Drug-Related Rhabdomyolysis: A Real-World FDA Adverse Event Reporting System Database Study

Drug-Related Rhabdomyolysis: A Real-World FDA Adverse Event Reporting System Database Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Drug-Related Rhabdomyolysis: A Real-World FDA Adverse Event Reporting System Database Study Jialu Hu, Yiqing Sun, Xutao Ma, Huanru Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7536398/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Drug-induced rhabdomyolysis is one of the major causes of non-traumatic rhabdomyolysis. This adverse reaction poses challenges to patient safety and places a substantial burden on public health systems. Aim Based on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database, this study systematically analyzes the association between drugs and rhabdomyolysis, providing scientific evidence for improving clinical drug safety management and optimizing pharmacovigilance systems. Method By cleaning and filtering FAERS data from 2004 to 2024, a total of 865,934 rhabdomyolysis cases were analyzed out of 18,278,243 reports. Four signal detection methods—Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS)—were employed for adverse event signal analysis. Additionally, regression models were used to further identify drugs closely associated with rhabdomyolysis. Results By combining disproportionality analysis methods and logistic regression, this study identified 16 drugs significantly associated with rhabdomyolysis. Among them, aprotinin (ROR = 131.87), multivitamins and minerals (ROR = 940.60), and sodium phosphate (ROR = 40.35) posed the highest risks. Antiviral drugs and cardiovascular medications constituted the major components of drug-induced rhabdomyolysis, with average onset times of 594.17 days and 1434.21 days, respectively. Conclusion This study revealed significant associations between various drugs and rhabdomyolysis, providing valuable references for drug safety management. Future efforts should focus on enhanced monitoring and clinical interventions for high-risk drugs to optimize pharmacovigilance systems and improve patient outcomes. Health sciences/Diseases Biological sciences/Drug discovery Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Rhabdomyolysis Adverse events Pharmacovigilance FAERS Drug-induced rhabdomyolysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Rhabdomyolysis is an acute syndrome caused by the rupture of skeletal muscle cells, which results in the release of myoglobin, creatine kinase, and other intracellular components into the bloodstream[ 1 ]. This pathological process can lead to severe complications, including acute kidney injury, electrolyte imbalances, and multiple organ failure, and, in extreme cases, may be life-threatening. Annual incidence of rhabdomyolysis is approximately 26,000 cases in USA[ 2 ], with a significant proportion attributed to drug-related causes. Drug-induced rhabdomyolysis, the one of most common type of non-traumatic etiology, is primarily caused by drug-induced myotoxicity[ 3 ] via mechanisms including mitochondrial dysfunction, calcium homeostasis disruption, and oxidative stress. Furthermore, specific drugs under certain physiological or pathological conditions (e.g., hepatic or renal dysfunction or polypharmacy) can substantially heighten the risk of rhabdomyolysis[ 4 ]. This issue presents challenges to the safety of individual patient treatments and imposes a considerable burden on public health systems, making it a critical focus in the field of pharmacovigilance. Drug-induced rhabdomyolysis is not uncommon in clinical practice, and current studies have mainly found that this adverse reaction is related to the cardiovascular system. cardiovascular drugs, which are commonly prescribed for lipid-lowering therapy, are recognized as major contributors to drug-induced rhabdomyolysis due to their impact on muscle cell metabolism[ 5 ]. Combined drug use, such as the co-administration of statins and fibrates, further amplifies the risk of muscle toxicity[ 6 ], posing significant challenges for clinical management. However, systematic research on drug-induced rhabdomyolysis is relatively scarce, primarily derived from individual case reports and clinical research. Hence, this study conducted analysis to explore association between various drugs and rhabdomyolysis based on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Given the high incidence and severe clinical outcomes of drug-induced rhabdomyolysis, a systematic and comprehensive analysis of drugs closely associated with rhabdomyolysis is of substantial clinical importance. This study aims to serve as a reference for the safety management of clinical drug use and to provide new insights into the optimization of pharmacovigilance systems. Materials and methods Data source and study design This research adopted a retrospective, observational pharmacovigilance design based on the publicly available FAERS database. FAERS is a spontaneous reporting platform that collects safety reports submitted by healthcare professionals, patients, manufacturers, and other reporters. All procedures were carried out following the methodological standards of the READUS-PV guideline, which provides recommendations for conducting and presenting disproportionality analyses using individual case safety reports. Adverse events were coded using the Medical Dictionary for Regulatory Activities (MedDRA), an internationally recognized terminology system. To facilitate data retrieval, Open Vigil 2.1-MedDRA was employed. For reliability, only reports submitted by qualified healthcare providers (e.g., physicians and pharmacists, coded as MD and PH) were retained. From January 2004 to September 2024, FAERS contained 21,964,449 initial records; after deduplication, 18,278,243 unique reports were available. Standardizing drug names further reduced the dataset to 2,482 distinct agents. A schematic of this cleaning process is provided in Figure 1.. [Figure 1] Data Extraction Cases of rhabdomyolysis were identified in the REAC file through the preferred term “Rhabdomyolysis” (MedDRA code 10039020). Corresponding report identifiers (PRIMARYID) were extracted and linked to other datasets to obtain detailed information, including Individual Safety Reports (ISR), patient demographics (age, sex, country), event details, medication exposure, dates of reporting, and clinical outcomes. Analyses focused exclusively on drugs coded as “primary suspects,” while concomitant, secondary suspect, or interaction drugs were excluded to minimize uncertainty regarding causality. Statistical analysis For signal detection, we applied four disproportionality methods—Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS)[7]—each derived from 2×2 contingency tables (see Supplementary Table S1). Operational formulas and thresholds are outlined in Supplementary Table S2. A drug was considered to have a valid signal only if it satisfied criteria across all four methods, suggesting a consistent association with rhabdomyolysis. To strengthen causal inference, drugs meeting disproportionality criteria were further assessed by univariate logistic regression (requirements: ROR 95% CI lower bound >1, case count >100, and adjusted p <0.01)[8]. Candidate drugs were subsequently filtered using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with the 20 most strongly associated drugs retained. Multivariable logistic regression was then conducted to confirm independent associations. Model performance was evaluated with receiver operating characteristic (ROC) curve analysis. Finally, we investigated the time-to-onset of rhabdomyolysis following drug initiation and compared latency patterns among different agents. Ethics approval Considering that the FAERS database is publicly accessible, and patient records are anonymous and de-identified, it does not involve informed consent or ethical approval. Results Basic information of patients with drug-induced rhabdomyolysis From 2004 to 2019, there was a consistent increase in the number of reports concerning drug-induced rhabdomyolysis. By September 2024, a total of 865,934 patients had reported adverse events linked to this condition, with the peak occurring in 2019, accounting for 80,468 cases [Figure 2A]. The average age of these patients was approximately 58.8±18.8 years, as detailed in Table 1. Notably, the age distribution of these patients approximated a normal curve, initially increasing before decreasing, with the most reports found in the 61-70 age group [Figure 2C]. The majority of the reports originated from the United States, which accounted for 53.8% of the total (n= 465,355) [Figures 2D]. Excluding cases with missing data, 32.4% of the reports indicated other serious outcomes. Notably, 90,048(10.4%) patients who suffered from drug-induced rhabdomyolysis were dead [Figure 2B]. Additional details were showed in Table 1. [Figure 2] [Table 1] Classification of Drugs Associated by therapeutic purpose In light of the fact that some medications are prescribed to treat conditions associated with rhabdomyolysis yet may show positive signals due to insufficient efficacy, we excluded these drugs from our analysis. We also verified the brand and generic names of these medications. After screening 126 drugs that met the four-disproportionality analysis and univariate logistic regression positives, we identified the top 20 drugs in reported cases ranked by ROR. These drugs were primarily categorized into seven classes, with lansoprazole reporting the highest incidence (n=15,035), as outlined in Table 2. The drugs with the highest ROR were aprotinin (ROR = 103.64, 95%CI= 94.92 - 113.17), multivitamins and minerals (ROR = 84.31, 95%CI= 70.21 - 101.25), sodium phosphate (ROR = 46.77, 95%CI= 41.03 - 53.31), lansoprazole (ROR = 25.14, 95%CI= 24.55 - 25.75), and dexlansoprazole (ROR = 18.09, 95%CI= 17.19 - 19.05), with detailed data provided in Table 2. “Multivitamins and minerals” are the general term of ascorbic acid, biotin, chromium, cholecalciferol, copper, cyanocobalamin, folic acid, iron, molybdenum, nicotinic acid and pantothenic. [Table 2] Multi-variate logistic regression analysis of drug-induced rhabdomyolysis Based on the results of the top 20 drugs with p<0.01 conforming to univariate analysis, we used LASSO regression to further analyze the association between these drugs and rhabdomyolysis. In cross-validation, the regularization parameter λmin=0.0004275009 (log(λmin) =−7.76) was selected as it minimizes the prediction error. The corresponding model includes 16 drugs (Figure 3). Furthermore, multi-variate regression and ROC curves were used to test the reliability of the analysis. Consequently, all 16 drugs were related to rhabdomyolysis, and the ROC of logistic regression analysis was 0.763, showing a relatively good level (Figure 4). The drugs with the highest association were multivitamins and minerals (OR= 940.60, 95%CI= 300.78-5697.98, P<0.001), aprotinin (OR= 131.87, 95%CI= 114.73-152.40, P<0.001), sodium phosphate (OR= 40.35, 95%CI= 32.54-50.46, P<0.001), telbivudine (OR= 17.86, 95%CI= 12.52-25.65, P<0.001) and lovastatin (OR= 12.42, 95%CI= 10.18-15.16, P<0.001). Detailed results are shown in Table 3. [Table 3] [Figure 3] [Figure 4] Comparison of Drug-Induced Onset Times for rhabdomyolysis We categorized the drugs based on their primary therapeutic uses into six groups: blood system (1 drug), metabolic-related drug (1 drug), orthopedic system (1 drug), digestive system (1 drug), cardiovascular system (5 drugs), antiviral drug (6 drugs) and urinary system (2 drugs). Comprehensive data on the onset times of drug-induced effects were collected, and differences between these groups were analyzed using cumulative risk curves. The analysis revealed that metabolic-related drug tends to exhibit a longest time to onset of drug-induced effects while