Antibiotics-associated pseudomembranous colitis: a disproportionality analysis of the US Food and Drug Administration Adverse Event Reporting System (FAERS) database

Antibiotics-associated pseudomembranous colitis: a disproportionality analysis of the US Food and Drug Administration Adverse Event Reporting System (FAERS) database | 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 Antibiotics-associated pseudomembranous colitis: a disproportionality analysis of the US Food and Drug Administration Adverse Event Reporting System (FAERS) database Jinhua Chen, Weijiang Yu, Cuicui Sun, Wenzhou Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3827087/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 Antibiotics have been established as an important risk factor for pseudomembranous colitis (PMC), a potential life-threatening complication. Evaluating the antibiotics most commonly associated with PMC is of great significance. In this study, we extracted the data from fourth quarter of 2003 to third quarter of 2023 in the US Food and Drug Administration Adverse Event Reporting System (FAERS). Disproportionality analysis was performed to evaluate the potential association between antibiotics and PMC. The results showed that eighty-one antibiotics which met the three algorithms simultaneously were enrolled. A total of 11737133 adverse event (ADE) reports were identified in the FAERS database, of which 1683 reports were associated with the enrolled antibiotics related PMC. It showed that the elderly and females are more susceptible to the antibiotics-associated PMC, especially for patients aged > 60 years. The top twenty-four antibiotics included four penicillins, eleven cephalosporins, three carbapenems, two lincosamides, one cephamycin, one aminoglycoside, one fosfomycin, and one echinocandin. This study also showed that cefoxitin, streptomycin, fosfomycin, and micafungin have a high risk of PMC, but there are few reports in the literature. This is helpful to reduce the potential damage of antibiotics-associated PMC. Health sciences/Gastroenterology Health sciences/Risk factors Pseudomembranous colitis antibiotic FAERS adverse event disproportionality analysis Introduction Pseudomembranous colitis (PMC) is a severe inflammatory condition of the colon characterized by colonic mucosa with the formation of pseudomembranes, with diarrhea as the main clinical manifestation 1,2 . Many different etiologies can cause PMC, such as Clostridium (reclassified as “ Clostridioides ”) difficile ( C. difficile ) infection, Staphylococcus aureus infection, Cytomegalovirus , and Behcet’s disease 3 . Among these, it is well-known that C. difficile infection is the most common cause 4 . Toxin A and toxin B produced by C. difficile might cause PMC by activating the immune system to cause inflammation in the colon 5 . Many drugs are associated to PMC, such as antibiotics, immune checkpoint inhibitors and glucocorticoids 6–8 . Antibiotics-induced PMC accounts for 25%-33% of all antibiotics-associated diarrhea, which is mainly associated with dysbacteriosis, thus leading to the overgrowth of C. difficile 9 . The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is a spontaneous reporting database to collect the adverse events of drugs approved by FDA worldwide 10–12 . This provides a large source of data about drug-induced adverse events (ADEs) in real-world clinical setting 13 . This study was to comprehensively assess the association between antibiotics and PMC by a disproportionality analysis using the FAERS, which can provide a reference for the marketed antibiotics that induced PMC. Materials and methods Data sources and processing. This study was a retrospective pharmacovigilance analysis to asses reports of antibiotics-associated PMC based on the FAERS database. OpenVigil 2.1, a validated pharmacovigilance data extraction, cleaning, and mining tool of the FAERS database, was used to retrieve FAERS database 14 . In this study, drug information was extracted by using PMC (including C. difficile colitis) as preferred term (PT) from the fourth quarter of 2003 to third quarter of 2023 in the FAERS database through OpenVigil 2.1. We selected all antibiotics from the extracted drugs. Moreover, this study excluded some antibiotics: ( 1 ) they are usually available as combination products, including β-lactamase inhibitors (tazobactam, clavulanate, avibactam, sulbactam), cilastatin, sulfamethoxazole, and trimethoprim. ( 2 ) several antibiotics are used to treat C. difficile infection and thus highly unlikely to cause PMC as adverse events, including metronidazole, vancomycin and fidaxomicin. We deduplicated the ADE reports of the enrolled antibiotics-associated PMC from the FAERS database. Reports with the same information including adverse event, ISR number, date received, drug, indication, gender, reporter country and age were identified as duplicate reports and excluded. The remaining reports were further screened by setting the main selection criterion as primary suspect (PS), to eliminating the effects of other factors. After deduplication, the remaining reports were used for follow-up analysis. Clinical characteristics in enrolled reports were analyzed, including sex, age, reporting region, outcome and reporting year. Serious outcomes included hospitalization, life-threatening, disability, and death. Signal mining. To improve the sensitivity, specificity and predictive value, three disproportionality analyses were performed to detect the potential association between ADEs and antibiotics using the reporting odds ratio (ROR), the proportional reporting ratio (PRR), and the information component (IC) 15 . Each algorithm has its own advantages and disadvantages, and they can complement each other to some extent. The ADE signals were considered to be positive when they met three algorithm criterias simultaneously. The equations and corresponding thresholds of the three algorithms are listed in Table 1 . Table 1 Three Algorithms Used for Signal Detection. Algorithms Equation Criteria ROR ROR=(a/c)/(b/d) a ≥ 3, 95%CI ≥ 1 95%CI = e ln(ROR)±1.96(1/a+1/b+1/c+1/d)^0.5 PRR PRR=[a/(c + d)]/[c/(a + b)] a ≥ 3, PRR ≥ 2, χ 2 ≥ 4 χ 2 =[(ad-bc)^2](a + b + c + d)/[(a + b)(c + d)(a + c)(b + d)] BCPNN IC = log 2 a(a + b + c + d)(a + c)(a + b) IC025 > 0 95%CI = E(IC) ± 2V(IC)^0.5 Equation: a, number of reports containing both the target drug and target adverse drug reaction; b, number of reports containing other adverse drug reaction of the target drug; c, number of reports containing the target adverse drug reaction of other drugs; d, number of reports containing other drugs and other adverse drug reactions. 