Fever lasting 48 hours as a predictive factor of ESBL-producing E. coli in non-critically ill patients with urinary tract infection

Fever lasting 48 hours as a predictive factor of ESBL-producing E. coli in non-critically ill patients with urinary tract infection | 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 Research Article Fever lasting 48 hours as a predictive factor of ESBL-producing E. coli in non-critically ill patients with urinary tract infection Sungbin Yoon, Hae-rim Kim, So Won Kim, Hoon Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3895719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Urinary tract infection (UTI) is the most prevalent urological condition worldwide; however, its management is increasingly challenging due to the increasing rates of antibiotic resistance. Choosing appropriate antibiotics for patients who have fever before receiving a culture result is challenging. We investigated the utility of fever at 48 h as a predictive factor for extended spectrum beta-lactamase -producing E. coli (EPEC). Method The study enrolled patients 394 patients hospitalized at Gangneung Asan Hospital for UTI from May 2017 to April 2021. Fever at 48 h of hospitalization was the analysis point, as this is when the response to antibiotic therapy manifest, although the results of antibiogram are not available. Multivariate analysis was performed to assess the correlation between EPEC and fever at 48 h. Result Overall, 36.3% of patients had EPEC and 27.9% had fever at 48 h. In multivariate analysis, a significant association was found between EPEC and fever (odds ratio 1.17, 95% confidence interval 1.05–1.30, P = 0.004) Conclusion Fever at 48 h is associated with EPEC, and could be considered a predictive factor for EPEC infection in patients with UTI. Antibiotic escalation may be considered in patients with fever at 48 h. Prolonged fever Predictive factor Extended spectrum b-lactamase inhibitor Escherichia coli Urinary tract infection Acute pyelonephritis Figures Figure 1 Figure 2 Figure 3 Background Urinary tract infection (UTI) ranks among the most prevalent conditions encountered in urological practice. Clinical protocols generally indicate the initiation of antibiotic therapy pre-emptively in patients with UTI, without awaiting urine culture and sensitivity results, with subsequent adjustments to the antibiotic regimen made as necessary( 1 ). However, the current landscape of escalating antibiotic resistance has led to a noticeable delay in the therapeutic efficacy of empirical antibiotic regimens for UTI( 2 ). The 2021 antimicrobial resistance report published by the World Health Organization highlights this pressing issue, showing that Escherichia coli , a primary UTI pathogen, exhibits a resistance rate to ciprofloxacin of 43.1% (interquartile range [IQR] 22.5–58.6) and a 33–46% resistance rate to third generation cephalosporins. Similarly, Klebsiella pneumoniae shows a ciprofloxacin resistance rate of 36.4% (IQR 28.5–52.3) and a 44–51% resistance to third-generation cephalosporins( 3 ). These results highlight the urgency for the medical community to prioritize the prediction of antibiotic-resistant bacterial strains and administration of targeted antibiotic treatments in advance of the availability of culture and sensitivity results. In clinical settings, empirical antibiotic therapy for UTI is initiated rapidly following admission, which is typically followed by a waiting period of 3–5 d before the results of bacterial culture and antimicrobial susceptibility testing can be confirmed. In response to this delay, research efforts to develop predictive models for antibiotic-resistant bacterial infections in UTI have intensified. Emerging evidence from various studies has indicated that certain clinical factors are strongly correlated with the risk of antibiotic-resistant infections. These factors include advanced age, male sex, history of UTIs within the past year, antibiotic treatment within the previous 3 months, and the presence of diabetes mellitus( 4 – 7 ) are particularly relevant prior to the initiation of treatment. Currently, no predictive indicators of antibiotic resistance have been identified that can be used following the commencement of empirical antibiotic therapy and before the availability of drug susceptibility test results. However, fever is recognized as a critical clinical marker in the context of antibiotic stewardship, particularly for its immediacy in signaling the response to treatment. The manifestation of fever in clinical infections can serve as an acute indicator of the efficacy of the administered treatment, particularly through its pattern of resolution. Typically in UTI, an appropriate treatment regimen results in the presence of fever for approximately 2–3 d, with a noticeable decline in fever usually initiating by the fourth day( 8 ). Conversely, the treatment response to antibiotics can be presumed to be suboptimal in cases of UTI caused by antibiotic-resistant bacteria. This is commonly evidenced by an extended duration and increased intensity of fever compared to infections caused by nonresistant bacteria. Consequently, the persistence of fever beyond the expected timeframe may be indicative of failure of the empirical antibiotic treatment. Thus, recommendation to alter the antibiotic regimen may be needed. This approach underscores the need for vigilant monitoring of fever as a parameter for adjusting treatment protocols according to the management of UTI( 9 ). We aimed to determine the potential of prolonged fever as a predictive tool for the presence of resistant bacterial strains, which could significantly influence the decision-making process in the empirical treatment of UTIs, thereby promoting the judicious use of antibiotics and optimizing patient outcomes. Methods Study population and design This retrospective analysis enrolled a cohort of 862 adult (> 18 years) patients admitted and treated for UTI at Gangneung Asan Hospital from May 2017 to April 2021. The Institutional Review Board (IRB) of the Gangneung Asan hospital approved this research plan, which was constructed in compliance with Declaration of Helsinki (IRB number: GNAH 2023-11-015), and further waived the requirement for informed consent because of the deidentified data collection and retrospective nature of the study. All participants had confirmed infections with E. coli in either the urine, serum, or both. The selection of patients for inclusion was guided by specific exclusion criteria, which were as follows: lack of available clinical data (missing fever record, absence of abdominal computed tomography (CT) scan, and lack of laboratory data at admission), presence of functional or anatomical abnormalities in the urinary tract, history of UTI within the previous 6 months, concurrent diagnosis requiring treatment for another disease, requirement of intensive care unit admission, and the initiation of meropenem or ertapenem therapy prior to receiving drug susceptibility test results. Following the application of these criteria, a total of 394 adult patients were included in the final analysis. The patient cohort was stratified into two groups based on the results of the drug susceptibility tests: the first comprised patients with infections caused by extended-spectrum beta-lactamase (ESBL)-producing E. coli (EPEC), and the other comprised patients with infections caused by ESBL-negative E. coli . We meticulously collected and recorded data of body temperature (in °C), from the onset of hospitalization (day 0) through to day 5 of admission. Specific time points for temperature collection were at onset (hour 0) and subsequently at 8-h intervals for the first 72 h, followed by additional recording on day 4 and day 5. In this study, fever at 48 h after admission (defined as body temperature above 37.7°C) was chosen as the point of analysis. Data collection Data for this investigation was extracted from the electronic medical records at Gangneung Asan Hospital. An array of demographic and clinical parameters was assembled, encompassing gender, age, height, body weight, length of hospital stays, and findings from abdominal CT scans. Additional clinical data collected included microbiological culture results, outcomes of drug susceptibility tests, the regimen of empirical antibiotic therapy, the number of days the patient was ill before hospital admission, and the presence of flank pain. Patient medical histories were thoroughly reviewed, noting the presence of conditions such as hypertension, diabetes, chronic kidney disease, liver cirrhosis, heart failure, and any form of malignancy. Past instances of UTIs were also recorded. Fever was measured using an infrared ear thermometer. Fever progression was tracked, alongside a suite of pertinent laboratory markers, including white blood cell (WBC) and platelet counts and C-reactive protein (CRP) and serum albumin levels. Statistical analysis Continuous variables are presented as the mean and standard deviation or median (interquartile range), while categorical variables are presented as frequency (percentage). Continuous variables were analyzed using a student's t-test or Mann–Whitney test, depending on normality, and categorical variables were analyzed using a chi-square test or Fisher’s exact test and ANOVA for group comparisons. Univariable with logistic regression was performed to assess the correlation between EPEC and fever at 48 h. To provide complementary analyses, we constructed a multivariate adjusted logistic regression model adjusted for patients' baseline characteristics (as selected by the univariate model) and clinically