Investigating Long-Term Risk of Aortic Aneurysm and Dissection from Fluoroquinolones and the Key Contributing Factors Using Machine Learning Methods

Investigating Long-Term Risk of Aortic Aneurysm and Dissection from Fluoroquinolones and the Key Contributing Factors Using Machine Learning Methods | 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 Investigating Long-Term Risk of Aortic Aneurysm and Dissection from Fluoroquinolones and the Key Contributing Factors Using Machine Learning Methods Hsiao-Wei Wang, Yen-Chun Huang, Yu-Wei Fang, Tsrang-Neng Jang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3990017/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 The connection between fluoroquinolones and severe heart conditions, such as aortic aneurysm (AA) and aortic dissection (AD), has been acknowledged, but the full extent of long-term risks remains uncertain. Addressing this knowledge deficit, a retrospective cohort study was conducted in Taiwan, utilizing data from the National Health Insurance Research Database spanning from 2004 to 2010, with follow-up lasting until 2019. The study included 232,552 people who took fluoroquinolones and the same number of people who didn't, matched for age, sex, and index year. The Cox regression model was enlisted to calculate the hazard ratio (HR) for AA/AD onset. Additionally, five machine learning algorithms assisted in pinpointing critical determinants for AA/AD among those with fluoroquinolones. Intriguingly, within the longest follow-up duration of 16 years, exposed patients presented with a markedly higher incidence of AA/AD. After adjusting for multiple factors, exposure to fluoroquinolones was linked to a higher risk of AA/AD (HR 1.62). Machine learning identified ten factors that significantly affected AA/AD risk in those exposed. These results show a 62% increase in long-term AA/AD risk after fluoroquinolone use, highlighting the need for healthcare professionals to carefully consider prescribing these antibiotics due to the risks and factors involved. Health sciences/Health care Health sciences/Risk factors Biological sciences/Drug discovery/Drug safety fluoroquinolones aortic aneurysm aortic dissection antibiotics machine learning feature selection Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Fluoroquinolone, a class of broad-spectrum antibacterial drugs effective against a variety of gram-negative and gram-positive pathogens, have been widely used for several decades 1 . Despite their efficacy in treating a range of infections, these agents have been associated with several safety concerns, including tendinopathy, QTc prolongation, and, potentially, adverse effects on collagen and other connective tissue structures 2–5 . In recent years, concerns have extended to the potential long-term risk of serious vascular complications, particularly aortic aneurysm (AA) and aortic dissection (AD), the latter being catastrophic events associated with high mortality rates 6,7 . Aortic aneurysm, a condition characterized by an abnormal focal dilation of the arterial wall, and its extreme sequel, dissection, pose significant health burdens due to their asymptomatic nature and potential for sudden, fatal rupture 8,9 . Emerging epidemiological evidence suggests that fluoroquinolones may contribute to these disorders by disrupting the integrity of collagen within the aortic wall, thus underlining a pressing need for a comprehensive review of their long-term vascular risks 10–12 . The concern was first raised when laboratory studies highlighted the capacity of fluoroquinolones to upregulate matrix metalloproteinases (MMPs), enzymes responsible for the degradation of collagen and elastin, which play a crucial role in maintaining the structural integrity of the aorta 13,14 . This biochemical mechanism provided a plausible link between fluoroquinolone use and increased risk of AA or AD, prompting observational studies and subsequent pharmacovigilance efforts. In 2008, the United States Food and Drug Administration issued a black box warning for fluoroquinolones based on post-marketing surveillance data indicating their association with post-treatment tendonitis and tendon rupture. Subsequent studies demonstrated the association of fluoroquinolones with increased risk of AA and AD 15,16 . In population-based cohort studies in Canada and Sweden, Daneman et al. 17 and Pasternak et al. 11 found that fluoroquinolones exposure was associated with greater risk of AA (hazard ratio [HR] 2.24, 95% confidence interval [CI] 2.02–2.49) and AA/AD (HR 1.66, 95%CI 1.12–2.46), respectively. These findings are supported by several case-control studies NHIRD 10,12,18–20 . Although previous studies have identified a potential association between AA/AD and fluoroquinolone exposure, there may be limitations regarding the long-term follow-up of patients taking fluoroquinolones. Our study embarked on a comprehensive exploration of the potential link between fluoroquinolone exposure and the development of aortic aneurysm (AA) and aortic dissection (AD) over an extended timeline. Utilizing the extensive data available from the National Health Insurance Research Database (NHIRD), our objective was to thoroughly assess the connection between fluoroquinolone use and the risk of AA/AD in a real-world context. Moreover, we aimed to identify predictive markers that could be leveraged in clinical settings to assess the risk of AA/AD in patients prescribed fluoroquinolones. To accomplish this, we employed advanced machine learning techniques to analyze the data, aiming to provide clinically relevant insights that could inform safer prescribing practices and enhance patient care. Materials and Methods Data sources Our study was designed as a retrospective cohort, utilizing the extensive information available from the NHIRD. The NHIRD, since its establishment in 1998, has been an exhaustive resource, encompassing coverage for a sweeping majority of the population in Taiwan, accounting for nearly 99 percent 21 . This expansive database holds a wealth of data points covering different aspects of healthcare services such as hospital stays, appointments with medical professionals in outpatient settings, along with a plethora of other healthcare-related information. It encompasses detailed records of surgical procedures undergone by individuals, the variety of medications prescribed, and the specific disease diagnosis codes assigned to patients' conditions. These diagnostic codes are formulated in alignment with the globally recognized International Classification of Diseases Ninth Revision Clinical Modification, commonly abbreviated as International Classification of Diseases Ninth Revision Clinical Modification 22 . Before releasing the data analysis, Taiwan's Ministry of Health and Welfare (MOHW) took important measures to protect the privacy of people represented in our study. The MOHW carefully anonymized the records of all claimants, stripping the dataset of any personal identifiers effectively. In preparation for our in-depth analysis, we ensured that all personal identifiers within the beneficiary claim records had been removed, rendering the data deidentified for the protection of personal privacy. Therefore, the Institutional Review Board of Shin-Kong Wu Ho-Su Memorial Hospital, responsible for overseeing the ethical aspects of our research, authorized the waiver of informed consent. This approval validated our approach and granted us permission to proceed with our investigation without requiring direct consent from the individuals in the dataset (protocol No.: 20200720R). All methods in this study were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki). Study population and baseline variables The database we used for our study included information from 2002 to 2019. Our attention was directed towards individuals who were registered between the dates of January 1, 2004, and December 31, 2010. Subsequently, we diligently observed their progress until the conclusion of the observation period on December 31, 2019. Figure 1 illustrates the scheme for selecting patients in our study from 2004 to 2010. We had a group of patients who were exposed to fluoroquinolones. This group consisted of 232,552 individuals who were prescribed either oral fluoroquinolones (specifically ciprofloxacin, levofloxacin, moxifloxacin, and gemifloxacin) or intravenous fluoroquinolones (specifically ciprofloxacin, levofloxacin, and moxifloxacin) during a visit to the outpatient department or as part of a hospital admission. To identify patients with new onset conditions, we applied the following exclusion criteria: ( 1 ) patients who had received fluoroquinolones prescriptions prior to 2002–2003, ( 2 ) patients who had already been diagnosed with AA or AD in 2002–2003, ( 3 ) patients with missing information, and ( 4 ) patients under the age of 18. The index date of the group with fluoroquinolones exposure was one month after the initial medication of fluoroquinolones records. To form a comparison group that hadn't been exposed to fluoroquinolones, we systematically paired individuals with counterparts from the group of those who did receive fluoroquinolones, ensuring that each match was made with a 1:1 ratio. This meticulous pairing process considered several important characteristics. Age, gender, and the specific year within the range of 2004 to 2010, also known as the index year, were the key factors considered to align the two groups as closely as possible. For the group that had not been exposed to fluoroquinolones, we assigned an index date that corresponded directly to the index date for their paired counterparts who had been exposed to the medication. This method allowed for consistent comparison across both groups. Definition of variables Three variable categories were included in the present study. The first category included those included in propensity score matching (index years, age, sex). The second category was composed of comorbidities including hypertension; hyperlipidemia; diabetes mellitus (DM); cirrhosis; chronic kidney disease; cerebrovascular disease; ischemic stroke; chronic obstructive pulmonary disease (COPD); coronary artery disease (CAD); asthma; genital tract infection (GTI), soft tissue and bone infection (STBI), and lower respiratory tract infections (LRTI); septicemia; and seizure disorder (Supplementary Table S1 online for codes of diseases). The third category was composed of medications including angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARB), beta-blockers, calcium channel blockers (CCB), insulin, nonsteroidal anti-inflammatory drugs (NSAID), diuretics, and oral and intravenous steroids (Supplementary Table S2 online for codes of medications). Study outcomes The main result of our study focused on patients who were admitted to the hospital for AA/AD, for which the specific codes used to identify these conditions are provided in further detail in Supplementary Table S1 online. The dataset was examined, starting from the index date, and continued until the specific endpoints were reached. This close examination was carried out up to the point at which the first instance of a new AA/AD diagnosis was identified, the occurrence of death from any cause, or until the preset conclusion of the observation period, which was determined to be December 31, 2019. The analysis concluded with whichever of these three events happened the earliest. Statistical analysis We presented all demographic results as percentages for categorical data and as means with standard deviations for continuous data. Categorical and continuous variables were compared using the chi-square and Student’s t tests, respectively. To eliminate discrepancies between groups, propensity score matching with a 1:1 ratio between groups with/without fluoroquinolones exposure was used in subsequent analyses. Cox proportional regression models were used to estimate HRs with 95%CIs for AA/AD risk. The first model was a crude analysis and the second model incorporated additional adjustments for all the baseline confounders. We tested the proportional hazard assumption by schoenfeld residuals. A two-tail P -value less than .05 was considered statistically significant. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA). Moreover, feature selection was used to find the important features of AA/AD in patients with fluoroquinolones exposure, thereby excluding unimportant variables and improving the accuracy of machine learning models 23,24 . Feature selection models used in the present study were logistic regression (LGR), random forest (RF), classification and regression tree, multivariate adaptive regression splines (MARS), multivariate adaptive regression splines (CART), and extreme gradient boosting (eXGBoost). Since the algorithm used for each model was different, the selection process for each variable varied as well. To ensure fairness, this research averaged all the features generated by each model and ranked the scores from high to low. The variable with the highest average score is considered the most important, and so on (Supplementary Figure S1 online). The R software (version 3.4.3; R Foundation for Statistical Computing, Vienna, Austria) was used for feature selection. Results Baseline characteristics of patients The distributions of age, sex, comorbidities, and medications of target population are presented in Table 1 . Briefly, the rates of comorbidities were significantly higher in the group with fluoroquinolones exposure than in those without fluoroquinolones exposure and included hypertension (13.22% vs. 7.84%, P < .001), hyperlipidemia (30.95% vs. 24.02%, P < .001), DM (33.21% vs. 19.67%, P < .001), cirrhosis (5.37% vs. 1.83%, P < .001), chronic kidney disease (16.18% vs. 4.84%, P < .001), stroke (26.02% vs. 12.48%, P < .001), ischemic stroke (8.89% vs. 2.87%, P < .001), COPD (42.87% vs. 22.03%, P < .001), CAD (11.88% vs. 3.84%, P < .001), asthma (23.92% vs. 11.83%, P < .001), GTI (60.98% vs. 31.56%, P < .001), STBI (50.26% vs. 30.56%, P < .001), LRTI (49.59% vs. 21.79%, P < .001), septicemia (10.77% vs. 3.73%, P < .001), and seizure disorder (0.83% vs. 0.21%, P < .001). Similarly, the use rates of specific medications were significantly higher in the group with fluoroquinolones exposure than in those without fluoroquinolones exposure and included ACEI/ARB (12.11% vs. 7.82%, P < .001), beta-blockers (14.83% vs. 11.18%, P < .001), CCB (26.18% vs. 15.7%, P < .001), insulin (11.13% vs. 1.08%, P < .001), NSAID (46.81% vs. 31.64%, P < .001), diuretics (2.62% vs. 2.02%, P < .001), oral steroids (65.66% vs. 45.08%, P < .001), and intravenous steroids (54.29% vs. 23.62%, P < .001). Table 1 Demographic and clinical characteristics of study population Variables fluoroquinolones exposure (n = 232,552) Non- fluoroquinolones exposure (n = 232,552) p value Sex Male (%) 127,710 (54.92 ) 128,032 (55.06 ) 0.342 Female (%) 104,842 (45.08 ) 104,520 (44.94 ) Age , years 60.7 \(\pm\) 17.9 60.7 \(\pm\) 17.8 0.168 Index year 2004 (%) 20,706 (8.90 ) 20,546 (8.84 ) 0.878 2005 (%) 24,510 (10.54 ) 24,343 (10.47 ) 2006 (%) 29,181 (12.55 ) 29,068 (12.50 ) 2007 (%) 33,787 (14.53 ) 33,786 (14.53 ) 2008 (%) 36,164 (15.55 ) 36,208 (15.57 ) 2009 (%) 40,278 (17.32 ) 40,398 (17.37 ) 2010 (%) 47,926 (20.61 ) 48,203 (20.73 ) Comorbidities Hypertension (%) 30,750 (13.22 ) 18,242 (7.84 ) < 0.001 Hyperlipidemia (%) 71,964 (30.95 ) 55,862 (24.02 ) < 0.001 Diabetes mellitus (%) 77,219 (33.21 ) 45,748 (19.67 ) < 0.001 Hepatobiliary disorders (%) 12,482 (5.37 ) 4,263 (1.83 ) < 0.001 Kidney disease (%) 37,627 (16.18 ) 11,246 (4.84 ) < 0.001 Cerebrovascular disease (%) 60,500 (26.02 ) 29,013 (12.48) < 0.001 Ischemic stroke (%) 20,668 (8.89 ) 6,673 (2.87) < 0.001 COPD (%) 99,688 (42.87) 51,241 (22.03) < 0.001 Coronary artery disease (%) 27,626 (11.88 ) 8,935 (3.84 ) < 0.001 Asthma (%) 55,627 (23.92 ) 27,521 (11.83 ) < 0.001 GTI (%) 141,809 (60.98 ) 73,392 (31.56 ) < 0.001 STBI (%) 116,885 (50.26 ) 71,071 (30.56 ) < 0.001 LRTI (%) 115,318 (49.59 ) 50,667 (21.79 ) < 0.001 Septicemia (%) 25,041 (10.77 ) 8,673 (3.73 ) < 0.001 Seizure disorder (%) 1,928 (0.83 ) 487 (0.21 ) < 0.001 Medications ACEI/ARB (%) 28,161 (12.11 ) 18,177 (7.82 ) < 0.001 Beta-blockers (%) 34,484 (14.83 ) 25,998 (11.18 ) < 0.001 Calcium channel blocker (%) 60,873 (26.18 ) 36,519 (15.7 ) < 0.001 Insulin (%) 25,887 (11.13 ) 2,508 (1.08 ) < 0.001 NSAIDs (%) 108,860 (46.81 ) 73,576 (31.64 ) < 0.001 Diuretics (%) 6,094 (2.62) 4,698 (2.02 ) < 0.001 Oral steroids (%) 152,699 (65.66 ) 104,839 (45.08 ) < 0.001 Intravenous steroids (%) 126,251 (54.29 ) 54,918 (23.62 ) < 0.001 Abbreviation: COPD, chronic obstructive pulmonary disease; GTI, genital tract infection; LRTI, lower respiratory tract infection; STBI, soft tissue and bone infection; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; NSAIDs, nonsteroidal anti-inflammatory drug. Risk factors for the development of AA/AD Table 2 shows the determinants of AA/AD in the study population. In multivariable analysis, in addition to fluoroquinolones (HR 1.61, 95%CI 1.45–1.78), old age (HR 2.21, 95%CI 1.55–3.14 for 40–55 years vs. <40 years; HR 9.39, 95%CI 6.83–12.92 for ≥ 55 years vs. <40 years), male sex (HR 2.47, 95%CI 2.23–2.74), hypertension (HR 1.14, 95%CI 1.02–1.28), DM (HR 0.75; 95%CI 0.68–0.83), cirrhosis (HR 0.75, 95%CI 0.57–0.97), cerebrovascular disease (HR 1.30, 95%CI 1.16–1.45), CAD (HR 1.50, 95 CI 1.16–1.93), asthma (HR 0.88, 95%CI 0.79–0.97), septicemia (HR 1.36, 95%CI 1.20–1.54), ACEI/ARB (HR 1.40, 95%CI 1.24–1.58), beta-blockers (HR 1.36, 95%CI 1.22–1.51), CCB (HR 1.59, 95%CI 1.44–1.75), insulin (HR 0.56, 95%CI 0.45–0.70), NSAIDs (HR 0.90, 95%CI 0.82–0.98), oral steroids (HR 1.75, 95%CI 1.56–1.96), and intravenous steroids (HR 4.29, 95%CI 3.82–4.81) were independently associated with the development of AA/AD. Table 2 Multivariable adjusting Cox regression analysis to determine risk covariates for aortic dissection and aneurysm. Characteristics Aortic dissection and aneurysm aHR (95%CI) * P- value FQs exposure vs. Non-FQs exposure 1.61 (1.45–1.78) < 0.001 Age group, years < 40 (reference) 1 < 0.001 40–55 2.21 (1.55–3.14) ≥ 55 9.39 (6.83–12.92) Male vs. female 2.47 (2.23–2.74) < 0.001 Comorbidities Hypertension 1.14 (1.02–1.28) 0.023 Hyperlipidemia 0.97 (0.89–1.07) 0.565 Hyperparathyroidism 0.84 (0.37–1.88) 0.669 Diabetes mellitus 0.75 (0.68–0.83) < 0.001 Hepatobiliary disorders 0.75 (0.57–0.97) 0.031 Kidney disease 0.93 (0.73–1.17) 0.508 Cerebrovascular disease 1.30 (1.16–1.45) < 0.001 Ischemic stroke 1.12 (0.95–1.31) 0.183 Chronic obstructive pulmonary disease 1.06 (0.96–1.17) 0.252 Coronary artery disease 1.50 (1.16–1.93) 0.001 Asthma 0.88 (0.79–0.97) 0.014 Genital tract infection 1.03 (0.93–1.13) 0.608 Soft tissue and bone infection 0.90 (0.82–0.98) 0.015 Lower respiratory tract infection 0.97 (0.88–1.07) 0.532 Septicemia 1.36 (1.20–1.54) < 0.001 Seizure 0.50 (0.25–1.01) 0.052 Medications ACEI/ARB 1.40 (1.24–1.58) < 0.001 Beta-blockers 1.36 (1.22–1.51) < 0.001 Calcium channel blocker 1.59 (1.44–1.75) < 0.001 Insulin 0.56 (0.45–0.70) < 0.001 NSAIDs 0.90 (0.82–0.98) 0.018 Diuretics 0.82 (0.65–1.04) 0.101 Oral steroids 1.75 (1.56–1.96) < 0.001 Intravenous steroids 4.29 (3.82–4.81) < 0.001 * The full multivariable adjusting model included all baseline comorbidities and medications. Abbreviation : FQs, fluoroquinolones; CI, confidence interval; aHR, adjusted hazard ratio; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; NSAIDs, non-steroidal anti-inflammatory drug. Association of fluoroquinolones exposure with AA/AD Over a longest 16-year follow-up period, the Kaplan-Meier plot in Fig. 2 reveals a significant difference in the occurrence of AA/AD events between groups exposed to fluoroquinolones and those not exposed (p < .001). Additionally, Table 3 illustrates that we compared 1,389 patients who recently developed AA or AD and were exposed to fluoroquinolones to 675 patients who also recently developed AA or AD but were not exposed to fluoroquinolones. The rate of AA/AD was higher in the group exposed to fluoroquinolones compared to those not exposed (80 vs. 30 per 100,000 person-years). Table 3 Comparison of the risk for aortic aneurysm and aortic dissection between patients with and without fluroquinolone exposure Clinical outcome FQs exposure (n = 232,552) Non- FQs exposure (n = 232,552) FQs exposure vs. Non- FQs exposure Events IR Events IR Crude HR (95% CI) P value aHR* (95% CI) P value AA/AD 1389 80 675 30 2.39 (2.18–2.62) < 0.001 1.61 (1.45–1.78) < 0.001 * The full multivariable adjusting model included all baseline comorbidities and medications. Abbreviation: AA, aortic aneurysm; AD, aortic dissection; aHR, adjusted hazard ratio; CI, confidence interval; FQ, fluroquinolone; HR, hazard ratio; IR, incident rate (per 100,000 person-years) The initial analysis showed a clear link between taking fluoroquinolones and a higher chance of developing AA/AD (HR 2.39, with a 95% CI of 2.18–2.62; the p < .001). Even after considering all the factors listed in Table 1 , the use of fluoroquinolones was still significantly linked to an increased risk of getting AA/AD for the first time (the adjusted HR is 1.62, with a 95% CI of 1.47–1.78; p < .001). Subgroup analysis We further performed subgroup analysis to determine the effect of the baseline variables on AA/AD risk (Fig. 3 ). Almost all patients had increased HRs of entering AA/AD indicators in the favor of fluoroquinolones non-exposure, the risk for AA/AD was higher in patients younger than 40 years (HR 3.94, 95%CI 1.82–8.55). In addition, the risk for AA/AD was not different between the patients with and without fluoroquinolones exposure among those using intravenous steroids. Critical factors to develop AA/AD in patients exposed to fluoroquinolones Figure 4 shows how variables rank according to their average importance scores, as determined by LGR, RF, MARS, CART, and eXGBoost methods. The top ten most significant variables, listed from the most to the least important, are intravenous steroid, insulin, CCB, DM, oral steroid, beta-blockers, ACEI/ARB, hyperlipidemia, COPD, and patient's age (Supplementary Table S3 online). Discussion In this nationwide cohort study examining the impact of fluoroquinolones exposure on AA/AD incidence, our analyses revealed a 1.6-fold increased long-term risk of AA/AD among patients exposed to fluoroquinolones, with a significant association noted across nearly all subgroups that were analyzed. Using machine learning