95%CI, 95% confidence interval; χ 2 , chi-squared. IC, information component; IC025, the lower limit of 95% CI of the IC; E(IC), the IC expectations; V(IC), the variance of IC. Statistical analysis. Descriptive analyses were performed to summarize the clinical characteristics in the ADE reports of antibiotics-associated PMC. Risk factors were compared using a Pearson’s chi-squared test. All data mining and statistical analyses were performed using Microsoft Excel 2019 and SPSS. Results Descriptive analysis. From the fourth quarter of 2003 to third quarter of 2023, there were 11737133 ADE reports in the FAERS database. Of these, there were 1683 reports of PMC associated with the enrolled antibiotics. Clinical characteristics in reports were described in Table 2 . There were 836 (49.67%) reports for males, 692 (41.12%) reports for females, and 155 (9.21%) with missing gender information. The number of reports aged 60 years was 86 (5.11%), 165 (9.80%), 322 (19.13%), and 831 (49.38%), respectively. United States (675, 40.11%) had the highest number of reports, followed by United Kingdom (143, 8.50%), Japan (134, 7.96%), France (123, 7.31%), and Canada (119, 7.07%). Analysis about reporting years showed that the number of antibiotics-associated PMC reports was increasing over time. Moreover, the majority (1229, 73.02%) were serious outcome events, including hospitalization, death, life-threatening, and disability. Hospitalization was the most frequent serious outcome event (805, 47.83%), followed by death (305, 18.12%), life-threatening (110, 6.54%), and disability (9, 0.53%). Table 2 Clinical Characteristics in Reports with PMC. Characteristics Report numbers(n) Report proportion (%) Gender Male 836 49.67% Female 692 41.12% Unknown 155 9.21% Age 60 831 49.38% Unknown 279 16.58% Reporting region United States 675 40.11 United Kingdom 143 8.50% Japan 134 7.96% France 123 7.31% Canada 119 7.07% Spain 70 4.16% Germany 65 3.86% Italy 56 3.33% Poland 35 2.08% Portugal 28 1.66% China 26 1.54% Romania 12 0.71% Other countries and unknown 197 11.71% Reporting years 2004–2008 262 15.57% 2009–2013 442 26.26% 2014–2018 451 26.80% 2019–2023 528 31.37% Outcomes Hospitalization 805 47.83% Death 305 18.12% Life-Threatening 110 6.54% Disability 9 0.53% Others and Unknown 454 26.98% Disproportionality analysis. There were eighty-one antibiotics that met the three algorithms simultaneously and were enrolled. The top twenty-four antibiotics in the descending order were showed in Table 3 according to the lower limit of 95% CI of ROR, PRR value, and the lower limit of 95% CI of the IC (IC025). Of the top twenty-four antibiotics, there were four penicillins, namely dicloxacillin, pivmecillinam, piperacillin, and piperacillin-tazobactam; there were eleven cephalosporins, including one first-generation cephalosporin (cefazolin), two second-generation cephalosporins (cefotiam and cefuroxime), six third-generation cephalosporins (ceftriaxone, cefixoral, ceftizoxime, cefpodoxime, cefditoren, and ceftazidime - avibactam), one fourth-generation cephalosporin (cefepime), and one fifth-generation cephalosporin (cefiderocol); three carbapenems (meropenesm, ertapenem, and imipenem) and two lincosamides (clindamycine and lincomycin) were included; Moreover, there was one cephamycin (cefoxitin), one aminoglycoside (streptomycin), one fosfomycin (fosfomycin), and one echinocandin (micafungin) (Table 4 ). Table 3 Top 24 antibiotics Associated with PMC Arranged by ROR, PRR and IC025. Drugs Report numbers ROR (95%CI) PRR (95%CI) IC(IC025) Pivmecillinam 12 120.12(67.16,119.54) 114.09(65.69,198.13) 6.83(5.26) Cefotiam 5 119.64(48.64,294.25) 113.65(48.35,267.10) 6.83(5.38) Lincomycin 8 74.70(36.92, 151.17) 72.34(36.56, 143.11) 6.17(4.66) Ceftazidime - avibactam 3 67.45(21.38, 212.76) 65.51(21.48,199.86) 6.03(4.74) Micafungin 66 53.21(41.63, 68.03) 52.02(40.92, 66.13) 5.68(4.04) Cefpodoxime 19 43.90(27.86, 69.18) 43.08(27.58, 67.31) 5.42(3.83) Cefoxitin 11 43.00(23.67, 78.13) 42.21(23.49,75.85) 5.40(3.85) Cefixoral 33 41.52(29.39, 58.65) 40.79(29.06, 57.26) 5.07(3.71) Cefazolin 91 41.33(33.53, 50.94) 40.62(33.07, 49.88) 5.32(3.67) Dicloxacillin 7 38.79(18.37, 81.93) 38.15(18.29, 79.57) 5.25(3.77) Fosfomycin 25 36.67(24.68, 54.50) 36.10(24.45, 53.31) 5.17(3.55) Ertapenem 64 34.11(26.61, 43.72) 33.62(26.32, 42.94) 5.05(3.41) Cefiderocol 4 34.07(12.69, 91.49) 33.58(12.69,88.85) 5.07(3.71) Streptomycin 11 32.76(18.05, 59.45) 32.31(17.96, 58.13) 5.01(3.46) Cefditoren 9 32.05(16.59, 61.92) 31.62(16.52, 60.52) 4.98(3.46) Clindamycine 269 31.14(27.52, 35.23) 30.75(27.22, 34.73) 4.87(3.21) Cefuroxime 114 27.87(23.12, 33.59) 27.55(22.90, 33.13) 4.75(3.10) Ceftriaxone 228 26.88(23.53, 30.72) 26.59(23.30,30.34) 4.67(3.01) Meropenem 119 24.66(20.54, 29.61) 24.41(20.37,29.25) 4.58(2.92) Imipenem 53 24.64(18.77, 32.34) 24.39(18.63, 31.92) 4.59(2.95) Ceftizoxime 30 24.44(17.03, 35.05) 24.19(16.92, 34.56) 4.59(2.97) Piperacillin-tazobactam 39 24.32(17.72, 33.39) 24.07(17.60, 32.93) 4.58(2.95) Cefepime 63 23.41(18.23, 30.04) 23.18(18.10, 29.68) 4.52(2.87) Piperacillin 157 22.99(19.60, 26.97) 22.77(19.44, 26.67) 4.47(2.81) ROR, reporting odds ratio; PRR, proportional reporting ratio; 95%CI, 95% confidence interval; IC, information component; IC025, the lower limit of 95% CI of the IC. Table 4 Classification of Top 24 Drugs Associated with PMC. Classifications Drug names Penicillins Penicillinase-resistant penicillin Dicloxacillin Broad-spectrum penicillins Pivmecillinam; Piperacillin; Piperacillin-tazobactam; Cephalosporins First generation Cefazolin Second generation Cefotiam; Cefuroxime Third generation Ceftriaxone; Cefixoral; Ceftizoxime; Cefpodoxime; Cefditoren; Ceftazidime - avibactam Fourth generation Cefepime Fifth generation Cefiderocol Carbapenems Meropenem; Ertapenem; Imipenem Cephamycin Cefoxitin Lincosamides Clindamycine; Lincomycin Aminoglycoside Streptomycin Fosfomycin Fosfomycin Echinocandin Micafungin Serious versus Non-Serious Cases. We explored the risk factors of antibiotics-associated PMC by comparing between serious and non-serious cases (Table 5 ). A higher proportion of female exhibited serious and non-serious ADEs than males, and the difference was statistically significant (χ2 = 13.79, P = 0.0002). Additionally, there was statistically difference in age (χ2 = 50.03, P < 0.0001). It showed that the elderly had a significantly higher incidence of serious ADEs. Table 5 Differences in clinical characteristics between severe and non-severe reports. Clinical characteristics Serious cases(N = 1229) Non-serious cases(N = 427) χ2 P -value Gender Male 547(44.51%) 134(31.38%) 13.79 0.0002 Female 594(48.33%) 230(53.86%) Age < 18 57(4.64%) 28(6.56%) 50.03 60 692(56.31%) 128(29.98%) Reporting region United States 475(38.65%) 174(40.75%) United Kingdom 113(9.19%) 5(1.17%) Japan 69(5.61%) 65(15.22%) France 95(7.73%) 28(6.56%) Canada 103(8.38%) 16(3.75%) Spain 57(4.64%) 13(3.04%) Germany 46(3.74%) 44(10.30%) Italy 53(4.31%) 3(0.70%) Poland 29(2.36%) 6(1.41%) Portugal 19(1.55%) 9(2.11%) China 16(1.30%) 9(2.11%) Romania 6(0.49%) 6(1.41%) Other countries and unknown 148(12.04%) 49(11.48%) Reporting years 2004–2008 171(13.91%) 86(20.14%) 2009–2013 319(25.96%) 109(25.53%) 2014–2018 329(26.77%) 116(27.17%) 2019–2023 410(33.36%) 116(27.17%) Discussion PMC is a potential life-threatening complication. Drugs are an important etiology of PMC, such as antibiotics and proton pump inhibitors 6 . Antibiotics-associated PMC has been widely reported, but there is no large-scale case study 16,17 . To our knowledge, this study is the first pharmacovigilance study on ADEs of antibiotics-associated PMC using the real-world data from the FAERS database. Currently, the main mechanism by which antibiotics cause PMC is that they suppress the growth of some normal micro-organisms, resulting in the