Clinical Characteristics of Moxifloxacin-Related Arrhythmias and Development of a Predictive Nomogram: A Case Control Study.
Peng LiMan ZhuAo GaoHaili GuoAn FuAnqi ZhaoDaihong GuoPublished in: Journal of clinical pharmacology (2024)
This study aimed to analyze the incidence, clinical characteristics, and risk factors of moxifloxacin-related arrhythmias and electrocardiographic alterations in hospitalized patients using real-world data. Concurrently, a nomogram was established and validated to provide a practical tool for prediction. Retrospective automatic monitoring of inpatients using moxifloxacin was performed in a Chinese hospital from January 1, 2017, to December 31, 2021, to obtain the incidence of drug-induced arrhythmias and electrocardiographic alterations. Propensity score matching was conducted to balance confounders and analyze clinical characteristics. Based on the risk and protective factors identified through logistic regression analysis, a prediction nomogram was developed and internally validated using the Bootstrap method. Arrhythmias and electrocardiographic alterations occurred in 265 of 21,711 cases taking moxifloxacin, with an incidence of 1.2%. Independent risk factors included medication duration (odds ratio [OR] 1.211, 95% confidence interval [CI] 1.156-1.270), concomitant use of meropenem (OR 4.977, 95% CI 2.568-9.644), aspartate aminotransferase >40 U/L (OR 3.728, 95% CI 1.800-7.721), glucose >6.1 mmol/L (OR 2.377, 95% CI 1.531-3.690), and abnormally elevated level of amino-terminal brain natriuretic peptide precursor (OR 2.908, 95% CI 1.640-5.156). Concomitant use of cardioprotective drugs (OR 0.430, 95% CI 0.220-0.841) was a protective factor. The nomogram showed good differentiation and calibration, with enhanced clinical benefit. The incidence of moxifloxacin-related arrhythmias and electrocardiographic alterations is in the range of common. The nomogram proves valuable in predicting the risk in the moxifloxacin-administered population, offering significant clinical applications.
Keyphrases
- risk factors
- drug induced
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- machine learning
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