methods, we identified crucial factors contributing to AA/AD development in patients exposed to fluoroquinolones. The findings presented here are intended to assist healthcare professionals in creating systematic methods for the continuous observation and proactive prevention of AA/AD among individuals who have been treated with fluoroquinolone antibiotics to reduce the risk of potential health complications related to these antibiotics. The mechanism underlying fluoroquinolones-induced AA/AD is not fully understood, although two hypotheses have been proposed. The first hypothesis posits that fluoroquinolones interfere with the integrity of the extracellular matrix, resulting in homeostatic dysregulation and impaired biomechanical strength in aorta, and ultimately triggering progressive aortic weakening, dissection, and rupture by upregulating the activity of matrix metalloproteinases (MMPs) and reducing the levels of tissue inhibitors of MMPs 25,26 . Increased MMP expression has been reported in smooth muscle cells in patients with abdominal AA 27 and in cornea and tendons in animals exposed to fluoroquinolones 14,28 . The second hypothesis proposes that fluoroquinolones, which are DNA topoisomerase inhibitors, promote mitochondrial dysfunction, suppress cell proliferation, and induce apoptosis 29,30 , ultimately leading to aortic damage. In addition to fluoroquinolones, risk factors of AA/AD include older age, male sex, lifestyle habits such as cigarette smoking and stimulant abuse, and clinical conditions such as COPD, prolonged hypertension, obesity, atherosclerosis, chronic kidney disease, trauma, vasculitis, bacterial infection, and congenital connective tissue disorders 31–33 . Indeed, we also found that these previously reported factors were associated with AA/AD risk in Table 2 . Further, we found that certain antihypertensive medications (ACEI/ARB, CCB, and beta-blockers) were associated with increased AA/AD risk. This finding contradicts previous studies linking the renin-angiotensin system to AA and suggesting that antihypertensive medications are beneficial for patient outcomes after the development of AA/AD 34,35 . One possible explanation for this discrepancy is that hypertension is a known risk factor for AA/AD 36 and that individuals with hypertension are often prescribed these antihypertensive medications. Therefore, in the present study, the association of antihypertensive medications with increased AA/AD risk might reflect the presence of high blood pressure, a known AA/AD risk factor, in these patients. Based on our machine learning analysis, the top ten important factors for the development of AA/AD in patients with fluoroquinolones exposure were age, comorbidities such as DM, hyperlipidemia, and COPD, and medications including intravenous steroids, insulin, CCB, beta-blockers, and ACEI/ARB. Of these, intravenous steroid use was the top-scoring predictor of AA/AD. Of note, antihypertensive medication use might reflect preexisting high blood pressure. As indicated in Table 2 , which outlines the risk determinants for AA/AD, and Fig. 4 , which ranks the important risk factors, most of the top ten significant factors for AA/AD are also related to an increased risk of AA/AD. The only exceptions are diabetes mellitus (DM) and insulin use, which may play a role in reducing the risk of AA/AD in patients exposed to fluoroquinolones. Overall, the results mentioned above offer important insights for tracking patients exposed to fluoroquinolones. Glucocorticoids are often used in combination with fluoroquinolones for inpatients. However, a case series reported that treatment with anabolic steroids increased the risk of AD in athletes, particularly in association with exercise 37 . Furthermore, Sendzik et al. reported that the combined use of steroids and fluoroquinolones increased the levels of MMPs and activated caspase 3, indicating apoptosis, in tenocyte cultures 38 . These results are consistent with the present study finding that steroid use, either intravenous or oral, might be associated with the development of AA/AD. DM is a well-established risk factor for coronary and cerebrovascular diseases. However, the DM prevalence is surprisingly lower in individuals with abdominal AA than in those without abdominal AA (6–14% vs. 17–36%) 39 . In fact, a 3-year follow-up study found that DM was independently associated with reduced abdominal AA growth 40 . Similarly, Prakash et al. reported an inverse association between DM and the rate of hospitalization for thoracic AD 41 . In a meta-analysis including 14 studies and 15 794 patients, Li et al. found that the DM prevalence was lower in patients with AD than in those without AD (odds ratio 0.51, 95%CI 0.33–0.81) 42 . However, the mechanism underlying the beneficial effects of hyperglycemia in thoracic AD is not fully understood. In the present study, insulin had a beneficial effect and prevented the development of AA/AD. Insulin use may play an important role in the negative association observed between DM and the development of AA/AD. Recent studies have increasingly shown the role of inflammation and macrophage infiltration in the development of AD 43,44 . In a murine model, Tomida et al. found that the use of indomethacin, an NSAID, prevented death due to abdominal AD and reduced the incidence of AD by up to 40% 45 . This effect might be attributed to the inhibition of monocyte transendothelial migration and blockade of the accumulation of monocytes/macrophages in the aortic wall. This is compatible with our findings, indicating the potential use of NSAIDs to prevent the development of AD. The present study boasts several key strengths. Firstly, utilization of a nationwide database supports the generalizability of the study results. Secondly, the sample size and follow-up duration ensured a robust collection of AA/AD events. Thirdly, we employed machine learning methods were used to pinpoint important factors for the development of AA/AD in individuals exposed to fluoroquinolones. Lastly, the cohort study design minimized the risk of sampling bias, a common issue in case-control studies 46 , that most previous research has used. Despite its strengths, the current study has a few potential weaknesses. Firstly, although the NHIRD database offered a large sample size, it did not include clinical information like imaging results, biochemical and microbiological data, blood pressure readings, and physical characteristics. Secondly, the study wasn't a randomized controlled trial, which meant that there were notable differences in the baseline characteristics of the two groups. However, to reduce these biases as much as possible, we used propensity score matching and multivariable adjustment. Finally, we were unable to confirm whether participants used fluoroquinolones before 2002 or after 2010 because of the limitations in our data availability and study design. Assuming the missing data was random in both groups, we could likely overlook the bias. Conclusion The long-term risk of AA/AD was 61% higher in patients with fluoroquinolones exposure than in those without fluoroquinolones exposure. Ten notable factors contributing to this correlation have been identified. We suggest that a sophisticated artificial intelligence system could be developed to predict the risk of developing AA/AD in patients treated with fluoroquinolone antibiotics based on our findings. Such powerful tool would enhance patient care by aiding healthcare professionals in making informed decisions regarding fluoroquinolone use. Abbreviations NHIRD national health insurance research database FQs Fluoroquinolone. Declarations Author contributions Conceived and designed the experiments: W-HW, F-YW, and C-Mingchih; performed the experiments: H-YC and T-MH; analyzed the data: H-YC, C-Mingchih, J-TN, and W-HW; contributed reagents/materials/analysis tools: all authors; wrote the manuscript: T-MH and W-HW; approved the manuscript: all authors. Acknowledgments This work was supported by the Artificial Intelligence Center of Shin Kong Wu Ho-Su Memorial Hospital sponsored this study (2021SKHADE034). Data availability statement The datasets used in this study are not available to the public because all claim records must be anonymized before they are released for analysis. The dataset is held by the Taiwan Ministry of Health and Welfare. (MOHW). Any researcher interested in accessing this dataset can apply for access. Please visit the website of the National Health Informatics Project of the MOHW (https://dep.mohw.gov.tw/DOS/mp-113.html.). If anyone would like to request the data from this study and is having difficulty accessing it, please contact the corresponding author, T-MH, for help. References Majalekar, P. P. & Shirote, P. J. Fluoroquinolones: Blessings Or Curses. Curr Drug Targets 21 , 1354-1370, doi:10.2174/1389450121666200621193355 (2020). van der Linden, P. D., van de Lei, J., Nab, H. W., Knol, A. & Stricker, B. H. Achilles tendinitis associated with fluoroquinolones. Br J Clin Pharmacol 48 , 433-437, doi:10.1046/j.1365-2125.1999.00016.x (1999). Kaleagasioglu, F. & Olcay, E. Fluoroquinolone-induced tendinopathy: etiology and preventive measures. Tohoku J Exp Med 226 , 251-258, doi:10.1620/tjem.226.251 (2012). Kapoor, K. G., Hodge, D. O., St Sauver, J. L. & Barkmeier, A. J. 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Global and regional burden of aortic dissection and aneurysms: mortality trends in 21 world regions, 1990 to 2010. Glob Heart 9 , 171-180.e110, doi:10.1016/j.gheart.2013.12.010 (2014). Powell, J. T. et al. Meta-analysis of individual-patient data from EVAR-1, DREAM, OVER and ACE trials comparing outcomes of endovascular or open repair for abdominal aortic aneurysm over 5 years. Br J Surg 104 , 166-178, doi:10.1002/bjs.10430 (2017). Lee, C. C. et al. Risk of Aortic Dissection and Aortic Aneurysm in Patients Taking Oral Fluoroquinolone. JAMA Intern Med 175 , 1839-1847, doi:10.1001/jamainternmed.2015.5389 (2015). Pasternak, B., Inghammar, M. & Svanström, H. Fluoroquinolone use and risk of aortic aneurysm and dissection: nationwide cohort study. Bmj 360 , k678, doi:10.1136/bmj.k678 (2018). Gopalakrishnan, C. et al. Association of Fluoroquinolones With the Risk of Aortic Aneurysm or Aortic Dissection. JAMA Intern Med 180 , 1596-1605, doi:10.1001/jamainternmed.2020.4199 (2020). Guzzardi, D. G. et al. Induction of human aortic myofibroblast-mediated extracellular matrix dysregulation: A potential mechanism of fluoroquinolone-associated aortopathy. J Thorac Cardiovasc Surg 157 , 109-119.e102, doi:10.1016/j.jtcvs.2018.08.079 (2019). Tsai, W. C. et al. Ciprofloxacin up-regulates tendon cells to express matrix metalloproteinase-2 with degradation of type I collagen. J Orthop Res 29 , 67-73, doi:10.1002/jor.21196 (2011). Jun, C. & Fang, B. Current progress of fluoroquinolones-increased risk of aortic aneurysm and dissection. BMC Cardiovasc Disord 21 , 470, doi:10.1186/s12872-021-02258-1 (2021). Wee, I. et al. The association between fluoroquinolones and aortic dissection and aortic aneurysms: a systematic review and meta-analysis. Sci Rep 11 , 11073, doi:10.1038/s41598-021-90692-8 (2021). Daneman, N., Lu, H. & Redelmeier, D. A. Fluoroquinolones and collagen