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<p>Antibiotic-related arrhythmias represent a major clinical challenge, characterized by risk patterns that remain not fully elucidated. This study employed the FP-growth algorithm to uncover associations between antibiotic classes and specific arrhythmias, and to identify key high-risk patient profiles. We included 246 cases of antibiotic-related arrhythmia from Center for Adverse Drug Reaction Monitoring of Chongqing (2013–2023). In the FP-growth association algorithm, we took antibiotic categories, age, gender, and time to arrhythmia onset as the antecedents, and the types of arrhythmias as the consequents. The minimum thresholds were set at a support of ≥ 1.0%, a confidence of ≥ 50.0%, and a lift of &gt; 1.8. Sinus tachycardia (36.18%), premature contractions (25.20%), and QT prolongation (16.26%) were the predominant arrhythmias. Key associations included antituberculosis drugs and quinolones with QT prolongation (lift 3.514—6.150 and 3.075—4.100, respectively), and macrolides with premature beats (lift 2.645—2.976). Crucially, onset timing differed:&#xa0;QT prolongation occurred mainly after 5&#xa0;days of anti-tuberculosis therapy or with prolonged quinolone use, whereas premature contractions and sinus tachycardia predominantly emerged&#xa0;during drug infusion. Moreover, females were more prone to QT prolongation induced by anti-tuberculosis drugs and premature contractions associated with macrolides, whereas males exhibited a stronger link to quinolones. Furthermore, the association with sinus tachycardia was more pronounced in children than in adults. In conclusion, The FP-growth algorithm effectively uncovered complex antibiotic-arrhythmia risk patterns. While controlling for other susceptible factors (including underlying disease, concomitant medication, and electrolyte disorders), these findings highlight distinct patterns of association that may inform more targeted monitoring during antibiotic therapy, particularly regarding the timing and type of arrhythmic events.</p>

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Medication association analysis of antimicrobial-induced arrhythmias based on FP-growth frequent pattern mining

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Shuang Chai,
  • Li Deng,
  • Jie Dong,
  • Jiaxu Zhou,
  • Xin Xi,
  • Guili Huang

摘要

Antibiotic-related arrhythmias represent a major clinical challenge, characterized by risk patterns that remain not fully elucidated. This study employed the FP-growth algorithm to uncover associations between antibiotic classes and specific arrhythmias, and to identify key high-risk patient profiles. We included 246 cases of antibiotic-related arrhythmia from Center for Adverse Drug Reaction Monitoring of Chongqing (2013–2023). In the FP-growth association algorithm, we took antibiotic categories, age, gender, and time to arrhythmia onset as the antecedents, and the types of arrhythmias as the consequents. The minimum thresholds were set at a support of ≥ 1.0%, a confidence of ≥ 50.0%, and a lift of > 1.8. Sinus tachycardia (36.18%), premature contractions (25.20%), and QT prolongation (16.26%) were the predominant arrhythmias. Key associations included antituberculosis drugs and quinolones with QT prolongation (lift 3.514—6.150 and 3.075—4.100, respectively), and macrolides with premature beats (lift 2.645—2.976). Crucially, onset timing differed: QT prolongation occurred mainly after 5 days of anti-tuberculosis therapy or with prolonged quinolone use, whereas premature contractions and sinus tachycardia predominantly emerged during drug infusion. Moreover, females were more prone to QT prolongation induced by anti-tuberculosis drugs and premature contractions associated with macrolides, whereas males exhibited a stronger link to quinolones. Furthermore, the association with sinus tachycardia was more pronounced in children than in adults. In conclusion, The FP-growth algorithm effectively uncovered complex antibiotic-arrhythmia risk patterns. While controlling for other susceptible factors (including underlying disease, concomitant medication, and electrolyte disorders), these findings highlight distinct patterns of association that may inform more targeted monitoring during antibiotic therapy, particularly regarding the timing and type of arrhythmic events.