Objective To investigate the characteristics,clinical indicators and risk factors of levofloxacin-induced arrhythmias in large hospitalized populations.Methods Using the"Adverse Event Active Monitoring and Intelligent Assessment Alert System-Ⅱ"(ADE-ASAS-Ⅱ),the electronic medical record of inpatients using levofloxacin in 2019 was monitored to obtain relevant data for patients with arrhythmias.Patients without arrhythmia were selected by propensity score matching,and the risk factors of levofloxacin-induced arrhythmias were analyzed by univariate and multivariate conditional logistic regression.Results The incidence of levofloxacin-induced arrhythmias was 1.64％in 12 879 people who used levofloxacin.The incidence in people over 65 years was 3.22％.The main manifestations of levofloxacin-induced arrhythmias were extrasystole(0.84％),tachycardia(0.63％),QT interval prolongation(0.44％),and no severe arrhythmias such as torsades de pointes and ventricular fibrillation.Multivariate Logistic regression analysis showed that the course of administration(OR=1.030,95％CI 1.009 to 1.050,P=0.004)and intravenous administration(OR=2.392,95％CI 1.478 to 3.870,P＜0.001)independent risk factors for levofloxacin-induced arrhythmias.Conclusion Arrhythmias caused by levofloxacin are common and have various types,among which the occurrence of QT interval prolongation is occasional.We should pay more attention to elderly patients who receive intravenous levofloxacin and try to avoid long courses of medication.

Objective To understand the incidence and characteristics of adverse drug reactions(ADRs)of carbapenems,explore the relevant risk signals,and provide a reference for clinically safe drug use.Methods All spontaneous reports of carbapenem drug-related ADRs from January 2008 to October 2022 in the Adverse Drug Reaction Monitoring Center,PLA General Hospital's ADR database were retrieved,and information such as patients'general conditions,involved systems and organs damage,and the names of ADRs involved were retrospectively analysed.Using the reporting odd ratio method,the proportional reporting ratio method,the Medicines and Healthcare Products Regulatory Agency method,and information component method to obtain risk signals of carbapenem antimicrobial drug-related ADR.Results A total of 2 642 ADR reports of carbapenems were reported,of which 410 serious ADR reports(15.52％)were serious ADR reports,five cabapenem antimirobial drug species were mainly involved.In descending order of composition were imipenem cilastatin(51.28％),meropenem(32.13％),biapenem(8.10％),ertapenem(7.68％),and panipenem(0.79％).The male to female ratio of patients was 1.74:1,with the most age＞60 years(59.69％).A total of 14"drug-ADR name"combinations generated risk signals in all four data mining methods,with meropenem being the most signals,and imipenem cilastatin and ertapenem had a high number of reported ADR in nervous system.Conclusion The results of risk signal mining are basically consistent with the known carbapenem ADR information,during the use of carbapenem antimicrobial drugs in the clinic,it is recommended to monitor patients'liver and kidney functions as well as blood biochemical indexes,so as to strengthen the awareness of vigilance in the clinical use of carbapenem antimicrobial drugs,and timely recognize and deal with ADRs in a timely manner,and to avoid the occurrence of serious ADRs.

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.