antiviral drug exhibits a shortest time. Specifically, the average onset time for foscarnet was 28.99 days, while it extended to 1739.99 days for efavirenz/emtricitabine/tenofovir disoproxil. For a detailed breakdown of these findings, refer to Table 4 Figure 5, and Figure 6. [Table 4] [Figure 5] [Figure 6] Discussion This study performed a data mining analysis of adverse drug reaction reports concerning drug-induced rhabdomyolysis and identified 80,468 relevant cases, primarily from the United States, France, and the United Kingdom. Regarding gender distribution, female patients reported more cases of drug-induced rhabdomyolysis than male patients, indicating a possible gender-related predisposition. In terms of age distribution, the reports included 3.0% of patients under 18 years and 3.9% of patients over 85 years, reflecting potential gaps in safety and efficacy studies for these age groups. Antiviral drugs were the most frequently classified category in this study and thus warrant particular attention. Tenofovir disoproxil (ROR = 13.55, 95%CI = 13.18–13.93), a key drug in HIV treatment, inhibits reverse transcriptase to disrupt viral replication [ 9 ]. However, its mechanism may cause mitochondrial toxicity and energy metabolism disorders. A case report documented rhabdomyolysis in a patient treated with tenofovir for 11 years, likely due to its direct toxicity on muscle cells or indirectly through Fanconi syndrome-associated hypophosphatemia and hyponatremia[ 10 ]. Tenofovir disoproxil's high affinity for mitochondrial DNA polymerase γ inhibits mitochondrial DNA synthesis, impairing mitochondrial function, energy production, and increasing oxidative stress [ 11 ]. Chronic mitochondrial damage may account for the prolonged latency of its adverse reactions (mean: 1552.83 days). Nevertheless, some studies attribute acute rhabdomyolysis in HIV patients to antiretroviral therapy or acute HIV-1 seroconversion symptoms [ 12 ], suggesting different mechanisms underlying acute and chronic rhabdomyolysis caused by antiviral drugs. Telbivudine (ROR = 12.83, 95%CI = 11.02–14.93), a nucleoside analog for chronic hepatitis B, acts by inhibiting viral DNA chain elongation but may induce rhabdomyolysis by impairing mitochondrial oxidative phosphorylation in skeletal muscle. One case reported rhabdomyolysis six months after switching from entecavir to telbivudine, with other common causes excluded [ 13 ]. Telbivudine significantly reduces mitochondrial DNA (mtDNA) copy numbers, increasing oxidative stress, mtDNA depletion, and mitochondrial dysfunction, disrupting homeostasis and causing muscle cell injury [ 14 ]. This rapid mechanism aligns with its shorter onset time (mean: 400.18 days). Foscarnet(ROR = 9.51, 95%CI = 8.18–11.05), a non-nucleoside antiviral drug, is widely used for the treatment of cytomegalovirus infections and other viral infections by inhibiting viral DNA polymerase and reverse transcriptase. While interfering with viral replication, foscarnet may exert toxic effects on host cell mitochondria, including inhibition of oxidative phosphorylation and increased oxidative stress[ 15 ]. The resulting mitochondrial dysfunction further compromises the energy metabolism of muscle cells, accelerating the process of cell damage. The latency of foscarnet-induced rhabdomyolysis is relatively short, averaging 28.99 days, making it one of the drugs with the shortest latency in this study. This rapid onset mechanism suggests a propensity to directly disrupt ion balance and induce acute muscle injury rather than a cumulative effect[ 16 ]. This rapid progression requires close clinical attention, particularly in the early stages of treatment when patients should be closely monitored. Additionally, combined antiviral regimens, such as efavirenz/emtricitabine/tenofovir disoproxil, significantly enhance antiviral efficacy but synergistically impair mitochondrial function, intensifying oxidative stress and mitochondrial DNA damage [ 17 ] [ 18 ]. The complex interactions between these drugs may further exacerbate the metabolic burden on patients, with this effect being particularly pronounced in elderly patients. The relationship between cardiovascular drugs and rhabdomyolysis is well-documented[ 19 ]. Statins, by inhibiting 3-hydroxy-3-methylglutaryl coenzyme A reductase, lower cholesterol synthesis while simultaneously reducing coenzyme Q10 (CoQ10) levels, thereby impairing mitochondrial function. CoQ10, a critical component of the electron transport chain, when reduced, leads to impaired ATP production and elevated oxidative stress, ultimately triggering rhabdomyolysis. A study based on the Vigibase database revealed that simvastatin is associated with the highest risk of rhabdomyolysis [ROR = 2.20 (2.11–2.29)], particularly among individuals over 74 years old and males[ 20 ]. Its moderate latency (mean: 663.49 days) is likely linked to cumulative mitochondrial damage [ 21 ] Lovastatin exhibits similar mechanisms, with a slightly shorter onset time (mean: 538.66 days), possibly due to pharmacokinetic properties resulting in more concentrated tissue exposure [ 22 ] [ 23 ]. Pitavastatin, a newer HMG-CoA reductase inhibitor, plays a crucial role in the treatment of cardiovascular diseases by lowering low-density lipoprotein levels. It is primarily metabolized through hepatic glucuronidation rather than CYP450 enzymes, thereby reducing the risk of drug interactions. However, in cases of polypharmacy or diminished metabolic capacity, its accumulation may still lead to muscle toxicity. An analysis of the JADER database reported a rhabdomyolysis rate of 0.09 for pitavastatin, which increased to 0.16 when combined with allopurinol and to 0.40 when combined with valsartan, findings that were corroborated by in vitro studies[ 24 ]. Conversely, other research has shown decreased rhabdomyolysis rates with specific drug combinations[ 25 ], suggesting that the adverse effects of pitavastatin may be closely related to treatment duration, a topic warranting further investigation. Ezetimibe, which inhibits intestinal cholesterol absorption, exhibits a synergistic effect when combined with simvastatin (ROR = 10.77, 95%CI = 10.06–11.52). However, this combination therapy may increase the risk of muscle toxicity. Its relatively short latency (mean: 355.99 days) suggests that the combined effects of the two drugs may accelerate mitochondrial damage and metabolic abnormalities. Furthermore, statins have been shown to interact with other commonly used drugs, such as daptomycin, exacerbating muscle toxicity [ 26 ]. Aprotinin, a protease inhibitor, is widely used to reduce intraoperative bleeding, particularly in cardiac surgery, where it plays a critical role. However, this study demonstrated a strong association between aprotinin and the risk of rhabdomyolysis (ROR = 103.64, 95%CI = 94.92–113.17), making it the highest-risk drug in this study. This finding highlights the potential for severe muscle-related adverse effects associated with aprotinin in certain patient populations. As a protease inhibitor, aprotinin suppresses the degradation of myoglobin, which may lead to its accumulation, causing nephrotoxic effects, renal impairment, and typical complications of rhabdomyolysis [ 27 ]. Aprotinin exerts hemostatic effects by inhibiting plasmin and other serine proteases. However, this inhibitory effect may disrupt the normal metabolic processes of muscle cell proteins, resulting in abnormal muscle fiber degradation and the accumulation of metabolic byproducts, thereby causing cytotoxic effects. Additionally, aprotinin use may increase oxidative stress, disrupt mitochondrial oxidative phosphorylation, and impair energy metabolism in skeletal muscle cells. These effects are particularly pronounced in high-dose usage or when combined with other metabolic inhibitors. The mean latency period for aprotinin-induced rhabdomyolysis is 272.12 days, making it one of the drugs with a relatively short latency period for rhabdomyolysis. This rapid onset may be related to its direct effects on protease activity and calcium channels. It is crucial to closely monitor patients’ serum creatine kinase levels and clinical symptoms during initial drug administration or dose escalation to promptly identify and manage rhabdomyolysis. Multivitamin and mineral supplements may increase the risk of rhabdomyolysis under certain conditions through synergistic toxicity. This study found an ROR of 940.60 (95% CI: 300.78–5697.98) for such combinations, suggesting that interactions between their components may significantly exacerbate muscle damage. Copper, selenium, and zinc, essential components of the antioxidant system, may, at high doses, exacerbate oxidative stress, thereby causing muscle cell damage[ 28 ]. Similarly, the metabolic toxicity of vitamin A may further exacerbate energy deficiencies in cells by impairing mitochondrial function[ 29 ]. Although no direct evidence links multivitamin and mineral combinations to rhabdomyolysis, their synergistic toxicity underscores the importance of carefully dosing supplements. For high-risk populations, such as the elderly or individuals with impaired renal function, close monitoring of component interactions is essential to prevent adverse effects. Sodium phosphate and parathyroid hormone were significantly associated with rhabdomyolysis, potentially through mechanisms involving calcium-phosphorus metabolism disturbances and direct damage to skeletal muscle cells. Sodium phosphate, commonly used as a laxative and bowel cleanser, was associated with an OR of 46.77 (95% CI: 41.03–53.31) for rhabdomyolysis, with a mean latency of 142.17 days. This relatively short latency period suggests that its toxic effects may manifest rapidly. High doses of sodium phosphate may lead to acute hyperphosphatemia, subsequently resulting in hypocalcemia and calcium-phosphorus precipitation[ 30 ]. Such metabolic disturbances can interfere with normal muscle cell contraction and function, ultimately leading to cell damage and rhabdomyolysis [ 31 ]. Additionally, the osmotic laxative effect of sodium phosphate may cause rapid fluid and electrolyte loss, exacerbating cellular stress, particularly in patients with preexisting metabolic or renal disorders [ 32 ]. Parathyroid hormone, widely used for the treatment of hypocalcemia and osteoporosis, was associated with an ROR of 11.41 (95% CI: 10.72–12.14) for rhabdomyolysis, with a mean latency of 603.68 days. Compared with sodium phosphate, its longer latency may reflect the cumulative effects of chronic metabolic disturbances. Parathyroid hormone promotes calcium release from bones into the bloodstream, which may result in chronic hypercalcemia [ 33 ]. Elevated calcium levels can disrupt intracellular calcium homeostasis, leading to muscle cell toxicity and functional impairment. Loop diuretics, including bumetanide and metolazone, play critical roles in managing edema and heart failure and are part of the treatment strategy for traumatic rhabdomyolysis [ 34 ]. However, excessive diuresis can cause significant fluid loss, further exacerbating metabolic stress and muscle cell damage [ 35 ]. Bumetanide, with an ROR of 9.73 (95% CI: 8.24–11.49) and a mean latency of 372.12 days, indicates its potential to cause rhabdomyolysis after prolonged use. Bumetanide strongly inhibits the Na⁺-K⁺-2Cl⁻ cotransporter in the renal loop of Henle [ 36 ], potentially causing severe hypokalemia and hypocalcemia. These electrolyte imbalances can disrupt muscle cell membrane potential, leading to impaired muscle contraction and eventual cell lysis [ 37 ]. Metolazone, with an OR of 7.60 (95% CI: 5.51–10.38) and a mean latency of 48.78 days, exhibits a relatively short latency period. Potent diuretic effect of metolazone (ROR = 8.71, 95%CI = 7.03–10.79) results from inhibiting sodium reabsorption in the distal convoluted tubules, causing