increased colonization and proliferation of toxinogenic strains of C. difficile and the enhanced cytotoxin synthesis of C. difficile 18–21 . Moreover, production of antibiotics-resistance C. difficile is also an important risk factor for PMC 22–24 . Accumulating evidence suggests that C. difficile infection is the most common cause of PMC 1 . Therefore, to ensure a more comprehensive data related to antibiotics-associated PMC, we included C. difficile colitis in this study. The FAERS database is a global database of ADE reports and collects ADE reports of drugs approved by FDA. The data from the FAERS database showed that the median age of antibiotics-associated PMC patients was 61 years. The distribution of age groups showed that the elderly are more susceptible to the antibiotics-associated PMC, especially for patients aged > 60 years. Currently, most studies on antibiotics-associated PMC are case reports, which lack of comprehensive analysis of age factor 24–26 . C. difficile is considered as the pathogenic microorganism in 90–100% of patients with antibiotics-associated PMC 27 . A 3-year cross-sectional study in eastern China showed that 55 years or older was a risk factor for C. difficile infection, while several studies surveyed C. difficile infection in Europe and United States and found that 65 years or older was its risk factor 28–30 . This study showed that United States had the highest number of antibiotics-associated PMC reports, followed by United Kingdom, Japan, and France. These reports were mainly from United States and Europe, thus it has a similar age to C. difficile infection in United States and Europe. Moreover, 73.02% of the patients with antibiotics-associated PMC had serious outcome events. Further risk factor analysis by comparing between serious and non-serious cases showed that sex and age may be related to the severity of ADEs. Female and the elderly had a significantly higher incidence of serious ADEs. In this study, there were eighty-one antibiotics that met the three algorithms simultaneously. Using the lower limit of 95% CI of ROR, PRR value, and the lower limit of 95% CI of the IC (IC025) in descending order, the top twenty-four drugs are the same, but in a slightly different order. The top twenty-four antibiotics included four penicillins, elven cephalosporins, three carbapenems, two lincosamides, one cephamycin, one aminoglycoside, one fosfomycin, and one echinocandin. Consistent with our results of antibiotics-associated PMC, studies have demonstrated that penicillins, cephalosporins, carbapenems, and clindamycin have higher risk of C. difficile infection than other antibiotics 31 . This study showed that pivmecillinam had the strongest correlation with PMC. Pivmecillinam was been used widely in Nordic countries, but not used extensively in other countries. Thus, this may be related to patient population, and more comprehensive evaluation is needed. Khanafer et al found that second-generation and third-generation cephalosporins have been up to 3 times less active for C. difficile than first-generation cephalosporins by culture-based susceptibility studies, indicating that second-generation and third-generation cephalosporins have higher risk for C. difficile infection 32 . In this study, cephalosporins made up eleven of the top twenty-four antibiotics, and there were two second-generation cephalosporins (cefotiam and cefuroxime) and six third-generation cephalosporins (ceftriaxone, cefixoral, ceftizoxime, cefpodoxime, cefditoren, and ceftazidime - avibactam). Lincosamides is broad-spectrum activity against Gram-positive and obligate anaerobic bacteria. Many reports have indicated that lincosamides (clindamycine and lincomycin) predisposes patients to PMC 33,34 . It is noteworthy that cefoxitin, streptomycin, fosfomycin, and micafungin have a high risk of PMC in this study, but there are few reports in the literature. This study had several limitations. Firstly, only cases with PMC were reported to FAERS, but the total populations taking antibiotics were unknown, thus the true incidence rate of PMC of each antibiotic was not estimated based on the FAERS database. Secondly, disproportionality analysis based on the FAERS database statistically evaluated signal strength, but it did not reveal whether there was a causal relationship between PMC and drugs. This needs to be confirmed by further clinical studies. Conclusion Our pharmacovigilance analysis indicated that broad-spectrum penicillins, cephalosporins, carbapenems, lincomycin, and clindamycin have higher risk of PMC, which is roughly consistent with studies about C. difficile infection. This study found that cefoxitin, streptomycin, fosfomycin, and micafungin had a high risk of PMC, but there are few reports in the literature, and it needs further clinical studies to confirm. This study provided a clue on the relationship between antibiotics and the risk of PMC. Declarations Ethical approval. This study didn’t contain any studies with human participants or animals performed by any of the authors. Author contributions. J.C., W.Z. and W.Y. designed the analysis. J.C. and C.S. collected the data and performed the analysis. J.C. wrote the paper. W.Z and C.S. revised the manuscript. Data availability. The datasets used and analyzed during the current study are available from the corresponding author at a reasonable request. Competing interests. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding. This work was supported by Tackling-plan Project of Henan Department of Science and Technology (212102310325). References Farooq, P.D., Urrunaga, N.H., Tang, D.M. & von Rosenvinge, E.C. Peudomembranous colitis. Dis. Mon. 61, 181–206(2015). Postma, N., Kiers, D. & Pickkers, P. The challenge of Clostridium difficile infection: Overview of clinical manifestations, diagnostic tools and therapeutic options. Int. J. Antimicrob. Agents. 46 Suppl 1, S47-50(2015). Kiliçarslan, R., Toprak, H., Ozturk, F. & Kocakoç, E. Pseudomembranous colitis due to Clostridium difficile. 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Lancet. 377, 63–73(2011). Tabak, Y.P., et al. Hospital-level high-risk antibiotic use in relation to hospital-associated Clostridioides difficile infections: Retrospective analysis of 2016–2017 data from US hospitals. Infect. Control. Hosp. Epidemiol. 40,1229–1235(2019). Alammari, K.M. & Thabit, A.K. Characteristics of patients infected with Clostridioides difficile at a Saudi Tertiary Academic Medical Center and assessment of antibiotic duration. Gut. Pathog. 13,10(2021). Khanafer, N., et al. Susceptibilities of clinical Clostridium difficile isolates to antimicrobials: a systematic review and meta-analysis of studies since 1970. Clin. Microbiol. Infect. 24,110–117(2018). Sullivan, A., Edlund, C. & Nord, C.E. Effect of antimicrobial agents on the ecological balance of human microflora. Lancet. Infect. Dis. 1,101 – 14(2001). Cavanenghi, D., Morra, C., Grassini, M. & Sorisio, V. Pseudomembranous colitis induced by clindamycin-lincomycin combination. Description of a clinical case. Minerva. Dietol. Gastroenterol. 31,343–345(1985). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3827087","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":265021841,"identity":"3ba0384c-371f-42f2-8c14-31d99536c93b","order_by":0,"name":"Jinhua Chen","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinhua","middleName":"","lastName":"Chen","suffix":""},{"id":265021842,"identity":"744b0e95-028e-4d49-a4df-54f7e86469dc","order_by":1,"name":"Weijiang Yu","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weijiang","middleName":"","lastName":"Yu","suffix":""},{"id":265021843,"identity":"cac9eb44-be26-4a33-ace3-035ebcce6e85","order_by":2,"name":"Cuicui Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFACxgYGBgMgzcx84MCHCpK0sLMlHpxxhiTb+HmMD/O2EKNwRnLjZ56CusTtzDwfDvA2MMjzix3Ar0XiRmKzNI/B4cSdzbwbDkjuYDCcOTsBvxYDicQ2Zh6DA4kbDgO1GJ5hSDC4TZyWOqAWngcHEtuI18IM0sJw4CAxWiTOPGyWnGNw2HjDYTaDgw1nJAj7hb89/eGHN3/qZDecP/z4858KG3l+aQJaMGwlTfkoGAWjYBSMAuwAAPgYRhzfoSDmAAAAAElFTkSuQmCC","orcid":"","institution":"Qilu hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Cuicui","middleName":"","lastName":"Sun","suffix":""},{"id":265021844,"identity":"8a119988-51c4-4a25-91d4-8152f36838a1","order_by":3,"name":"Wenzhou Zhang","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Zhengzhou University \u0026 Henan Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenzhou","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-01-01 08:14:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3827087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3827087/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49789634,"identity":"96911137-49d3-4e61-ac4a-fbede72da820","added_by":"auto","created_at":"2024-01-18 04:52:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":369421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3827087/v1/f3197701-e1b0-46c1-8d63-61db9eedd4df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Antibiotics-associated pseudomembranous colitis: a disproportionality analysis of the US Food and Drug Administration Adverse Event Reporting System (FAERS) database","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePseudomembranous colitis (PMC) is a severe inflammatory condition of the colon characterized by colonic mucosa with the formation of pseudomembranes, with diarrhea as the main clinical manifestation\u003csup\u003e1,2\u003c/sup\u003e. Many different etiologies can cause PMC, such as \u003cem\u003eClostridium\u003c/em\u003e (reclassified as \u0026ldquo;\u003cem\u003eClostridioides\u003c/em\u003e\u0026rdquo;) \u003cem\u003edifficile\u003c/em\u003e (\u003cem\u003eC. difficile\u003c/em\u003e) infection, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infection, \u003cem\u003eCytomegalovirus\u003c/em\u003e, and Behcet\u0026rsquo;s disease\u003csup\u003e3\u003c/sup\u003e. Among these, it is well-known that \u003cem\u003eC. difficile\u003c/em\u003e infection is the most common cause\u003csup\u003e4\u003c/sup\u003e. Toxin A and toxin B produced by \u003cem\u003eC. difficile\u003c/em\u003e might cause PMC by activating the immune system to cause inflammation in the colon\u003csup\u003e5\u003c/sup\u003e. Many drugs are associated to PMC, such as antibiotics, immune checkpoint inhibitors and glucocorticoids\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. Antibiotics-induced PMC accounts for 25%-33% of all antibiotics-associated diarrhea, which is mainly associated with dysbacteriosis, thus leading to the overgrowth of \u003cem\u003eC. difficile\u003c/em\u003e\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is a spontaneous reporting database to collect the adverse events of drugs approved by FDA worldwide\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. This provides a large source of data about drug-induced adverse events (ADEs) in real-world clinical setting\u003csup\u003e13\u003c/sup\u003e. This study was to comprehensively assess the association between antibiotics and PMC by a disproportionality analysis using the FAERS, which can provide a reference for the marketed antibiotics that induced PMC.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eData sources and processing.\u003c/b\u003e This study was a retrospective pharmacovigilance analysis to asses reports of antibiotics-associated PMC based on the FAERS database. OpenVigil 2.1, a validated pharmacovigilance data extraction, cleaning, and mining tool of the FAERS database, was used to retrieve FAERS database\u003csup\u003e14\u003c/sup\u003e. In this study, drug information was extracted by using PMC (including \u003cem\u003eC. difficile\u003c/em\u003e colitis) as preferred term (PT) from the fourth quarter of 2003 to third quarter of 2023 in the FAERS database through OpenVigil 2.1. We selected all antibiotics from the extracted drugs. Moreover, this study excluded some antibiotics: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) they are usually available as combination products, including β-lactamase inhibitors (tazobactam, clavulanate, avibactam, sulbactam), cilastatin, sulfamethoxazole, and trimethoprim. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) several antibiotics are used to treat \u003cem\u003eC. difficile\u003c/em\u003e infection and thus highly unlikely to cause PMC as adverse events, including metronidazole, vancomycin and fidaxomicin.\u003c/p\u003e \u003cp\u003eWe deduplicated the ADE reports of the enrolled antibiotics-associated PMC from the FAERS database. Reports with the same information including adverse event, ISR number, date received, drug, indication, gender, reporter country and age were identified as duplicate reports and excluded. The remaining reports were further screened by setting the main selection criterion as primary suspect (PS), to eliminating the effects of other factors. After deduplication, the remaining reports were used for follow-up analysis.\u003c/p\u003e \u003cp\u003eClinical characteristics in enrolled reports were analyzed, including sex, age, reporting region, outcome and reporting year. Serious outcomes included hospitalization, life-threatening, disability, and death.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSignal mining.\u003c/b\u003e To improve the sensitivity, specificity and predictive value, three disproportionality analyses were performed to detect the potential association between ADEs and antibiotics using the reporting odds ratio (ROR), the proportional reporting ratio (PRR), and the information component (IC)\u003csup\u003e15\u003c/sup\u003e. Each algorithm has its own advantages and disadvantages, and they can complement each other to some extent. The ADE signals were considered to be positive when they met three algorithm criterias simultaneously. The equations and corresponding thresholds of the three algorithms are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree Algorithms Used for Signal Detection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlgorithms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eROR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROR=(a/c)/(b/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ea\u0026thinsp;\u0026ge;\u0026thinsp;3, 