Risk Factors For Antibiotic Resistance Research Articles

Success of susceptibility-guided eradication of Helicobacter pylori in a region with high secondary clarithromycin and levofloxacin resistance rates.

Resistance to clarithromycin (CLA) and levofloxacin (LFX) of Helicobacter pylori (H. pylori) is increasing in severity, and successful eradication is essential. Presently, the eradication success rate has greatly declined, leaving a large number of patients with previous treatment histories. To investigate secondary resistance rates, explore risk factors for antibiotic resistance, and assess the efficacy of susceptibility-guided therapy. We recruited 154 subjects positive for Urea Breath Test who attended The First Affiliated Hospital of China Medical University between July 2022 and April 2023. Participants underwent a string test after an overnight fast. The gastric juice was obtained and transferred to vials containing storage solution. Subsequently, DNA extraction and the specific DNA amplification were performed using quantitative polymerase chain reaction (qPCR). Demographic information was also analyzed as part of the study. Based on these results, the participants were administered susceptibility-guided treatment. Efficacy was compared with that of the empiric treatment group. A total of 132 individuals tested positive for the H. pylori ureA gene by qPCR technique. CLA resistance rate reached a high level of 82.6% (n = 109), LFX resistance rate was 69.7% (n = 92) and dual resistance was 62.1% (n = 82). Gastric symptoms [odds ratio (OR) = 2.782; 95% confidence interval (95%CI): 1.076-7.194; P = 0.035] and rural residence (OR = 5.152; 95%CI: 1.407-18.861; P = 0.013) were independent risk factors for secondary resistance to CLA and LFX, respectively. A total of 102 and 100 individuals received susceptibility-guided therapies and empiric treatment, respectively. The antibiotic susceptibility-guided treatment and empiric treatment groups achieved successful eradication rates of 75.5% (77/102) and 59.0% (59/411) by the intention-to-treat (ITT) analysis and 90.6% (77/85) and 70.2% (59/84) by the per-protocol (PP) analysis, respectively. The eradication rates of these two treatment strategies were significantly different in both ITT (P = 0.001) and PP (P = 0.012) analyses. H. pylori presented high secondary resistance rates to CLA and LFX. For patients with previous treatment failures, treatments should be guided by antibiotic susceptibility tests or regional antibiotic resistance profile.

PREDICTION OF THE RISK OF ANTIBIOTIC RESISTANCE OF UROPATHOGENS IN PATIENTS WITH URINARY TRACT INFECTIONS

Urinary tract infection is the most common disease in urological practice. The growth of antibiotic resistance in general and in urological practice in particular determines the need to develop methods for determining the risks of the presence of microflora resistance to antimicrobial drugs. The article discusses the existing methods for predicting the resistance of microorganisms to antibiotics, as well as the risk factor for antibiotic resistance developed by the author in community-acquired urinary tract infections. This indicator takes into account the individual characteristics and clinical and laboratory data of the patient with urinary tract infections that occur against the background of urolithiasis.

Antibiotic Resistance of Helicobacter pylori and Related Risk Factors in Yangzhou, China: A Cross-Sectional Study.

The antibiotic resistance of Helicobacter pylori (H. pylori) is a common cause of treatment failure. Previous studies showed that H. pylori resistance may be related to some characteristics of patients. This study intended to investigate the resistance of H. pylori to five commonly used antibiotics and risk factors in Yangzhou, China. We recruited the subjects who joined the endoscopic screening program organized by the Affiliated Hospital of Yangzhou University between April 2018 and September 2019 and endoscopists would take biopsy samples from the antrum and the corpus of the stomach. The antrum biopsy specimens were used to culture H. pylori. Next, we extracted DNA from H. pylori strains and performed the specific DNA amplification. Finally, we use gene chip technology to test the susceptibility to clarithromycin, levofloxacin, metronidazole, amoxicillin and tetracycline. Multivariate logistic analyses were also performed to determine the risk factors for antibiotic resistance of H. pylori. A total of 461 H. pylori strains were finally collected. The resistance rate of H. pylori to clarithromycin, levofloxacin, metronidazole, amoxicillin and tetracycline was 41.0%, 44.9%, 38.8%, 6.3% and 1.1%, respectively. In addition, 16 multi-resistance patterns were detected, and strains resistant to all five antibiotics were not found. Multivariate analysis showed that past medical history and clinical outcomes were significantly associated with the resistance to clarithromycin. Drinking, gastrointestinal symptoms and a family history of gastric cancer were significantly associated with the resistance of H. pylori to levofloxacin. Especially gastrointestinal symptoms were significantly associated with the resistance of H. pylori to any antibiotic. The resistance rates of H. pylori to clarithromycin, levofloxacin and metronidazole were very high in Yangzhou, China, various factors were related to bacterial resistance, and grasping these influencing factors can guide treatment.