associated severe adverse events: a longitudinal cohort study. BMJ Open 5 , e010077, doi:10.1136/bmjopen-2015-010077 (2015). Maumus-Robert, S. et al. Short-Term Risk of Aortoiliac Aneurysm or Dissection Associated With Fluoroquinolone Use. J Am Coll Cardiol 73 , 875-877, doi:10.1016/j.jacc.2018.12.012 (2019). Lee, C. C. et al. Oral Fluoroquinolone and the Risk of Aortic Dissection. J Am Coll Cardiol 72 , 1369-1378, doi:10.1016/j.jacc.2018.06.067 (2018). Sommet, A. et al. What Fluoroquinolones Have the Highest Risk of Aortic Aneurysm? A Case/Non-case Study in VigiBase®. J Gen Intern Med 34 , 502-503, doi:10.1007/s11606-018-4774-2 (2019). Hsieh, C. Y. et al. Taiwan's National Health Insurance Research Database: past and future. Clin Epidemiol 11 , 349-358, doi:10.2147/clep.S196293 (2019). Tsai, M. H., Chen, M., Huang, Y. C., Liou, H. H. & Fang, Y. W. Corrigendum: The Protective Effects of Lipid-Lowering Agents on Cardiovascular Disease and Mortality in Maintenance Dialysis Patients: Propensity Score Analysis of a Population-Based Cohort Study. Front Pharmacol 13 , 894462, doi:10.3389/fphar.2022.894462 (2022). Alsahaf, A., Petkov, N., Shenoy, V. & Azzopardi, G. A framework for feature selection through boosting. Expert Systems with Applications 187 , 115895 (2022). Borboudakis, G. & Tsamardinos, I. Forward-backward selection with early dropping. The Journal of Machine Learning Research 20 , 276-314 (2019). Hadi, T. et al. Macrophage-derived netrin-1 promotes abdominal aortic aneurysm formation by activating MMP3 in vascular smooth muscle cells. Nat Commun 9 , 5022, doi:10.1038/s41467-018-07495-1 (2018). Sendzik, J., Shakibaei, M., Schäfer-Korting, M., Lode, H. & Stahlmann, R. Synergistic effects of dexamethasone and quinolones on human-derived tendon cells. Int J Antimicrob Agents 35 , 366-374, doi:10.1016/j.ijantimicag.2009.10.009 (2010). Dilme, J. F. et al. Influence of cardiovascular risk factors on levels of matrix metalloproteinases 2 and 9 in human abdominal aortic aneurysms. Eur J Vasc Endovasc Surg 48 , 374-381, doi:10.1016/j.ejvs.2014.05.023 (2014). Sharma, C. et al. Effect of fluoroquinolones on the expression of matrix metalloproteinase in debrided cornea of rats. Toxicol Mech Methods 21 , 6-12, doi:10.3109/15376516.2010.529183 (2011). LeMaire, S. A. et al. Effect of Ciprofloxacin on Susceptibility to Aortic Dissection and Rupture in Mice. JAMA Surg 153 , e181804, doi:10.1001/jamasurg.2018.1804 (2018). Tsai, W. C. et al. Ciprofloxacin-mediated inhibition of tenocyte migration and down-regulation of focal adhesion kinase phosphorylation. Eur J Pharmacol 607 , 23-26, doi:10.1016/j.ejphar.2009.02.006 (2009). Kessler, V., Klopf, J., Eilenberg, W., Neumayer, C. & Brostjan, C. AAA Revisited: A Comprehensive Review of Risk Factors, Management, and Hallmarks of Pathogenesis. Biomedicines 10 , doi:10.3390/biomedicines10010094 (2022). Wei, L. et al. Global Burden of Aortic Aneurysm and Attributable Risk Factors from 1990 to 2017. Glob Heart 16 , 35, doi:10.5334/gh.920 (2021). Howard, D. P. et al. Population‐based study of incidence of acute abdominal aortic aneurysms with projected impact of screening strategy. Journal of the American Heart Association 4 , e001926 (2015). Fatini, C. et al. ACE DD genotype: a predisposing factor for abdominal aortic aneurysm. Eur J Vasc Endovasc Surg 29 , 227-232, doi:10.1016/j.ejvs.2004.12.018 (2005). Nagashima, H. et al. Angiotensin II type 2 receptor mediates vascular smooth muscle cell apoptosis in cystic medial degeneration associated with Marfan's syndrome. Circulation 104 , I282-287, doi:10.1161/hc37t1.094856 (2001). Hibino, M. et al. Blood Pressure, Hypertension, and the Risk of Aortic Dissection Incidence and Mortality: Results From the J-SCH Study, the UK Biobank Study, and a Meta-Analysis of Cohort Studies. Circulation 145 , 633-644, doi:10.1161/circulationaha.121.056546 (2022). Heydari, A., Asadmobini, A. & Sabzi, F. Anabolic Steroid Use and Aortic Dissection in Athletes: A Case Series. Oman Med J 35 , e179, doi:10.5001/omj.2020.120 (2020). Sendzik, J., Shakibaei, M., Schafer-Korting, M., Lode, H. & Stahlmann, R. Synergistic effects of dexamethasone and quinolones on human-derived tendon cells. Int J Antimicrob Agents 35 , 366-374, doi:10.1016/j.ijantimicag.2009.10.009 (2010). Shantikumar, S., Ajjan, R., Porter, K. E. & Scott, D. J. Diabetes and the abdominal aortic aneurysm. Eur J Vasc Endovasc Surg 39 , 200-207, doi:10.1016/j.ejvs.2009.10.014 (2010). Golledge, J. et al. Reduced expansion rate of abdominal aortic aneurysms in patients with diabetes may be related to aberrant monocyte-matrix interactions. Eur Heart J 29 , 665-672, doi:10.1093/eurheartj/ehm557 (2008). Prakash, S. K., Pedroza, C., Khalil, Y. A. & Milewicz, D. M. Diabetes and reduced risk for thoracic aortic aneurysms and dissections: a nationwide case-control study. J Am Heart Assoc 1 , doi:10.1161/JAHA.111.000323 (2012). Li, S. et al. Diabetes Mellitus Lowers the Risk of Aortic Dissection: a Systematic Review and Meta-Analysis. Ann Vasc Surg 74 , 209-219, doi:10.1016/j.avsg.2020.12.016 (2021). Anzai, A. et al. Adventitial CXCL1/G-CSF expression in response to acute aortic dissection triggers local neutrophil recruitment and activation leading to aortic rupture. Circ Res 116 , 612-623, doi:10.1161/circresaha.116.304918 (2015). Ju, X. et al. Interleukin-6-signal transducer and activator of transcription-3 signaling mediates aortic dissections induced by angiotensin II via the T-helper lymphocyte 17-interleukin 17 axis in C57BL/6 mice. Arterioscler Thromb Vasc Biol 33 , 1612-1621, doi:10.1161/atvbaha.112.301049 (2013). Tomida, S. et al. Indomethacin reduces rates of aortic dissection and rupture of the abdominal aorta by inhibiting monocyte/macrophage accumulation in a murine model. Sci Rep 9 , 10751, doi:10.1038/s41598-019-46673-z (2019). Mansournia, M. A., Jewell, N. P. & Greenland, S. Case-control matching: effects, misconceptions, and recommendations. Eur J Epidemiol 33 , 5-14, doi:10.1007/s10654-017-0325-0 (2018). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1feasureselection.tiff SupplementaryTableS1Codeforcomorbidities.docx SupplementaryTableS2Codefordrugs.docx SupplementaryTableS3ThescoreofML.docx 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. 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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-3990017","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":278807932,"identity":"93ae60a1-00cb-4541-b218-cde5a908243f","order_by":0,"name":"Hsiao-Wei Wang","email":"","orcid":"","institution":"Shin-Kong Wu Ho-Su Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hsiao-Wei","middleName":"","lastName":"Wang","suffix":""},{"id":278807933,"identity":"96c06d34-3df6-4c70-9bed-ff81bd65ce8d","order_by":1,"name":"Yen-Chun Huang","email":"","orcid":"","institution":"Tamkang University","correspondingAuthor":false,"prefix":"","firstName":"Yen-Chun","middleName":"","lastName":"Huang","suffix":""},{"id":278807934,"identity":"0c2eb751-15ac-4266-8c66-3e0451b53bd1","order_by":2,"name":"Yu-Wei Fang","email":"","orcid":"","institution":"Shin-Kong Wu Ho-Su Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu-Wei","middleName":"","lastName":"Fang","suffix":""},{"id":278807935,"identity":"40f0b997-36b3-44fa-809a-ef7d9aacea4d","order_by":3,"name":"Tsrang-Neng Jang","email":"","orcid":"","institution":"Shin-Kong Wu Ho-Su Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tsrang-Neng","middleName":"","lastName":"Jang","suffix":""},{"id":278807936,"identity":"bda13a1b-381a-47fc-8d76-6c4222658f9d","order_by":4,"name":"Mingchih Chen","email":"","orcid":"","institution":"Fu Jen Catholic University","correspondingAuthor":false,"prefix":"","firstName":"Mingchih","middleName":"","lastName":"Chen","suffix":""},{"id":278807937,"identity":"6d2776ed-60c3-48b2-8272-71f1f9a27d43","order_by":5,"name":"Ming-Hsien Tsai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACCShtACI+ADEbO5FaJAwYmBkYZ4C0MJOihZkHxCSkhX9288OHP3MO15kz8B+Ttvm1TZ4PaNuHjzl4LLlzzNiYd9thCcsGZjbp3L7bhm1A2yRnbsOtxUAiwUyaEajF4AAz2+3cntuMQC1szLx4taR/k/wJ02LZc9ueCC05ZhK8MC0MP24nEtQicSOnGOiXdMkNh5nNf/Y23E5uY2ZsxusX/hnpGx/+3GbNb3C88bHBjz+3bee3Nx/88BGPFihohkQHYxuIw9hAUD0Q1EHpP8QoHgWjYBSMgpEGAIFpTDZgnouLAAAAAElFTkSuQmCC","orcid":"","institution":"Shin-Kong Wu Ho-Su Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ming-Hsien","middleName":"","lastName":"Tsai","suffix":""}],"badges":[],"createdAt":"2024-02-26 05:30:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3990017/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3990017/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52619282,"identity":"757b07e7-61c5-4d85-8c5a-8e2b29842eb8","added_by":"auto","created_at":"2024-03-13 16:35:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336719,"visible":true,"origin":"","legend":"\u003cp\u003eSchema of patient enrollment in the study.\u003c/p\u003e\n\u003cp\u003eAbbreviation: NHIRD, national health insurance research database; FQs, Fluoroquinolone.\u003c/p\u003e","description":"","filename":"Figure1flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/caa5b57f566d83817740a25f.png"},{"id":52619284,"identity":"2d7f042e-68df-4a3a-bc2b-7ce1f1ebdef4","added_by":"auto","created_at":"2024-03-13 16:35:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142562,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves on aortic aneurysm or aortic dissection associated with fluoroquinolone between groups exposed to fluoroquinolones and those not exposed.\u003c/p\u003e\n\u003cp\u003eAbbreviation: FQs, fluoroquinolone.\u003c/p\u003e","description":"","filename":"figure2KM.png","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/0b2b639ee4d5885edd283b89.png"},{"id":52620104,"identity":"f5e1b8cb-e84f-4fb9-a665-231fa1c44519","added_by":"auto","created_at":"2024-03-13 16:43:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":669202,"visible":true,"origin":"","legend":"\u003cp\u003eThe subgroup analysis of the effect of fluoroquinolone exposure on the aortic aneurysm or aortic dissection\u003c/p\u003e\n\u003cp\u003eAbbreviation: FQs: Fluoroquinolone; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; STBI, soft tissue and bone infection; LRTI, lower respiratory tract infection; GTI, genital tract infection.\u003c/p\u003e","description":"","filename":"Figure3subgroup.png","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/c3f1ee903cd5fd55199a60bf.png"},{"id":52619285,"identity":"85889eef-d042-4d17-9544-242830605ee4","added_by":"auto","created_at":"2024-03-13 16:35:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21767,"visible":true,"origin":"","legend":"\u003cp\u003eRanking of parameters important factors for aortic dissection and aneurysm in patients with fluoroquinolones exposure based on machine learning methods\u003c/p\u003e\n\u003cp\u003eAbbreviation: IV, intravenous; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CCB, calcium channel blocker; COPD, chronic obstructive pulmonary disease.