severe hypokalemia that disrupts the sodium-potassium pump in muscle cells, further leading to muscle cell necrosis [ 38 ]. In patients with coexisting renal dysfunction, metolazone may exacerbate renal metabolic disturbances [ 39 ], significantly increasing the risk of rhabdomyolysis. The use of bumetanide and metolazone requires particular attention to the risk of electrolyte imbalances, especially at high doses or in combination therapies. This study has several limitations. First, the FAERS database relies on spontaneous reporting, which may result in overreporting of severe events like rhabdomyolysis, potentially overestimating the association between drugs and rhabdomyolysis. Second, the lack of critical clinical details, such as dosage, frequency of administration, duration of exposure, and patient comorbidities, limits the depth of the analysis. Finally, the exclusion of "concomitant medications," "secondary suspect drugs," and "interaction drugs" may have overlooked other important factors, which could be addressed in future case-control studies. Conclusion We analyzed a large dataset from the FAERS database to identify medications potentially associated with rhabdomyolysis, focusing on both risk values and the timing of drug-related reactions. Our findings emphasize the importance of continuous pharmacovigilance and further research to enhance understanding of drug-related rhabdomyolysis. In the long term, this work could improve medication safety protocols, guide clinical practices, and ultimately benefit patient outcomes. Abbreviations FDA: U.S.Food and DrugAdministration; FAERS: FDAAdverse Event Reporting System; READUS-PV: The Reporting of ADisproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance;ISR:Individual Safety Reports; ROR: Ratio of Odds Ratios; PRR: Proportional Reporting Ratio; LASSO:Least Absolute Shrinkage and Selection Operator; MedDRA: Medical Dictionary for Regulatory Activities; ROC:receiver operator characteristic curve; PT: Preferred Term; DEMO: Demographic Record; REAC:Adverse Event Record;DRUG: DrugRecord;OUTC:OutcomeRecord;RPSR: Report Source Record; THER: Therapy Record; INDI: Indication Record; PS: Primary Suspected cases; BCPNN: Bayesian Confidence Propagation Neural Network; MGPS: Multi-Item Gamma Poisson Shrinker; CI: Confidence Interval; MDs: medical doctors; PHs: pharmacists; mtDNA:mitochondrial DNA;CoQ10: coenzyme Q10 Declarations Acknowledgments The FAERS database, which was made available by the FDA, was used to conduct this study. The FDA does not have any opinion about the data, findings, or interpretation of the current study. Author contributions JH: Writing- original manuscript and editing. YS: Formal Analysis. XM: Visualization. HQ: Conceptualization, Writing–review. Funding No funding is available for this article. Availability of data and material The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: FAERS Publish Dashboard (https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard). Ethics approval and consent to participate Considering that the FAERS database is publicly accessible, and patient records are anonymous and de-identified, it does not involve informed consent or ethical approval. Consent for publication Not applicable Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author details a Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China. b The First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi Xi'an 710061, China. c Shanghai Putuo Hospital of Traditional Chinese Medicine, Shanghai, China，200062. # Corresponding Author: Huanru Qu, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China. Email: [email protected] References Cabral, B. M. I., Edding, S. N., Portocarrero, J. P. & Lerma, E. V. Rhabdomyolysis. Dis Mon 66, 101015 (2020). Yang, C.-W., Li, S., Dong, Y., Paliwal, N. & Wang, Y. Epidemiology and the Impact of Acute Kidney Injury on Outcomes in Patients with Rhabdomyolysis. Journal of Clinical Medicine 10, 1950 (2021). 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High phosphate induces skeletal muscle atrophy and suppresses myogenic differentiation by increasing oxidative stress and activating Nrf2 signaling. Aging (Albany NY) 12, 21446–21468 (2020). Komaba, H. & Fukagawa, M. Phosphate-a poison for humans? Kidney Int 90, 753–763 (2016). Alexander, R. T. & Dimke, H. Effects of parathyroid hormone on renal tubular calcium and phosphate handling. Acta Physiol (Oxf) 238, e13959 (2023). Sawhney, J. S. et al. Management of rhabdomyolysis: A practice management guideline from the Eastern Association for the Surgery of Trauma. The American Journal of Surgery 224, 196–204 (2022). Bartoli, E. et al. Use, misuse and abuse of diuretics. Eur J Intern Med 39, 9–17 (2017). Tao, D. et al. Bumetanide: A review of its neuroplasticity and behavioral effects after stroke. Restor Neurol Neurosci 37, 397–407 (2019). Fredrickson, K. A. & Carver, T. W. Trauma-related electrolyte disturbances: From resuscitation to rhabdomyolysis. Nutr Clin Pract 37, 1004–1014 (2022). Ayoti, R., Khan, Z., Mlawa, G. & Gupta, A. Assessing the Effectiveness and Safety of Combination Diuretic Therapy in Heart Failure: A Systematic Review and Meta-Analysis. Cureus 16, e72118 (2024). Brisco‐Bacik, M. A. et al. Outcomes Associated With a Strategy of Adjuvant Metolazone or High‐Dose Loop Diuretics in Acute Decompensated Heart Failure: A Propensity Analysis. JAHA 7, e009149 (2018). Tables Table 1 Baseline Data of Rhabdomyolysis Patients Reported in the FAERS Database. Variables Value Age (year) 58.8±18.8 Weight (Kg) 77.20±20.63 Gender Female 417134 (48.2%) Male 354666 (41.0%) Missing 94133 (10.8%) Outcome Other Serious (Important Medical Event) 280,131(32.4%) Hospitalization -Initial or Prolonged 268,911(31.1%) Death 90,048(10.4%) Life-Threatening 48,136(5.6%) Disability 21,098(2.4%) Required Intervention to Prevent Permanent Impairment/Damage Congenital Anomaly 2,499(0.3%) Missing 154,692(17.8%) Country United States 465,355(53.8%) France 52,092(6.0%) United Kingdom 48,494(5.6%) Japan 41,883(4.9%) Canada 41,461(4.8%) Denmark 27,250(3.1%) Italy 15,904(1.8%) Missing 24,505 (2.8%) Others 148,990(17.2%) Notes: Continuous numerical variables are expressed as mean ± standard deviation, and categorical variables are presented as n(%) Table2 Statistical Values and Distribution of Top 20 Drugs of Drug-Related Rhabdomyolysis Medication Drug N ROR (95%Cl) PRR (χ 2 ) EBGM(EBGM05) IC(IC025) Blood system Aprotinin 3051 103.64 (94.92 - 113.17) 17.73 (50372.59) 17.67 (16.42) 4.14 (4.07) Metabolic-related drug Multivitamins and minerals 595 84.31 (70.21 - 101.25) 17.05 (9431.59) 17.04 (14.62) 4.09 (3.93) Digestive system Sodium phosphate 746 46.77 (41.03 - 53.31) 14.77 (10044.34) 14.76 (13.23) 3.88 (3.75) Digestive system Lansoprazole 15035 25.14 (24.55 - 25.75) 11.83 (153741.63) 11.64 (11.41) 3.54 (3.51) Digestive system Dexlansoprazole 2763 18.09 (17.19 - 19.05) 10.01 (23447.6) 9.98 (9.56) 3.32 (3.25) Cardiovascular system Simvastatin 11575 14.44 (14.1 - 14.79) 8.86 (83631.01) 8.76 (8.58) 3.13 (3.1) Cardiovascular system Lovastatin 436 13.62 (12.06 - 15.38) 8.53 (3038.63) 8.52 (7.7) 3.09 (2.93) Antiviral drug Tenofovir disoproxil 8459 13.55 (13.18 - 13.93) 8.53 (58411.9) 8.45 (8.26) 3.08 (3.04) Antiviral drug Telbivudine 273 12.83 (11.02 - 14.93) 8.22 (1817.56) 8.22 (7.24) 3.04 (2.83) Digestive system Pantoprazole 11291 11.74 (11.47 - 12.01) 7.81 (69460.64) 7.72 (7.57) 2.95 (2.92) Orthopedic system Parathyroid hormone 1563 11.41 (10.72 - 12.14) 7.65 (9460.23) 7.63 (7.25) 2.93 (2.85) Cardiovascular system Ezetimibe/Simvastatin 1293 10.77 (10.06 - 11.52) 7.36 (7453.83) 7.35 (6.95) 2.88 (2.79) Antiviral drug Efavirenz/Emtricitabine/Tenofovir disoproxil 3238 10.12 (9.7 - 10.56) 7.07 (17663.3) 7.05 (6.81) 2.82 (2.76) Cardiovascular system Fluvastatin 539 9.8 (8.84 - 10.86) 6.92 (2861.39) 6.91 (6.34) 2.79 (2.65) Urinary system Bumetanide 206 9.73 (8.24 - 11.49) 6.88 (1086.78) 6.88 (5.99) 2.78 (2.55) Antiviral drug Foscarnet 251 9.51 (8.18 - 11.05) 6.78 (1297.1) 6.78 (5.98) 2.76 (2.55) Digestive system Esomeprazole 21402 9.22 (9.07 - 9.37) 6.67 (105668) 6.53 (6.44) 2.71 (2.68) Antiviral drug Emtricitabine/Tenofovir disoproxil 6699 9.03 (8.77 - 9.29) 6.55 (32832.04) 6.51 (6.35) 2.7 (2.66) Cardiovascular system Pitavastatin 545 8.89 (8.03 - 9.83) 6.47 (2644.04) 6.47 (5.94) 2.69 (2.55) Urinary system Metolazone 120 8.71 (7.03 - 10.79) 6.38 (571.53) 6.38 (5.33) 2.67 (2.37) Notes: Drugs are sorted according to ROR values. “Multivitamins and minerals” are the general term of ascorbic acid, biotin, chromium, cholecalciferol, copper, cyanocobalamin, folic acid, iron, molybdenum, nicotinic acid and pantothenic. Table 3 Multi-variate Logistic Regression Analysis of Drugs Associated with Rhabdomyolysis Variables OR 95%CI P-Value Aprotinin 131.87 114.73-152.40 ＜0.001 Multivitamins and minerals 940.60 300.78-5697.98 ＜0.001 Sodium phosphate 40.35 32.54-50.46 ＜0.001 Simvastatin 10.64 10.23-11.07 ＜0.001 Lovastatin 12.42 10.18-15.16 ＜0.001 Tenofovir disoproxil 11.83 10.69-13.07 ＜0.001 Telbivudine 17.86 12.52-25.65 ＜0.001 Parathyroid hormone 10.02 8.24-12.16 ＜0.001 Ezetimibe/simvastatin 8.81 7.82-9.92 ＜0.001 Efavirenz/emtricitabine/tenofovir disoproxil 9.92 8.84-11.12 ＜0.001 Fluvastatin 7.89 6.55-9.48 ＜0.001 Bumetanide 8.55 6.28-11.58 ＜0.001 Foscarnet 8.18 5.89-11.25 ＜0.001 Emtricitabine/tenofovir disoproxil 6.51 6.01-7.05 ＜0.001 Pitavastatin 6.73 5.40-8.34 ＜0.001 Metolazone 7.60 5.51-10.38 ＜0.001 Table 4. Drug-Induced Time Distribution of Drug-Related Rhabdomyolysis Caused by Different Drugs Drug Mean Q1 Q3 Foscarnet 28.99 3.00 15.50 Metolazone 48.78 6.00 40.00 Multivitamins and minerals 120.58 4.00 97.00 Sodium phosphate 142.17 2.00 67.00 Pitavastatin 191.89 13.75 138.00 Aprotinin 272.12 2.00 94.50 Ezetimibe/simvastatin 355.99 32.00 365.50 Bumetanide 372.12 15.50 218.50 Telbivudine 400.18 151.00 401.00 Fluvastatin 406.93 25.00 434.00 Lovastatin 538.66 31.25 543.75 Parathyroid hormone 603.68 133.00 932.50 Simvastatin 663.49 32.00 818.00 Emtricitabine/tenofovir disoproxil 1413.11 304.50 2188.75 Tenofovir disoproxil 1552.83 196.50 2577.50 Efavirenz/emtricitabine/tenofovir disoproxil 1739.99 600.72 2678.25 Additional Declarations No competing interests reported. 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11:52:24","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45698,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/a23486e33dd627d8883245a2.png"},{"id":94758898,"identity":"ad3a48a5-9fae-4496-be06-39eddc3516f0","added_by":"auto","created_at":"2025-10-30 11:52:23","extension":"xml","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151989,"visible":true,"origin":"","legend":"","description":"","filename":"c47d3286e0244ee99d86db901a2ce23d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/c06a9d5b56c654172ea5617f.xml"},{"id":94824042,"identity":"380f6691-487b-490b-bb5a-958862b6d6e6","added_by":"auto","created_at":"2025-10-31 06:48:23","extension":"html","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159159,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/d95d39ddfe5e5ebd4c9e9468.html"},{"id":94758865,"identity":"aeeea7f8-a3be-4ec9-b799-ae47d6aa1bcd","added_by":"auto","created_at":"2025-10-30 11:52:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":619709,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of screening reports containing rhabdomyolysiselicited by diverse agents from the FAERS database.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/6553ac48e66a5d3ba040550e.jpg"},{"id":94824513,"identity":"2a0e3595-3a19-4792-9a4c-09c888c38b76","added_by":"auto","created_at":"2025-10-31 06:49:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":458409,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of reports involved in drug-induced rhabdomyolysisfrom the FAERS database. Notes: Figure 2A displays a timeline chart showing the distribution of reported adverse events of rhabdomyolysis over time. Figure 2B shows the distribution of outcomes among patients experiencing adverse events of rhabdomyolysis. Figure 2C depicts age distribution among patients reporting adverse events of rhabdomyolysis, categorized by gender. Figure 2D shows distribution of reported adverse events of rhabdomyolysis by country.