Adherence to and usefulness of the national treatment guideline for urinary tract infections (UTI) in a risk area

BackgroundTo optimize antibiotic treatment and decrease antibiotic resistance, national treatment guidelines are available for urinary tract infections (UTIs) in general practice. The usefulness of these guidelines in risk areas for antimicrobial resistance such as cross border regions or areas with dense agriculture, is unknown.MethodsMidstream urine samples from women with symptoms of acute UTI visiting general practitioners (GPs) in the Westland area, a dense agriculture area, were microbiologically analysed, and patient characteristics, symptoms, previous and present antibiotic treatment were collected. The National Nivel data were used as reference for antibiotic resistance.ResultsOf 310 women with symptoms of uncomplicated UTI, 247 (80%) had a culture proven E. coli UTI. Empirical antibiotic therapy was prescribed to 148 patients (48%) in total; in 7% of women with a negative and 52% with a positive urine culture. Having more than one symptom was associated with the prescription of antibiotics; travel history or previous antibiotic use for UTI were not. The isolated uropathogens were susceptible to the empiric antibiotic therapy in 98% of patients. Resistance to co-amoxiclav was higher (22%) than reported in the national data of 2004 (12%), 2009 (13%) and 2014 (9%), as was the prevalence of extended spectrum β-lactamase (ESBL): 3.4% in our study versus 0.1%, 1% and 2.2% in the national data respectively.ConclusionThe presence of environmental and socio-demographic risk factors for antibiotic resistance did not influence the empiric choice nor susceptibility for antibiotics advised by the national guidelines in women with uncomplicated UTI.

Self-Medication as an Important Risk Factor for Antibiotic Resistance: A Multi-Institutional Survey among Students.

Self-medication is an important issue, especially in developing countries. Self-medication is the concept in which individuals use medicine to ease and manage their minor illnesses. The current survey was designed to conduct interviews at different universities based on the availability of the students from August 2021 to October 2021 in Hazara region of Khyber Pakhtunkhwa (KPK), Pakistan. Overall, 1250 questionnaires were distributed to students from various departments. Students of microbiology (n = 305, 24.4%) and agriculture 236 (n = 18.8%) were the most elevated members in this study, while other participants were from medical lab technology (n = 118, 9.4%), chemistry (n = 103, 8.2%), food science (n = 92, 7.3%), business administration (n = 83, 6.6%), sociology (n = 78, 6.2%), math/physics (n = 6, 14.8%), Pak study (n = 58, 4.6%), English (n = 47, 3.7%), and psychology (n = 19, 1.5%). Students working towards their Bachelor numbered (n = 913, 73.0%), Master (minor) numbered (n = 80, 6.4%), Master (major) numbered (n = 221, 17.6%), and Doctorate numbered (n = 36, 2.8%). The age group of participants was majorly 20–25 years (61.0%), while others belonged to the age groups 25–30 years (20.6%), 30–35 years (9.8%), and 35–40 years (8.4%). The mean and standard deviation of daily practices of self-medication were observed (M = 416.667, SD = 1,026,108.667) and p = 0.002. The mean and standard deviation of daily practices of antibiotic knowledge was (M = 431.5, SD = 1,615,917) and p = 0.002. Antimicrobial agents were leading over others with 631 (50.4%), followed by anti-inflammatory with 331 (26.4%), multivitamins with 142 (11.3%), gynecological purpose with 59 (4.7%), and analgesic with 72 (5.7%), while the lowest frequency rate was observed against herbal remedies with 15 (1.2%). The results of the current study concluded that students practiced self-medication for reasons such as convenience to obtain these medications from cheap sources and to avoid the fee of a physician. They searched for the medicine on social media platforms and purchased it blindly from the pharmacy without any prescription from a physician.