important variables. Two-sided P-values of < 0.05 were considered significant. All statistical analyses and visualizations were conducted using R version 4.3.1 (The R Foundation, www.R-project.org ) and IBM SPSS Statistics for Windows, version 27.0 (IBM Corp., Armonk, NY). Results Figure 1 presents a flow chart of the study design. Of the 862 patients initially considered for the study, 468 were excluded based on the predetermined exclusion criteria. The reasons for exclusion were as follows: absence of essential clinical data (81 patients), presence of functional or structural anomalies within the urinary tract (68 patients), a history of UTI within the past 6 months (49 patients), a diagnosis requiring treatment for other diseases (82 patients), necessity for admission to the intensive care unit (98 patients), and the initiation of meropenem therapy before the availability of drug susceptibility test results (90 patients) After applying these criteria, 394 patients were deemed eligible for inclusion in the study, and were subsequently categorized into two groups based on the results of their drug susceptibility tests: 143 patients were identified with infections caused by ESBL-producing E. coli (ESBL group), while 251 patients had infections caused by ESBL-nonproducing E. coli (non-ESBL group). Table 1 shows the baseline characteristics of the study population. The majority of the patients were females (82.99% of the total), with females accounting for 76.22 and 86.85% of the ESBL and non-ESBL groups, respectively. The mean age of the entire group was 70.90 ± 14.10 years. Regarding body mass index, the overall mean was 24.31 ± 4.41, with the ESBL group averaging 23.99 ± 4.60 and the non-ESBL group 24.50 ± 4.30. Diabetes prevalence was 31.98% across all patients, with a higher incidence in the ESBL group (37.76%) than that in the non-ESBL group (28.69%), although this difference did not reach statistical significance (P = 0.081). Chronic kidney disease (CKD) of any stage was present in 6.35, 6.99, and 5.98% of all patients and ESBL and non-ESBL groups, respectively (P = 0.855). Hospital stay duration varied significantly between groups; the ESBL group had a mean hospitalization of 9.95 ± 5.18 d, compared to 6.70 ± 4.07 d in the non-ESBL group (P < 0.001). The initiation of antibiotic treatment occurred sooner in the ESBL group (2.44 ± 1.22 d) than in the non-ESBL group (2.74 ± 1.32 d). Over 95% of patients received either cephalosporin or fluoroquinolone as their initial antibiotic therapy. No notable disparities were observed in CT findings across the groups. However, fever patterns differed; at 48 h postadmission, the ESBL group had a higher average body temperature (37.55 ± 0.79℃) compared to the non-ESBL group (37.34 ± 0.63℃, P = 0.009). Furthermore, a body temperature exceeding 37.7℃ was more common in the ESBL group (36.36%) than in the non-ESBL group (23.20%). Figure 2 provides a graphical depiction of the variation in fever over time between the two patient groups. Overall, we observed that the ESBL group exhibited a marginally higher fever than the non-ESBL group, which was particularly noticeable from 24 h postadmission. Our study showed pivotal plot for antibiotic start time and hospital day between two groups in Figure 3. The findings from both univariate and multivariate logistic regression analyses exploring the relationship between prolonged fever and EPEC are presented in Table 2. The univariate analysis indicated a positive association between prolonged fever at 48 h and the presence of EPEC (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.05–1.29, P = 0.005). This association remained consistent in both multivariate models: model 1 (OR 1.17, 95% CI 1.05–1.30, P = 0.005) and model 2 (OR 1.17, 95% CI 1.05–1.30, P = 0.004). Female gender exhibited a negative association with EPEC in the univariate analysis (OR 0.84, 95% CI 0.74–0.95, P = 0.007), a finding which was confirmed in both multivariate models: model 1 (OR 0.83, 95% CI 0.73–0.94, P = 0.003) and model 2 (OR 0.83, 95% CI 0.73–0.94, P = 0.004). Early initiation of antibiotic therapy showed an association with EPEC across both univariate and multivariate analyses (univariate OR 0.96, 95% CI 0.92–1.00, P = 0.028; multivariate model 1 OR 0.96, 95% CI 0.93–1.00, P = 0.039; model 2 OR 0.96, 95% CI 0.93–1.00, P = 0.045). Diabetes, however, did not demonstrate a significant association with EPEC in any of the analyses (univariate OR 1.10, 95% CI 0.99–1.22, P = 0.063; multivariate model 1 OR 1.09, 95% CI 0.99–1.21, P = 0.084; model 2 OR 1.10, 95% CI 0.99–1.22, P = 0.072). While CRP levels appeared to be statistically associated with EPEC, they were deemed not to be a reliable marker because of the poor OR (0.99 to 1.00 in the univariate and multivariate model 1,2, respectively). Discussion Overall, our study showed that fever at 48 h was strongly associated with the presence of ESBL-producing E. coli in newly diagnosed, non-critically ill UTI patients. Even when we adjusted for other known risk factors (advanced age, the presence of diabetes mellitus) of antibiotic-resistant infections, prolonged fever was still strongly associated with ESBL producing E. coli . The findings of our research indicated that 36.3% of the E. coli isolates were producers of ESBL. Among these EPEC, a high percentage (90.9%) demonstrated resistance to cephalosporin-class antibiotics. Additionally, a significant proportion (66.4%) were resistant to fluoroquinolones. These resistance patterns closely mirror those observed in national surveillance data. For example, antibiogram data from urinary cultures in Korea spanning the years 2018–2020 revealed considerable resistance among the isolated bacteria( 10 ). In our investigation, we observed a predominant initial prescription pattern for UTIs that favored fluoroquinolones (49.5%) and cephalosporins (48.2%), which is in agreement with current clinical protocols. The preference for fluoroquinolones in South Korea is attributed to the elevated resistance rates to co-trimoxazole. Nevertheless, considerable resistance to fluoroquinolones exists among UTI pathogens in Korea. Consequently, intravenous cephalosporins have arisen as a common therapeutic recourse for UTIs within the region( 2 ). In our study, we observed that among the non-ESBL group, 21/251 patients were initially treated with fluoroquinolone as the empirical antibiotic, despite subsequent culture drug susceptibility tests indicating resistance to fluoroquinolone. Furthermore, only one patient was initially treated with cephalosporin as the empirical antibiotic, despite subsequent culture drug susceptibility tests indicating resistance to cephalosporin. Interestingly, the fever patterns at 48 h post administration in fluoroquinolone-resistant patients did not significantly differ from fluoroquinolone-susceptible patients in the ESBL negative group (Supplementary table 1). This suggests that even in cases where the causative microorganism is resistant to the empirical antibiotic, the presence of ESBL positivity may be a more significant factor in the manifestation of high fever at the 48-h mark. Investigation of the primary outcome of our study revealed a significant association between fever at 48 h and the presence of ESBL-producing bacteria. This result indicates that both the duration and intensity of fever are linked to the existence of antibiotic-resistant organisms. Specifically, an extended period of fever could be indicative of the ineffectiveness of empirical antibiotic therapy. A prior European prospective observational study investigating pediatric patients with fever lasting 5 d or more concluded that prolonged fever is associated with a higher risk of serious illness( 11 ). Under normal circumstances, successful empirical antibiotic treatment should lead to a reduction in fever as the bacterial infection is resolved( 12 ). However, the intricacies of fever height in relation to antibiotic-resistant organisms remain somewhat ambiguous. Insights from a prior study conducted in the United States on infants with invasive bacterial infections indicated that more severe infections (such as meningitis) were associated with higher fevers compared to less severe infections (such as bacteremia)( 13 ). This pediatric study provides a hint that increased bacterial activity may correlate with higher fever. In other studies, the association between prolonged fever and severe bacterial infection was evident in adult patients presenting with high fever. However, the relationship between the height of the fever and the severity of the bacterial infection has not been as clearly defined( 14 , 15 ). Prolonged fever can be a strong indicator of a serious infection in adults, the peak temperature reached does not necessarily correlate directly with the severity of the infection. Typically, the treatment duration for urinary tract infections ranges 5–10 d, and depends on a variety of clinical factors, including negative culture results, improvements in laboratory parameters, and resolution of symptoms. Importantly, our study highlights that patients in the ESBL group experienced longer hospital stays. Despite the initiation of antibiotic therapy being swifter in the ESBL group compared to the non-ESBL group, we observed a noticeable prolongation in their hospitalization duration. Additionally, a higher proportion of patients in the ESBL group presented with elevated fever after 48 h of admission compared to the non-ESBL group. These findings suggest that delays in administering the appropriate antibiotic can lead to prolonged fever episodes. Consequently, such prolonged