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/eb95962aceb056842df16f14.jpg"},{"id":94823922,"identity":"8baee8c4-12c5-464c-93c1-3e8276c7b6f5","added_by":"auto","created_at":"2025-10-31 06:48:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74134,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of drugs associated with rhabdomyolysis by LASSO regression. LASSO, Least Absolute Shrinkage and Selection Operator.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/cc3db7d46c2bf75defe10198.png"},{"id":94758868,"identity":"5a228d9c-f5b6-4210-9020-d1aa77ae5cbf","added_by":"auto","created_at":"2025-10-30 11:52:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158631,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of drug-related rhabdomyolysis. ROC, Receiver Operating Characteristic; AUC, Area Under Curve.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/c94d6e7942feb2a4c7e51f8e.jpg"},{"id":94823370,"identity":"e24ca2af-e293-4b53-9641-244b6e8c59eb","added_by":"auto","created_at":"2025-10-31 06:47:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":149173,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative risk timeline of drug induction for different system drugs.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/c8d1f36fc32b29047e808505.jpg"},{"id":94758875,"identity":"5df55f8d-ea5c-457e-b2c5-7642c3865449","added_by":"auto","created_at":"2025-10-30 11:52:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":161307,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot for time disparities in different group induction.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/e16987cf18c0eb4f11d4e899.jpg"},{"id":101751156,"identity":"d3ef6313-018c-4e24-9809-a1c0beb49f50","added_by":"auto","created_at":"2026-02-03 10:17:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2740199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/1a17916e-bb29-4780-9e3a-380f784e7284.pdf"},{"id":94758863,"identity":"103bc41a-c411-421c-a12b-1c907c6457de","added_by":"auto","created_at":"2025-10-30 11:52:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18254,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7536398/v1/e57c8e7f24f4e31deaf88f0e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Drug-Related Rhabdomyolysis: A Real-World FDA Adverse Event Reporting System Database Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRhabdomyolysis is an acute syndrome caused by the rupture of skeletal muscle cells, which results in the release of myoglobin, creatine kinase, and other intracellular components into the bloodstream[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This pathological process can lead to severe complications, including acute kidney injury, electrolyte imbalances, and multiple organ failure, and, in extreme cases, may be life-threatening. Annual incidence of rhabdomyolysis is approximately 26,000 cases in USA[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], with a significant proportion attributed to drug-related causes. Drug-induced rhabdomyolysis, the one of most common type of non-traumatic etiology, is primarily caused by drug-induced myotoxicity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] via mechanisms including mitochondrial dysfunction, calcium homeostasis disruption, and oxidative stress. Furthermore, specific drugs under certain physiological or pathological conditions (e.g., hepatic or renal dysfunction or polypharmacy) can substantially heighten the risk of rhabdomyolysis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This issue presents challenges to the safety of individual patient treatments and imposes a considerable burden on public health systems, making it a critical focus in the field of pharmacovigilance.\u003c/p\u003e\u003cp\u003eDrug-induced rhabdomyolysis is not uncommon in clinical practice, and current studies have mainly found that this adverse reaction is related to the cardiovascular system. cardiovascular drugs, which are commonly prescribed for lipid-lowering therapy, are recognized as major contributors to drug-induced rhabdomyolysis due to their impact on muscle cell metabolism[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Combined drug use, such as the co-administration of statins and fibrates, further amplifies the risk of muscle toxicity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], posing significant challenges for clinical management.\u003c/p\u003e\u003cp\u003eHowever, systematic research on drug-induced rhabdomyolysis is relatively scarce, primarily derived from individual case reports and clinical research. Hence, this study conducted analysis to explore association between various drugs and rhabdomyolysis based on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Given the high incidence and severe clinical outcomes of drug-induced rhabdomyolysis, a systematic and comprehensive analysis of drugs closely associated with rhabdomyolysis is of substantial clinical importance. This study aims to serve as a reference for the safety management of clinical drug use and to provide new insights into the optimization of pharmacovigilance systems.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eData source and study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research adopted a retrospective, observational pharmacovigilance design based on the publicly available FAERS database. FAERS is a spontaneous reporting platform that collects safety reports submitted by healthcare professionals, patients, manufacturers, and other reporters. All procedures were carried out following the methodological standards of the READUS-PV guideline, which provides recommendations for conducting and presenting disproportionality analyses using individual case safety reports. Adverse events were coded using the Medical Dictionary for Regulatory Activities (MedDRA), an internationally recognized terminology system. To facilitate data retrieval, Open Vigil 2.1-MedDRA was employed. For reliability, only reports submitted by qualified healthcare providers (e.g., physicians and pharmacists, coded as MD and PH) were retained. From January 2004 to September 2024, FAERS contained 21,964,449 initial records; after deduplication, 18,278,243 unique reports were available. Standardizing drug names further reduced the dataset to 2,482 distinct agents. A schematic of this cleaning process is provided in Figure 1..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 1]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCases of rhabdomyolysis were identified in the REAC file through the preferred term\u0026nbsp;“Rhabdomyolysis”\u0026nbsp;(MedDRA code 10039020). Corresponding report identifiers (PRIMARYID) were extracted and linked to other datasets to obtain detailed information, including Individual Safety Reports (ISR), patient demographics (age, sex, country), event details, medication exposure, dates of reporting, and clinical outcomes. Analyses focused exclusively on drugs coded as\u0026nbsp;“primary suspects,”\u0026nbsp;while concomitant, secondary suspect, or interaction drugs were excluded to minimize uncertainty regarding causality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor signal detection, we applied four disproportionality methods—Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS)[7]—each derived from 2×2 contingency tables (see Supplementary Table S1). Operational formulas and thresholds are outlined in Supplementary Table S2. A drug was considered to have a valid signal only if it satisfied criteria across all four methods, suggesting a consistent association with rhabdomyolysis. To strengthen causal inference, drugs meeting disproportionality criteria were further assessed by univariate logistic regression (requirements: ROR 95% CI lower bound \u0026gt;1, case count \u0026gt;100, and adjusted p \u0026lt;0.01)[8]. Candidate drugs were subsequently filtered using Least Absolute Shrinkage and Selection Operator (LASSO) regression, with the 20 most strongly associated drugs retained. Multivariable logistic regression was then conducted to confirm independent associations. Model performance was evaluated with receiver operating characteristic (ROC) curve analysis. Finally, we investigated the time-to-onset of rhabdomyolysis following drug initiation and compared latency patterns among different agents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering that the FAERS database is publicly accessible, and patient records are anonymous and de-identified, it does not involve informed consent or ethical approval.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBasic information of patients with drug-induced rhabdomyolysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2004 to 2019, there was a consistent increase in the number of reports concerning drug-induced rhabdomyolysis. By September 2024, a total of 865,934 patients had reported adverse events linked to this condition, with the peak occurring in 2019, accounting for 80,468 cases [Figure 2A]. The average age of these patients was approximately 58.8±18.8 years, as detailed in Table 1. Notably, the age distribution of these patients approximated a normal curve, initially increasing before decreasing, with the most reports found in the 61-70 age group [Figure 2C]. The majority of the reports originated from the United States, which accounted for 53.8% of the total (n= 465,355) [Figures 2D]. Excluding cases with missing data, 32.4% of the reports indicated other serious outcomes. Notably, 90,048(10.4%) patients who suffered from drug-induced rhabdomyolysis were dead [Figure 2B]. Additional details were showed in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 2]\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 1]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification of Drugs Associated by therapeutic purpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn light of the fact that some medications are prescribed to treat conditions associated with rhabdomyolysis yet may show positive signals due to insufficient efficacy, we excluded these drugs from our analysis. We also verified the brand and generic names of these medications. After screening 126 drugs that met the four-disproportionality analysis and univariate logistic regression positives, we identified the top 20 drugs in reported cases ranked by ROR. These drugs were primarily categorized into seven classes, with lansoprazole reporting the highest incidence (n=15,035), as outlined in Table 2. The drugs with the highest ROR were aprotinin (ROR = 103.64, 95%CI= 94.92 - 113.17), multivitamins and minerals (ROR = 84.31, 95%CI= 70.21 - 101.25), sodium phosphate (ROR = 46.77, 95%CI= 41.03 - 53.31), lansoprazole (ROR = 25.14, 95%CI= 24.55 - 25.75), and dexlansoprazole (ROR = 18.09, 95%CI= 17.19 - 19.05), with detailed data provided in Table 2. “Multivitamins and minerals” are the general term of ascorbic acid, biotin, chromium, cholecalciferol, copper, cyanocobalamin, folic acid, iron, molybdenum, nicotinic acid and pantothenic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 2]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-variate logistic regression analysis of drug-induced rhabdomyolysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the results of the top 20 drugs with p\u0026lt;0.01 conforming to univariate analysis, we used LASSO regression to further analyze the association between these drugs and rhabdomyolysis. In cross-validation, the regularization parameter λmin=0.0004275009 (log(λmin) =−7.76) was selected as it minimizes the prediction error. The corresponding model includes 16 drugs (Figure 3). Furthermore, multi-variate regression and ROC curves were used to test the reliability of the analysis. Consequently, all 16 drugs were related to rhabdomyolysis, and the ROC of logistic regression analysis was 0.763, showing a relatively good level (Figure 4). The drugs with the highest association were multivitamins and minerals\u0026nbsp;(OR= 940.60, 95%CI= 300.78-5697.98, P\u0026lt;0.001), aprotinin (OR= 131.87, 95%CI= 114.73-152.40, P\u0026lt;0.001), sodium phosphate (OR= 40.35, 95%CI= 32.54-50.46, P\u0026lt;0.001), telbivudine (OR= 17.86, 95%CI= 12.52-25.65, P\u0026lt;0.001) and lovastatin (OR= 12.42, 95%CI= 10.18-15.16, P\u0026lt;0.001).