Risk factors for antibiotic resistance in hospital-acquired and ventilator-associated pneumonia

IntroductionUnderstanding risk factors for antibiotic resistance (AR) in patients with hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) is important to select appropriate initial antibiotics and reduce broad-spectrum antibiotic overuse. However, available evidence is limited. We aimed to identify risk factors for AR in those patients. MethodsThis prospective observational study was conducted at a tertiary-care hospital. Pathogens with AR were defined as those resistant to ampicillin-sulbactam or ceftriaxone. Risk factors for AR in patients with HAP and VAP were assessed using penalized logistic regression analysis. ResultsIn total, 557 patients with HAP and VAP were enrolled. Pathogens were isolated from 315 patients, with AR identified in 68.3% (215/315). Among antibiotic-resistant pathogens (ARPs), Pseudomonas aeruginosa was isolated most frequently, followed by methicillin-resistant Staphylococcus aureus (MRSA). Significant risk factors for AR were chronic renal diseases (adjusted odds ratio: 2.82, 95% confidence interval: 1.79–7.83), history of ARP infection/colonization within the past 1 year (2.80, 1.90–7.02), bedridden state (1.84, 1.28–3.91), tube feeding (1.58, 1.09–2.98), and peripheral or central venous catheterization (1.57, 1.06–2.96). Additionally, a risk factor for ARPs that should be treated with anti-MRSA antibiotics was prior MRSA infection/colonization history. Those for ARPs requiring dual antipseudomonal antibiotics included prior non-MRSA ARP or MRSA infection/colonization history and bedridden state. ConclusionsThe five factors we highlighted can be important criteria for identifying patients at risk of AR. Physicians should consider these potential risk factors when selecting antibiotics for initial empirical therapy in patients with HAP and VAP.

Risk factors for antibiotic resistance and mortality in patients with bloodstream infection of Escherichia coli.

This study aimed to investigate the risk factors for bloodstream infection (BSI) caused by extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (E. coli) and related mortality. The clinical data of 388 patients with E. coli BSI were analyzed. Blood cultures were performed and the antimicrobial susceptibility profiles of the resulting isolates were determined. Four single-nucleotide polymorphisms (rs231775, rs12343816, rs16944, and rs2233406) were genotyped using real-time PCR. ESBL were detected by disk diffusion confirmatory testing. Univariate and multivariate regression analyses were applied to identify the risk factors for ESBL-producing isolates and the BSI-induced mortality. The prevalence of ESBL-producing E. coli in BSI patients was 40.98%. E. coli isolates were commonly susceptible to carbapenem and β-lactam/β-lactamase inhibitor combinations. The major ESBL genes were CTX-M-14, CTX-M-55, CTX-M-15, and CTX-M-27. The proportion of CTX-M-15 was significantly higher in patients over 70years and those receiving stomach tube catheterization. Nosocomial infection, biliary tract infection, stomach tube catheterization, and previous cephalosporin administration were independent risk factors for ESBL-producing isolates. ESBL positivity, nosocomial infection, and cancer were independent risk factors of mortality. Two genetic polymorphisms associated with inflammation activation, rs231775 A allele and rs2233406 T allele, significantly increased the mortality risk of E. coli BSI with a risk ratio (95% CI) of 1.93 (1.05-3.55) and 4.38 (2.07-9.29), respectively. For patients with nosocomial infection, biliary tract infection, and cancer, the monitor of BSI and antibiotic susceptibility should be enhanced. The invasive procedures should be minimized. rs231775 and rs2233406 are promising prognostic markers for E. coli BSI patients.

Prevalence, Patterns, Association with Biofilm Formation, Effects on Milk Quality and Risk Factors for Antibiotic Resistance of Staphylococci from Bulk-Tank Milk of Goat Herds.