\u003c/p\u003e","description":"","filename":"Figure4importanceoffactor.png","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/d6f291312fbaf874fa344523.png"},{"id":60376971,"identity":"e1646ebc-5daf-400e-95f9-642ee0687f72","added_by":"auto","created_at":"2024-07-16 06:26:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1992417,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/3ac71eb0-90e8-4b95-83f2-3f408eaab355.pdf"},{"id":52619287,"identity":"01b8d50b-5801-420c-b1d9-5b9c37f0ca4b","added_by":"auto","created_at":"2024-03-13 16:35:32","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":421766,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1feasureselection.tiff","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/07d23489d58aae8e86449d7f.tiff"},{"id":52620103,"identity":"0cdb092c-4a13-42a1-939a-8c6c43ce8679","added_by":"auto","created_at":"2024-03-13 16:43:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20483,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1Codeforcomorbidities.docx","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/19fdaed3b54394b1d3af8ee4.docx"},{"id":52619288,"identity":"f53387f4-508d-4143-be4d-44c44a2246ed","added_by":"auto","created_at":"2024-03-13 16:35:32","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17971,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2Codefordrugs.docx","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/6c74c8642f4869c718ecdf33.docx"},{"id":52619289,"identity":"d06056c0-dc7a-416e-b002-3e032cfbff62","added_by":"auto","created_at":"2024-03-13 16:35:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21975,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3ThescoreofML.docx","url":"https://assets-eu.researchsquare.com/files/rs-3990017/v1/00b597445b438faf08037c67.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating Long-Term Risk of Aortic Aneurysm and Dissection from Fluoroquinolones and the Key Contributing Factors Using Machine Learning Methods","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFluoroquinolone, a class of broad-spectrum antibacterial drugs effective against a variety of gram-negative and gram-positive pathogens, have been widely used for several decades \u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite their efficacy in treating a range of infections, these agents have been associated with several safety concerns, including tendinopathy, QTc prolongation, and, potentially, adverse effects on collagen and other connective tissue structures \u003csup\u003e2\u0026ndash;5\u003c/sup\u003e. In recent years, concerns have extended to the potential long-term risk of serious vascular complications, particularly aortic aneurysm (AA) and aortic dissection (AD), the latter being catastrophic events associated with high mortality rates\u003csup\u003e6,7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAortic aneurysm, a condition characterized by an abnormal focal dilation of the arterial wall, and its extreme sequel, dissection, pose significant health burdens due to their asymptomatic nature and potential for sudden, fatal rupture \u003csup\u003e8,9\u003c/sup\u003e. Emerging epidemiological evidence suggests that fluoroquinolones may contribute to these disorders by disrupting the integrity of collagen within the aortic wall, thus underlining a pressing need for a comprehensive review of their long-term vascular risks \u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. The concern was first raised when laboratory studies highlighted the capacity of fluoroquinolones to upregulate matrix metalloproteinases (MMPs), enzymes responsible for the degradation of collagen and elastin, which play a crucial role in maintaining the structural integrity of the aorta\u003csup\u003e13,14\u003c/sup\u003e. This biochemical mechanism provided a plausible link between fluoroquinolone use and increased risk of AA or AD, prompting observational studies and subsequent pharmacovigilance efforts.\u003c/p\u003e \u003cp\u003eIn 2008, the United States Food and Drug Administration issued a black box warning for fluoroquinolones based on post-marketing surveillance data indicating their association with post-treatment tendonitis and tendon rupture. Subsequent studies demonstrated the association of fluoroquinolones with increased risk of AA and AD\u003csup\u003e15,16\u003c/sup\u003e. In population-based cohort studies in Canada and Sweden, Daneman et al.\u003csup\u003e17\u003c/sup\u003e and Pasternak et al.\u003csup\u003e11\u003c/sup\u003e found that fluoroquinolones exposure was associated with greater risk of AA (hazard ratio [HR] 2.24, 95% confidence interval [CI] 2.02\u0026ndash;2.49) and AA/AD (HR 1.66, 95%CI 1.12\u0026ndash;2.46), respectively. These findings are supported by several case-control studies NHIRD \u003csup\u003e10,12,18\u0026ndash;20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough previous studies have identified a potential association between AA/AD and fluoroquinolone exposure, there may be limitations regarding the long-term follow-up of patients taking fluoroquinolones. Our study embarked on a comprehensive exploration of the potential link between fluoroquinolone exposure and the development of aortic aneurysm (AA) and aortic dissection (AD) over an extended timeline. Utilizing the extensive data available from the National Health Insurance Research Database (NHIRD), our objective was to thoroughly assess the connection between fluoroquinolone use and the risk of AA/AD in a real-world context. Moreover, we aimed to identify predictive markers that could be leveraged in clinical settings to assess the risk of AA/AD in patients prescribed fluoroquinolones. To accomplish this, we employed advanced machine learning techniques to analyze the data, aiming to provide clinically relevant insights that could inform safer prescribing practices and enhance patient care.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eOur study was designed as a retrospective cohort, utilizing the extensive information available from the NHIRD. The NHIRD, since its establishment in 1998, has been an exhaustive resource, encompassing coverage for a sweeping majority of the population in Taiwan, accounting for nearly 99 percent\u003csup\u003e21\u003c/sup\u003e. This expansive database holds a wealth of data points covering different aspects of healthcare services such as hospital stays, appointments with medical professionals in outpatient settings, along with a plethora of other healthcare-related information. It encompasses detailed records of surgical procedures undergone by individuals, the variety of medications prescribed, and the specific disease diagnosis codes assigned to patients' conditions. These diagnostic codes are formulated in alignment with the globally recognized International Classification of Diseases Ninth Revision Clinical Modification, commonly abbreviated as International Classification of Diseases Ninth Revision Clinical Modification \u003csup\u003e22\u003c/sup\u003e. Before releasing the data analysis, Taiwan's Ministry of Health and Welfare (MOHW) took important measures to protect the privacy of people represented in our study. The MOHW carefully anonymized the records of all claimants, stripping the dataset of any personal identifiers effectively.\u003c/p\u003e \u003cp\u003eIn preparation for our in-depth analysis, we ensured that all personal identifiers within the beneficiary claim records had been removed, rendering the data deidentified for the protection of personal privacy. Therefore, the Institutional Review Board of Shin-Kong Wu Ho-Su Memorial Hospital, responsible for overseeing the ethical aspects of our research, authorized the waiver of informed consent. This approval validated our approach and granted us permission to proceed with our investigation without requiring direct consent from the individuals in the dataset (protocol No.: 20200720R). All methods in this study were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and baseline variables\u003c/h2\u003e \u003cp\u003eThe database we used for our study included information from 2002 to 2019. Our attention was directed towards individuals who were registered between the dates of January 1, 2004, and December 31, 2010. Subsequently, we diligently observed their progress until the conclusion of the observation period on December 31, 2019.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the scheme for selecting patients in our study from 2004 to 2010. We had a group of patients who were exposed to fluoroquinolones. This group consisted of 232,552 individuals who were prescribed either oral fluoroquinolones (specifically ciprofloxacin, levofloxacin, moxifloxacin, and gemifloxacin) or intravenous fluoroquinolones (specifically ciprofloxacin, levofloxacin, and moxifloxacin) during a visit to the outpatient department or as part of a hospital admission. To identify patients with new onset conditions, we applied the following exclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients who had received fluoroquinolones prescriptions prior to 2002\u0026ndash;2003, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) patients who had already been diagnosed with AA or AD in 2002\u0026ndash;2003, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) patients with missing information, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) patients under the age of 18. The index date of the group with fluoroquinolones exposure was one month after the initial medication of fluoroquinolones records.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo form a comparison group that hadn't been exposed to fluoroquinolones, we systematically paired individuals with counterparts from the group of those who did receive fluoroquinolones, ensuring that each match was made with a 1:1 ratio. This meticulous pairing process considered several important characteristics. Age, gender, and the specific year within the range of 2004 to 2010, also known as the index year, were the key factors considered to align the two groups as closely as possible. For the group that had not been exposed to fluoroquinolones, we assigned an index date that corresponded directly to the index date for their paired counterparts who had been exposed to the medication. This method allowed for consistent comparison across both groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of variables\u003c/h2\u003e \u003cp\u003eThree variable categories were included in the present study. The first category included those included in propensity score matching (index years, age, sex). The second category was composed of comorbidities including hypertension; hyperlipidemia; diabetes mellitus (DM); cirrhosis; chronic kidney disease; cerebrovascular disease; ischemic stroke; chronic obstructive pulmonary disease (COPD); coronary artery disease (CAD); asthma; genital tract infection (GTI), soft tissue and bone infection (STBI), and lower respiratory tract infections (LRTI); septicemia; and seizure disorder (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e online for codes of diseases). The third category was composed of medications including angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARB), beta-blockers, calcium channel blockers (CCB), insulin, nonsteroidal anti-inflammatory drugs (NSAID), diuretics, and oral and intravenous steroids (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e online for codes of medications).