\u0026nbsp;Detailed results are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 3]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 3]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 4]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of Drug-Induced Onset Times for rhabdomyolysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe categorized the drugs based on their primary therapeutic uses into six groups: blood system (1 drug), metabolic-related drug (1 drug), orthopedic system (1 drug), digestive system (1 drug), cardiovascular system (5 drugs), antiviral drug (6 drugs) and urinary system (2 drugs). Comprehensive data on the onset times of drug-induced effects were collected, and differences between these groups were analyzed using cumulative risk curves. The analysis revealed that metabolic-related drug tends to exhibit a longest time to onset of drug-induced effects while antiviral drug exhibits a shortest time. Specifically, the average onset time for foscarnet was 28.99 days, while it extended to 1739.99 days for\u0026nbsp;efavirenz/emtricitabine/tenofovir disoproxil. For a detailed breakdown of these findings, refer to Table 4 Figure 5, and Figure 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 4]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 5]\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 6]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study performed a data mining analysis of adverse drug reaction reports concerning drug-induced rhabdomyolysis and identified 80,468 relevant cases, primarily from the United States, France, and the United Kingdom. Regarding gender distribution, female patients reported more cases of drug-induced rhabdomyolysis than male patients, indicating a possible gender-related predisposition. In terms of age distribution, the reports included 3.0% of patients under 18 years and 3.9% of patients over 85 years, reflecting potential gaps in safety and efficacy studies for these age groups.\u003c/p\u003e\u003cp\u003eAntiviral drugs were the most frequently classified category in this study and thus warrant particular attention. Tenofovir disoproxil (ROR\u0026thinsp;=\u0026thinsp;13.55, 95%CI\u0026thinsp;=\u0026thinsp;13.18\u0026ndash;13.93), a key drug in HIV treatment, inhibits reverse transcriptase to disrupt viral replication [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, its mechanism may cause mitochondrial toxicity and energy metabolism disorders. A case report documented rhabdomyolysis in a patient treated with tenofovir for 11 years, likely due to its direct toxicity on muscle cells or indirectly through Fanconi syndrome-associated hypophosphatemia and hyponatremia[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Tenofovir disoproxil's high affinity for mitochondrial DNA polymerase γ inhibits mitochondrial DNA synthesis, impairing mitochondrial function, energy production, and increasing oxidative stress [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Chronic mitochondrial damage may account for the prolonged latency of its adverse reactions (mean: 1552.83 days). Nevertheless, some studies attribute acute rhabdomyolysis in HIV patients to antiretroviral therapy or acute HIV-1 seroconversion symptoms [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], suggesting different mechanisms underlying acute and chronic rhabdomyolysis caused by antiviral drugs. Telbivudine (ROR\u0026thinsp;=\u0026thinsp;12.83, 95%CI\u0026thinsp;=\u0026thinsp;11.02\u0026ndash;14.93), a nucleoside analog for chronic hepatitis B, acts by inhibiting viral DNA chain elongation but may induce rhabdomyolysis by impairing mitochondrial oxidative phosphorylation in skeletal muscle. One case reported rhabdomyolysis six months after switching from entecavir to telbivudine, with other common causes excluded [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Telbivudine significantly reduces mitochondrial DNA (mtDNA) copy numbers, increasing oxidative stress, mtDNA depletion, and mitochondrial dysfunction, disrupting homeostasis and causing muscle cell injury [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This rapid mechanism aligns with its shorter onset time (mean: 400.18 days). Foscarnet(ROR\u0026thinsp;=\u0026thinsp;9.51, 95%CI\u0026thinsp;=\u0026thinsp;8.18\u0026ndash;11.05), a non-nucleoside antiviral drug, is widely used for the treatment of cytomegalovirus infections and other viral infections by inhibiting viral DNA polymerase and reverse transcriptase. While interfering with viral replication, foscarnet may exert toxic effects on host cell mitochondria, including inhibition of oxidative phosphorylation and increased oxidative stress[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The resulting mitochondrial dysfunction further compromises the energy metabolism of muscle cells, accelerating the process of cell damage. The latency of foscarnet-induced rhabdomyolysis is relatively short, averaging 28.99 days, making it one of the drugs with the shortest latency in this study. This rapid onset mechanism suggests a propensity to directly disrupt ion balance and induce acute muscle injury rather than a cumulative effect[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This rapid progression requires close clinical attention, particularly in the early stages of treatment when patients should be closely monitored. Additionally, combined antiviral regimens, such as efavirenz/emtricitabine/tenofovir disoproxil, significantly enhance antiviral efficacy but synergistically impair mitochondrial function, intensifying oxidative stress and mitochondrial DNA damage [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The complex interactions between these drugs may further exacerbate the metabolic burden on patients, with this effect being particularly pronounced in elderly patients.\u003c/p\u003e\u003cp\u003eThe relationship between cardiovascular drugs and rhabdomyolysis is well-documented[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Statins, by inhibiting 3-hydroxy-3-methylglutaryl coenzyme A reductase, lower cholesterol synthesis while simultaneously reducing coenzyme Q10 (CoQ10) levels, thereby impairing mitochondrial function. CoQ10, a critical component of the electron transport chain, when reduced, leads to impaired ATP production and elevated oxidative stress, ultimately triggering rhabdomyolysis. A study based on the Vigibase database revealed that simvastatin is associated with the highest risk of rhabdomyolysis [ROR\u0026thinsp;=\u0026thinsp;2.20 (2.11\u0026ndash;2.29)], particularly among individuals over 74 years old and males[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Its moderate latency (mean: 663.49 days) is likely linked to cumulative mitochondrial damage [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Lovastatin exhibits similar mechanisms, with a slightly shorter onset time (mean: 538.66 days), possibly due to pharmacokinetic properties resulting in more concentrated tissue exposure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Pitavastatin, a newer HMG-CoA reductase inhibitor, plays a crucial role in the treatment of cardiovascular diseases by lowering low-density lipoprotein levels. It is primarily metabolized through hepatic glucuronidation rather than CYP450 enzymes, thereby reducing the risk of drug interactions. However, in cases of polypharmacy or diminished metabolic capacity, its accumulation may still lead to muscle toxicity. An analysis of the JADER database reported a rhabdomyolysis rate of 0.09 for pitavastatin, which increased to 0.16 when combined with allopurinol and to 0.40 when combined with valsartan, findings that were corroborated by in vitro studies[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Conversely, other research has shown decreased rhabdomyolysis rates with specific drug combinations[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], suggesting that the adverse effects of pitavastatin may be closely related to treatment duration, a topic warranting further investigation. Ezetimibe, which inhibits intestinal cholesterol absorption, exhibits a synergistic effect when combined with simvastatin (ROR\u0026thinsp;=\u0026thinsp;10.77, 95%CI\u0026thinsp;=\u0026thinsp;10.06\u0026ndash;11.52). However, this combination therapy may increase the risk of muscle toxicity. Its relatively short latency (mean: 355.99 days) suggests that the combined effects of the two drugs may accelerate mitochondrial damage and metabolic abnormalities. Furthermore, statins have been shown to interact with other commonly used drugs, such as daptomycin, exacerbating muscle toxicity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAprotinin, a protease inhibitor, is widely used to reduce intraoperative bleeding, particularly in cardiac surgery, where it plays a critical role. However, this study demonstrated a strong association between aprotinin and the risk of rhabdomyolysis (ROR\u0026thinsp;=\u0026thinsp;103.64, 95%CI\u0026thinsp;=\u0026thinsp;94.92\u0026ndash;113.17), making it the highest-risk drug in this study. This finding highlights the potential for severe muscle-related adverse effects associated with aprotinin in certain patient populations. As a protease inhibitor, aprotinin suppresses the degradation of myoglobin, which may lead to its accumulation, causing nephrotoxic effects, renal impairment, and typical complications of rhabdomyolysis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Aprotinin exerts hemostatic effects by inhibiting plasmin and other serine proteases. However, this inhibitory effect may disrupt the normal metabolic processes of muscle cell proteins, resulting in abnormal muscle fiber degradation and the accumulation of metabolic byproducts, thereby causing cytotoxic effects. Additionally, aprotinin use may increase oxidative stress, disrupt mitochondrial oxidative phosphorylation, and impair energy metabolism in skeletal muscle cells. These effects are particularly pronounced in high-dose usage or when combined with other metabolic inhibitors. The mean latency period for aprotinin-induced rhabdomyolysis is 272.12 days, making it one of the drugs with a relatively short latency period for rhabdomyolysis. This rapid onset may be related to its direct effects on protease activity and calcium channels. It is crucial to closely monitor patients\u0026rsquo; serum creatine kinase levels and clinical symptoms during initial drug administration or dose escalation to promptly identify and manage rhabdomyolysis.\u003c/p\u003e\u003cp\u003eMultivitamin and mineral supplements may increase the risk of rhabdomyolysis under certain conditions through synergistic toxicity. This study found an ROR of 940.60 (95% CI: 300.78\u0026ndash;5697.98) for such combinations, suggesting that interactions between their components may significantly exacerbate muscle damage. Copper, selenium, and zinc, essential components of the antioxidant system, may, at high doses, exacerbate oxidative stress, thereby causing muscle cell damage[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Similarly, the metabolic toxicity of vitamin A may further exacerbate energy deficiencies in cells by impairing mitochondrial function[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although no direct evidence links multivitamin and mineral combinations to rhabdomyolysis, their synergistic toxicity underscores the importance of carefully dosing supplements. For high-risk populations, such as the elderly or individuals with impaired renal function, close monitoring of component interactions is essential to prevent adverse effects.