The objectives of this work were to study the prevalence and the patterns of antibiotic resistance of staphylococcal isolates from bulk-tank milk of goat herds across Greece, to assess possible associations of the presence of antibiotic resistance with the quality of milk in these herds and to evaluate herd-related factors potentially associated with the presence of antibiotic resistance among these staphylococcal isolates. A cross-sectional study was performed on 119 goat herds in Greece. Bulk-tank milk samples were collected for bacteriological examination; staphylococcal isolates were evaluated for resistance to 20 antibiotics. Oxacillin-resistant, resistant to at least one antibiotic, and multi-resistant staphylococcal isolates were recovered from 5.0%, 30.3%, and 16.0% of herds, respectively. Of 80 isolates, 7.5% were resistant to oxacillin, 50.0% were resistant to at least one antibiotic and 27.5% were multi-resistant. Resistance was seen more frequently among coagulase-negative staphylococci (59.3%) than among Staphylococcus aureus (23.8%). Resistance was more frequent against penicillin and ampicillin (41.3% of isolates) and fosfomycin (27.5%). No association was found with biofilm formation by staphylococci. For recovery of oxacillin-resistant isolates, the presence of working staff in the herds emerged as a significant factor; respective factors for the isolation of staphylococci resistant to at least one antibiotic were part-time farming and high (>10) number of systemic disinfections in the farm annually. The same three factors concurrently were also identified to be significant for the recovery of multi-resistant isolates.

571Effect of a computer network-based feedback program on antibiotic prescription rates of primary care physicians

Abstract Background Antibiotic overuse is one of the major prescription problems in rural China and a major risk factor for antibiotic resistance. Low antibiotic prescription rates can effectively reduce the risk of antibiotic resistance. Methods A cluster randomized crossover open controlled trial was conducted in 31 hospitals. These hospitals were randomly allocated to two groups to receive the intervention for three months followed by no intervention for three months in a random sequence. The feedback intervention information, which displayed the physicians’ antibiotic prescription rates and ranking, was updated every 10 days. The primary outcome was the 10-day antibiotic prescription rate of the physicians. Results There were 82 physicians in group 1 (intervention first followed by control) and 81 in group 2 (control first followed by intervention). Baseline comparison showed no significant difference in antibiotic prescription rate between the two groups (30.8% vs 35.2%, P-value = 0.07). At the crossover point, the relative reduction in antibiotic prescription rate was significantly higher among physicians in the intervention group than in the control group (33.1% vs 20.3%, P-value < 0.001). After a further 3 months, the rate of decline in antibiotic prescriptions was also significantly greater in the intervention group compared to the control group (14.2% vs 4.6%, P-value < 0.001). Conclusions A computer network-based feedback intervention can significantly reduce the antibiotic prescription rates of primary care outpatient physicians. Key messages The feedback intervention continuously affected their prescription behavior for up to six months.

Prevalence and risk factors for antimicrobial resistance among newborns with gram-negative sepsis.

Newborn sepsis accounts for more than a third of neonatal deaths globally and one in five neonatal deaths in Ethiopia. The first-line treatment recommended by WHO is the combination of gentamicin with ampicillin or benzylpenicillin. Gram-negative bacteria (GNB) are increasingly resistant to previously effective antibiotics. Our goal was to estimate the prevalence of antibiotic-resistant gram-negative bacteremia and identify risk factors for antibiotic resistance, among newborns with GNB sepsis. At a tertiary hospital in Ethiopia, we enrolled a cohort pregnant women and their newborns, between March and December 2017. Newborns who were followed up until 60 days of life for clinical signs of sepsis. Among the newborns with clinical signs of sepsis, blood samples were cultured; bacterial species were identified and tested for antibiotic susceptibility. We described the prevalence of antibiotic resistance, identified newborn, maternal, and environmental factors associated with multidrug resistance (MDR), and combined resistance to ampicillin and gentamicin (AmpGen), using multivariable regression. Of the 119 newborns with gram-negative bacteremia, 80 (67%) were born preterm and 82 (70%) had early-onset sepsis. The most prevalent gram-negative species were Klebsiella pneumoniae 94 (79%) followed by Escherichia coli 10 (8%). Ampicillin resistance was found in 113 cases (95%), cefotaxime 104 (87%), gentamicin 101 (85%), AmpGen 101 (85%), piperacillin-tazobactam 47 (39%), amikacin 10 (8.4%), and Imipenem 1 (0.8%). Prevalence of MDR was 88% (n = 105). Low birthweight and late-onset sepsis (LOS) were associated with higher risks of AmpGen-resistant infections. All-cause mortality was higher among newborns treated with ineffective antibiotics. There was significant resistance to current first-line antibiotics and cephalosporins. Additional data are needed from primary care and community settings. Amikacin and piperacillin-tazobactam had lower rates of resistance; however, context-specific assessments of their potential adverse effects, their local availability, and cost-effectiveness would be necessary before selecting a new first-line regimen to help guide clinical decision-making.