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy outcomes\u003c/h2\u003e \u003cp\u003eThe main result of our study focused on patients who were admitted to the hospital for AA/AD, for which the specific codes used to identify these conditions are provided in further detail in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e online. The dataset was examined, starting from the index date, and continued until the specific endpoints were reached. This close examination was carried out up to the point at which the first instance of a new AA/AD diagnosis was identified, the occurrence of death from any cause, or until the preset conclusion of the observation period, which was determined to be December 31, 2019. The analysis concluded with whichever of these three events happened the earliest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe presented all demographic results as percentages for categorical data and as means with standard deviations for continuous data. Categorical and continuous variables were compared using the chi-square and Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e tests, respectively. To eliminate discrepancies between groups, propensity score matching with a 1:1 ratio between groups with/without fluoroquinolones exposure was used in subsequent analyses. Cox proportional regression models were used to estimate HRs with 95%CIs for AA/AD risk. The first model was a crude analysis and the second model incorporated additional adjustments for all the baseline confounders. We tested the proportional hazard assumption by schoenfeld residuals. A two-tail \u003cem\u003eP\u003c/em\u003e-value less than .05 was considered statistically significant. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA).\u003c/p\u003e \u003cp\u003eMoreover, feature selection was used to find the important features of AA/AD in patients with fluoroquinolones exposure, thereby excluding unimportant variables and improving the accuracy of machine learning models \u003csup\u003e23,24\u003c/sup\u003e. Feature selection models used in the present study were logistic regression (LGR), random forest (RF), classification and regression tree, multivariate adaptive regression splines (MARS), multivariate adaptive regression splines (CART), and extreme gradient boosting (eXGBoost). Since the algorithm used for each model was different, the selection process for each variable varied as well. To ensure fairness, this research averaged all the features generated by each model and ranked the scores from high to low. The variable with the highest average score is considered the most important, and so on (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e online). The R software (version 3.4.3; R Foundation for Statistical Computing, Vienna, Austria) was used for feature selection.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics of patients\u003c/h2\u003e\n \u003cp\u003eThe distributions of age, sex, comorbidities, and medications of target population are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Briefly, the rates of comorbidities were significantly higher in the group with fluoroquinolones exposure than in those without fluoroquinolones exposure and included hypertension (13.22% vs. 7.84%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), hyperlipidemia (30.95% vs. 24.02%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), DM (33.21% vs. 19.67%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), cirrhosis (5.37% vs. 1.83%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), chronic kidney disease (16.18% vs. 4.84%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), stroke (26.02% vs. 12.48%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), ischemic stroke (8.89% vs. 2.87%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), COPD (42.87% vs. 22.03%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), CAD (11.88% vs. 3.84%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), asthma (23.92% vs. 11.83%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), GTI (60.98% vs. 31.56%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), STBI (50.26% vs. 30.56%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), LRTI (49.59% vs. 21.79%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), septicemia (10.77% vs. 3.73%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and seizure disorder (0.83% vs. 0.21%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Similarly, the use rates of specific medications were significantly higher in the group with fluoroquinolones exposure than in those without fluoroquinolones exposure and included ACEI/ARB (12.11% vs. 7.82%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), beta-blockers (14.83% vs. 11.18%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), CCB (26.18% vs. 15.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), insulin (11.13% vs. 1.08%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), NSAID (46.81% vs. 31.64%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), diuretics (2.62% vs. 2.02%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), oral steroids (65.66% vs. 45.08%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and intravenous steroids (54.29% vs. 23.62%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and clinical characteristics of study population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003efluoroquinolones exposure\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;232,552)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon- fluoroquinolones exposure\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;232,552)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127,710 (54.92 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128,032 (55.06 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104,842 (45.08 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104,520 (44.94 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.7\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.7\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2004 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,706 (8.90 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,546 (8.84 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2005 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,510 (10.54 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,343 (10.47 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2006 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,181 (12.55 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,068 (12.50 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2007 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,787 (14.53 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,786 (14.53 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,164 (15.55 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,208 (15.57 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40,278 (17.32 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40,398 (17.37 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47,926 (20.61 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48,203 (20.73 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30,750 (13.22 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,242 (7.84 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71,964 (30.95 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55,862 (24.02 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77,219 (33.21 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45,748 (19.67 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatobiliary disorders (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,482 (5.37 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,263 (1.83 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,627 (16.18 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,246 (4.84 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60,500 (26.02 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,013 (12.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIschemic stroke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,668 (8.89 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,673 (2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOPD (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99,688 (42.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51,241 (22.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary artery disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27,626 (11.88 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,935 (3.84 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsthma (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55,627 (23.92 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27,521 (11.83 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141,809 (60.98 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73,392 (31.56 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTBI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116,885 (50.26 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71,071 (30.56 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLRTI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115,318 (49.59 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50,667 (21.79 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSepticemia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,041 (10.77 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,673 (3.73 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeizure disorder (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,928 (0.83 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e487 (0.21 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEI/ARB (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,161 (12.11 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,177 (7.82 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta-blockers (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34,484 (14.83 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,998 (11.18 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium channel blocker (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60,873 (26.18 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,519 (15.7 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,887 (11.13 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,508 (1.08 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSAIDs (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108,860 (46.81 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73,576 (31.64 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiuretics (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,094 (2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,698 (2.02 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOral steroids (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152,699 (65.66 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104,839 (45.08 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntravenous steroids (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126,251 (54.29 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54,918 (23.62 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e COPD, chronic obstructive pulmonary disease; GTI, genital tract infection; LRTI, lower respiratory tract infection; STBI, soft tissue and bone infection; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; NSAIDs, nonsteroidal anti-inflammatory drug.