\u003c/p\u003e\u003cp\u003eSodium phosphate and parathyroid hormone were significantly associated with rhabdomyolysis, potentially through mechanisms involving calcium-phosphorus metabolism disturbances and direct damage to skeletal muscle cells. Sodium phosphate, commonly used as a laxative and bowel cleanser, was associated with an OR of 46.77 (95% CI: 41.03\u0026ndash;53.31) for rhabdomyolysis, with a mean latency of 142.17 days. This relatively short latency period suggests that its toxic effects may manifest rapidly. High doses of sodium phosphate may lead to acute hyperphosphatemia, subsequently resulting in hypocalcemia and calcium-phosphorus precipitation[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Such metabolic disturbances can interfere with normal muscle cell contraction and function, ultimately leading to cell damage and rhabdomyolysis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, the osmotic laxative effect of sodium phosphate may cause rapid fluid and electrolyte loss, exacerbating cellular stress, particularly in patients with preexisting metabolic or renal disorders [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Parathyroid hormone, widely used for the treatment of hypocalcemia and osteoporosis, was associated with an ROR of 11.41 (95% CI: 10.72\u0026ndash;12.14) for rhabdomyolysis, with a mean latency of 603.68 days. Compared with sodium phosphate, its longer latency may reflect the cumulative effects of chronic metabolic disturbances. Parathyroid hormone promotes calcium release from bones into the bloodstream, which may result in chronic hypercalcemia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Elevated calcium levels can disrupt intracellular calcium homeostasis, leading to muscle cell toxicity and functional impairment.\u003c/p\u003e\u003cp\u003eLoop diuretics, including bumetanide and metolazone, play critical roles in managing edema and heart failure and are part of the treatment strategy for traumatic rhabdomyolysis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, excessive diuresis can cause significant fluid loss, further exacerbating metabolic stress and muscle cell damage [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Bumetanide, with an ROR of 9.73 (95% CI: 8.24\u0026ndash;11.49) and a mean latency of 372.12 days, indicates its potential to cause rhabdomyolysis after prolonged use. Bumetanide strongly inhibits the Na⁺-K⁺-2Cl⁻ cotransporter in the renal loop of Henle [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], potentially causing severe hypokalemia and hypocalcemia. These electrolyte imbalances can disrupt muscle cell membrane potential, leading to impaired muscle contraction and eventual cell lysis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Metolazone, with an OR of 7.60 (95% CI: 5.51\u0026ndash;10.38) and a mean latency of 48.78 days, exhibits a relatively short latency period. Potent diuretic effect of metolazone (ROR\u0026thinsp;=\u0026thinsp;8.71, 95%CI\u0026thinsp;=\u0026thinsp;7.03\u0026ndash;10.79) results from inhibiting sodium reabsorption in the distal convoluted tubules, causing severe hypokalemia that disrupts the sodium-potassium pump in muscle cells, further leading to muscle cell necrosis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In patients with coexisting renal dysfunction, metolazone may exacerbate renal metabolic disturbances [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], significantly increasing the risk of rhabdomyolysis. The use of bumetanide and metolazone requires particular attention to the risk of electrolyte imbalances, especially at high doses or in combination therapies.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the FAERS database relies on spontaneous reporting, which may result in overreporting of severe events like rhabdomyolysis, potentially overestimating the association between drugs and rhabdomyolysis. Second, the lack of critical clinical details, such as dosage, frequency of administration, duration of exposure, and patient comorbidities, limits the depth of the analysis. Finally, the exclusion of \"concomitant medications,\" \"secondary suspect drugs,\" and \"interaction drugs\" may have overlooked other important factors, which could be addressed in future case-control studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe analyzed a large dataset from the FAERS database to identify medications potentially associated with rhabdomyolysis, focusing on both risk values and the timing of drug-related reactions. Our findings emphasize the importance of continuous pharmacovigilance and further research to enhance understanding of drug-related rhabdomyolysis. In the long term, this work could improve medication safety protocols, guide clinical practices, and ultimately benefit patient outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFDA: U.S.Food and DrugAdministration; FAERS: FDAAdverse Event Reporting System; READUS-PV: The Reporting of ADisproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance;ISR:Individual Safety Reports; ROR: Ratio of Odds Ratios; PRR: Proportional Reporting Ratio; LASSO:Least Absolute Shrinkage and Selection Operator; MedDRA: Medical Dictionary for Regulatory Activities; ROC:receiver operator characteristic curve; PT: Preferred Term; DEMO: Demographic Record; REAC:Adverse Event Record;DRUG: DrugRecord;OUTC:OutcomeRecord;RPSR: Report Source Record; THER: Therapy Record; INDI: Indication Record; PS: Primary Suspected cases; BCPNN: Bayesian Confidence Propagation Neural Network; MGPS: Multi-Item Gamma Poisson Shrinker; CI: Confidence Interval; MDs: medical doctors; PHs: pharmacists; mtDNA:mitochondrial DNA;CoQ10: coenzyme Q10\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eThe FAERS database, which was made available by the FDA, was used to conduct this study. The FDA does not have any opinion about the data, findings, or interpretation of the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eJH: Writing- original manuscript and editing. YS: Formal Analysis. XM: Visualization. HQ: Conceptualization, Writing\u0026ndash;review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNo funding is available for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: FAERS Publish Dashboard (https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eConsidering that the FAERS database is publicly accessible, and patient records are anonymous and de-identified, it does not involve informed consent or ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e \u003csup\u003ea\u003c/sup\u003e Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China. \u003csup\u003eb\u003c/sup\u003e The First Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University, Shaanxi Xi\u0026apos;an 710061, China. \u003csup\u003ec\u003c/sup\u003e Shanghai Putuo Hospital of Traditional Chinese Medicine, Shanghai, China，200062.\u003c/p\u003e\n\u003cp\u003e# Corresponding Author: Huanru Qu, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China. 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M. \u003cem\u003eet al.\u003c/em\u003e Two-drug regimens for HIV treatment. \u003cem\u003eLancet HIV\u003c/em\u003e \u003cstrong\u003e9,\u003c/strong\u003e e868\u0026ndash;e883 (2022).\u003c/li\u003e\n\u003cli\u003eWallace, J. \u003cem\u003eet al.\u003c/em\u003e Anti-HIV Drugs Cause Mitochondrial Dysfunction in Monocyte-Derived Macrophages. \u003cem\u003eAntimicrob Agents Chemother\u003c/em\u003e \u003cstrong\u003e66,\u003c/strong\u003e e0194121 (2022).\u003c/li\u003e\n\u003cli\u003eCollins, R. \u003cem\u003eet al.\u003c/em\u003e Interpretation of the evidence for the efficacy and safety of statin therapy. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cstrong\u003e388,\u003c/strong\u003e 2532\u0026ndash;2561 (2016).\u003c/li\u003e\n\u003cli\u003eMontastruc, J.-L. Rhabdomyolysis and statins: A pharmacovigilance comparative study between statins. \u003cem\u003eBr J Clin Pharmacol\u003c/em\u003e \u003cstrong\u003e89,\u003c/strong\u003e 2636\u0026ndash;2638 (2023).\u003c/li\u003e\n\u003cli\u003eWojcicki, K., Budzinska, A. \u0026amp; Jarmuszkiewicz, W. Effects of Atorvastatin and Simvastatin on the Bioenergetic Function of Isolated Rat Brain Mitochondria. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e25,\u003c/strong\u003e 8494 (2024).\u003c/li\u003e\n\u003cli\u003eAjima, H. \u003cem\u003eet al.\u003c/em\u003e Effects of fenofibrate and its combination with lovastatin on the expression of genes involved in skeletal muscle atrophy, including FoxO1 and its targets. \u003cem\u003eJ Toxicol Sci\u003c/em\u003e \u003cstrong\u003e46,\u003c/strong\u003e 11\u0026ndash;24 (2021).\u003c/li\u003e\n\u003cli\u003eLu, B. \u003cem\u003eet al.\u003c/em\u003e Effect of SLCO1B1 T521C on Statin-Related Myotoxicity With Use of Lovastatin and Atorvastatin. \u003cem\u003eClin Pharmacol Ther\u003c/em\u003e \u003cstrong\u003e110,\u003c/strong\u003e 733\u0026ndash;740 (2021).\u003c/li\u003e\n\u003cli\u003eNegishi, A. \u003cem\u003eet al.\u003c/em\u003e Possibility of Multiple Drug-Drug Interactions in Patients Treated with Statins: Analysis of Data from the Japanese Adverse Drug Event Report (JADER) Database and Verification by Animal Experiments. \u003cem\u003eInt J Med Sci\u003c/em\u003e \u003cstrong\u003e19,\u003c/strong\u003e 1816\u0026ndash;1823 (2022).\u003c/li\u003e\n\u003cli\u003eAkimoto, H. \u003cem\u003eet al.\u003c/em\u003e Onset timing of statin-induced musculoskeletal adverse events and concomitant drug-associated shift in onset timing of MAEs. \u003cem\u003ePharmacol Res Perspect\u003c/em\u003e \u003cstrong\u003e6,\u003c/strong\u003e e00439 (2018).\u003c/li\u003e\n\u003cli\u003eChuma, M. \u003cem\u003eet al.\u003c/em\u003e Association Between Statin Use and Daptomycin-related Musculoskeletal Adverse Events: A Mixed Approach Combining a Meta-analysis and a Disproportionality Analysis. \u003cem\u003eClin Infect Dis\u003c/em\u003e \u003cstrong\u003e75,\u003c/strong\u003e 1416\u0026ndash;1422 (2022).\u003c/li\u003e\n\u003cli\u003eZorova, L. D. \u003cem\u003eet al.\u003c/em\u003e The role of myoglobin degradation in nephrotoxicity after rhabdomyolysis. \u003cem\u003eChemico-Biological Interactions\u003c/em\u003e \u003cstrong\u003e256,\u003c/strong\u003e 64\u0026ndash;70 (2016).\u003c/li\u003e\n\u003cli\u003eBj\u0026oslash;rklund, G. \u003cem\u003eet al.\u003c/em\u003e The Role of Zinc and Copper in Insulin Resistance and Diabetes Mellitus. \u003cem\u003eCurr Med Chem\u003c/em\u003e \u003cstrong\u003e27,\u003c/strong\u003e 6643\u0026ndash;6657 (2020).\u003c/li\u003e\n\u003cli\u003eCarazo, A. \u003cem\u003eet al.\u003c/em\u003e Vitamin A Update: Forms, Sources, Kinetics, Detection, Function, Deficiency, Therapeutic Use and Toxicity. \u003cem\u003eNutrients\u003c/em\u003e \u003cstrong\u003e13,\u003c/strong\u003e 1703 (2021).\u003c/li\u003e\n\u003cli\u003eHamilton Smith, R., Eddleston, M. \u0026amp; Bateman, D. N. Toxicity of phosphate enemas - an updated review. \u003cem\u003eClin Toxicol (Phila)\u003c/em\u003e \u003cstrong\u003e60,\u003c/strong\u003e 672\u0026ndash;680 (2022).\u003c/li\u003e\n\u003cli\u003eChung, L.-H. \u003cem\u003eet al.\u003c/em\u003e High phosphate induces skeletal muscle atrophy and suppresses myogenic differentiation by increasing oxidative stress and activating Nrf2 signaling. \u003cem\u003eAging (Albany NY)\u003c/em\u003e \u003cstrong\u003e12,\u003c/strong\u003e 21446\u0026ndash;21468 (2020).