General practice after COVID-19: lessons learned.

<h3>Abstract</h3> <h3>Background</h3> Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning algorithms. However, they are scarcely used for empiric antibiotic therapy. Here we accurately predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using machine learning algorithms applied to patients’ electronic medical records. <h3>Methods</h3> The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their electronic medical records. Five antibiotics were examined: Ceftazidime (n=2942), Gentamicin (n=4360), Imipenem (n=2235), Ofloxacin (n=3117) and Sulfamethoxazole-Trimethoprim (n=3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble combining all three algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis. <h3>Results</h3> The ensemble model outperformed the separate models and produced accurate predictions on a test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble model yielded area under the receiver-operating-characteristic (auROC) scores of 0.73-0.79, for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. The effects of different variables on the predictions were assessed and found consistent with previously identified risk factors for antibiotic resistance. <h3>Conclusions</h3> Our study demonstrates the potential of machine learning models to accurately predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapid information regarding the infecting bacterial species can improve predictions substantially. The implementation of such systems should be seriously considered by clinicians to aid correct empiric therapy and to potentially reduce antibiotic misuse. <h3>40-word summary</h3> Machine learning models were applied to large and diverse datasets of medical records of hospitalized patients, to predict antibiotic resistance profiles of bacterial infections. The models achieved high accuracy predictions and interpretable results regarding the drivers of antibiotic resistance.

A Pragmatic Machine Learning Model To Predict Carbapenem Resistance.

Infection caused by carbapenem-resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are well known, there remains a large need for the early identification of antibiotic-resistant infections. Using machine learning (ML), we sought to develop a prediction model for carbapenem resistance. All patients >18 years of age admitted to a tertiary-care academic medical center between 1 January 2012 and 10 October 2017 with ≥1 bacterial culture were eligible for inclusion. All demographic, medication, vital sign, procedure, laboratory, and culture/sensitivity data were extracted from the electronic health record. Organisms were considered CR if a single isolate was reported as intermediate or resistant. Patients with CR and non-CR organisms were temporally matched to maintain the positive/negative case ratio. Extreme gradient boosting was used for model development. In total, 68,472 patients met inclusion criteria, with 1,088 patients identified as having CR organisms. Sixty-seven features were used for predictive modeling. The most important features were number of prior antibiotic days, recent central venous catheter placement, and inpatient surgery. After model training, the area under the receiver operating characteristic curve was 0.846. The sensitivity of the model was 30%, with a positive predictive value (PPV) of 30% and a negative predictive value of 99%. Using readily available clinical data, we were able to create a ML model capable of predicting CR infections at the time of culture collection with a high PPV.

Self-medication with antibiotics among youths from Myanmar

Background: Self-medication with antibiotics becomes a public health concern in both developed and developing countries. It is a risk factor for antibiotic resistance, one of the biggest threats to global health, food security, and development today. The prevalence of self-medication with antibiotics varies across the countries, and such studies are still limited in Myanmar. Therefore, we aimed at describing the prevalence of and the factors facilitating self-medication with antibiotics in Myanmar.Methods: This cross-sectional study included face-to-face interviewing of 360 youths randomly selected from 6 townships of Mandalay city, Myanmar.Results: In this study, 36.67% of the youths self-medicated with antibiotics in the six months before the survey. The most common antibiotic used for self-medication was amoxicillin (70.94%). Runny nose (35.61%), cough (26.52%), and fever (25.76%) were the most typical symptoms for which antibiotics were taken. The main factor facilitating self-medication with antibiotics was easy accessibility (86.36%). One-third of youths selected antibiotics based on the advice of drugstore’s sellers. Most of the youths bought antibiotics at nearby drugstores. About two-thirds of youths stopped taking antibiotics after taking them one or two days, regardless of the outcome.Conclusions: The prevalence of self-medication with antibiotics was high among Myanmar youths. Law enforcement and regulations for inappropriate use of antibiotics is an urgent need to alleviate the consequences of self-medication with antibiotics. Awareness-raising and educational program targeting both drugstore owners and the public through various channels is necessary to reduce the inappropriate use of antibiotics.