95%CI\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95%CI\u0026thinsp;=\u0026thinsp;e\u003csup\u003eln(ROR)\u0026plusmn;1.96(1/a+1/b+1/c+1/d)^0.5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRR=[a/(c\u0026thinsp;+\u0026thinsp;d)]/[c/(a\u0026thinsp;+\u0026thinsp;b)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ea\u0026thinsp;\u0026ge;\u0026thinsp;3, PRR\u0026thinsp;\u0026ge;\u0026thinsp;2, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e=[(ad-bc)^2](a\u0026thinsp;+\u0026thinsp;b\u0026thinsp;+\u0026thinsp;c\u0026thinsp;+\u0026thinsp;d)/[(a\u0026thinsp;+\u0026thinsp;b)(c\u0026thinsp;+\u0026thinsp;d)(a\u0026thinsp;+\u0026thinsp;c)(b\u0026thinsp;+\u0026thinsp;d)]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eBCPNN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC\u0026thinsp;=\u0026thinsp;log\u003csub\u003e2\u003c/sub\u003ea(a\u0026thinsp;+\u0026thinsp;b\u0026thinsp;+\u0026thinsp;c\u0026thinsp;+\u0026thinsp;d)(a\u0026thinsp;+\u0026thinsp;c)(a\u0026thinsp;+\u0026thinsp;b)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIC025\u0026thinsp;\u0026gt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95%CI\u0026thinsp;=\u0026thinsp;E(IC)\u0026thinsp;\u0026plusmn;\u0026thinsp;2V(IC)^0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eEquation: a, number of reports containing both the target drug and target adverse drug reaction; b, number of reports containing other adverse drug reaction of the target drug; c, number of reports containing the target adverse drug reaction of other drugs; d, number of reports containing other drugs and other adverse drug reactions. 95%CI, 95% confidence interval; χ\u003csup\u003e2\u003c/sup\u003e, chi-squared. IC, information component; IC025, the lower limit of 95% CI of the IC; E(IC), the IC expectations; V(IC), the variance of IC.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis.\u003c/b\u003e Descriptive analyses were performed to summarize the clinical characteristics in the ADE reports of antibiotics-associated PMC. Risk factors were compared using a Pearson\u0026rsquo;s chi-squared test. All data mining and statistical analyses were performed using Microsoft Excel 2019 and SPSS.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDescriptive analysis.\u003c/b\u003e From the fourth quarter of 2003 to third quarter of 2023, there were 11737133 ADE reports in the FAERS database. Of these, there were 1683 reports of PMC associated with the enrolled antibiotics. Clinical characteristics in reports were described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There were 836 (49.67%) reports for males, 692 (41.12%) reports for females, and 155 (9.21%) with missing gender information. The number of reports aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years, 18\u0026ndash;40 years, 41\u0026ndash;60 years, and \u0026gt;\u0026thinsp;60 years was 86 (5.11%), 165 (9.80%), 322 (19.13%), and 831 (49.38%), respectively. United States (675, 40.11%) had the highest number of reports, followed by United Kingdom (143, 8.50%), Japan (134, 7.96%), France (123, 7.31%), and Canada (119, 7.07%). Analysis about reporting years showed that the number of antibiotics-associated PMC reports was increasing over time. Moreover, the majority (1229, 73.02%) were serious outcome events, including hospitalization, death, life-threatening, and disability. Hospitalization was the most frequent serious outcome event (805, 47.83%), followed by death (305, 18.12%), life-threatening (110, 6.54%), and disability (9, 0.53%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Characteristics in Reports with PMC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReport numbers(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReport proportion (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReporting region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther countries and unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReporting years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife-Threatening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers and Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDisproportionality analysis.\u003c/b\u003e There were eighty-one antibiotics that met the three algorithms simultaneously and were enrolled. The top twenty-four antibiotics in the descending order were showed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e according to the lower limit of 95% CI of ROR, PRR value, and the lower limit of 95% CI of the IC (IC025). Of the top twenty-four antibiotics, there were four penicillins, namely dicloxacillin, pivmecillinam, piperacillin, and piperacillin-tazobactam; there were eleven cephalosporins, including one first-generation cephalosporin (cefazolin), two second-generation cephalosporins (cefotiam and cefuroxime), six third-generation cephalosporins (ceftriaxone, cefixoral, ceftizoxime, cefpodoxime, cefditoren, and ceftazidime - avibactam), one fourth-generation cephalosporin (cefepime), and one fifth-generation cephalosporin (cefiderocol); three carbapenems (meropenesm, ertapenem, and imipenem) and two lincosamides (clindamycine and lincomycin) were included; Moreover, there was one cephamycin (cefoxitin), one aminoglycoside (streptomycin), one fosfomycin (fosfomycin), and one echinocandin (micafungin) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 24 antibiotics Associated with PMC Arranged by ROR, PRR and IC025.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReport numbers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePRR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIC(IC025)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePivmecillinam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.12(67.16,119.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114.09(65.69,198.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.83(5.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefotiam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119.64(48.64,294.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.65(48.35,267.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.83(5.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLincomycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.70(36.92, 151.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.34(36.56, 143.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.17(4.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftazidime - avibactam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.45(21.38, 212.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.51(21.48,199.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.03(4.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicafungin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.21(41.63, 68.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.02(40.92, 66.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.68(4.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefpodoxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.90(27.86, 69.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.08(27.58, 67.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.42(3.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefoxitin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.00(23.67, 78.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.21(23.49,75.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.40(3.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefixoral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.52(29.39, 58.