fever can hinder clinical recovery, thereby extending the duration of hospitalization and delaying patient discharge. This outcome emphasizes the critical importance of timely and effective antibiotic therapy in managing UTIs, particularly in the context of antibiotic resistance (Fig. 3 ). Several studies have indicated that certain factors are strongly correlated with the risk of antibiotic-resistant infections. These factors include advanced age, male gender, history of UTIs within the past year, antibiotic treatment within the previous 3 months, and the presence of diabetes mellitus( 4 – 7 ). Unfortunately, these identified factors are particularly relevant prior to the initiation of treatment and do not affect antibiotic choice. Overall, our study aimed to evaluate prolonged fever during empirical treatment as a predictive factor for escalation of antibiotics. As such, patients with a history of UTIs within the previous 6 months, or those who required of intensive care unit admission was excluded. This is because patients with sepsis or septic are recommended to take two broad-spectrum antibiotics or carbapenem as empirical antibiotics, due to the poor clinical outcomes( 16 , 17 ). In our study, female sex was negatively correlated with the ESBL group, which is in agreement with existing studies. However, we found no significant association between diabetes and the ESBL group. This finding diverges from some studies which previously identified risk factors( 4 – 7 ). This discrepancy may stem from the due to variations in the study populations. We focused on a cohort that primarily consisted of patients with non-critically ill UTIs. Additionally, our study population only included patients who had not been administered antibiotics for more than 6 months prior to the study. This contrasts with other studies, which generally included a broader range of patients, including those with more complex medical histories and recent antibiotic usage. Analysis of the laboratory data collected at the treatment onset (day 0), encompassing CRP levels, WBC count, and hemoglobin and albumin levels, revealed no significant disparities between the two groups in our study. However, due to our clinical policy to sublate habitual laboratory test in non-critically ill UTIs and the retrospective nature of the study design, we only had access to 65% of comprehensive laboratory data at the 2-d hospitalization mark. This limitation hindered our ability to assess the trajectory of laboratory recovery for the enrolled patients. Our study possesses several positive aspects. Firstly, it reinforces the notion that prolonged fever can serve as a key factor in deciding to escalate antibiotic treatment prior to culture results, particularly in patients with non-critically ill UTIs. Secondly, we used a convenient tool – the infrared ear thermometer – to aid in the formulation of appropriate antibiotic strategies. However, this study also has several notable limitations. Firstly, it was conducted as a single-center retrospective analysis, which may limit the generalizability of the findings. Therefore, larger-scale, multi-center, or prospective studies are warranted to validate these findings for broader clinical application. Secondly, there is currently no standardized protocol for measuring body temperature using an infrared ear thermometer. Without a uniform approach, the recorded body temperatures may vary depending on the attending nurse who conducted the measurement. This variability could introduce inconsistencies in the temperature data, potentially affecting the reliability and accuracy of our findings regarding fever patterns in the patient groups. Conclusion Prolonged fever is associated with EPEC identification and could be considered as a predictive factor for infection caused by EPEC in the treatment of UTI patients. Overall, our results showed that antibiotics escalation may be considered in patients with prolonged fever at 48 h post-treatment to achieve better clinical outcome. Abbreviations BMI Body mass index CI Confidence Interval CKD Chronic kidney disease CT computed tomography DM Diabetes Mellitus E. coli Escherichia coli EPEC Extended-spectrum beta-lactamase producing E. coli ESBL Extended-spectrum beta-lactamase FQ Fluoroquinolone HF Heart failure HTN Hypertension IRB:Institutional Review Board IQR interquartile range K. pneumoniae Klebsiella pneumoniae LC Liver cirrhosis P/β Penicillin/β-lactamase inhibitor OR Odds Ratio UTI Urinary tract infection WBC white blood cell Declarations Ethics approval statement: The Institutional Review Board (IRB) of the Gangneung Asan hospital approved the study protocol, which was constructed in compliance with the Declaration of Helsinki (IRB number: GNAH 2023-11-015) and waived the requirement for informed consent because of deidentified data collection and retrospective nature of the study. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contributions: SY contributed to methodology and investigation of this study, conducted formal analysis, and wrote the original draft and visualization. HK contributed to the formal analysis and visualization. SK and YH aided in the conceptualization, methodology, writing-review and editing, and supervised the study. All authors contributed to the manuscript and read and approved the final manuscript. Acknowledgements: Not applicable References Gupta K, Hooton TM, Naber KG, Wullt B, Colgan R, Miller LG, et al. 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Baseline characteristics of the study participants Total (N = 394) ESBL (N = 143) Non-ESBL (N = 251) P-value Female sex 327 (82.99%) 109 (76.22%) 218 (86.85%) 0.010 Age, years 70.90 ± 14.10 71.48 ± 14.09 70.57 ± 14.12 0.537 Body mass index, kg/m² 24.31 ± 4.41 23.99 ± 4.60 24.50 ± 4.30 0.269 Hypertension 228 (57.87%) 78 (54.55%) 150 (59.76%) 0.367 Diabetes 126 (31.98%) 54 (37.76%) 72 (28.69%) 0.081 Liver cirrhosis 4 (1.02%) 1 (0.70%) 3 (1.20%) 1.000 Heart failure 22 (5.58%) 10 (6.99%) 12 (4.78%) 0.489 Chronic kidney disease 25 (6.35%) 10 (6.99%) 15 (5.98%) 0.855 Hospitalization, days 7.88 ± 4.76 9.95 ± 5.18 6.70 ± 4.07 <.001 Antibiotic start time, hours 2.63 ± 1.29 2.44 ± 1.22 2.74 ± 1.32 0.028 Initial antibiotic 0.937 Cephalosporin 190 (48.22%) 67 (46.85%) 123 (49.00%) Fluoroquinolone 195 (49.49%) 72 (50.35%) 123 (49.00%) P/β 7 (1.78%) 3 (2.10%) 4 (1.59%) Others 2 (0.51%) 1 (0.70%) 1 (0.40%) Hemoglobin, g/dL 12.01 ± 1.65 11.95 ± 1.64 12.04 ± 1.65 0.610 WBC counts, x10 3 /uL 13.35 ± 5.22 13.11 ± 5.18 13.48 ± 5.25 0.502 C-reactive protein, mg/dL 16.48 ± 9.73 15.14 ± 8.10 17.24 ± 10.49 0.030 Serum albumin, g/dL 3.58 ± 0.44 3.60 ± 0.41 3.58 ± 0.45 0.690 CT finding 0.897 Negative 121 (30.71%) 42 (29.37%) 79 (31.47%) Unilateral 174 (44.16%) 65 (45.45%) 109 (43.43%) Bilateral 99 (25.13%) 36 (25.17%) 63 (25.10%) Fever at 48 h 37.42 ± 0.70 37.55 ± 0.79 37.34 ± 0.63 0.009 Body temp. over 37.7℃ Hospital hour: 24 159 (40.36%) 68 (47.55%) 91 (36.25%) 0.037 Hospital hour: 40 110 (27.99%) 36 (25.17%) 74 (29.60%) 0.410 Hospital hour: 48 110 (27.99%) 52 (36.36%) 58 (23.20%) 0.007 Hospital hour: 56 82 (20.92%) 38 (26.76%) 44 (17.60%) 0.044 Hospital hour: 64 75(19.18%) 38(26.76%) 37(14.86%) 0.016 Hospital hour: 72 63(16.28%) 31(21.99%) 32(13.01%) 0.071 CT: computed tomography, P/β : penicillin/β-lactamase inhibitor, WBC: white blood cell. Continuous variables are presented as the mean ± standard deviation and categorical variables are presented as number (percentage). Table 2. Logistic regression model assessing the link between prolonged fever and ESBL-producing E. coli Univariate Model 1 Model 2 OR (95% CI) p OR (95% CI) p OR (95% CI) p Fever at 48 h 1.16 (1.05–1.29) 0.005 1.17 (1.05–1.30) 0.005 1.17 (1.05–1.30) 0.004 Sex (female) 0.84 (0.74–0.95) 0.007 0.83 (0.73–0.94) 0.003 0.83 (0.73–0.94) 0.004 Age 1.00 (1.00–1.00) 0.537 1.00 (1.00–1.00) 0.494 1.00 (1.00–1.00) 0.586 BMI 0.99 (0.98–1.00) 0.269 0.99 (0.98–1.00) 0.242 DM 1.10 (0.99–1.22) 0.063 1.09 (0.99–1.21) 0.084 1.10 (0.99–1.22) 0.072 CKD 1.04 (0.86–1.26) 0.691 1.00 (0.82–1.22) 0.973 HTN 0.95 (0.86–1.05) 0.315 LC 0.89 (0.55–1.43) 0.638 HF 1.10 (0.90–1.36) 0.359 Antibiotic start time 0.96 (0.92–1.00) 0.028 0.96 (0.93–1.00) 0.039 0.96 (0.93–1.00) 0.045 Initial antibiotic Cephalosporin Ref Fluoroquinolone 1.02 (0.92–1.12) 0.736 P/β 1.08 (0.75–1.55) 0.683 Others 1.16 (0.59–2.27) 0.668 Hemoglobin 0.99 (0.96–1.02) 0.610 WBC count 1.00 (0.99–1.01) 0.502 CRP 0.99 (0.99–1.00) 0.043 0.99 (0.99–1.00) 0.042 1.00 (0.99–1.00) 0.048 Albumin 1.02 (0.92–1.14) 0.690 Model 1: sex, antibiotic start, CRP, DM, age Model 2: sex, antibiotic start, CRP, DM, age, BMI, CKD, BMI: body mass index, CKD: chronic kidney disease, CT: computed tomography, DM: diabetes mellitus, HF: heart failure, HTN: hypertension, LC: liver cirrhosis, P/β : penicillin/β-lactamase inhibitor, WBC: white blood cell Logistic regression model for prolonged fever. Adjustable items were selected by univariate model and clinically importance. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3895719","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269176929,"identity":"5dcf6b54-4d90-4309-97ce-9a04c983fcab","order_by":0,"name":"Sungbin Yoon","email":"","orcid":"","institution":"University of Ulsan College of Medicine, Gangneung Asan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sungbin","middleName":"","lastName":"Yoon","suffix":""},{"id":269176930,"identity":"4c0e07d7-98d0-4ca4-8341-3badd08cf204","order_by":1,"name":"Hae-rim Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Hae-rim","middleName":"","lastName":"Kim","suffix":""},{"id":269176931,"identity":"7d85fe2e-00ec-433e-8ab6-745c0fffc796","order_by":2,"name":"So Won Kim","email":"","orcid":"","institution":"University of Ulsan College of Medicine, Asan Medical Center","correspondingAuthor":false,"prefix":"","firstName":"So","middleName":"Won","lastName":"Kim","suffix":""},{"id":269176932,"identity":"4cd2dfc2-761b-45b0-81a7-a43c0b224c9b","order_by":3,"name":"Hoon Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACgwM8IOq/HBt7A4hrQbQWZmM+ngMgrgRhLZINEC2J8yQSQAwitPBL5B588HEHG2Ob5POrG34USDDwt3cn4NXCJpGXbDjzDA8zm3RO2c0eoMMkzpzdQEBLjpk0b5sEG1BL2g0eoBYDiVyitBjwsEmeSbv5hwQtCRJsEuzHbhNnC88bY8OZbQcM2Hhy2G7LGEjwEPYLe47hg49tB+rntx9/dvPNHxs5/vZe/FqQAI8BmCRWOQiwPyBF9SgYBaNgFIwgAAADzD7zKgynywAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ulsan College of Medicine, Gangneung Asan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hoon","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-01-25 02:29:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3895719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3895719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50329899,"identity":"811c6998-8797-4cf6-869e-33b110ff0fe6","added_by":"auto","created_at":"2024-01-29 21:31:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":285438,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flowchart. \u003c/strong\u003eOverall,\u003cstrong\u003e \u003c/strong\u003e862 patients were screened, of whom 468 patients were excluded based on the exclusions criteria. Finally, 394 patients were deemed eligible for the study. Patients were divided into two groups based on whether or not they had ESBL.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3895719/v1/9458be161d9a56e255923755.jpeg"},{"id":50329901,"identity":"83970b87-3b67-4b59-9a40-f5e3502f337e","added_by":"auto","created_at":"2024-01-29 21:31:15","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":205500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFever change over time between the two groups\u003c/strong\u003e. Graphs showing the average fever in each group, plotted over time. Fever at 24 and 48 h showed statistical differences between the ESBL positive group and ESBL negative group.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3895719/v1/cc86868e5636aaf1f22b283b.jpeg"},{"id":50329900,"identity":"ad7f00bb-99eb-4ce5-8545-0afd572bfbd6","added_by":"auto","created_at":"2024-01-29 21:31:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePivotal plots for antibiotic start time and hospital days between the two groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAntibiotics start time (a) and hospitalization day (b) of all patients between the ESBL positive negative groups.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3895719/v1/f60bb6f80360e2d8c8f65ba7.jpeg"},{"id":50464890,"identity":"244d8499-dfba-45e6-bc9e-9cbb6890b5e9","added_by":"auto","created_at":"2024-01-31 23:52:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":508681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3895719/v1/1e4701ab-d7c9-4bc0-a56b-80a5f8e97c14.pdf"},{"id":50329898,"identity":"1abaf3f5-815b-4d9d-b0fa-92d604f970ae","added_by":"auto","created_at":"2024-01-29 21:31:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16619,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-3895719/v1/b6b83f40d614be57e9bec2e9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fever lasting 48 hours as a predictive factor of ESBL-producing E. coli in non-critically ill patients with urinary tract infection","fulltext":[{"header":"Background","content":"\u003cp\u003eUrinary tract infection (UTI) ranks among the most prevalent conditions encountered in urological practice. Clinical protocols generally indicate the initiation of antibiotic therapy pre-emptively in patients with UTI, without awaiting urine culture and sensitivity results, with subsequent adjustments to the antibiotic regimen made as necessary(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, the current landscape of escalating antibiotic resistance has led to a noticeable delay in the therapeutic efficacy of empirical antibiotic regimens for UTI(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The 2021 antimicrobial resistance report published by the World Health Organization highlights this pressing issue, showing that \u003cem\u003eEscherichia coli\u003c/em\u003e, a primary UTI pathogen, exhibits a resistance rate to ciprofloxacin of 43.1% (interquartile range [IQR] 22.5\u0026ndash;58.6) and a 33\u0026ndash;46% resistance rate to third generation cephalosporins. Similarly, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e shows a ciprofloxacin resistance rate of 36.4% (IQR 28.5\u0026ndash;52.3) and a 44\u0026ndash;51% resistance to third-generation cephalosporins(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These results highlight the urgency for the medical community to prioritize the prediction of antibiotic-resistant bacterial strains and administration of targeted antibiotic treatments in advance of the availability of culture and sensitivity results.\u003c/p\u003e \u003cp\u003eIn clinical settings, empirical antibiotic therapy for UTI is initiated rapidly following admission, which is typically followed by a waiting period of 3\u0026ndash;5 d before the results of bacterial culture and antimicrobial susceptibility testing can be confirmed. In response to this delay, research efforts to develop predictive models for antibiotic-resistant bacterial infections in UTI have intensified. Emerging evidence from various studies has indicated that certain clinical factors are strongly correlated with the risk of antibiotic-resistant infections. These factors include advanced age, male sex, history of UTIs within the past year, antibiotic treatment within the previous 3 months, and the presence of diabetes mellitus(\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) are particularly relevant prior to the initiation of treatment.\u003c/p\u003e \u003cp\u003eCurrently, no predictive indicators of antibiotic resistance have been identified that can be used following the commencement of empirical antibiotic therapy and before the availability of drug susceptibility test results. However, fever is recognized as a critical clinical marker in the context of antibiotic stewardship, particularly for its immediacy in signaling the response to treatment. The manifestation of fever in clinical infections can serve as an acute indicator of the efficacy of the administered treatment, particularly through its pattern of resolution. Typically in UTI, an appropriate treatment regimen results in the presence of fever for approximately 2\u0026ndash;3 d, with a noticeable decline in fever usually initiating by the fourth day(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, the treatment response to antibiotics can be presumed to be suboptimal in cases of UTI caused by antibiotic-resistant bacteria. This is commonly evidenced by an extended duration and increased intensity of fever compared to infections caused by nonresistant bacteria. Consequently, the persistence of fever beyond the expected timeframe may be indicative of failure of the empirical antibiotic treatment. Thus, recommendation to alter the antibiotic regimen may be needed. This approach underscores the need for vigilant monitoring of fever as a parameter for adjusting treatment protocols according to the management of UTI(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe aimed to determine the potential of prolonged fever as a predictive tool for the presence of resistant bacterial strains, which could significantly influence the decision-making process in the empirical treatment of UTIs, thereby promoting the judicious use of antibiotics and optimizing patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and design\u003c/h2\u003e \u003cp\u003eThis retrospective analysis enrolled a cohort of 862 adult (\u0026gt;\u0026thinsp;18 years) patients admitted and treated for UTI at Gangneung Asan Hospital from May 2017 to April 2021. The Institutional Review Board (IRB) of the Gangneung Asan hospital approved this research plan, which was constructed in compliance with Declaration of Helsinki (IRB number: GNAH 2023-11-015), and further waived the requirement for informed consent because of the deidentified data collection and retrospective nature of the study.\u003c/p\u003e \u003cp\u003eAll participants had confirmed infections with \u003cem\u003eE. coli\u003c/em\u003e in either the urine, serum, or both. The selection of patients for inclusion was guided by specific exclusion criteria, which were as follows: lack of available clinical data (missing fever record, absence of abdominal computed tomography (CT) scan, and lack of laboratory data at admission), presence of functional or anatomical abnormalities in the urinary tract, history of UTI within the previous 6 months, concurrent diagnosis requiring treatment for another disease, requirement of intensive care unit admission, and the initiation of meropenem or ertapenem therapy prior to receiving drug susceptibility test results. Following the application of these criteria, a total of 394 adult patients were included in the final analysis.