145. Monthly Antibiotic Prescribing Peer Comparison Combined with In-person Education Decreases Antibiotic Prescribing for Acute Respiratory Infections

BackgroundInappropriate antibiotic prescribing is an important modifiable risk factor for antibiotic resistance. Approximately half of all antibiotics prescribed for acute respiratory infections (ARIs) in the United States may be inappropriate or unnecessary. The purpose of this quality improvement (QI) project was to evaluate the effect of three consecutive interventions on improving antibiotic prescribing for ARIs (i.e., pharyngitis, rhinosinusitis, bronchitis, common cold).MethodsThis was a pre-post analysis of an antimicrobial stewardship QI initiative to improve antibiotic prescribing for ARIs in six Veterans Affairs (VA) primary care clinics. Three distinct intervention phases occurred. Educational interventions included training on appropriate antibiotic prescribing for ARIs. During the first intervention period (8/2017-1/2019), education was presented virtually to primary care providers on a single occasion. In the second intervention period (2/2019-10/2019), in-person education with peer comparison was presented on a single occasion. In the third intervention period (11/2019-4/2020), education and prescribing feedback with peer comparison was presented once in-person followed by monthly emails of prescribing feedback with peer comparison. January 2016-July 2017 was used as a pre-intervention baseline period.The primary outcome was the antibiotic prescribing rate for all classifications of ARIs. Secondary outcomes included adherence to antibiotic prescribing guidance for pharyngitis and rhinosinusitis. Descriptive statistics and interrupted time series segmented regression were used to analyze the outcomes.ResultsMonthly antibiotic prescribing peer comparison emails in combination with in-person education was associated with a statistically significant 12.5% reduction in the rate of antibiotic prescribing for ARIs (p=0.0019). When provider education alone was used, the reduction in antibiotic prescribing was nonsignificant. ConclusionEducation alone does not significantly reduce antibiotic prescribing for ARIs, regardless of the delivery mode. In contrast, education followed by monthly prescribing feedback with peer comparison was associated with a statistically significant reduction in ARI antibiotic prescribing rates.Disclosures All Authors: No reported disclosures

1677. Evaluation of Optimal Treatment for Urinary Tract Infections in Outpatient Clinics at an Academic Medical Center

BackgroundInappropriate prescribing of antibiotics is an important modifiable risk factor for antibiotic resistance. The Joint Commission has identified the need for outpatient antimicrobial stewardship efforts. The purpose of this study was to assess the incidence of optimal empiric antibiotic therapy for urinary tract infections (UTIs) in outpatient clinics at VCU Health.MethodsThis was a retrospective study of patients seen in internal medicine (IM) and urology clinics between July 1, 2018 and June 30, 2019. Patients were included if they were ≥ 18 years old, had a diagnosis of UTI per ICD-10 code, and received a prescription to treat a UTI at the visit. Patients were excluded if they had a concurrent infection, currently prescribed antibiotics, or pregnant. The primary outcome was to evaluate the incidence of optimal empiric treatment for UTIs. Appropriateness of antibiotic therapy was assessed based on prior culture data along with our institutional UTI treatment guideline.ResultsTwo hundred and twenty-six patients were included: 136 in IM clinics and 90 in urology clinics. Patients in the IM clinics were significantly older (mean age 64.8 vs. 60.5, p= 0.033) and more were female (88% vs. 38%, p< 0.001). More patients in the urology clinics had a history of a UTI within 24 months (72% vs. 57%, p= 0.016), history of fluoroquinolone-resistant Gram-Negative UTIs (35% vs. 13%, p= 0.007), and history of genitourinary cancer (28% vs. 1%, p< 0.001). Overall, 61% of patients were treated with optimal empiric antibiotics. Incidence of optimal prescribing in the IM clinics was significantly higher compared to urology clinics (69% vs 49%, p= 0.002). See table 1 for additional results.Table 1. Optimal UTI Treatment in Internal Medicine Clinics vs Urology Clinics ConclusionIM clinics more frequently prescribed optimal empiric antibiotics for UTIs compared to urology clinics. Resident prescribers were more likely to prescribe optimal empiric therapy. Presence of a beta-lactam allergy was not predictive of optimal prescribing. These data highlight opportunities for antibiotic therapy optimization for UTIs at our health system.Disclosures All Authors: No reported disclosures

Travel to Mexico and uropathogen-antibiotic susceptibility mismatch in the emergency department.

