Implementation and Impact of an Antimicrobial Stewardship Post-Prescription Review Program: Challenges Amid Rising Resistance in a Tertiary Hospital

Implementation and Impact of an Antimicrobial Stewardship Post-Prescription Review Program: Challenges Amid Rising Resistance in a Tertiary Hospital | 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 Implementation and Impact of an Antimicrobial Stewardship Post-Prescription Review Program: Challenges Amid Rising Resistance in a Tertiary Hospital Lea Nadia Marvulli, Marco Vecchia, Paolo Sacchi, Patrizia Cambieri, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7236630/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 Antimicrobial resistance (AMR) represents a major global health emergency. This study evaluated a pilot Antimicrobial Stewardship (AMS) program based on post-prescription review of selected antibiotics at Fondazione IRCCS Policlinico San Matteo in Pavia, Italy. Methods This quasi-experimental study analysed the first six months (March-August 2024) of an AMS program in Internal Medicine, General Surgery, and Hematology departments. The intervention focused on post-prescription review of carbapenems, new beta-lactam/beta-lactamase inhibitors, and cefiderocol. Multiple outcome measures were assessed, including prescription appropriateness, intervention effects, microbiological patterns, antibiotic consumption, and costs, with comparison to the corresponding period in 2023. Results We analysed 210 antibiotic prescriptions (64 in Internal Medicine, 40 in General Surgery, 106 in Hematology). Overall appropriateness was 50.0% (95% CI: 43.2–56.8%), with therapy duration being the principal factor of inappropriateness (75.0% of inappropriate cases). Inappropriate therapy was associated with a significantly higher risk of unfavourable outcomes (χ2 = 9.41; p = 0.002) and longer hospital stays (median difference: 2 days; p < 0.001). Antibiotic consumption increased significantly (+ 41.93%), primarily in Hematology (+ 54.17%), with overall costs rising by 56.12%. The prevalence of multidrug-resistant organisms increased substantially, with NDM-producing Klebsiella spp. showing increases in both colonization (+ 77.58%) and infection (+ 95.10%). There was a significant association between inappropriate prescriptions and subsequent modification (χ²=6.42; p = 0.011), more evident for targeted than for empirical therapies. Conclusions The pilot AMS program identified significant challenges in optimizing antibiotic use amid increasing resistance rates. Department-specific responses suggest tailored approaches may be more effective than uniform strategies. Enhanced infection control, targeted economic stewardship, and an improved handshake model of intervention are recommended to improve future outcomes. Antimicrobial stewardship Antimicrobial resistance Post-prescription review Multidrug-resistant organisms Carbapenemase-producing Enterobacterales Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Antimicrobial resistance (AMR) represents a major global health emergency with significant consequences for therapeutic failures, mortality, healthcare costs, and economic impact [ 1 , 2 ]. By 2050, AMR is predicted to cause approximately 2 million deaths annually, with an additional 8 million deaths being associated with AMR complications [ 3 ]. The European Health Emergency Preparedness and Response Authority has identified AMR as one of three cross-border health threats [ 4 ]. Italy faces particularly serious AMR challenges. According to the European Centre for Disease Prevention and Control, the percentage of infections caused by multidrug-resistant pathogens in Italy is 40% compared to the 21.8% European average, and the prevalence of patients receiving at least one antibiotic treatment is 41.67% versus 36% across Europe [ 5 ]. Notably, approximately one-third of European deaths attributable to AMR occur in Italy [ 6 ]. Antimicrobial Stewardship (AMS) is defined as a coherent set of actions promoting responsible antimicrobial use to ensure sustainable access to effective therapy for all who need it [ 7 ]. The aim of a AMS program is to improve prescription appropriateness and achieve high rates of cure, with minimal toxicity for the patient and minimal impact on the subsequent development of resistance. The Centers for Disease Control and Prevention has outlined essential components for a functioning AMS program, including leadership commitment, accountability, pharmacy expertise, interventions, monitoring, reporting, and education. Among the several types of interventions, post-prescription review and preauthorization are considered the two most effective antibiotic stewardship interventions in hospitals [ 8 ]. However, choosing the best program for a specific care setting remains a challenge. This study aimed to conduct a process and outcome analysis of a pilot AMS program implemented in specific departments of Fondazione IRCCS Policlinico San Matteo, a tertiary-level university hospital in Pavia, Italy. We evaluated the impact of post-prescription review intervention on prescription appropriateness, clinical outcomes, microbiological patterns, antibiotic consumption, and costs during the first six months of implementation. Methods Setting and context The Fondazione IRCCS Policlinico San Matteo is a tertiary-level university hospital with 781 ordinary beds, serving approximately 700,000 people. According to data from the 2022 point-prevalence study promoted by ECDC, the local percentage of patients receiving at least one antibiotic (49.9%) reflects the national trend, with significant use of antibiotics in the Watch category of the WHO AWaRe classification [ 9 ]. Prior to the pilot program, the hospital relied primarily on restrictive AMS approaches, requiring an infectious diseases (ID) consultation for carbapenem and Reserve category antibiotic prescriptions. A hospital antimicrobial use protocol for empirical therapy, derived from international guidelines, was implemented in June 2023. Intervention Beginning March 1st, 2024, a pilot AMS program based on post-prescription review was implemented in the following departments: Internal Medicine, General Surgery, and Hematology. We decided to start from these departments because of both the high number of patients admitted and the high rate of antibiotic prescriptions. The intervention targeted specific antibiotics from the Watch and Reserve categories: carbapenems (meropenem, imipenem, ertapenem); new beta-lactam/beta-lactamase inhibitors (ceftazidime/avibactam, ceftolozane/tazobactam, meropenem/vaborbactam, imipenem/cilastatin/relebactam, ceftazidime/avibactam + aztreonam); cefiderocol. The program consisted of: Alert phase - Daily electronic alerts were sent via email with reports of prescriptions for targeted antibiotics initiated or ongoing in the previous 24 hours. Proactive intervention phase - Within 48–72 hours, the ID consultant reviewed the prescription and discussed it with prescribing physicians, considering clinical, biochemical, radiological, and microbiological data, resulting in a documented therapeutic decision. Study design and measurements This quality improvement study followed a quasi-experimental design according to Revised Standards for Quality Improvement Reporting Excellence 2.0 guidelines [ 10 ]. The analysis covered the first six months of implementation (March-August 2024), with antibiotic consumption and cost comparisons to the corresponding period in 2023. Microbiological analysis examined broader periods (January-October 2024 vs January-October 2023). Process indicators Appropriateness of empirical and targeted antibiotic therapy Defined as adherence to hospital protocol guidelines for both choice of molecule and duration of therapy. In case of targeted therapy, the choice of molecule according to the identified microorganism was considered. We calculated the overall appropriateness rate with 95% confidence intervals. Outcomes of proactive reviews Categorized as continuation, intensification, simplification, discontinuation, or transition to targeted therapy. Outcome indicators