\u003c/li\u003e\n\u003cli\u003eKomaba, H. \u0026amp; Fukagawa, M. Phosphate-a poison for humans? \u003cem\u003eKidney Int\u003c/em\u003e \u003cstrong\u003e90,\u003c/strong\u003e 753\u0026ndash;763 (2016).\u003c/li\u003e\n\u003cli\u003eAlexander, R. T. \u0026amp; Dimke, H. Effects of parathyroid hormone on renal tubular calcium and phosphate handling. \u003cem\u003eActa Physiol (Oxf)\u003c/em\u003e \u003cstrong\u003e238,\u003c/strong\u003e e13959 (2023).\u003c/li\u003e\n\u003cli\u003eSawhney, J. S. \u003cem\u003eet al.\u003c/em\u003e Management of rhabdomyolysis: A practice management guideline from the Eastern Association for the Surgery of Trauma. \u003cem\u003eThe American Journal of Surgery\u003c/em\u003e \u003cstrong\u003e224,\u003c/strong\u003e 196\u0026ndash;204 (2022).\u003c/li\u003e\n\u003cli\u003eBartoli, E. \u003cem\u003eet al.\u003c/em\u003e Use, misuse and abuse of diuretics. \u003cem\u003eEur J Intern Med\u003c/em\u003e \u003cstrong\u003e39,\u003c/strong\u003e 9\u0026ndash;17 (2017).\u003c/li\u003e\n\u003cli\u003eTao, D. \u003cem\u003eet al.\u003c/em\u003e Bumetanide: A review of its neuroplasticity and behavioral effects after stroke. \u003cem\u003eRestor Neurol Neurosci\u003c/em\u003e \u003cstrong\u003e37,\u003c/strong\u003e 397\u0026ndash;407 (2019).\u003c/li\u003e\n\u003cli\u003eFredrickson, K. A. \u0026amp; Carver, T. W. Trauma-related electrolyte disturbances: From resuscitation to rhabdomyolysis. \u003cem\u003eNutr Clin Pract\u003c/em\u003e \u003cstrong\u003e37,\u003c/strong\u003e 1004\u0026ndash;1014 (2022).\u003c/li\u003e\n\u003cli\u003eAyoti, R., Khan, Z., Mlawa, G. \u0026amp; Gupta, A. Assessing the Effectiveness and Safety of Combination Diuretic Therapy in Heart Failure: A Systematic Review and Meta-Analysis. \u003cem\u003eCureus\u003c/em\u003e \u003cstrong\u003e16,\u003c/strong\u003e e72118 (2024).\u003c/li\u003e\n\u003cli\u003eBrisco‐Bacik, M. A. \u003cem\u003eet al.\u003c/em\u003e Outcomes Associated With a Strategy of Adjuvant Metolazone or High‐Dose Loop Diuretics in Acute Decompensated Heart Failure: A Propensity Analysis. \u003cem\u003eJAHA\u003c/em\u003e \u003cstrong\u003e7,\u003c/strong\u003e e009149 (2018).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"502\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Baseline Data of Rhabdomyolysis Patients Reported in the FAERS Database.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e58.8\u0026plusmn;18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eWeight (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e77.20\u0026plusmn;20.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e417134 (48.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e354666 (41.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e94133 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eOther Serious (Important Medical Event)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e280,131(32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eHospitalization -Initial or Prolonged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e268,911(31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e90,048(10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eLife-Threatening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e48,136(5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eDisability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e21,098(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eRequired Intervention to Prevent Permanent Impairment/Damage Congenital Anomaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e2,499(0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e154,692(17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e465,355(53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e52,092(6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e48,494(5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e41,883(4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e41,461(4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e27,250(3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e15,904(1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e24,505 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5299%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52.3904%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.0797%;\"\u003e\n \u003cp\u003e148,990(17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100%;\"\u003e\n \u003cp\u003eNotes: Continuous numerical variables are expressed as mean \u0026plusmn; standard deviation, and categorical variables are presented as n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatistical Values and Distribution of Top 20 Drugs of Drug-Related\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRhabdomyolysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6927%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.209%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROR (95%Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRR\u003c/strong\u003e\u003cstrong\u003e(\u0026chi;\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEBGM(EBGM05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIC(IC025)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eBlood system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eAprotinin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e3051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e103.64 (94.92 - 113.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e17.73 (50372.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e17.67 (16.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e4.14 (4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eMetabolic-related drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eMultivitamins and minerals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e84.31 (70.21 - 101.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e17.05 (9431.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e17.04 (14.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e4.09 (3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eDigestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eSodium phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e46.77 (41.03 - 53.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e14.77 (10044.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e14.76 (13.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e3.88 (3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eDigestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eLansoprazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e15035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e25.14 (24.55 - 25.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e11.83 (153741.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e11.64 (11.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e3.54 (3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eDigestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eDexlansoprazole\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e2763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e18.09 (17.19 - 19.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e10.01 (23447.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e9.98 (9.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e3.32 (3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eCardiovascular system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eSimvastatin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e11575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e14.44 (14.1 - 14.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e8.86 (83631.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e8.76 (8.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e3.13 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eCardiovascular system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eLovastatin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e13.62 (12.06 - 15.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e8.53 (3038.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e8.52 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e3.09 (2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eAntiviral drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eTenofovir disoproxil\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e8459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e13.55 (13.18 - 13.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e8.53 (58411.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e8.45 (8.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e3.08 (3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eAntiviral drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eTelbivudine\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e12.83 (11.02 - 14.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e8.22 (1817.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e8.22 (7.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e3.04 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eDigestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003ePantoprazole\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e11291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e11.74 (11.47 - 12.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e7.81 (69460.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e7.72 (7.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.95 (2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eOrthopedic system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eParathyroid hormone\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e1563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e11.41 (10.72 - 12.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e7.65 (9460.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e7.63 (7.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.93 (2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eCardiovascular system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eEzetimibe/Simvastatin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e1293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e10.77 (10.06 - 11.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e7.36 (7453.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e7.35 (6.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.88 (2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eAntiviral drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eEfavirenz/Emtricitabine/Tenofovir disoproxil\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e3238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e10.12 (9.7 - 10.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e7.07 (17663.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e7.05 (6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.82 (2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eCardiovascular system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eFluvastatin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e9.8 (8.84 - 10.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e6.92 (2861.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e6.91 (6.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.79 (2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eUrinary system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eBumetanide\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e9.73 (8.24 - 11.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e6.88 (1086.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e6.88 (5.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.78 (2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eAntiviral drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eFoscarnet\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e9.51 (8.18 - 11.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e6.78 (1297.