International travel results in an increased risk of colonization and infection with multidrug-resistant organisms. This study aimed to determine if recent travel to Mexico affects the rate of uropathogen-antibiotic susceptibility mismatch (UASM) in outpatients treated for urinary tract infection (UTI) in a South Texas emergency department (ED). A retrospective cohort of adult patients presenting to the ED and treated outpatient for UTI from October 1, 2014, to February 25, 2020, was conducted at a community hospital located within approximately 15miles of the United States-Mexico border. Rates of UASM were compared between patients with a history of recent travel to Mexico and those who have not recently traveled. A total of 192 patients were included, with 64 in the travel to Mexico group and 128 in the no travel group. UASM was significantly higher in the recent travel to Mexico group when compared to the no travel group (RR 1.49, 95% CI 1.03-2.13). Antibiotics most commonly associated with UASM included fluoroquinolones, cephalexin, and sulfamethoxazole-trimethoprim. There was no significant difference between the rates of resistance to first-line agents for the treatment of UTI among the two groups. In addition to known antibiotic resistance risk factors, recent travel to Mexico may increase the risk of UASM for ED patients with UTI. Considering the potential consequences of UTI treatment failure, antimicrobial stewardship services in the ED should include screening for antibiotic resistance risk factors and urine culture follow-up to ensure appropriate outpatient antibiotic therapy, especially among patients with recent international travel.

Predicting Antibiotic Resistance in Hospitalized Patients by Applying Machine Learning to Electronic Medical Records.

Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning (ML) algorithms. However, they are scarcely used for empiric antibiotic therapy. Here, we predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using ML algorithms applied to patients' electronic medical records (EMRs). The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their EMRs. Five antibiotics were examined: ceftazidime (n = 2942), gentamicin (n = 4360), imipenem (n = 2235), ofloxacin (n = 3117), and sulfamethoxazole-trimethoprim (n = 3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble that combined all 3 algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis. The ensemble outperformed the separate models and produced accurate predictions on test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble yielded area under the receiver-operating characteristic (auROC) scores of 0.73-0.79 for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. Variables' effects on predictions were assessed and found to be consistent with previously identified risk factors for antibiotic resistance. We demonstrate the potential of ML to predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapidly gained information regarding the infecting bacterial species can improve predictions substantially. Clinicians should consider the implementation of such systems to aid correct empiric therapy and to potentially reduce antibiotic misuse.

Effect of a computer network-based feedback program on antibiotic prescription rates of primary care physicians: A cluster randomized crossover-controlled trial

ObjectiveAntibiotic overuse is one of the major prescription problems in rural China and a major risk factor for antibiotic resistance. Low antibiotic prescription rates can effectively reduce the risk of antibiotic resistance. We hypothesized that under a paperless, computer-based feedback system the rates of antibiotic prescriptions among primary care physicians can be reduced. MethodsA cluster randomized crossover open controlled trial was conducted in 31 hospitals. These hospitals were randomly allocated to two groups to receive the intervention for three months followed by no intervention for three months in a random sequence. The feedback intervention information, which displayed the physicians’ antibiotic prescription rates and ranking, was updated every 10 days. The primary outcome was the 10-day antibiotic prescription rate of the physicians. ResultsThere were 82 physicians in group 1 (intervention first followed by control) and 81 in group 2 (control first followed by intervention). Baseline comparison showed no significant difference in antibiotic prescription rate between the two groups (30.8% vs 35.2%, P-value=0.07). At the crossover point, the relative reduction in antibiotic prescription rate was significantly higher among physicians in the intervention group than in the control group (33.1% vs 20.3%, P-value<0.001). After a further 3 months, the rate of decline in antibiotic prescriptions was also significantly greater in the intervention group compared to the control group (14.2% vs 4.6%, P-value<0.001). The characteristics of physicians did not significantly determine the change in rate of antibiotic prescriptions. ConclusionA computer network-based feedback intervention can significantly reduce the antibiotic prescription rates of primary care outpatient physicians and continuously affected their prescription behavior for up to six months. Trial registrationChiCTR1900021823

181. Antimicrobial Susceptibility Trends and Risk Factors for Antibiotic Resistance in Pseudomonas aeruginosa Bacteremia: A 10-Year Experience at a Korean Tertiary Hospital