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.79(29.06, 57.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.07(3.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefazolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.33(33.53, 50.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.62(33.07, 49.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.32(3.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDicloxacillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.79(18.37, 81.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.15(18.29, 79.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.25(3.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFosfomycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.67(24.68, 54.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.10(24.45, 53.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.17(3.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErtapenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.11(26.61, 43.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.62(26.32, 42.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.05(3.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefiderocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.07(12.69, 91.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.58(12.69,88.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.07(3.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStreptomycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.76(18.05, 59.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.31(17.96, 58.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.01(3.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefditoren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.05(16.59, 61.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.62(16.52, 60.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.98(3.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClindamycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.14(27.52, 35.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.75(27.22, 34.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.87(3.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefuroxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.87(23.12, 33.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.55(22.90, 33.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.75(3.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftriaxone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.88(23.53, 30.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.59(23.30,30.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.67(3.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.66(20.54, 29.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.41(20.37,29.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.58(2.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImipenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.64(18.77, 32.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.39(18.63, 31.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.59(2.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftizoxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.44(17.03, 35.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.19(16.92, 34.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.59(2.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiperacillin-tazobactam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.32(17.72, 33.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.07(17.60, 32.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.58(2.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefepime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.41(18.23, 30.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.18(18.10, 29.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.52(2.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiperacillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.99(19.60, 26.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.77(19.44, 26.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.47(2.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eROR, reporting odds ratio; PRR, proportional reporting ratio; 95%CI, 95% confidence interval; IC, information component; IC025, the lower limit of 95% CI of the IC.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of Top 24 Drugs Associated with PMC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassifications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrug names\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePenicillins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePenicillinase-resistant\u0026nbsp;penicillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDicloxacillin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBroad-spectrum penicillins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePivmecillinam; Piperacillin; Piperacillin-tazobactam;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCephalosporins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCefazolin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCefotiam; Cefuroxime\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThird generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCeftriaxone; Cefixoral; Ceftizoxime;\u003c/p\u003e \u003cp\u003eCefpodoxime; Cefditoren;\u003c/p\u003e \u003cp\u003eCeftazidime - avibactam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFourth generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCefepime\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFifth generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCefiderocol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbapenems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeropenem; Ertapenem; Imipenem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephamycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCefoxitin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLincosamides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClindamycine; Lincomycin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAminoglycoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStreptomycin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFosfomycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFosfomycin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEchinocandin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMicafungin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSerious versus Non-Serious Cases.\u003c/b\u003e We explored the risk factors of antibiotics-associated PMC by comparing between serious and non-serious cases (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A higher proportion of female exhibited serious and non-serious ADEs than males, and the difference was statistically significant (χ2\u0026thinsp;=\u0026thinsp;13.79, P\u0026thinsp;=\u0026thinsp;0.0002). Additionally, there was statistically difference in age (χ2\u0026thinsp;=\u0026thinsp;50.03, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). It showed that the elderly had a significantly higher incidence of serious ADEs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in clinical characteristics between severe and non-severe reports.