Clinical measures Length of hospitalization, Intensive Care Unit (ICU) admission rate, Clostridioides difficile infection rate, unfavourable outcome rate (defined as death, treatment failure, or chronic infection). Microbiological measures Prevalence of colonization and infection with resistant pathogens, with particular focus on carbapenemase-producing Enterobacterales (CPE), difficult-to-treat Pseudomonas aeruginosa (DTR-PA), and carbapenem-resistant Acinetobacter baumannii (CRAB). Antibiotic consumption Expressed in defined daily doses (DDDs) per 100 patient-days. Economic measures Cost of selected antibiotics. Data collection Clinical, demographic and microbiological data, as well as data for clinical measures, were extracted from the electronic medical records of the patients and were inserted to a dedicated spreadsheet. Data for microbiological measures were provided by the Microbiology Laboratory. Antibiotic consumption and costs were inferred by the monthly dispensation from the Hospital Pharmacy to the wards. Statistical analysis Continuous variables were represented as median and interquartile range (IQR), while categorical variables as count and percentage with 95% confidence intervals. Associations between categorical variables were evaluated using chi-square tests with Yates' correction. Differences in medians were assessed using Mann-Whitney test for two groups and Kruskal-Wallis test for multiple groups, with Tukey's post-hoc tests for pairwise comparisons. The relationship between intervention type and therapy duration was analysed using group comparisons with Mann-Whitney tests. To evaluate the association between prescription appropriateness and subsequent modification, we calculated adjusted odds ratios using logistic regression models that controlled for patient age, department, and infection type. For the analysis of antibiotic consumption trends, we calculated DDDs per 100 patient-days for each antibiotic class and department. Differences in consumption between 2023 and 2024 were evaluated using paired t-tests. Economic analyses included calculation of cost per DDD, and cost proportions by antibiotic class and department. For microbiological data, we calculated prevalence of colonization and infection for resistant organisms in both study periods. Multivariate logistic regression was performed to identify independent predictors of prescription inappropriateness, controlling for patient characteristics, department, infection type, and prior antibiotic exposure. Finally, linear regression was used to describe the relationship between time and antibiotic consumption. All statistical analyses were performed using MATLAB R2023b (MathWorks, Natick, MA, USA) with a significance level set at p < 0.05. Results Prescription patterns and population characteristics During the study period, 210 antibiotic prescriptions were initiated, of which 64 (30.5%) were started in Internal Medicine, 40 (19.0%) in General Surgery, and 106 (50.5%) in Hematology. The most prescribed antibiotics were carbapenems, exclusively represented by meropenem (n = 181, 86.2%), followed by ceftazidime/avibactam + aztreonam (n = 14, 6.7%), ceftazidime/avibactam (n = 7, 3.3%), ceftolozane/tazobactam (n = 4, 1.9%), and cefiderocol (n = 4, 1.9%). The study involved 180 patients (107 males, 59.4%), with a median age of 69 years (IQR 58–79). The prevalence of suspected/confirmed infections varied by department: genitourinary tract infections were more frequent in Internal Medicine (n = 20, 31.3%), gastrointestinal/intra-abdominal infections in General Surgery (n = 24, 60.0%), and febrile neutropenia in Hematology (n = 51, 48.1%) followed by respiratory tract infections (n = 30, 28.3%). Significant differences were observed between departments regarding patient age (p < 0.001), ICU admission (p < 0.001), and unfavorable clinical outcome (p < 0.001). Detailed characteristics are presented in Table 1 . Table 1 Characteristics of the study population and antibiotic prescriptions. Characteristic Overall Internal Medicine General Surgery Hematology p-value Prescriptions, n (%) 210 64 (30.5) 40 (19.0) 106 (50.5) - Patients, n (%) 180 62 (34.5) 33 (18.3) 85 (47.2) - Patient characteristics Median age, years (IQR) 69 (58–79) 79 (69–86) 70 (61–79) 61 (49–70) < 0.001 Male gender, n (%) 107 (59.4) 32 (51.6) 18 (54.5) 57 (67.1) 0.139 Median length of hospitalization, days (IQR) 30 (17.75-44) 26.5 (11-50.5) 34 (18–58) 31 (21–39) 0.121 ICU admission, n (%) 36 (20.0) 11 (17.7) 19 (57.6) 6 (7.1) < 0.001 Known colonization, n (%) 56 (26.7) 10 (15.6) 15 (37.5) 31 (29.2) 0.132 Unfavorable outcome, n (%) 53 (25.2) 27 (42.2) 9 (22.5) 17 (16.0) < 0.001 C. difficile infection, n (%) 7 (3.9) 4 (6.5) 1 (3.0) 2 (2.4) 0.429 Treatment characteristics Meropenem, n (%) 181 (86,2) 59 (92,2) 29 (72,5) 93 (87,7) - Imipenem, n (%) 0 (0) 0 (0) 0 (0) 0 (0) - Ertapenem, n (%) 0 (0) 0 (0) 0 (0) 0 (0) - Ceftolozane/tazobactam, n (%) 4 (1,9) 1 (1,6) 0 (0) 3 (2,8) - Ceftazidime/avibactam, n (%) 7 (3,3) 2 (3,1) 4 (10,0) 1 (0,9) - Ceftazidime/avibactam + ATM, n (%) 14 (6,7) 2 (3,1) 4 (10,0) 8 (7,5) - Cefiderocol, n (%) 4 (1,9) 0 (0) 3 (7,5) 1 (0,9) - Meropenem/vaborbactam, n (%) 0 (0) 0 (0) 0 (0) 0 (0) - Imipenem/relebactam, n (%) 0 (0) 0 (0) 0 (0) 0 (0) - Empirical therapy, n (%) 131 (62.4) 35 (54.7) 16 (40.0) 80 (75.5) < 0.001 Targeted therapy, n (%) 79 (37.6) 29 (45.3) 24 (60.0) 26 (24.5) < 0.001 Combination therapy, n (%) 111 (52.9) 25 (39.1) 27 (67.5) 59 (55.7) 0.012 Previous antibiotic therapy, n (%) 173 (82.4) 47 (73.4) 29 (72.5) 97 (91.5) 0.002 Median therapy duration, days (IQR) 9.5 (7–13) 10 (7–12) 9 (7.75-15) 9 (7–12) 0.327 Infection type, n (%) Other 4 (1,9) 0 (0) 0 (0) 4 (3,8) - Bacteremia 13 (6,2) 8 (12,5) 0 (0) 5 (4,7) - Endocarditis and CLABSI 10 (4,8) 3 (4,7) 1 (2,5) 6 (5,7) - Gastrointestinal/intra-abdominal 40 (19,0) 13 (20,3) 24 (60,0) 3 (2,8) - Meningitis and CNS infections 2 (1,0) 2 (3,1) 0 (0) 0 (0) - Febrile neutropenia 51 (24,3) 0 (0) 0 (0) 51 (48,1) - Respiratory tract infections 52 (24,8) 17 (26,6) 5 (12,5) 30 (28,3) - SSTIs 12 (5,7) 1 (1,6) 7 (17,5) 4 (3,8) - UTIs 26 (12,4) 20 (31,3) 3 (7,5) 3 (2,8) - (ATM: aztreonam; CLABSI: central line-associated bloodstream infection; CNS: central nervous system; ICU: intensive care unit; SSTIs: skin and soft tissue infections; UTIs: urinary tract infections) Prescriptive appropriateness and patient outcomes Among the 210 antibiotic prescriptions started during the study period, 116 (55,2%) were classified as inappropriate, and most of them were empirical therapies (n=79, 60,3%). Overall appropriateness of antibiotic prescriptions was 50.0% (95% CI: 43.2-56.8%), with no statistically significant differences between departments (p=0.266). For empirical therapy, Internal Medicine had the highest appropriateness (51.4%; 95% CI: 39.5-63.2%) compared to General Surgery (31.3%; 95% CI: 17.7-48.7%) and Hematology (36.3%; 95% CI: 27.4-46.1%). The same trend was observed for targeted therapy: Internal Medicine (55.2%; 95% CI: 42.3-67.5%), General Surgery (50.0%; 95% CI: 35.8-64.2%), and Hematology (53.8%; 95% CI: 39.1-67.9%) (Figure 1 A and 1 B). The primary factor of inappropriateness was therapy duration, alone (n=57, 49,1%) or as a contributing factor (n=30, 25.9%) (Figure 1 C). Substantial variability in treatment duration was observed across different infection types, with community-acquired pneumonia (median: 13 days; IQR: 8.5-16.5) and skin/soft tissue infections, including purulent and non-purulent infections and necrotizing fasciitis (median overall: 9 days; IQR: 8-18) exceeding hospital protocol recommendations (Figure 2 ). There is an association between inappropriate therapy and unfavourable outcome (χ2 = 9.41; p = 0.002); furthermore, time-to-event analysis revealed that patients with inappropriate prescriptions had a median time to clinical resolution that was 2 days longer than those with appropriate prescriptions (p < 0.001). We performed a multivariate analysis to identify independent predictors of prescription inappropriateness (Table 2 ). Although none of the factors included reached a statistical significance, the