\u003c/p\u003e \u003cp\u003eThe patient cohort was stratified into two groups based on the results of the drug susceptibility tests: the first comprised patients with infections caused by extended-spectrum beta-lactamase (ESBL)-producing \u003cem\u003eE. coli\u003c/em\u003e (EPEC), and the other comprised patients with infections caused by ESBL-negative \u003cem\u003eE. coli\u003c/em\u003e. We meticulously collected and recorded data of body temperature (in \u0026deg;C), from the onset of hospitalization (day 0) through to day 5 of admission. Specific time points for temperature collection were at onset (hour 0) and subsequently at 8-h intervals for the first 72 h, followed by additional recording on day 4 and day 5. In this study, fever at 48 h after admission (defined as body temperature above 37.7\u0026deg;C) was chosen as the point of analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData for this investigation was extracted from the electronic medical records at Gangneung Asan Hospital. An array of demographic and clinical parameters was assembled, encompassing gender, age, height, body weight, length of hospital stays, and findings from abdominal CT scans. Additional clinical data collected included microbiological culture results, outcomes of drug susceptibility tests, the regimen of empirical antibiotic therapy, the number of days the patient was ill before hospital admission, and the presence of flank pain.\u003c/p\u003e \u003cp\u003ePatient medical histories were thoroughly reviewed, noting the presence of conditions such as hypertension, diabetes, chronic kidney disease, liver cirrhosis, heart failure, and any form of malignancy. Past instances of UTIs were also recorded. Fever was measured using an infrared ear thermometer. Fever progression was tracked, alongside a suite of pertinent laboratory markers, including white blood cell (WBC) and platelet counts and C-reactive protein (CRP) and serum albumin levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as the mean and standard deviation or median (interquartile range), while categorical variables are presented as frequency (percentage). Continuous variables were analyzed using a student's t-test or Mann\u0026ndash;Whitney test, depending on normality, and categorical variables were analyzed using a chi-square test or Fisher\u0026rsquo;s exact test and ANOVA for group comparisons. Univariable with logistic regression was performed to assess the correlation between EPEC and fever at 48 h. To provide complementary analyses, we constructed a multivariate adjusted logistic regression model adjusted for patients' baseline characteristics (as selected by the univariate model) and clinically important variables. Two-sided P-values of \u0026lt;\u0026thinsp;0.05 were considered significant. All statistical analyses and visualizations were conducted using R version 4.3.1 (The R Foundation, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.R-project.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and IBM SPSS Statistics for Windows, version 27.0 (IBM Corp., Armonk, NY).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFigure 1 presents a flow chart of the study design. Of the 862 patients initially considered for the study, 468 were excluded based on the predetermined exclusion criteria. The reasons for exclusion were as follows: absence of essential clinical data (81 patients), presence of functional or structural anomalies within the urinary tract (68 patients), a history of UTI within the past 6 months (49 patients), a diagnosis requiring treatment for other diseases (82 patients), necessity for admission to the intensive care unit (98 patients), and the initiation of meropenem therapy before the availability of drug susceptibility test results (90 patients) After applying these criteria, 394 patients were deemed eligible for inclusion in the study, and were subsequently categorized into two groups based on the results of their drug susceptibility tests: 143 patients were identified with infections caused by ESBL-producing \u003cem\u003eE. coli\u003c/em\u003e (ESBL group), while 251 patients had infections caused by ESBL-nonproducing \u003cem\u003eE. coli\u003c/em\u003e (non-ESBL group).\u003c/p\u003e\n\u003cp\u003eTable 1 shows the baseline characteristics of the study population. The majority of the patients were females (82.99% of the total), with females accounting for 76.22 and 86.85% of the ESBL and non-ESBL groups, respectively. The mean age of the entire group was 70.90 \u0026plusmn; 14.10 years. Regarding body mass index, the overall mean was 24.31 \u0026plusmn; 4.41, with the ESBL group averaging 23.99 \u0026plusmn; 4.60 and the non-ESBL group 24.50 \u0026plusmn; 4.30. Diabetes prevalence was 31.98% across all patients, with a higher incidence in the ESBL group (37.76%) than that in the non-ESBL group (28.69%), although this difference did not reach statistical significance (P = 0.081). Chronic kidney disease (CKD) of any stage was present in 6.35, 6.99, and 5.98% of all patients and ESBL and non-ESBL groups, respectively (P = 0.855). Hospital stay duration varied significantly between groups; the ESBL group had a mean hospitalization of 9.95 \u0026plusmn; 5.18 d, compared to 6.70 \u0026plusmn; 4.07 d in the non-ESBL group (P \u0026lt; 0.001). The initiation of antibiotic treatment occurred sooner in the ESBL group (2.44 \u0026plusmn; 1.22 d) than in the non-ESBL group (2.74 \u0026plusmn; 1.32 d). Over 95% of patients received either cephalosporin or fluoroquinolone as their initial antibiotic therapy. No notable disparities were observed in CT findings across the groups. However, fever patterns differed; at 48 h postadmission, the ESBL group had a higher average body temperature (37.55 \u0026plusmn; 0.79℃) compared to the non-ESBL group (37.34 \u0026plusmn; 0.63℃, P = 0.009). Furthermore, a body temperature exceeding 37.7℃ was more common in the ESBL group (36.36%) than in the non-ESBL group (23.20%). Figure 2 provides a graphical depiction of the variation in fever over time between the two patient groups. Overall, we observed that the ESBL group exhibited a marginally higher fever than the non-ESBL group, which was particularly noticeable from 24 h postadmission. Our study showed pivotal plot for antibiotic start time and hospital day between two groups in Figure 3.\u003c/p\u003e\n\u003cp\u003eThe findings from both univariate and multivariate logistic regression analyses exploring the relationship between prolonged fever and EPEC are presented in Table 2. The univariate analysis indicated a positive association between prolonged fever at 48 h and the presence of EPEC (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.05\u0026ndash;1.29, P = 0.005). This association remained consistent in both multivariate models: model 1 (OR 1.17, 95% CI 1.05\u0026ndash;1.30, P = 0.005) and model 2 (OR 1.17, 95% CI 1.05\u0026ndash;1.30, P = 0.004). Female gender exhibited a negative association with EPEC in the univariate analysis (OR 0.84, 95% CI 0.74\u0026ndash;0.95, P = 0.007), a finding which was confirmed in both multivariate models: model 1 (OR 0.83, 95% CI 0.73\u0026ndash;0.94, P = 0.003) and model 2 (OR 0.83, 95% CI 0.73\u0026ndash;0.94, P = 0.004). Early initiation of antibiotic therapy showed an association with EPEC across both univariate and multivariate analyses (univariate OR 0.96, 95% CI 0.92\u0026ndash;1.00, P = 0.028; multivariate model 1 OR 0.96, 95% CI 0.93\u0026ndash;1.00, P = 0.039; model 2 OR 0.96, 95% CI 0.93\u0026ndash;1.00, P = 0.045). Diabetes, however, did not demonstrate a significant association with EPEC in any of the analyses (univariate OR 1.10, 95% CI 0.99\u0026ndash;1.22, P = 0.063; multivariate model 1 OR 1.09, 95% CI 0.99\u0026ndash;1.21, P = 0.084; model 2 OR 1.10, 95% CI 0.99\u0026ndash;1.22, P = 0.072). While CRP levels appeared to be statistically associated with EPEC, they were deemed not to be a reliable marker because of the poor OR (0.99 to 1.00 in the univariate and multivariate model 1,2, respectively).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOverall, our study showed that fever at 48 h was strongly associated with the presence of ESBL-producing \u003cem\u003eE. coli\u003c/em\u003e in newly diagnosed, non-critically ill UTI patients. Even when we adjusted for other known risk factors (advanced age, the presence of diabetes mellitus) of antibiotic-resistant infections, prolonged fever was still strongly associated with ESBL producing \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe findings of our research indicated that 36.3% of the \u003cem\u003eE. coli\u003c/em\u003e isolates were producers of ESBL. Among these EPEC, a high percentage (90.9%) demonstrated resistance to cephalosporin-class antibiotics. Additionally, a significant proportion (66.4%) were resistant to fluoroquinolones. These resistance patterns closely mirror those observed in national surveillance data. For example, antibiogram data from urinary cultures in Korea spanning the years 2018\u0026ndash;2020 revealed considerable resistance among the isolated bacteria(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e In our investigation, we observed a predominant initial prescription pattern for UTIs that favored fluoroquinolones (49.5%) and cephalosporins (48.2%), which is in agreement with current clinical protocols. The preference for fluoroquinolones in South Korea is attributed to the elevated resistance rates to co-trimoxazole. Nevertheless, considerable resistance to fluoroquinolones exists among UTI pathogens in Korea. Consequently, intravenous cephalosporins have arisen as a common therapeutic recourse for UTIs within the region(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, we observed that among the non-ESBL group, 21/251 patients were initially treated with fluoroquinolone as the empirical antibiotic, despite subsequent culture drug susceptibility tests indicating resistance to fluoroquinolone. Furthermore, only one patient was initially treated with cephalosporin as the empirical antibiotic, despite subsequent culture drug susceptibility tests indicating resistance to cephalosporin. Interestingly, the fever patterns at 48 h post administration in fluoroquinolone-resistant patients did not significantly differ from fluoroquinolone-susceptible patients in the ESBL negative group (Supplementary table 1). This suggests that even in cases where the causative microorganism is resistant to the empirical antibiotic, the presence of ESBL positivity may be a more significant factor in the manifestation of high fever at the 48-h mark.