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerious cases(N\u0026thinsp;=\u0026thinsp;1229)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-serious cases(N\u0026thinsp;=\u0026thinsp;427)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e547(44.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(31.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e594(48.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230(53.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57(4.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28(6.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105(8.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59(13.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e229(18.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87(20.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e692(56.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128(29.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReporting region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e475(38.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174(40.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113(9.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(1.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69(5.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95(7.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28(6.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103(8.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(3.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57(4.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(3.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46(3.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44(10.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53(4.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(0.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29(2.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(1.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19(1.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(2.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(1.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(2.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6(0.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(1.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther countries and unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148(12.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49(11.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReporting years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171(13.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86(20.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e319(25.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109(25.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e329(26.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116(27.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410(33.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116(27.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePMC is a potential life-threatening complication. Drugs are an important etiology of PMC, such as antibiotics and proton pump inhibitors\u003csup\u003e6\u003c/sup\u003e. Antibiotics-associated PMC has been widely reported, but there is no large-scale case study\u003csup\u003e16,17\u003c/sup\u003e. To our knowledge, this study is the first pharmacovigilance study on ADEs of antibiotics-associated PMC using the real-world data from the FAERS database.\u003c/p\u003e \u003cp\u003eCurrently, the main mechanism by which antibiotics cause PMC is that they suppress the growth of some normal micro-organisms, resulting in the increased colonization and proliferation of toxinogenic strains of \u003cem\u003eC. difficile\u003c/em\u003e and the enhanced cytotoxin synthesis of \u003cem\u003eC. difficile\u003c/em\u003e\u003csup\u003e18\u0026ndash;21\u003c/sup\u003e. Moreover, production of antibiotics-resistance \u003cem\u003eC. difficile\u003c/em\u003e is also an important risk factor for PMC\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. Accumulating evidence suggests that \u003cem\u003eC. difficile\u003c/em\u003e infection is the most common cause of PMC\u003csup\u003e1\u003c/sup\u003e. Therefore, to ensure a more comprehensive data related to antibiotics-associated PMC, we included \u003cem\u003eC. difficile colitis\u003c/em\u003e in this study.\u003c/p\u003e \u003cp\u003eThe FAERS database is a global database of ADE reports and collects ADE reports of drugs approved by FDA. The data from the FAERS database showed that the median age of antibiotics-associated PMC patients was 61 years. The distribution of age groups showed that the elderly are more susceptible to the antibiotics-associated PMC, especially for patients aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years. Currently, most studies on antibiotics-associated PMC are case reports, which lack of comprehensive analysis of age factor\u003csup\u003e24\u0026ndash;26\u003c/sup\u003e. \u003cem\u003eC. difficile\u003c/em\u003e is considered as the pathogenic microorganism in 90\u0026ndash;100% of patients with antibiotics-associated PMC\u003csup\u003e27\u003c/sup\u003e. A 3-year cross-sectional study in eastern China showed that 55 years or older was a risk factor for \u003cem\u003eC. difficile\u003c/em\u003e infection, while several studies surveyed \u003cem\u003eC. difficile\u003c/em\u003e infection in Europe and United States and found that 65 years or older was its risk factor\u003csup\u003e28\u0026ndash;30\u003c/sup\u003e. This study showed that United States had the highest number of antibiotics-associated PMC reports, followed by United Kingdom, Japan, and France. These reports were mainly from United States and Europe, thus it has a similar age to \u003cem\u003eC. difficile\u003c/em\u003e infection in United States and Europe. Moreover, 73.02% of the patients with antibiotics-associated PMC had serious outcome events. Further risk factor analysis by comparing between serious and non-serious cases showed that sex and age may be related to the severity of ADEs. Female and the elderly had a significantly higher incidence of serious ADEs.