age of the patient and the department of admission were more likely associated with an inappropriate antibiotic therapy. Table 2 multivariate analysis of factors associated with prescription inappropriateness. Variable Adjusted Odds Ratio 95% CI p-value Prior antibiotic exposure 0.77 0.35–1.70 0.516 Febrile neutropenia 1.04 0.44–2.48 0.929 Age > 70 years 1.65 1.03–2.64 0.062 Combination therapy 0.74 0.41–1.34 0.314 Department (ref: Internal Medicine) - General Surgery 1.87 0.73–4.80 0.192 - Hematology 1.46 0.65–3.29 0.358 Male gender 0.62 0.33–1.14 0.120 ICU admission 0.93 0.56–1.55 0.119 (ICU: intensive care unit) Outcomes of proactive reviews Of 210 prescriptions, 156 (74.3%) were reviewed, of which 88 (56.4%) were empirical and 68 (43.6%) targeted. In most cases, the consultant confirmed the ward-initiated therapy for both empirical (n = 49, 31.4%) and targeted (n = 54, 34.6%) treatments. Reviews were not performed for 54 prescriptions (25.7%), mainly in Hematology (n = 50, representing 47.2% of the department's prescriptions and 92.59% of non-reviews) (details are available in Supplemental Table S1 ). There was a significant association between inappropriate prescriptions and subsequent modification (χ²=6.42; p = 0.011), more evident for targeted (χ²=5.76; p = 0.016) than for empirical therapies (χ²=1.21; p = 0.271): inappropriate targeted prescriptions were more than four times as likely to be modified compared to appropriate ones (adjusted OR: 4.94; 95% CI: 1.24–19.76). The most frequent intervention for targeted therapy was simplification (n = 6, 42.9%), while empirical prescriptions have mostly been converted to targeted therapies (n = 20, 51.3%). Overall, Internal Medicine has the highest modification rate (24/61 prescriptions, 39.3%), followed by Hematology (20/56, 35.7%), while General Surgery had a much lower rate (9/39, 23.1%). Microbiological patterns Of the 210 antibiotic prescriptions, 127 (60.5%) had at least one microbiological isolation, with 189 microorganisms identified in total. Gram-negative bacteria were the most common pathogens, with Enterobacterales representing 40.7% (n = 77), followed by Pseudomonas aeruginosa (15.3%) and Acinetobacter baumannii (4.2%). Approximately half of isolated Enterobacterales were extended-spectrum beta-lactamases (ESBL) producers (44.2%), while 13% were CPE (3 KPC, 7 NDM). Among P. aeruginosa isolates, 27.6% were carbapenem-resistant, and nearly all A. baumannii isolates (87.5%) were carbapenem-resistant (details are provided in Supplemental Table S2 ). Comparing January-October 2024 to the same period in 2023, we observed a concerning pattern of increasing antimicrobial resistance. Considering the number of infections caused by all monitored resistant pathogens (excluding rectal swabs), overall resistance burden increased by 25.53%, with a peak of 80.00% for Pseudomonas aeruginosa (Figure 3 A). The most frequent multidrug-resistant pathogen was NDM-producing Klebsiella spp.: colonization rates have more than doubled (from 1.2% to 2.5%), while NDM infections have risen, especially as regards bloodstream infections ( Figure 3B and 3C ). (NDM: New Delhi metallo-beta-lactamase) Antibiotic consumption and cost analysis The overall percentage increase in consumption of all analysed antibiotics in March-August 2024 compared to the same period in 2023 was + 41.93%, with variations between departments (Table 3 ). For carbapenems, the general percentage increase was + 28.40% (from 294.75 to 378.46 DDD/100 patient-days). Ceftolozane/tazobactam consumption increased by 1,375.89% (from 1.41 to 20.81 DDD/100 patient-days), while ceftazidime/avibactam + aztreonam increased by 501.97% (from 5.08 to 30.58 DDD/100 patient-days). Statistically significant increases in consumption were observed in Hematology (+ 54.18%, p = 0.041) and General Surgery (+ 22.91%, p = 0.048), while Internal Medicine showed a non-significant trend toward reduced consumption (-3.87%, p = 0.847) (Fig. 4 ). Hematology contributed 94.36% of the total increase in consumption. The proportion of new beta-lactam/beta-lactamase inhibitors relative to carbapenems increased from 2.20% in 2023 to 13.58% in 2024 (p < 0.001), driving the overall increase. Total antibiotic cost increased by 56.12% (from €37,402.94 to €58,393.54), primarily affecting Hematology (+ 75.32%) and General Surgery (+ 140.35%) departments, while Internal Medicine saw a 22.42% decrease (Table 3 ). Correlation analysis revealed a significant positive association between antibiotic consumption and resistance rates, with the strongest correlation observed for carbapenem consumption and NDM-producing Klebsiella prevalence (r = 0.82; p < 0.001). Table 3 Changes in antibiotic consumption and costs by departments and by molecule. Consumption is expressed by DDD/100 patient-days. Consumption 2023 Consumption 2024 % Change p-value Cost 2023 (€) Cost 2024 (€) % Change p-value Total 305.37 433.41 + 41.93 0.033 37,402.94 58,393.54 + 56.12 0.012 By department Internal Medicine 43.50 41.82 -3.87 0.847 11,648.35 9,036.93 -22.42 0.127 General Surgery 38.89 47.80 + 22.91 0.048 6,465.61 15,540.02 + 140.35 < 0.001 Hematology 222.98 343.79 + 54.18 0.041 19,288.98 33,816.58 + 75.32 0.008 By molecule Carbapenems 294.75 378.46 + 28.40 0.047 11,651.67 11,121.25 -4.55 0.713 Ceftolozane/tazobactam 1.41 20.81 + 1,375.89 < 0.001 1,617.90 10,786.00 + 566.67 < 0.001 Ceftazidime/avibactam 5.08 19.21 + 278.25 < 0.001 6349.20 24761.88 + 290 < 0.001 CAZAVI + aztreonam 5.08 30.58 + 502.22 < 0.001 6,349.20 25,485.77 + 301.40 < 0.001 Cefiderocol 4.13 3.55 -13.99 0.083 17,784.17 11,000.52 -38.14 0.042 (CAZAVI: ceftazidime/avibactam) Figure legend: Carbapenems consumption has been separated to allow a better visualisation (ATM: aztreonam; C-T: ceftolozane/tazobactam; CAZAVI: ceftazidime/avibactam; FDC; cefiderocol). Forecasting antibiotic consumption The line graph in Fig. 5 shows a linear regression model using data on overall antibiotic consumption of all analysed antibiotics in the three departments from 2016 to 2024 (shown in Supplemental Table S3 ). Although irregular, the average trend over time appears to be upward, with an important increase between 2020 and 2024. Assuming the trend linearity, the consumption will increase further in the coming years. Discussion This study analysed the process and outcomes of a pilot AMS program in a tertiary hospital in Italy, revealing several important findings. First, overall prescriptive appropriateness was low (~50%), suggesting inadequacy of previous AMS measures. The primary factor of inappropriateness was therapy duration, which generally exceeded guideline recommendations despite evidence supporting shorter antibiotic courses [11,12]. Appropriateness as a measure of antibiotic use aims to assess prescribing quality; however, it appears difficult to define in a reliable, valid, and widely accepted manner [13]. Following other studies, we defined appropriateness as concordance with hospital-endorsed guidelines, considered as proxy for international and evidence-based indications [14, 15], in terms of both the right agent to treat the infection of concern or the pathogen when available, and the right duration of therapy. Second, although proactive prescription review interventions most often confirmed ongoing therapeutic choices, we found a significant association between inappropriate therapies and their modification, suggesting good targeting of interventions. This is relevant since we demonstrated that inappropriateness more frequently leads to an unfavourable outcome and to a longer time to clinical resolution. Particularly, inappropriate targeted therapies were more likely to be modified, and in this case the review prompted a simplification of the antibiotic therapy: this result underlines the importance of microbiological data in therapeutic decisions and advocates the implementation of innovative diagnostic tools or other diagnostic strategies to decrease unnecessary or broad-spectrum antibiotic use, thus promoting antimicrobial stewardship. Examples of successful common infection-based interventions are the use of rapid