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e6.78 (5.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.76 (2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eDigestive system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eEsomeprazole\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e21402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e9.22 (9.07 - 9.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e6.67 (105668)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e6.53 (6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.71 (2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eAntiviral drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eEmtricitabine/Tenofovir disoproxil\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e6699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e9.03 (8.77 - 9.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e6.55 (32832.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e6.51 (6.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.7 (2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eCardiovascular system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003ePitavastatin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e8.89 (8.03 - 9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e6.47 (2644.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e6.47 (5.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.69 (2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6927%;\"\u003e\n \u003cp\u003eUrinary system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.209%;\"\u003e\n \u003cp\u003eMetolazone\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.86427%;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.7925%;\"\u003e\n \u003cp\u003e8.71 (7.03 - 10.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1685%;\"\u003e\n \u003cp\u003e6.38 (571.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6006%;\"\u003e\n \u003cp\u003e6.38 (5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.67239%;\"\u003e\n \u003cp\u003e2.67 (2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Drugs are sorted according to ROR values. \u0026ldquo;Multivitamins and minerals\u0026rdquo; are the general term of ascorbic acid, biotin, chromium, cholecalciferol, copper, cyanocobalamin, folic acid, iron, molybdenum, nicotinic acid and pantothenic.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Multi-variate Logistic Regression Analysis of Drugs Associated with Rhabdomyolysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"518\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eAprotinin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e131.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e114.73-152.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eMultivitamins and minerals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e940.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e300.78-5697.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eSodium phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e40.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e32.54-50.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eSimvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e10.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e10.23-11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eLovastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e10.18-15.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eTenofovir disoproxil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e10.69-13.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eTelbivudine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e17.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e12.52-25.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eParathyroid hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e8.24-12.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eEzetimibe/simvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e7.82-9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eEfavirenz/emtricitabine/tenofovir disoproxil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e8.84-11.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eFluvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e6.55-9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eBumetanide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e6.28-11.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eFoscarnet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e5.89-11.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eEmtricitabine/tenofovir disoproxil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e6.01-7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003ePitavastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e5.40-8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51.8375%;\"\u003e\n \u003cp\u003eMetolazone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2515%;\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7582%;\"\u003e\n \u003cp\u003e5.51-10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1528%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Drug-Induced Time Distribution of Drug-Related\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRhabdomyolysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Caused by Different Drugs\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eFoscarnet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e28.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e15.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eMetolazone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e48.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e40.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eMultivitamins and minerals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e120.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e97.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eSodium phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e142.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e67.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003ePitavastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e191.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e13.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e138.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eAprotinin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e272.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e94.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eEzetimibe/simvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e355.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e365.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eBumetanide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e372.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e15.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e218.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eTelbivudine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e400.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e151.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e401.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eFluvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e406.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e434.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eLovastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e538.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e543.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eParathyroid hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e603.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e133.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e932.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eSimvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e663.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e818.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eEmtricitabine/tenofovir disoproxil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1413.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e304.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e2188.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eTenofovir disoproxil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1552.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e196.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e2577.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.7657%;\"\u003e\n \u003cp\u003eEfavirenz/emtricitabine/tenofovir disoproxil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1739.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e600.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1914%;\"\u003e\n \u003cp\u003e2678.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rhabdomyolysis, Adverse events, Pharmacovigilance, FAERS, Drug-induced rhabdomyolysis","lastPublishedDoi":"10.21203/rs.3.rs-7536398/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7536398/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDrug-induced rhabdomyolysis is one of the major causes of non-traumatic rhabdomyolysis. This adverse reaction poses challenges to patient safety and places a substantial burden on public health systems.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e\u003cp\u003eBased on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database, this study systematically analyzes the association between drugs and rhabdomyolysis, providing scientific evidence for improving clinical drug safety management and optimizing pharmacovigilance systems.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eBy cleaning and filtering FAERS data from 2004 to 2024, a total of 865,934 rhabdomyolysis cases were analyzed out of 18,278,243 reports. Four signal detection methods\u0026mdash;Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS)\u0026mdash;were employed for adverse event signal analysis. Additionally, regression models were used to further identify drugs closely associated with rhabdomyolysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBy combining disproportionality analysis methods and logistic regression, this study identified 16 drugs significantly associated with rhabdomyolysis. Among them, aprotinin (ROR\u0026thinsp;=\u0026thinsp;131.87), multivitamins and minerals (ROR\u0026thinsp;=\u0026thinsp;940.60), and sodium phosphate (ROR\u0026thinsp;=\u0026thinsp;40.35) posed the highest risks. Antiviral drugs and cardiovascular medications constituted the major components of drug-induced rhabdomyolysis, with average onset times of 594.17 days and 1434.21 days, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study revealed significant associations between various drugs and rhabdomyolysis, providing valuable references for drug safety management. Future efforts should focus on enhanced monitoring and clinical interventions for high-risk drugs to optimize pharmacovigilance systems and improve patient outcomes.\u003c/p\u003e","manuscriptTitle":"Drug-Related Rhabdomyolysis: A Real-World FDA Adverse Event Reporting System Database Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 11:52:18","doi":"10.21203/rs.3.rs-7536398/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e6c122c-fa99-40bd-a4ba-02c49e201965","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56906350,"name":"Health sciences/Diseases"},{"id":56906351,"name":"Biological sciences/Drug discovery"},{"id":56906352,"name":"Health sciences/Health care"},{"id":56906353,"name":"Health sciences/Medical research"},{"id":56906354,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-29T10:03:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 11:52:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7536398","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7536398","identity":"rs-7536398","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}