\u003c/p\u003e \u003cp\u003eInvestigation of the primary outcome of our study revealed a significant association between fever at 48 h and the presence of ESBL-producing bacteria. This result indicates that both the duration and intensity of fever are linked to the existence of antibiotic-resistant organisms. Specifically, an extended period of fever could be indicative of the ineffectiveness of empirical antibiotic therapy. A prior European prospective observational study investigating pediatric patients with fever lasting 5 d or more concluded that prolonged fever is associated with a higher risk of serious illness(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Under normal circumstances, successful empirical antibiotic treatment should lead to a reduction in fever as the bacterial infection is resolved(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, the intricacies of fever height in relation to antibiotic-resistant organisms remain somewhat ambiguous. Insights from a prior study conducted in the United States on infants with invasive bacterial infections indicated that more severe infections (such as meningitis) were associated with higher fevers compared to less severe infections (such as bacteremia)(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This pediatric study provides a hint that increased bacterial activity may correlate with higher fever. In other studies, the association between prolonged fever and severe bacterial infection was evident in adult patients presenting with high fever. However, the relationship between the height of the fever and the severity of the bacterial infection has not been as clearly defined(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Prolonged fever can be a strong indicator of a serious infection in adults, the peak temperature reached does not necessarily correlate directly with the severity of the infection.\u003c/p\u003e \u003cp\u003eTypically, the treatment duration for urinary tract infections ranges 5\u0026ndash;10 d, and depends on a variety of clinical factors, including negative culture results, improvements in laboratory parameters, and resolution of symptoms. Importantly, our study highlights that patients in the ESBL group experienced longer hospital stays. Despite the initiation of antibiotic therapy being swifter in the ESBL group compared to the non-ESBL group, we observed a noticeable prolongation in their hospitalization duration. Additionally, a higher proportion of patients in the ESBL group presented with elevated fever after 48 h of admission compared to the non-ESBL group. These findings suggest that delays in administering the appropriate antibiotic can lead to prolonged fever episodes. Consequently, such prolonged fever can hinder clinical recovery, thereby extending the duration of hospitalization and delaying patient discharge. This outcome emphasizes the critical importance of timely and effective antibiotic therapy in managing UTIs, particularly in the context of antibiotic resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have indicated that certain factors are strongly correlated with the risk of antibiotic-resistant infections. These factors include advanced age, male gender, history of UTIs within the past year, antibiotic treatment within the previous 3 months, and the presence of diabetes mellitus(\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Unfortunately, these identified factors are particularly relevant prior to the initiation of treatment and do not affect antibiotic choice. Overall, our study aimed to evaluate prolonged fever during empirical treatment as a predictive factor for escalation of antibiotics. As such, patients with a history of UTIs within the previous 6 months, or those who required of intensive care unit admission was excluded. This is because patients with sepsis or septic are recommended to take two broad-spectrum antibiotics or carbapenem as empirical antibiotics, due to the poor clinical outcomes(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, female sex was negatively correlated with the ESBL group, which is in agreement with existing studies. However, we found no significant association between diabetes and the ESBL group. This finding diverges from some studies which previously identified risk factors(\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This discrepancy may stem from the due to variations in the study populations. We focused on a cohort that primarily consisted of patients with non-critically ill UTIs. Additionally, our study population only included patients who had not been administered antibiotics for more than 6 months prior to the study. This contrasts with other studies, which generally included a broader range of patients, including those with more complex medical histories and recent antibiotic usage.\u003c/p\u003e \u003cp\u003eAnalysis of the laboratory data collected at the treatment onset (day 0), encompassing CRP levels, WBC count, and hemoglobin and albumin levels, revealed no significant disparities between the two groups in our study. However, due to our clinical policy to sublate habitual laboratory test in non-critically ill UTIs and the retrospective nature of the study design, we only had access to 65% of comprehensive laboratory data at the 2-d hospitalization mark. This limitation hindered our ability to assess the trajectory of laboratory recovery for the enrolled patients.\u003c/p\u003e \u003cp\u003eOur study possesses several positive aspects. Firstly, it reinforces the notion that prolonged fever can serve as a key factor in deciding to escalate antibiotic treatment prior to culture results, particularly in patients with non-critically ill UTIs. Secondly, we used a convenient tool \u0026ndash; the infrared ear thermometer \u0026ndash; to aid in the formulation of appropriate antibiotic strategies. However, this study also has several notable limitations. Firstly, it was conducted as a single-center retrospective analysis, which may limit the generalizability of the findings. Therefore, larger-scale, multi-center, or prospective studies are warranted to validate these findings for broader clinical application. Secondly, there is currently no standardized protocol for measuring body temperature using an infrared ear thermometer. Without a uniform approach, the recorded body temperatures may vary depending on the attending nurse who conducted the measurement. This variability could introduce inconsistencies in the temperature data, potentially affecting the reliability and accuracy of our findings regarding fever patterns in the patient groups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eProlonged fever is associated with EPEC identification and could be considered as a predictive factor for infection caused by EPEC in the treatment of UTI patients. Overall, our results showed that antibiotics escalation may be considered in patients with prolonged fever at 48 h post-treatment to achieve better clinical outcome.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE. coli\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEscherichia coli\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtended-spectrum beta-lactamase producing E. coli\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESBL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtended-spectrum beta-lactamase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFluoroquinolone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHTN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHypertension IRB:Institutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eK. pneumoniae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKlebsiella pneumoniae\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiver cirrhosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eP/β\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePenicillin/β-lactamase inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrinary tract infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhite blood cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval statement:\u003c/strong\u003e The\u0026nbsp;Institutional Review Board (IRB) of the Gangneung Asan hospital approved the study protocol, which was constructed in compliance with the Declaration of Helsinki (IRB number: GNAH 2023-11-015) and waived\u0026nbsp;the requirement for\u0026nbsp;informed consent because of deidentified data collection and retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e SY contributed to methodology and investigation of this study, conducted formal analysis, and wrote the original draft and visualization. HK contributed to the formal analysis and visualization. SK and YH aided in the conceptualization, methodology, writing-review and editing, and supervised the study. All authors contributed to the manuscript and read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGupta K, Hooton TM, Naber KG, Wullt B, Colgan R, Miller LG, et al. International Clinical Practice Guidelines for the Treatment of Acute Uncomplicated Cystitis and Pyelonephritis in Women: A 2010 Update by the Infectious Diseases Society of America and the European Society for Microbiology and Infectious Diseases. Clin Infect Dis. 2011;52(5):e103\u0026ndash;e20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang C-I, Kim J, Park DW, Kim B-N, Ha U-S, Lee S-J, et al. Clinical practice guidelines for the antibiotic treatment of community-acquired urinary tract infections. Infect Chemother. 