molecular tests for patients with suspected community-acquired pneumonia [16] or the selective reporting of urine cultures [17]. Third, microbiological data showed increased prevalence of multidrug-resistant organisms, particularly NDM-producing Klebsiella spp., both in colonization and infection contexts, aligning with European surveillance reports showing rising isolations of carbapenemase-producing Enterobacterales [18]. This increase should be considered within the hospital's role as a tertiary-level regional hub, receiving complex patients with comorbidities. The epidemiological relationship between the hospital and territorial/long-term care facilities, known reservoirs for resistant bacteria dissemination [19], likely plays a role. Regarding antibiotic consumption data, the implementation of post-prescription review was associated with significantly increased consumption of targeted antibiotics, contrary to expected AMS outcomes. This may reflect the transition from a restrictive to a post-prescription review approach, which can initially increase antibiotic use [20], as observed in our General Surgery department with the introduction of previously unused antibiotics. Finally, we tried to forecast antibiotic consumption in our setting by analysing data from years 2016-2024 and building a linear regression model. In such a long-time frame, we observed a continuous upward trend, with a more pronounced surge in 2024 and the projection of a further increase in subsequent years. Such finding reflects increased circulation of multidrug-resistant pathogens within the hospital, highlighting the importance of complementing AMS with effective infection control practices, also to improve resource distribution. A recent study shows that new anti-infective molecules have a favourable impact on the allocation of resources in the public health system [21]. Several systematic reviews have demonstrated the efficacy of AMS programs in improving prescriptive compliance, reducing therapy duration and hospitalization without increasing mortality [22,23]. However, determining the impact of individual interventions remains challenging, as they are often combined and their effects may vary across settings and populations [24]. Despite strongly recommended in evidenced-based guidelines, both restrictive and proactive modes of intervention have several limits. The first ones are sometimes poorly effective, while the second ones are resource- and time- consuming and need a dedicated multidisciplinary team. Our intervention based on an electronic alert system showed to be effective in detecting inappropriate prescriptions and may be an interesting way to combine the two principal modes of controlling antibiotic use. The "handshake stewardship" model, which includes face-to-face meetings with ward medical staff, has shown greater effectiveness than traditional approaches [25] and may represent an improvement opportunity for our program. This study has several strengths. First, we conducted a comprehensive evaluation examining multiple dimensions of the AMS intervention, including process indicators, clinical outcomes, microbiological trends, and economic impacts. In this context, we chose to use appropriateness of empirical and targeted antibiotic therapy as a measure, along with antibiotic consumption. Second, we employed robust statistical methods including multivariate analyses to control for confounding factors. Third, the real-world implementation context provides valuable insights for similar healthcare settings. Limitations include the retrospective design, arbitrary time selection, heterogeneous patient sample, and limited sample size affecting statistical power. Interpretation biases include the lack of documentation regarding when treatments were prescribed based on informal infectious disease consultation. Microbiological data collection lacked dedicated software and required post-hoc processing. Antibiotic consumption was calculated based on department deliveries rather than actual patient administration, although this limitation was mitigated by examining sufficiently long periods to average out anomalous orders. Conclusions The pilot AMS program at Fondazione IRCCS Policlinico San Matteo operated in a context of low prescriptive appropriateness of antibiotic therapies. After six months, it was associated with increased consumption and cost of targeted antibiotics, partly justified by increased circulation of multidrug-resistant bacteria. Based on our findings, we recommend several improvements to the program: increase prescriptive appropriateness through targeted educational interventions on hospital guidelines; enhance proactive reviews based on electronic alerts; implement stronger infection control measures, particularly for screening and containing multidrug-resistant organisms; develop department-specific approaches rather than uniform strategies; consider long-term cost analyses that account for incremental cost and patient quality of life. This study highlights the challenges of implementing effective AMS programs in the context of rising antimicrobial resistance, suggesting the need for comprehensive, multidisciplinary approaches that combine stewardship with robust infection control practices. Abbreviations AMR Antimicrobial resistance AMS Antimicrobial Stewardship ATM Aztreonam C-T Ceftolozane/tazobactam CAP Community-acquired pneumonia CAZAVI Ceftazidime/avibactam CLABSI Central line-associated bloodstream infection CNS Central nervous system CPE Carbapenemase-producing Enterobacterales CRAB Carbapenem-resistant Acinetobacter baumannii DDD Defined daily dose DTR-PA Difficult-to-treat Pseudomonas aeruginosa ESBL Extended-spectrum beta-lactamases FDC Cefiderocol HAP Hospital-acquired pneumonia ICU Intensive Care Unit ID Infectious diseases IQR Interquartile range KPC Klebsiella pneumoniae carbapenemase NDM New Delhi metallo-beta-lactamase SSTIs Skin and soft tissue infections UTIs Urinary tract infections Declarations Ethics approval and consent to participate The experimental protocol was approved by the Pavia Ethics Committee (protocol no. 2023311590). All data were anonymised and remained so throughout the study. Given the retrospective and anonymous nature of the analysis, the ethics committee waived the requirement for specific written informed consent. However, upon admission to hospital, all patients signed a consent form regarding the management of sensitive data and their use in clinical studies. Consent for publication Not applicable. 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 Not applicable. Funding Not applicable. Authors' contributions LMN, MV and PS conceived and designed the analysis. LMN, MV and EA collected the data. PC and MC provided microbiological data. EP and CM analyzed and interpreted data regarding Internal Medicine patients. RL and EC analyzed and interpreted data regarding General Surgery patients. AR and PZ analyzed and interpreted data regarding Hematology patients. LMN, MV and PS performed the analysis. LMN, PS, VZ and RB wrote the paper. All authors have read and agreed to the published version of the manuscript. Acknowledgements We acknowledge Professor Lucia Sacchi for the support in the statistical analysis. References Goossens H, Ferech M, Stichele RV, Elseviers M. Outpatient Antibiotic Use in Europe and Association with Resistance: A Cross-National Database Study. The Lancet. 2005;365:579–587. 10.1016/S0140-6736(05)17907-0 . PMID: 15708101. Ajulo S, Awosile B, Global Antimicrobial Resistance and Use Surveillance System (GLASS 2022). Investigating the Relationship between Antimicrobial Resistance and Antimicrobial Consumption Data across the Participating Countries. PLoS ONE. 2024;19. 10.1371/journal.pone.0297921 . PMID: 38315668; PMCID: PMC10843100. GBD 2021 Antimicrobial Resistance Collaborators. Global Burden of Bacterial Antimicrobial Resistance 1990–2021: A Systematic Analysis with Forecasts to 2050. The Lancet. 2024;404:1199–1226. doi: 10.1016/S0140-6736(24)01867-1. Epub 2024 Sep 16. PMID: 39299261; PMCID: PMC11718157. Health Emergency Preparedness and Response Authority (HERA). Health Union: Identifying Top 3 Priority Health Targets. 