keywords, with a positive predictive value (PPV) of 13.63% and a recall rate of 100%. Expanding the monitoring to 8 222 medication users using this module, 281 cases of positive causality were identified, with an incidence rate of 3.42%. Among them, patients over 60 years old accounted for 50.17%, and the main manifestations of CNS-ADR were epileptic seizures, headaches, mania, and delirium.

Conclusion The CNS-ADR automatic monitoring module established based on ADE-ASAS-II provides fast and reliable text data mining support for conducting real-world research on CNS-ADR.

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References

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