2018;50(1):67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Global antimicrobial resistance and use surveillance system (GLASS) report: 2021. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen Ayed H, Koubaa M, Hammami F, Marrakchi C, Rekik K, Ben Jemaa T et al. Performance of an Easy and Simple New Scoring Model in Predicting Multidrug-Resistant Enterobacteriaceae in Community-Acquired Urinary Tract Infections. Open Forum Infectious Diseases. 2019;6(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Tello A, Gimbernat H, Redondo C, Meil\u0026aacute;n E, Arana DM, Cacho J, et al. Prediction of infection caused by extended-spectrum beta-lactamase-producing Enterobacteriaceae: development of a clinical decision-making nomogram. Scandinavian J Urol. 2018;52(1):70\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoyal D, Dean N, Neill S, Jones P, Dascomb K. Risk Factors for Community-Acquired Extended-Spectrum Beta-Lactamase-Producing Enterobacteriaceae Infections-A Retrospective Study of Symptomatic Urinary Tract Infections. Open Forum Infect Dis. 2019;6(2):ofy357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeinstein EJ, Han JH, Lautenbach E, Nachamkin I, Garrigan C, Bilker WB et al. A Clinical Prediction Tool for Extended-Spectrum Cephalosporin Resistance in Community-Onset Enterobacterales Urinary Tract Infection. Open Forum Infectious Diseases. 2019;6(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarcel ABaRDaMDLaJSDaJSD. Fever duration in hospitalized acute pyelonephritis patients. Am J Med. 1996;101(3):277\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandel G, Bennet J. Bennett's principles and practice of infectious diseases2015. 901 p.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwak B, Hong J, Bae HG, Park YS, Lee MK, Lee K, Lee KR. Microorganisms Isolated from Urine Cultures and Their Antimicrobial Susceptibility Patterns at a Commercial Laboratory during 2018\u0026ndash;2020. Korean J healthc assoc Infect Control Prev. 2022;27(1):51\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNijman RG, Tan CD, Hagedoorn NN, Nieboer D, Herberg JA, Balode A, et al. Are children with prolonged fever at a higher risk for serious illness? A prospective observational study. Archives of Disease in Childhood; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgoina D. Fever, fever patterns and diseases called \u0026lsquo;fever\u0026rsquo; \u0026ndash; A review. J Infect Public Health. 2011;4(3):108\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichelson KA, Neuman MI, Pruitt CM, Desai S, Wang ME, DePorre AG et al. Height of fever and invasive bacterial infection. Arch Dis Child. 2020:archdischild\u0026ndash;2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBor DH, Makadon HJ, Friedland G, Dasse P, Komaroff AL, Aronson MD. Fever in hospitalized medical patients: characteristics and significance. J Gen Intern Med. 1988;3:119\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCirciumaru B, Baldock G, Cohen J. A prospective study of fever in the intensive care unit. Intensive Care Med. 1999;25:668\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonkat G, Cai T, Veeratterapillay R, Bruyere F, Bartoletti R, Pilatz A, et al. Management of Urosepsis in 2018. Eur Urol focus. 2019;5(1):5\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLertwattanachai T, Montakantikul P, Tangsujaritvijit V, Sanguanwit P, Sueajai J, Auparakkitanon S, Dilokpattanamongkol P. Clinical outcomes of empirical high-dose meropenem in critically ill patients with sepsis and septic shock: a randomized controlled trial. J Intensive Care. 2020;8(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of the study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"664\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" rowspan=\"2\" style=\"width: 31.9658%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" rowspan=\"2\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003cbr\u003e\u0026nbsp;(N = 394)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" rowspan=\"2\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003eESBL\u003cbr\u003e\u0026nbsp;(N = 143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" rowspan=\"2\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-ESBL\u003cbr\u003e\u0026nbsp;(N = 251)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" rowspan=\"2\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"41\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\" height=\"5\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e327 (82.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e109 (76.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e218 (86.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e70.90 \u0026plusmn; 14.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e71.48 \u0026plusmn; 14.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e70.57 \u0026plusmn; 14.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eBody mass index, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e24.31 \u0026plusmn; 4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e23.99 \u0026plusmn; 4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e24.50 \u0026plusmn; 4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e228 (57.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e78 (54.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e150 (59.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e126 (31.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e54 (37.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e72 (28.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eLiver cirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e4 (1.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e1 (0.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e3 (1.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e22 (5.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e10 (6.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e12 (4.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e25 (6.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e10 (6.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e15 (5.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHospitalization, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e7.88 \u0026plusmn; 4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e9.95 \u0026plusmn; 5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e6.70 \u0026plusmn; 4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" valign=\"bottom\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eAntibiotic start time, hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" valign=\"bottom\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e2.63 \u0026plusmn; 1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" valign=\"bottom\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e2.44 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" valign=\"bottom\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e2.74 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" valign=\"bottom\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eInitial antibiotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"11\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003e\u0026nbsp;Cephalosporin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e190 (48.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e67 (46.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e123 (49.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003e\u0026nbsp;Fluoroquinolone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e195 (49.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e72 (50.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e123 (49.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003e\u0026nbsp;P/\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e7 (1.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e3 (2.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e4 (1.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003e\u0026nbsp;Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e2 (0.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e1 (0.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e1 (0.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e12.01 \u0026plusmn; 1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e11.95 \u0026plusmn; 1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e12.04 \u0026plusmn; 1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eWBC counts, x10\u003csup\u003e3\u003c/sup\u003e/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e13.35 \u0026plusmn; 5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e13.11 \u0026plusmn; 5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e13.48 \u0026plusmn; 5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eC-reactive protein, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e16.48 \u0026plusmn; 9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e15.14 \u0026plusmn; 8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e17.24 \u0026plusmn; 10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eSerum albumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e3.58 \u0026plusmn; 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e3.60 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e3.58 \u0026plusmn; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eCT finding \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"13\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003e\u0026nbsp;Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e121 (30.