2022. European Centre for Disease Prevention and Control. Point Prevalence Survey of Healthcare-Associated Infections and Antimicrobial Use in European Acute Care Hospitals 2022–2023. Stockholm: ECDC; 2024. European Centre for Disease Prevention and Control. Assessing the Health Burden of Infections with Antibiotic-Resistant Bacteria in the EU/EEA, 2016–2020. Stockholm: ECDC; 2022. Dyar OJ, Huttner B, Schouten J, Pulcini C, ESGAP (ESCMID Study Group for Antimicrobial stewardshiP). What Is Antimicrobial Stewardship? Clin Microbiol Infect. 2017;23:793–8. 10.1016/j.cmi.2017.08.026 . Epub 2017 Sep 4. PMID: 28882725. US Department of Health and Human Services. CDC. Core Elements of Hospital Antibiotic Stewardship Programs. 2019. World Health Organization. The WHO AWaRe (Access, Watch, Reserve) Antibiotic Book. 1st ed. Geneva: World Health Organization; 2022. Ogrinc G, Davies L, Goodman D, Batalden P, Davidoff F, Stevens D. SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): Revised Publication Guidelines from a Detailed Consensus Process. BMJ Qual Saf. 2016;25:986–99. 10.1136/bmjqs-2015-004411 . Epub 2015 Sep 14. PMID: 26369893; PMCID: PMC5256233. Spellberg B. The New Antibiotic Mantra-Shorter Is Better. JAMA Intern Med. 2016;176:1254–5. 10.1001/jamainternmed.2016.3646 . PMID: 27455385; PMCID: PMC5233409. Vitiello A, Sabbatucci M, Salzano A, Zovi A. The Importance of Antibiotic Treatment Duration in Antimicrobial Resistance. Eur J Clin Microbiol Infect Dis. 2024;43:1673–5. 10.1007/s10096-024-04867-y . Epub 2024 Jun 4. PMID: 38833103. Morris AM. Antimicrobial Stewardship Programs: Appropriate Measures and Metrics to Study their Impact. Curr Treat Options Infect Dis. 2014;6(2):101–12. 10.1007/s40506-014-0015-3 . PMID: 25999798; PMCID: PMC4431704. Talpaert MJ, Gopal Rao G, Cooper BS, Wade P. Impact of guidelines and enhanced antibiotic stewardship on reducing broad-spectrum antibiotic usage and its effect on incidence of Clostridium difficile infection. J Antimicrob Chemother. 2011;66(9):2168–74. 10.1093/jac/dkr253 . Epub 2011 Jun 14. PMID: 21676904. McCabe C, Kirchner C, Zhang H, Daley J, Fisman DN. Guideline-concordant therapy and reduced mortality and length of stay in adults with community-acquired pneumonia: playing by the rules. Arch Intern Med. 2009;169(16):1525-31. 10.1001/archinternmed.2009.259 . PMID: 19752411. Markussen DL, Serigstad S, Ritz C, Knoop ST, Ebbesen MH, Faurholt-Jepsen D, Heggelund L, van Werkhoven CH, Clark TW, Bjørneklett RO, Kommedal Ø, Ulvestad E, Grewal HMS. Diagnostic Stewardship in Community-Acquired Pneumonia With Syndromic Molecular Testing: A Randomized Clinical Trial. JAMA Netw Open. 2024;7(3):e240830. 10.1001/jamanetworkopen.2024.0830 . Daley P, Garcia D, Inayatullah R, Penney C, Boyd S. Modified Reporting of Positive Urine Cultures to Reduce Inappropriate Treatment of Asymptomatic Bacteriuria Among Nonpregnant, Noncatheterized Inpatients: A Randomized Controlled Trial. Infect Control Hosp Epidemiol. 2018;39(7):814–9. Epub 2018 May 28. PMID: 29804552. European Centre for Disease Prevention and Control and World Health Organization. Antimicrobial Resistance Surveillance in Europe 2023 – 2021 Data. Stockholm: ECDC; 2023. Feldstein D, Sloane PD, Feltner C. Antibiotic Stewardship Programs in Nursing Homes: A Systematic Review. J Am Med Dir Assoc. 2018;19:110–6. 10.1016/j.jamda.2017.06.019 . Epub 2017 Aug 7. PMID: 28797590. Mehta JM, Haynes K, Wileyto EP, et al. Comparison of Prior Authorization and Prospective Audit with Feedback for Antimicrobial Stewardship. Infect Control Hosp Epidemiol. 2014;35:1092–9. doi: 10.1086/677624. Epub 2014 Jul 23. PMID: 25111916; PMCID: PMC4198070. Naci H, Murphy P, Woods B, Lomas J, Wei J, Papanicolas I. Population-health impact of new drugs recommended by the National Institute for Health and Care Excellence in England during 2000-20: a retrospective analysis. Lancet. 2025;405(10472):50–60. 10.1016/S0140-6736(24)02352-3 . Epub 2024 Dec 12. Davey P, Marwick CA, Scott CL, et al. Interventions to Improve Antibiotic Prescribing Practices for Hospital Inpatients. Cochrane Database Syst Rev. 2017;2. 10.1002/14651858.CD003543.pub4 . PMID: 28178770; PMCID: PMC6464541. Baur D, Gladstone BP, Burkert F, et al. Effect of Antibiotic Stewardship on the Incidence of Infection and Colonisation with Antibiotic-Resistant Bacteria and Clostridium Difficile Infection: A Systematic Review and Meta-Analysis. Lancet Infect Dis. 2017;17:990–1001. 10.1016/S1473-3099(17)30325-0 . Epub 2017 Jun 16. PMID: 28629876. Schuts EC, Hulscher MEJL, Mouton JW, et al. Current Evidence on Hospital Antimicrobial Stewardship Objectives: A Systematic Review and Meta-Analysis. Lancet Infect Dis. 2016;16:847–56. 10.1016/S1473-3099(16)00065-7 . Epub 2016 Mar 3. MacBrayne CE, Williams MC, Levek C et al. Sustainability of Handshake Stewardship: Extending a Hand Is Effective Years Later. Clin Infect Dis. 2020;70:2325–2332. 10.1093/cid/ciz650 . PMID: 31584641. Additional Declarations No competing interests reported. Supplementary Files ARICSupplementalMaterials2707.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":91355,"visible":true,"origin":"","legend":"\u003cp\u003eAppropriateness index for both empirical (A) and targeted (B) therapies in Internal Medicine, General Surgery and Hematology. Factors of inappropriateness are shown in the pie chart (C).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236630/v1/0b16484dd9203a151ede41db.jpg"},{"id":100388756,"identity":"4171072e-60dd-4335-92da-ffeb3cd100ae","added_by":"auto","created_at":"2026-01-16 11:18:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105572,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing duration of therapy for different infection types.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(CAP: community-acquired pneumonia; CLABSI: central line-associated bloodstream infection; HAP: hospital-acquired pneumonia; SSTI: skin and soft tissue infection; UTI: urinary tract infection)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236630/v1/f5c7420c62b93018fafc4835.jpg"},{"id":100388566,"identity":"92af137c-c4db-487e-b0de-1db69a312b1f","added_by":"auto","created_at":"2026-01-16 11:17:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74350,"visible":true,"origin":"","legend":"\u003cp\u003eChange in number of infections caused by multidrug-resistant pathogens (A) and by NDM-producing Klebsiella spp. (B), registered in the three departments of interest in 2023 and 2024. Colonisation rates for NDM-producing Klebsiella spp. in the two years are also shown (C).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236630/v1/7b451b4e5e9a1719629759f1.jpg"},{"id":100388839,"identity":"f1d5cd25-7aef-4b60-95b2-898d950b3c07","added_by":"auto","created_at":"2026-01-16 11:18:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160804,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of consumption of the analysed antibiotics in 2023 and 2024, overall (A) and in the three departments (Internal Medicine - B, General Surgery - C, and Hematology - D).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure legend: Carbapenems consumption has been separated to allow a better visualisation (ATM: aztreonam; C-T: ceftolozane/tazobactam; CAZAVI: ceftazidime/avibactam; FDC; cefiderocol).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236630/v1/2a4410d9ca1755d36eb3eda2.jpg"},{"id":100388658,"identity":"411cdd06-18ed-4891-b341-a50c5c042784","added_by":"auto","created_at":"2026-01-16 11:17:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71293,"visible":true,"origin":"","legend":"\u003cp\u003eChange in overall antibiotic consumption from 2016 to 2024. Consumption is expressed by DDD/100 patient-days.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure legend: Blue dots represent real consumption data. The red dashed line represents a linear regression line calculated on historical data and shows the average upward trend in consumption over time. The green dashed line represents a future forecast for the years 2025 and 2026 based on the linear model.