\u003c/p\u003e \u003cp\u003eIn this study, there were eighty-one antibiotics that met the three algorithms simultaneously. Using the lower limit of 95% CI of ROR, PRR value, and the lower limit of 95% CI of the IC (IC025) in descending order, the top twenty-four drugs are the same, but in a slightly different order. The top twenty-four antibiotics included four penicillins, elven cephalosporins, three carbapenems, two lincosamides, one cephamycin, one aminoglycoside, one fosfomycin, and one echinocandin. Consistent with our results of antibiotics-associated PMC, studies have demonstrated that penicillins, cephalosporins, carbapenems, and clindamycin have higher risk of \u003cem\u003eC. difficile\u003c/em\u003e infection than other antibiotics\u003csup\u003e31\u003c/sup\u003e. This study showed that pivmecillinam had the strongest correlation with PMC. Pivmecillinam was been used widely in Nordic countries, but not used extensively in other countries. Thus, this may be related to patient population, and more comprehensive evaluation is needed. Khanafer et al found that second-generation and third-generation cephalosporins have been up to 3 times less active for \u003cem\u003eC. difficile\u003c/em\u003e than first-generation cephalosporins by culture-based susceptibility studies, indicating that second-generation and third-generation cephalosporins have higher risk for \u003cem\u003eC. difficile\u003c/em\u003e infection\u003csup\u003e32\u003c/sup\u003e. In this study, cephalosporins made up eleven of the top twenty-four antibiotics, and there were two second-generation cephalosporins (cefotiam and cefuroxime) and six third-generation cephalosporins (ceftriaxone, cefixoral, ceftizoxime, cefpodoxime, cefditoren, and ceftazidime - avibactam). Lincosamides is broad-spectrum activity against Gram-positive and obligate anaerobic bacteria. Many reports have indicated that lincosamides (clindamycine and lincomycin) predisposes patients to PMC\u003csup\u003e33,34\u003c/sup\u003e. It is noteworthy that cefoxitin, streptomycin, fosfomycin, and micafungin have a high risk of PMC in this study, but there are few reports in the literature.\u003c/p\u003e \u003cp\u003eThis study had several limitations. Firstly, only cases with PMC were reported to FAERS, but the total populations taking antibiotics were unknown, thus the true incidence rate of PMC of each antibiotic was not estimated based on the FAERS database. Secondly, disproportionality analysis based on the FAERS database statistically evaluated signal strength, but it did not reveal whether there was a causal relationship between PMC and drugs. This needs to be confirmed by further clinical studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur pharmacovigilance analysis indicated that broad-spectrum penicillins, cephalosporins, carbapenems, lincomycin, and clindamycin have higher risk of PMC, which is roughly consistent with studies about \u003cem\u003eC. difficile\u003c/em\u003e infection. This study found that cefoxitin, streptomycin, fosfomycin, and micafungin had a high risk of PMC, but there are few reports in the literature, and it needs further clinical studies to confirm. This study provided a clue on the relationship between antibiotics and the risk of PMC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval.\u0026nbsp;\u003c/strong\u003eThis study didn\u0026rsquo;t contain any studies with human participants or animals performed by any of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u0026nbsp;\u003c/strong\u003eJ.C., W.Z. and W.Y. designed the analysis. J.C. and C.S. collected the data and performed the analysis. J.C. wrote the paper. W.Z and C.S. revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability.\u0026nbsp;\u003c/strong\u003eThe datasets used and analyzed during the current study are available from the corresponding author at a reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u0026nbsp;\u003c/strong\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eThis work was supported by Tackling-plan Project of Henan Department of Science and Technology (212102310325).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFarooq, P.D., Urrunaga, N.H., Tang, D.M. \u0026amp; von Rosenvinge, E.C. Peudomembranous colitis. Dis. 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Lancet. 377, 63\u0026ndash;73(2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabak, Y.P., et al. Hospital-level high-risk antibiotic use in relation to hospital-associated Clostridioides difficile infections: Retrospective analysis of 2016\u0026ndash;2017 data from US hospitals. Infect. Control. Hosp. Epidemiol. 40,1229\u0026ndash;1235(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlammari, K.M. \u0026amp; Thabit, A.K. Characteristics of patients infected with Clostridioides difficile at a Saudi Tertiary Academic Medical Center and assessment of antibiotic duration. Gut. Pathog. 13,10(2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanafer, N., et al. Susceptibilities of clinical Clostridium difficile isolates to antimicrobials: a systematic review and meta-analysis of studies since 1970. Clin. Microbiol. Infect. 24,110\u0026ndash;117(2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSullivan, A., Edlund, C. \u0026amp; Nord, C.E. Effect of antimicrobial agents on the ecological balance of human microflora. Lancet. Infect. Dis. 1,101\u0026thinsp;\u0026ndash;\u0026thinsp;14(2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavanenghi, D., Morra, C., Grassini, M. \u0026amp; Sorisio, V. Pseudomembranous colitis induced by clindamycin-lincomycin combination. Description of a clinical case. Minerva. Dietol. Gastroenterol. 31,343\u0026ndash;345(1985).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Pseudomembranous colitis, antibiotic, FAERS, adverse event, disproportionality analysis","lastPublishedDoi":"10.21203/rs.3.rs-3827087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3827087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntibiotics have been established as an important risk factor for pseudomembranous colitis (PMC), a potential life-threatening complication. Evaluating the antibiotics most commonly associated with PMC is of great significance. In this study, we extracted the data from fourth quarter of 2003 to third quarter of 2023 in the US Food and Drug Administration Adverse Event Reporting System (FAERS). Disproportionality analysis was performed to evaluate the potential association between antibiotics and PMC. The results showed that eighty-one antibiotics which met the three algorithms simultaneously were enrolled. A total of 11737133 adverse event (ADE) reports were identified in the FAERS database, of which 1683 reports were associated with the enrolled antibiotics related PMC. It showed that the elderly and females are more susceptible to the antibiotics-associated PMC, especially for patients aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years. The top twenty-four antibiotics included four penicillins, eleven cephalosporins, three carbapenems, two lincosamides, one cephamycin, one aminoglycoside, one fosfomycin, and one echinocandin. This study also showed that cefoxitin, streptomycin, fosfomycin, and micafungin have a high risk of PMC, but there are few reports in the literature. This is helpful to reduce the potential damage of antibiotics-associated PMC.\u003c/p\u003e","manuscriptTitle":"Antibiotics-associated pseudomembranous colitis: a disproportionality analysis of the US Food and Drug Administration Adverse Event Reporting System (FAERS) database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-04 09:18:58","doi":"10.21203/rs.3.rs-3827087/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":"2f6d5744-92dd-4a68-9acf-ad599fb2b567","owner":[],"postedDate":"January 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27931255,"name":"Health sciences/Gastroenterology"},{"id":27931256,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-01-18T04:44:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-04 09:18:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3827087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3827087","identity":"rs-3827087","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}