BackgroundBacteremia due to Pseudomonas aeruginosa is associated with high mortality and inappropriate initial antimicrobial therapy leads to worse outcomes. We aimed to analyze clinical characteristics of P. aeruginosa bacteremia and risk factors for antibiotic resistance and investigate their antimicrobial susceptibility trends.MethodsWe retrospectively reviewed the medical records of patients with P. aeruginosa bacteremia admitted to a tertiary hospital between January 2009 and March 2019.ResultsA total of 242 patients were identified and the median age was 70 years [interquartile range (IQR) 57.6–75.4]. Hepatobiliary tract (28.5%) was most common primary site of infection, followed by respiratory tract (20.2%) and urinary tract (15.7%). Out of 197 (81.4%) patients treated with susceptible antibiotics and the median duration of active antibiotic therapy was 10 days (IQR 4–15.5). The percentages of susceptible P. aeruginosa to amikacin, aztreonam, cefepime, ceftazidime, ciprofloxacin, colistin, gentamicin, imipenem, meropenem, piperacillin–tazobactam, and ticarcillin-clavulanate were 90.1%, 57.9%, 77.3%, 74.8%, 74.4%, 99.2%, 91.3%, 76.0%, 76.0%, 69.4%, and 51.2%. There were 24.8% carbapenem-resistant P. aeruginosa (CRPA), 36.4% multidrug-resistant P. aeruginosa (MDRPA), and 15.3% extensively drug-resistant P. aeruginosa (XDRPA). Susceptible P. aeruginosa to gentamycin and ticarcillin-clavulanate were significantly decreased in 2014–2019 than that in 2009–2013 (both; P < 0.001). Resistance rates to carbapenems and fluoroquinolones tended to increase over time. CRPA, MDRPA, and XDRPA were significantly associated with delayed active therapy (>48 h) (all; P < 0.001). Independent risk factors for CRPA were urinary tract infection (adjusted odds ratio [aOR], 3.4; 95% confidence interval [CI], 1.5–7.8), underlying hematologic malignancy (aOR, 3.0; 95% CI, 1.1–8.3) and cerebrovascular accident (aOR, 2.6; 95% CI, 1.1–5.9), hospital-acquired infection (aOR, 2.5; 95% CI, 1.0–6.1), and co-colonization with multidrug-resistant organisms (aOR, 2.2; 95% CI, 1.1–4.4).ConclusionThe identification of risk factors for antibiotic resistance and analysis of antibiotics susceptibility are useful for early initiation of appropriate antibiotics in patients with P. aeruginosa bacteremia.Disclosures All authors: No reported disclosures.

1450. Risk Factors for Antibiotic Resistance of Escherichia coli Urinary Isolates in Outpatients

BackgroundAntibiotic-resistant E. coli infections represent a major cause of morbidity and mortality, and pose a challenge to antibiotic stewardship. Patient age has been suggested as a key determinant of resistance patterns in studies based in the United States and Europe, although local antibiotic use patterns may affect this relationship. We analyzed results from clinical antibiotic susceptibility tests performed at a large reference laboratory to further examine the association of age with E. coli urinary tract resistance patterns in WA State.MethodsWe analyzed 5 years of E. coli antibiotic susceptibility data for outpatient urinary tract infections in WA State from a national clinical reference laboratory. We included only the first isolate recorded for each patient and calculated crude rates of resistance to antibiotics for the age groups of 50 years. In a multivariate logistic model, we tested the effect of patient age, year of antimicrobial susceptibility test submission, and sex on antibiotic resistance.ResultsUnivariate analyses indicated that resistance rates differed significantly across patient age groups for ciprofloxacin and nitrofurantoin. Among females, resistance rates also differed significantly across patients age groups for amoxicillin-clavulanate and gentamicin. Logistic regression using data from male patients found the odds of resistance to be significantly greater in older individuals for ciprofloxacin (OR 2.59) and lower in older individuals for amoxicillin-clavulanate (OR 0.56). For females, logistic regression found the odds of resistance to be significantly greater for older individuals for amoxicillin-clavulanate (OR 1.43), ciprofloxacin (OR 3.04), ceftriaxone (OR 2.58), nitrofurantoin (OR 2.20), and gentamicin (OR 1.62).ConclusionIn WA State, the distribution of antibiotic resistance in E. coli urinary isolates varies with age, sex and the antibiotic of interest. Greater and more timely use of databases of susceptibility testing of clinical isolates from outpatient settings can allow for the creation of age-specific antibiograms to guide and improve stewardship. Disclosures All authors: No reported disclosures.