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e42 (29.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e79 (31.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003e\u0026nbsp;Unilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e174 (44.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e65 (45.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e109 (43.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003e\u0026nbsp;Bilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e99 (25.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e36 (25.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e63 (25.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eFever at 48 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e37.42 \u0026plusmn; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e37.55 \u0026plusmn; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e37.34 \u0026plusmn; 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eBody temp. over 37.7℃\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"15\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHospital hour: 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e159 (40.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e68 (47.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e91 (36.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHospital hour: 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e110 (27.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e36 (25.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e74 (29.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHospital hour: 48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e110 (27.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e52 (36.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e58 (23.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHospital hour: 56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e82 (20.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e38 (26.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e44 (17.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHospital hour: 64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e75(19.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e38(26.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e37(14.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.506787330316744%\" valign=\"bottom\" style=\"width: 31.9658%;\"\u003e\n \u003cp\u003eHospital hour: 72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e63(16.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" style=\"width: 17.6068%;\"\u003e\n \u003cp\u003e31(21.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.043740573152338%\" style=\"width: 19.1453%;\"\u003e\n \u003cp\u003e32(13.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.95475113122172%\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\" style=\"width: 1.0256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCT: computed tomography, P/\u0026beta;\u0026nbsp;:\u0026nbsp;penicillin/\u0026beta;-lactamase inhibitor,\u0026nbsp;WBC: white blood cell.\u003cbr\u003e\u0026nbsp;Continuous variables are presented as the mean \u0026plusmn; standard deviation and categorical variables are presented as number (percentage).\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Logistic regression model assessing the link between prolonged fever and ESBL-producing E. coli\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.554848966613672%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"27.344992050874403%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.55007949125596%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.55007949125596%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eFever at 48 h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.16 (1.05\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e1.17 (1.05\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e1.17 (1.05\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eSex (female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e0.84 (0.74\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e0.83 (0.73\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e0.83 (0.73\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e0.99 (0.98\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e0.99 (0.98\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.10 (0.99\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e1.09 (0.99\u0026ndash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e1.10 (0.99\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.04 (0.86\u0026ndash;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e1.00 (0.82\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e0.95 (0.86\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e0.89 (0.55\u0026ndash;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.10 (0.90\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eAntibiotic start time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e0.96 (0.92\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e0.96 (0.93\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e0.96 (0.93\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eInitial antibiotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e\u0026nbsp;Cephalosporin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e\u0026nbsp;Fluoroquinolone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.02 (0.92\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e\u0026nbsp;P/\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.08 (0.75\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e\u0026nbsp;Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.16 (0.59\u0026ndash;2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e0.99 (0.96\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eWBC count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e0.99 (0.99\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e0.99 (0.99\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e1.00 (0.99\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.617224880382775%\"\u003e\n \u003cp\u003e1.02 (0.92\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.660287081339714%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.814992025518341%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eModel 1: sex, antibiotic start, CRP, DM, age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eModel 2: sex, antibiotic start, CRP, DM, age, BMI, CKD,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI: body mass index, CKD: chronic kidney disease, CT: computed tomography, DM: diabetes mellitus, HF: heart failure, HTN: hypertension, LC: liver cirrhosis, P/\u0026beta;\u0026nbsp;:\u0026nbsp;penicillin/\u0026beta;-lactamase inhibitor,\u0026nbsp;WBC: white blood cell\u003c/p\u003e\n\u003cp\u003eLogistic regression model for prolonged fever. Adjustable items were selected by univariate model and clinically importance.\u003c/p\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":"Prolonged fever, Predictive factor, Extended spectrum b-lactamase inhibitor, Escherichia coli, Urinary tract infection, Acute pyelonephritis","lastPublishedDoi":"10.21203/rs.3.rs-3895719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3895719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUrinary tract infection (UTI) is the most prevalent urological condition worldwide; however, its management is increasingly challenging due to the increasing rates of antibiotic resistance. Choosing appropriate antibiotics for patients who have fever before receiving a culture result is challenging. We investigated the utility of fever at 48 h as a predictive factor for extended spectrum beta-lactamase -producing E. coli (EPEC).\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe study enrolled patients 394 patients hospitalized at Gangneung Asan Hospital for UTI from May 2017 to April 2021. Fever at 48 h of hospitalization was the analysis point, as this is when the response to antibiotic therapy manifest, although the results of antibiogram are not available. Multivariate analysis was performed to assess the correlation between EPEC and fever at 48 h.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eOverall, 36.3% of patients had EPEC and 27.9% had fever at 48 h. In multivariate analysis, a significant association was found between EPEC and fever (odds ratio 1.17, 95% confidence interval 1.05\u0026ndash;1.30, P\u0026thinsp;=\u0026thinsp;0.004)\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFever at 48 h is associated with EPEC, and could be considered a predictive factor for EPEC infection in patients with UTI. Antibiotic escalation may be considered in patients with fever at 48 h.\u003c/p\u003e","manuscriptTitle":"Fever lasting 48 hours as a predictive factor of ESBL-producing E. coli in non-critically ill patients with urinary tract infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 21:31:09","doi":"10.21203/rs.3.rs-3895719/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":"6d8d1df3-fb29-4238-9634-662cc601ac73","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-15T07:03:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-29 21:31:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3895719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3895719","identity":"rs-3895719","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}