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eRisk factors for the development of AA/AD\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eshows the determinants of AA/AD in the study population. In multivariable analysis, in addition to fluoroquinolones (HR 1.61, 95%CI 1.45\u0026ndash;1.78), old age (HR 2.21, 95%CI 1.55\u0026ndash;3.14 for 40\u0026ndash;55 years vs. \u0026lt;40 years; HR 9.39, 95%CI 6.83\u0026ndash;12.92 for \u0026ge;\u0026thinsp;55 years vs. \u0026lt;40 years), male sex (HR 2.47, 95%CI 2.23\u0026ndash;2.74), hypertension (HR 1.14, 95%CI 1.02\u0026ndash;1.28), DM (HR 0.75; 95%CI 0.68\u0026ndash;0.83), cirrhosis (HR 0.75, 95%CI 0.57\u0026ndash;0.97), cerebrovascular disease (HR 1.30, 95%CI 1.16\u0026ndash;1.45), CAD (HR 1.50, 95 CI 1.16\u0026ndash;1.93), asthma (HR 0.88, 95%CI 0.79\u0026ndash;0.97), septicemia (HR 1.36, 95%CI 1.20\u0026ndash;1.54), ACEI/ARB (HR 1.40, 95%CI 1.24\u0026ndash;1.58), beta-blockers (HR 1.36, 95%CI 1.22\u0026ndash;1.51), CCB (HR 1.59, 95%CI 1.44\u0026ndash;1.75), insulin (HR 0.56, 95%CI 0.45\u0026ndash;0.70), NSAIDs (HR 0.90, 95%CI 0.82\u0026ndash;0.98), oral steroids (HR 1.75, 95%CI 1.56\u0026ndash;1.96), and intravenous steroids (HR 4.29, 95%CI 3.82\u0026ndash;4.81) were independently associated with the development of AA/AD.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable adjusting Cox regression analysis to determine risk covariates for aortic dissection and aneurysm.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAortic dissection and aneurysm\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaHR (95%CI) *\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFQs exposure vs. Non-FQs exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61 (1.45\u0026ndash;1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAge group, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;40 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21 (1.55\u0026ndash;3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.39 (6.83\u0026ndash;12.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale vs. female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47 (2.23\u0026ndash;2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (1.02\u0026ndash;1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97 (0.89\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHyperparathyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84 (0.37\u0026ndash;1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.68\u0026ndash;0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHepatobiliary disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.57\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eKidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.73\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30 (1.16\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIschemic stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.95\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (0.96\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (1.16\u0026ndash;1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.79\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGenital tract infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.93\u0026ndash;1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSoft tissue and bone infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 (0.82\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLower respiratory tract infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97 (0.88\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSepticemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (1.20\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSeizure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50 (0.25\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eACEI/ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (1.24\u0026ndash;1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBeta-blockers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (1.22\u0026ndash;1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCalcium channel blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59 (1.44\u0026ndash;1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56 (0.45\u0026ndash;0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNSAIDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 (0.82\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 (0.65\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOral steroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75 (1.56\u0026ndash;1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIntravenous steroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.29 (3.82\u0026ndash;4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e* The full multivariable adjusting model included all baseline comorbidities and medications.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e: FQs, fluoroquinolones; CI, confidence interval; aHR, adjusted hazard ratio; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; NSAIDs, non-steroidal anti-inflammatory drug.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation of fluoroquinolones exposure with AA/AD\u003c/h2\u003e\n \u003cp\u003eOver a longest 16-year follow-up period, the Kaplan-Meier plot in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reveals a significant difference in the occurrence of AA/AD events between groups exposed to fluoroquinolones and those not exposed (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additionally, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eillustrates that we compared 1,389 patients who recently developed AA or AD and were exposed to fluoroquinolones to 675 patients who also recently developed AA or AD but were not exposed to fluoroquinolones. The rate of AA/AD was higher in the group exposed to fluoroquinolones compared to those not exposed (80 vs. 30 per 100,000 person-years).\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of the risk for aortic aneurysm and aortic dissection between patients with and without fluroquinolone exposure\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eClinical outcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFQs exposure\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;232,552)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon- FQs exposure\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;232,552)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFQs exposure vs. Non- FQs exposure\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude HR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eaHR*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA/AD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.39 (2.18\u0026ndash;2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61 (1.45\u0026ndash;1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e* The full multivariable adjusting model included all baseline comorbidities and medications.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation:\u0026nbsp;\u003c/strong\u003eAA, aortic aneurysm; AD, aortic dissection; aHR, adjusted hazard ratio; CI, confidence interval; FQ, fluroquinolone; HR, hazard ratio; IR, incident rate (per 100,000 person-years)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe initial analysis showed a clear link between taking fluoroquinolones and a higher chance of developing AA/AD (HR 2.39, with a 95% CI of 2.18\u0026ndash;2.62; the \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Even after considering all the factors listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the use of fluoroquinolones was still significantly linked to an increased risk of getting AA/AD for the first time (the adjusted HR is 1.62, with a 95% CI of 1.47\u0026ndash;1.78; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup analysis\u003c/h2\u003e\n \u003cp\u003eWe further performed subgroup analysis to determine the effect of the baseline variables on AA/AD risk (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Almost all patients had increased HRs of entering AA/AD indicators in the favor of fluoroquinolones non-exposure, the risk for AA/AD was higher in patients younger than 40 years (HR 3.94, 95%CI 1.82\u0026ndash;8.55). In addition, the risk for AA/AD was not different between the patients with and without fluoroquinolones exposure among those using intravenous steroids.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCritical factors to develop AA/AD in patients exposed to fluoroquinolones\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows how variables rank according to their average importance scores, as determined by LGR, RF, MARS, CART, and eXGBoost methods. The top ten most significant variables, listed from the most to the least important, are intravenous steroid, insulin, CCB, DM, oral steroid, beta-blockers, ACEI/ARB, hyperlipidemia, COPD, and patient\u0026apos;s age (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e online).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide cohort study examining the impact of fluoroquinolones exposure on AA/AD incidence, our analyses revealed a 1.6-fold increased long-term risk of AA/AD among patients exposed to fluoroquinolones, with a significant association noted across nearly all subgroups that were analyzed. Using machine learning methods, we identified crucial factors contributing to AA/AD development in patients exposed to fluoroquinolones. The findings presented here are intended to assist healthcare professionals in creating systematic methods for the continuous observation and proactive prevention of AA/AD among individuals who have been treated with fluoroquinolone antibiotics to reduce the risk of potential health complications related to these antibiotics.\u003c/p\u003e \u003cp\u003eThe mechanism underlying fluoroquinolones-induced AA/AD is not fully understood, although two hypotheses have been proposed. The first hypothesis posits that fluoroquinolones interfere with the integrity of the extracellular matrix, resulting in homeostatic dysregulation and impaired biomechanical strength in aorta, and ultimately triggering progressive aortic weakening, dissection, and rupture by upregulating the activity of matrix metalloproteinases (MMPs) and reducing the levels of tissue inhibitors of MMPs\u003csup\u003e25,26\u003c/sup\u003e. Increased MMP expression has been reported in smooth muscle cells in patients with abdominal AA\u003csup\u003e27\u003c/sup\u003e and in cornea and tendons in animals exposed to fluoroquinolones\u003csup\u003e14,28\u003c/sup\u003e. The second hypothesis proposes that fluoroquinolones, which are DNA topoisomerase inhibitors, promote mitochondrial dysfunction, suppress cell proliferation, and induce apoptosis\u003csup\u003e29,30\u003c/sup\u003e, ultimately leading to aortic damage.