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

1.Montané E, Santesmases J. Adverse drug reactions[J]. Med Clin (Barc), 2020, 154(5): 178-184. DOI: 10.1016/j.medcli.2019.08.007.

2.冯殿伟. 药源性中枢神经系统疾病1 262例文献分析 [J]. 中国医院药学杂志, 2016, 36(19): 1680-1684. [Feng DW. Literature analysis of 1262 cases of drug-induced central nervous system diseases[J]. Chinese Journal of Hosipital Pharmacy, 2016, 36(19): 1680-1684.] DOI: 10.13286/j.cnki.chinhosppharmacyj.2016.19.15.

3.Miller AD, Ball AM, Bookstaver PB, et al. Epileptogenic potential of carbapenem agents: mechanism of action, seizure rates, and clinical considerations[J]. Pharmacotherapy, 2011, 31(4): 408-423. DOI: 10.1592/phco.31.4.408.

4.姚翀, 刘东杰, 郭代红, 等. 临床药物不良事件主动监测与智能评估警示系统Ⅱ的研发[J]. 中国药物应用与监测, 2020, 17(6): 387-391. [Yao C, Liu DJ, Guo DH, et al. The development on active surveillance and assessment system-Ⅱ of adverse drug events[J]. Chinese Journal of Drug Application and Monitoring, 2020, 17(6): 387-391.] DOI: 10.3969/j.issn.1672-8157.2020.06.009.

5.Naranjo CA, Busto U, Sellers EM, et al. A method for estimating the probability of adverse drug reactions[J]. Clin Pharmacol Ther, 1981, 30(2): 239-245. DOI: 10.1038/clpt.1981.154.

6.中国研究型医院学会药物评价专业委员会, 解放军药品不良反应监测中心. 基于医院信息系统数据的临床用药风险自动监测评价专家共识[J]. 中国药物应用与监测, 2021, 18(5): 277-287. DOI: 10.3969/j.issn.1672-8157.2021.05.001.

7.国家药品监督管理局. 食品药品监管总局关于适用国际人用药品注册技术协调会二级指导原则的公告[EB/OL]. (2018-01-25) [2018-12]. https://www.nmpa.gov.cn/zhuanti/ypqxgg/ggzhcfg/20180125175101846.html

8.卢京川, 王嘉熙, 郭代红, 等. 863例药物相关严重神经系统不良反应自发报告分析[J]. 药物流行病学杂志, 2022, 31(7): 444-447, 453. [Lu JC, Wang JX, Guo DH, et al. Analysis of 863 spontaneous cases of drug-related severe nervous system adverse reactions[J]. Chinese Journal of Pharmacoepidemiology, 2022, 31(7): 444-447, 453.] DOI: 10.19960/j.cnki.issn1005-0698.2022.07.003.

9.Lane RJ, Routledge PA. Drug-induced neurological disorders[J]. Drugs, 1983, 26(2): 124-147. DOI: 10.2165/ 00003495-198326020-00002.

10.中国抗癫痫协会, 编著. 临床诊疗指南癫痫病分册（2015 修订版）[M]. 北京: 人民卫生出版社, 2015: 1-297.

11.Hirsch LJ, Fong MWK, Leitinger M, et al. American Clinical Neurophysiology Society's standardized critical care EEG terminology: 2021 version[J]. J Clin Neurophysiol, 2021, 38: 1-29. DOI: 10.1097/WNP. 0000000000000806.

12.USNCI. Common terminology criteria for adverse events (CTCAE) version 5[S]. 2017.

13.吴宗友, 白昆龙, 杨林蕊, 等. 电子病历文本挖掘研究综述[J]. 计算机研究与发展, 2021, 58(3): 513-527. [Wu ZY, Bai KL, Yang LR, et al. Review on text mining of electronic medical record[J]. Journal of Computer Research and Development, 2021, 58(3): 513-527.] DOI: 10.7544/issn1000-1239.2021.20200402.

14.卢京川, 郭代红, 高奥, 等. 基于HIS数据的住院人群癫痫发作自动监测模块的建立与优化[J]. 中国药物应用与监测, 2022, 19(4): 248-253. [Lu JC, Guo DH, Gao A, et al. Establishment and optimization of a module for automatic monitoring epileptic seizures in hospitalized population based on HIS[J]. Chinese Journal of Drug Application and Monitoring, 2022, 19(4): 248-253.] DOI: 10.3969/j.issn.1672-8157.2022.04.011.

15.郭海丽, 郭代红, 高奥, 等. 基于HIS数据的药源性心律失常自动监测模块规则建立与验证[J]. 中国药物应用与监测, 2022, 19(3): 176-181. [Guo HL,Guo DH, Gao A, et al. Establishment and validation of automatic monitoring module for drug-induced arrhythmia based on data of HIS[J]. Chinese Journal of Drug Application and Monitoring, 2022, 19(3): 176-181.] DOI: 10.3969/j.issn.1672-8157.2022.03.010.

16.Norrby SR. Neurotoxicity of carbapenem antibacterials[J]. Drug Saf, 1996, 15(2): 87-90. DOI: 10.2165/00002018-199615020-00001.

17.Sutter R, Rüegg S, Tschudin-Sutter S. Seizures as adverse events of antibiotic drugs: a systematic review[J]. Neurology, 2015, 85(15): 1332-1341. DOI: 10.1212/WNL.0000000000002023.

18.Struck AF, Tabaeizadeh M, Schmitt SE, et al. Assessment of the validity of the 2HELPS2B score for inpatient seizure risk prediction[J]. JAMA Neurol, 2020, 77: 500. DOI: 10.1001/jamaneurol.2019.4656.

19.马莉莉, 张健. 碳青霉烯类抗生素的神经毒性[J]. 药物不良反应杂志, 2010, 12(3): 178-182. [Ma LL, Zhang J. Neurotoxicity of carbapenem antibiotics[J]. Adverse Drug Reactions Journal, 2010, 12(3): 178-182.] DOI: 10.3969/j.issn.1008-5734.2010.03.008.

20.Hornik CP, Herring AH, Benjamin DK Jr, et al. Adverse events associated with meropenem versus imipenem/cilastatin therapy in a large retrospective cohort of hospitalized infants[J]. Pediatr Infect Dis J. 2013, 32(7): 748-753. DOI: 10.1097/INF.0b013e31828be70b.

21.Schliamser SE. Neurotoxicity of beta-lactam antibiotics: experimental kinetic and neurophysiological studies[J]. Scand J Infect Dis Suppl, 1988, 55: 1-61. DOI: 10.3109/inf.1988.20.suppl-55.01.

22.宗慧君, 张齐, 杨阳, 等. 恶性血液病患者合并呼吸道感染的病毒病原学分析[J]. 解放军医学院学报, 2023,44(7):750-754. [Zong HJ，Zhang Q, Yang Y, et al. Viral etiology of respiratory tract infection in patients with hematologic malignancy[J]. Academic Journal of Chinese PLA Medical School, 2023, 44(7): 750-754.] DOI: 10.12435/j.issn.2095-5227.2023.061.