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236630/v1/aaed8904b349e68f2a60ad59.jpg"},{"id":104089364,"identity":"d5a3d6a9-fc8b-443b-8d1a-14557a4940f3","added_by":"auto","created_at":"2026-03-06 15:56:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1752783,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7236630/v1/8dccf35b-b980-4f04-841b-1de60e09a544.pdf"},{"id":100388804,"identity":"2a203f5b-ac58-40d3-acda-233e9a5e2ade","added_by":"auto","created_at":"2026-01-16 11:18:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":30690,"visible":true,"origin":"","legend":"","description":"","filename":"ARICSupplementalMaterials2707.docx","url":"https://assets-eu.researchsquare.com/files/rs-7236630/v1/c9c6b1bc17606bc4d9448527.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Implementation and Impact of an Antimicrobial Stewardship Post-Prescription Review Program: Challenges Amid Rising Resistance in a Tertiary Hospital","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntimicrobial resistance (AMR) represents a major global health emergency with significant consequences for therapeutic failures, mortality, healthcare costs, and economic impact [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2050, AMR is predicted to cause approximately 2\u0026nbsp;million deaths annually, with an additional 8\u0026nbsp;million deaths being associated with AMR complications [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The European Health Emergency Preparedness and Response Authority has identified AMR as one of three cross-border health threats [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eItaly faces particularly serious AMR challenges. According to the European Centre for Disease Prevention and Control, the percentage of infections caused by multidrug-resistant pathogens in Italy is 40% compared to the 21.8% European average, and the prevalence of patients receiving at least one antibiotic treatment is 41.67% versus 36% across Europe [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, approximately one-third of European deaths attributable to AMR occur in Italy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAntimicrobial Stewardship (AMS) is defined as a coherent set of actions promoting responsible antimicrobial use to ensure sustainable access to effective therapy for all who need it [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The aim of a AMS program is to improve prescription appropriateness and achieve high rates of cure, with minimal toxicity for the patient and minimal impact on the subsequent development of resistance. The Centers for Disease Control and Prevention has outlined essential components for a functioning AMS program, including leadership commitment, accountability, pharmacy expertise, interventions, monitoring, reporting, and education. Among the several types of interventions, post-prescription review and preauthorization are considered the two most effective antibiotic stewardship interventions in hospitals [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, choosing the best program for a specific care setting remains a challenge.\u003c/p\u003e \u003cp\u003eThis study aimed to conduct a process and outcome analysis of a pilot AMS program implemented in specific departments of Fondazione IRCCS Policlinico San Matteo, a tertiary-level university hospital in Pavia, Italy. We evaluated the impact of post-prescription review intervention on prescription appropriateness, clinical outcomes, microbiological patterns, antibiotic consumption, and costs during the first six months of implementation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eSetting and context\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe Fondazione IRCCS Policlinico San Matteo is a tertiary-level university hospital with 781 ordinary beds, serving approximately 700,000 people. According to data from the 2022 point-prevalence study promoted by ECDC, the local percentage of patients receiving at least one antibiotic (49.9%) reflects the national trend, with significant use of antibiotics in the Watch category of the WHO AWaRe classification [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrior to the pilot program, the hospital relied primarily on restrictive AMS approaches, requiring an infectious diseases (ID) consultation for carbapenem and Reserve category antibiotic prescriptions. A hospital antimicrobial use protocol for empirical therapy, derived from international guidelines, was implemented in June 2023.\u003c/p\u003e\u003cp\u003e \u003cb\u003eIntervention\u003c/b\u003e \u003c/p\u003e\u003cp\u003eBeginning March 1st, 2024, a pilot AMS program based on post-prescription review was implemented in the following departments: Internal Medicine, General Surgery, and Hematology. We decided to start from these departments because of both the high number of patients admitted and the high rate of antibiotic prescriptions. The intervention targeted specific antibiotics from the Watch and Reserve categories: carbapenems (meropenem, imipenem, ertapenem); new beta-lactam/beta-lactamase inhibitors (ceftazidime/avibactam, ceftolozane/tazobactam, meropenem/vaborbactam, imipenem/cilastatin/relebactam, ceftazidime/avibactam + aztreonam); cefiderocol.\u003c/p\u003e\u003cp\u003eThe program consisted of:\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eAlert phase\u003c/em\u003e - Daily electronic alerts were sent via email with reports of prescriptions for targeted antibiotics initiated or ongoing in the previous 24 hours.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eProactive intervention phase\u003c/em\u003e - Within 48–72 hours, the ID consultant reviewed the prescription and discussed it with prescribing physicians, considering clinical, biochemical, radiological, and microbiological data, resulting in a documented therapeutic decision.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003cp\u003e \u003cb\u003eStudy design and measurements\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThis quality improvement study followed a quasi-experimental design according to Revised Standards for Quality Improvement Reporting Excellence 2.0 guidelines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The analysis covered the first six months of implementation (March-August 2024), with antibiotic consumption and cost comparisons to the corresponding period in 2023. Microbiological analysis examined broader periods (January-October 2024 vs January-October 2023).\u003c/p\u003e\u003cp\u003e \u003cb\u003eProcess indicators\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cstrong\u003eAppropriateness of empirical and targeted antibiotic therapy\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e Defined as adherence to hospital protocol guidelines for both choice of molecule and duration of therapy. In case of targeted therapy, the choice of molecule according to the identified microorganism was considered. We calculated the overall appropriateness rate with 95% confidence intervals.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eOutcomes of proactive reviews\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eCategorized as continuation, intensification, simplification, discontinuation, or transition to targeted therapy.\u003c/p\u003e\u003cp\u003e \u003cb\u003eOutcome indicators\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cstrong\u003eClinical measures\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eLength of hospitalization, Intensive Care Unit (ICU) admission rate, \u003cem\u003eClostridioides difficile\u003c/em\u003e infection rate, unfavourable outcome rate (defined as death, treatment failure, or chronic infection).\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eMicrobiological measures\u003c/strong\u003e \u003c/p\u003e\u003cp\u003ePrevalence of colonization and infection with resistant pathogens, with particular focus on carbapenemase-producing \u003cem\u003eEnterobacterales\u003c/em\u003e (CPE), difficult-to-treat \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (DTR-PA), and carbapenem-resistant \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (CRAB).