\u003c/p\u003e \u003cp\u003eIn addition to fluoroquinolones, risk factors of AA/AD include older age, male sex, lifestyle habits such as cigarette smoking and stimulant abuse, and clinical conditions such as COPD, prolonged hypertension, obesity, atherosclerosis, chronic kidney disease, trauma, vasculitis, bacterial infection, and congenital connective tissue disorders \u003csup\u003e31\u0026ndash;33\u003c/sup\u003e. Indeed, we also found that these previously reported factors were associated with AA/AD risk in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Further, we found that certain antihypertensive medications (ACEI/ARB, CCB, and beta-blockers) were associated with increased AA/AD risk. This finding contradicts previous studies linking the renin-angiotensin system to AA and suggesting that antihypertensive medications are beneficial for patient outcomes after the development of AA/AD\u003csup\u003e34,35\u003c/sup\u003e. One possible explanation for this discrepancy is that hypertension is a known risk factor for AA/AD\u003csup\u003e36\u003c/sup\u003e and that individuals with hypertension are often prescribed these antihypertensive medications. Therefore, in the present study, the association of antihypertensive medications with increased AA/AD risk might reflect the presence of high blood pressure, a known AA/AD risk factor, in these patients.\u003c/p\u003e \u003cp\u003eBased on our machine learning analysis, the top ten important factors for the development of AA/AD in patients with fluoroquinolones exposure were age, comorbidities such as DM, hyperlipidemia, and COPD, and medications including intravenous steroids, insulin, CCB, beta-blockers, and ACEI/ARB. Of these, intravenous steroid use was the top-scoring predictor of AA/AD. Of note, antihypertensive medication use might reflect preexisting high blood pressure. As indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which outlines the risk determinants for AA/AD, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which ranks the important risk factors, most of the top ten significant factors for AA/AD are also related to an increased risk of AA/AD. The only exceptions are diabetes mellitus (DM) and insulin use, which may play a role in reducing the risk of AA/AD in patients exposed to fluoroquinolones. Overall, the results mentioned above offer important insights for tracking patients exposed to fluoroquinolones.\u003c/p\u003e \u003cp\u003eGlucocorticoids are often used in combination with fluoroquinolones for inpatients. However, a case series reported that treatment with anabolic steroids increased the risk of AD in athletes, particularly in association with exercise \u003csup\u003e37\u003c/sup\u003e. Furthermore, Sendzik et al. reported that the combined use of steroids and fluoroquinolones increased the levels of MMPs and activated caspase 3, indicating apoptosis, in tenocyte cultures\u003csup\u003e38\u003c/sup\u003e. These results are consistent with the present study finding that steroid use, either intravenous or oral, might be associated with the development of AA/AD.\u003c/p\u003e \u003cp\u003eDM is a well-established risk factor for coronary and cerebrovascular diseases. However, the DM prevalence is surprisingly lower in individuals with abdominal AA than in those without abdominal AA (6\u0026ndash;14% vs. 17\u0026ndash;36%)\u003csup\u003e39\u003c/sup\u003e. In fact, a 3-year follow-up study found that DM was independently associated with reduced abdominal AA growth\u003csup\u003e40\u003c/sup\u003e. Similarly, Prakash et al. reported an inverse association between DM and the rate of hospitalization for thoracic AD\u003csup\u003e41\u003c/sup\u003e. In a meta-analysis including 14 studies and 15 794 patients, Li et al. found that the DM prevalence was lower in patients with AD than in those without AD (odds ratio 0.51, 95%CI 0.33\u0026ndash;0.81)\u003csup\u003e42\u003c/sup\u003e. However, the mechanism underlying the beneficial effects of hyperglycemia in thoracic AD is not fully understood. In the present study, insulin had a beneficial effect and prevented the development of AA/AD. Insulin use may play an important role in the negative association observed between DM and the development of AA/AD.\u003c/p\u003e \u003cp\u003eRecent studies have increasingly shown the role of inflammation and macrophage infiltration in the development of AD\u003csup\u003e43,44\u003c/sup\u003e. In a murine model, Tomida et al. found that the use of indomethacin, an NSAID, prevented death due to abdominal AD and reduced the incidence of AD by up to 40%\u003csup\u003e45\u003c/sup\u003e. This effect might be attributed to the inhibition of monocyte transendothelial migration and blockade of the accumulation of monocytes/macrophages in the aortic wall. This is compatible with our findings, indicating the potential use of NSAIDs to prevent the development of AD.\u003c/p\u003e \u003cp\u003eThe present study boasts several key strengths. Firstly, utilization of a nationwide database supports the generalizability of the study results. Secondly, the sample size and follow-up duration ensured a robust collection of AA/AD events. Thirdly, we employed machine learning methods were used to pinpoint important factors for the development of AA/AD in individuals exposed to fluoroquinolones. Lastly, the cohort study design minimized the risk of sampling bias, a common issue in case-control studies \u003csup\u003e46\u003c/sup\u003e, that most previous research has used. Despite its strengths, the current study has a few potential weaknesses. Firstly, although the NHIRD database offered a large sample size, it did not include clinical information like imaging results, biochemical and microbiological data, blood pressure readings, and physical characteristics. Secondly, the study wasn't a randomized controlled trial, which meant that there were notable differences in the baseline characteristics of the two groups. However, to reduce these biases as much as possible, we used propensity score matching and multivariable adjustment. Finally, we were unable to confirm whether participants used fluoroquinolones before 2002 or after 2010 because of the limitations in our data availability and study design. Assuming the missing data was random in both groups, we could likely overlook the bias.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe long-term risk of AA/AD was 61% higher in patients with fluoroquinolones exposure than in those without fluoroquinolones exposure. Ten notable factors contributing to this correlation have been identified. We suggest that a sophisticated artificial intelligence system could be developed to predict the risk of developing AA/AD in patients treated with fluoroquinolone antibiotics based on our findings. Such powerful tool would enhance patient care by aiding healthcare professionals in making informed decisions regarding fluoroquinolone use.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHIRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enational health insurance research database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFQs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFluoroquinolone.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the experiments: W-HW, F-YW, and C-Mingchih; performed the experiments: H-YC and T-MH; analyzed the data: H-YC, C-Mingchih, J-TN, and W-HW; contributed reagents/materials/analysis tools: all authors; wrote the manuscript: T-MH and W-HW; approved the manuscript: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Artificial Intelligence Center of Shin Kong Wu Ho-Su Memorial Hospital sponsored this study (2021SKHADE034).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are not available to the public because all claim records must be anonymized before they are released for analysis. The dataset is held by the Taiwan Ministry of Health and Welfare. (MOHW). Any researcher interested in accessing this dataset can apply for access. Please visit the website of the National Health Informatics Project of the MOHW (https://dep.mohw.gov.tw/DOS/mp-113.html.). If anyone would like to request the data from this study and is having difficulty accessing it, please contact the corresponding author, T-MH, for help.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMajalekar, P. P. \u0026amp; Shirote, P. J. Fluoroquinolones: Blessings Or Curses. \u003cem\u003eCurr Drug Targets\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 1354-1370, doi:10.2174/1389450121666200621193355 (2020).\u003c/li\u003e\n\u003cli\u003evan der Linden, P. D., van de Lei, J., Nab, H. W., Knol, A. \u0026amp; Stricker, B. H. 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A., Jewell, N. P. \u0026amp; Greenland, S. Case-control matching: effects, misconceptions, and recommendations. \u003cem\u003eEur J Epidemiol\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 5-14, doi:10.1007/s10654-017-0325-0 (2018).\u003c/li\u003e\n\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":"fluoroquinolones, aortic aneurysm, aortic dissection, antibiotics, machine learning, feature selection","lastPublishedDoi":"10.21203/rs.3.rs-3990017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3990017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe connection between fluoroquinolones and severe heart conditions, such as aortic aneurysm (AA) and aortic dissection (AD), has been acknowledged, but the full extent of long-term risks remains uncertain. Addressing this knowledge deficit, a retrospective cohort study was conducted in Taiwan, utilizing data from the National Health Insurance Research Database spanning from 2004 to 2010, with follow-up lasting until 2019. The study included 232,552 people who took fluoroquinolones and the same number of people who didn't, matched for age, sex, and index year. The Cox regression model was enlisted to calculate the hazard ratio (HR) for AA/AD onset. Additionally, five machine learning algorithms assisted in pinpointing critical determinants for AA/AD among those with fluoroquinolones. Intriguingly, within the longest follow-up duration of 16 years, exposed patients presented with a markedly higher incidence of AA/AD. After adjusting for multiple factors, exposure to fluoroquinolones was linked to a higher risk of AA/AD (HR 1.62). Machine learning identified ten factors that significantly affected AA/AD risk in those exposed. These results show a 62% increase in long-term AA/AD risk after fluoroquinolone use, highlighting the need for healthcare professionals to carefully consider prescribing these antibiotics due to the risks and factors involved.\u003c/p\u003e","manuscriptTitle":"Investigating Long-Term Risk of Aortic Aneurysm and Dissection from Fluoroquinolones and the Key Contributing Factors Using Machine Learning Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 16:35:27","doi":"10.21203/rs.3.rs-3990017/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":"299be36a-6ff2-4a38-a627-5e3c1f4fc1f0","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29364847,"name":"Health sciences/Health care"},{"id":29364848,"name":"Health sciences/Risk factors"},{"id":29364849,"name":"Biological sciences/Drug discovery/Drug safety"}],"tags":[],"updatedAt":"2024-07-16T06:26:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 16:35:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3990017","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3990017","identity":"rs-3990017","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}