\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eAntibiotic consumption\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eExpressed in defined daily doses (DDDs) per 100 patient-days.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eEconomic measures\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eCost of selected antibiotics.\u003c/p\u003e\u003cp\u003e \u003cb\u003eData collection\u003c/b\u003e \u003c/p\u003e\u003cp\u003eClinical, demographic and microbiological data, as well as data for clinical measures, were extracted from the electronic medical records of the patients and were inserted to a dedicated spreadsheet. Data for microbiological measures were provided by the Microbiology Laboratory. Antibiotic consumption and costs were inferred by the monthly dispensation from the Hospital Pharmacy to the wards.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were represented as median and interquartile range (IQR), while categorical variables as count and percentage with 95% confidence intervals. Associations between categorical variables were evaluated using chi-square tests with Yates' correction. Differences in medians were assessed using Mann-Whitney test for two groups and Kruskal-Wallis test for multiple groups, with Tukey's post-hoc tests for pairwise comparisons. The relationship between intervention type and therapy duration was analysed using group comparisons with Mann-Whitney tests. To evaluate the association between prescription appropriateness and subsequent modification, we calculated adjusted odds ratios using logistic regression models that controlled for patient age, department, and infection type. For the analysis of antibiotic consumption trends, we calculated DDDs per 100 patient-days for each antibiotic class and department. Differences in consumption between 2023 and 2024 were evaluated using paired t-tests. Economic analyses included calculation of cost per DDD, and cost proportions by antibiotic class and department. For microbiological data, we calculated prevalence of colonization and infection for resistant organisms in both study periods. Multivariate logistic regression was performed to identify independent predictors of prescription inappropriateness, controlling for patient characteristics, department, infection type, and prior antibiotic exposure. Finally, linear regression was used to describe the relationship between time and antibiotic consumption. All statistical analyses were performed using MATLAB R2023b (MathWorks, Natick, MA, USA) with a significance level set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003ePrescription patterns and population characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDuring the study period, 210 antibiotic prescriptions were initiated, of which 64 (30.5%) were started in Internal Medicine, 40 (19.0%) in General Surgery, and 106 (50.5%) in Hematology. The most prescribed antibiotics were carbapenems, exclusively represented by meropenem (n\u0026thinsp;=\u0026thinsp;181, 86.2%), followed by ceftazidime/avibactam\u0026thinsp;+\u0026thinsp;aztreonam (n\u0026thinsp;=\u0026thinsp;14, 6.7%), ceftazidime/avibactam (n\u0026thinsp;=\u0026thinsp;7, 3.3%), ceftolozane/tazobactam (n\u0026thinsp;=\u0026thinsp;4, 1.9%), and cefiderocol (n\u0026thinsp;=\u0026thinsp;4, 1.9%).\u003c/p\u003e \u003cp\u003eThe study involved 180 patients (107 males, 59.4%), with a median age of 69 years (IQR 58\u0026ndash;79). The prevalence of suspected/confirmed infections varied by department: genitourinary tract infections were more frequent in Internal Medicine (n\u0026thinsp;=\u0026thinsp;20, 31.3%), gastrointestinal/intra-abdominal infections in General Surgery (n\u0026thinsp;=\u0026thinsp;24, 60.0%), and febrile neutropenia in Hematology (n\u0026thinsp;=\u0026thinsp;51, 48.1%) followed by respiratory tract infections (n\u0026thinsp;=\u0026thinsp;30, 28.3%). Significant differences were observed between departments regarding patient age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ICU admission (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and unfavorable clinical outcome (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population and antibiotic prescriptions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternal Medicine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneral Surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHematology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrescriptions, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age, years (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (58\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (69\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (61\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (49\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian length of hospitalization, days (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (17.75-44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5 (11-50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (18\u0026ndash;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (21\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU admission, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnown colonization, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnfavorable outcome, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC. difficile\u003c/em\u003e infection, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeropenem, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (86,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (92,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (72,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93 (87,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImipenem, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErtapenem, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftolozane/tazobactam, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftazidime/avibactam, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (10,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftazidime/avibactam\u0026thinsp;+\u0026thinsp;ATM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (6,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (10,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (7,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefiderocol, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (7,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeropenem/vaborbactam, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImipenem/relebactam, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpirical therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (75.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious antibiotic therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173 (82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian therapy duration, days (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5 (7\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (7.75-15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfection type, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (3,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteremia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (6,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (4,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocarditis and CLABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (4,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (5,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal/intra-abdominal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (19,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (20,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (60,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeningitis and CNS infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebrile neutropenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (24,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (48,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory tract infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (24,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (26,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (12,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (28,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSTIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (5,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (17,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (3,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (12,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (31,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (7,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e(ATM: aztreonam; CLABSI: central line-associated bloodstream infection; CNS: central nervous system; ICU: intensive care unit; SSTIs: skin and soft tissue infections; UTIs: urinary tract infections)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003ePrescriptive appropriateness and patient outcomes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong the 210 antibiotic prescriptions started during the study period, 116 (55,2%) were classified as inappropriate, and most of them were empirical therapies (n=79, 60,3%). Overall appropriateness of antibiotic prescriptions was 50.0% (95% CI: 43.2-56.8%), with no statistically significant differences between departments (p=0.266). For empirical therapy, Internal Medicine had the highest appropriateness (51.4%; 95% CI: 39.5-63.2%) compared to General Surgery (31.3%; 95% CI: 17.7-48.7%) and Hematology (36.3%; 95% CI: 27.4-46.1%). The same trend was observed for targeted therapy: Internal Medicine (55.2%; 95% CI: 42.3-67.5%), General Surgery (50.0%; 95% CI: 35.8-64.2%), and Hematology (53.8%; 95% CI: 39.1-67.9%) (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The primary factor of inappropriateness was therapy duration, alone (n=57, 49,1%) or as a contributing factor (n=30, 25.9%) (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Substantial variability in treatment duration was observed across different infection types, with community-acquired pneumonia (median: 13 days; IQR: 8.5-16.5) and skin/soft tissue infections, including purulent and non-purulent infections and necrotizing fasciitis (median overall: 9 days; IQR: 8-18) exceeding hospital protocol recommendations (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is an association between inappropriate therapy and unfavourable outcome (χ2\u0026thinsp;=\u0026thinsp;9.41; p\u0026thinsp;=\u0026thinsp;0.002); furthermore, time-to-event analysis revealed that patients with inappropriate prescriptions had a median time to clinical resolution that was 2 days longer than those with appropriate prescriptions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWe performed a multivariate analysis to identify independent predictors of prescription inappropriateness (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although none of the factors included reached a statistical significance, the age of the patient and the department of admission were more likely associated with an inappropriate antibiotic therapy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emultivariate analysis of factors associated with prescription inappropriateness.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior antibiotic exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026ndash;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebrile neutropenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;70 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026ndash;2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026ndash;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartment (ref: Internal Medicine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- General Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Hematology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026ndash;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e(ICU: intensive care unit)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcomes of proactive reviews\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOf 210 prescriptions, 156 (74.3%) were reviewed, of which 88 (56.4%) were empirical and 68 (43.6%) targeted. In most cases, the consultant confirmed the ward-initiated therapy for both empirical (n\u0026thinsp;=\u0026thinsp;49, 31.4%) and targeted (n\u0026thinsp;=\u0026thinsp;54, 34.6%) treatments. Reviews were not performed for 54 prescriptions (25.7%), mainly in Hematology (n\u0026thinsp;=\u0026thinsp;50, representing 47.2% of the department's prescriptions and 92.59% of non-reviews) (details are available in \u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThere was a significant association between inappropriate prescriptions and subsequent modification (χ\u0026sup2;=6.42; p\u0026thinsp;=\u0026thinsp;0.011), more evident for targeted (χ\u0026sup2;=5.76; p\u0026thinsp;=\u0026thinsp;0.016) than for empirical therapies (χ\u0026sup2;=1.21; p\u0026thinsp;=\u0026thinsp;0.271): inappropriate targeted prescriptions were more than four times as likely to be modified compared to appropriate ones (adjusted OR: 4.94; 95% CI: 1.24\u0026ndash;19.76). The most frequent intervention for targeted therapy was simplification (n\u0026thinsp;=\u0026thinsp;6, 42.9%), while empirical prescriptions have mostly been converted to targeted therapies (n\u0026thinsp;=\u0026thinsp;20, 51.3%).\u003c/p\u003e \u003cp\u003eOverall, Internal Medicine has the highest modification rate (24/61 prescriptions, 39.3%), followed by Hematology (20/56, 35.7%), while General Surgery had a much lower rate (9/39, 23.1%).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrobiological patterns\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOf the 210 antibiotic prescriptions, 127 (60.5%) had at least one microbiological isolation, with 189 microorganisms identified in total. Gram-negative bacteria were the most common pathogens, with \u003cem\u003eEnterobacterales\u003c/em\u003e representing 40.7% (n\u0026thinsp;=\u0026thinsp;77), followed by \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (15.3%) and \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (4.2%). Approximately half of isolated \u003cem\u003eEnterobacterales\u003c/em\u003e were extended-spectrum beta-lactamases (ESBL) producers (44.2%), while 13% were CPE (3 KPC, 7 NDM). Among \u003cem\u003eP. aeruginosa\u003c/em\u003e isolates, 27.6% were carbapenem-resistant, and nearly all \u003cem\u003eA. baumannii\u003c/em\u003e isolates (87.5%) were carbapenem-resistant (details are provided in \u003cb\u003eSupplemental Table S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eComparing January-October 2024 to the same period in 2023, we observed a concerning pattern of increasing antimicrobial resistance. Considering the number of infections caused by all monitored resistant pathogens (excluding rectal swabs), overall resistance burden increased by 25.53%, with a peak of 80.00% for \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The most frequent multidrug-resistant pathogen was NDM-producing \u003cem\u003eKlebsiella\u003c/em\u003e spp.: colonization rates have more than doubled (from 1.2% to 2.5%), while NDM infections have risen, especially as regards bloodstream infections (\u003cb\u003eFigure\u003c/b\u003e \u003cb\u003e3B\u003c/b\u003e and \u003cb\u003e3C\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003e(NDM: New Delhi metallo-beta-lactamase)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAntibiotic consumption and cost analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe overall percentage increase in consumption of all analysed antibiotics in March-August 2024 compared to the same period in 2023 was +\u0026thinsp;41.93%, with variations between departments (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For carbapenems, the general percentage increase was +\u0026thinsp;28.40% (from 294.75 to 378.46 DDD/100 patient-days). Ceftolozane/tazobactam consumption increased by 1,375.89% (from 1.41 to 20.81 DDD/100 patient-days), while ceftazidime/avibactam\u0026thinsp;+\u0026thinsp;aztreonam increased by 501.97% (from 5.08 to 30.58 DDD/100 patient-days).\u003c/p\u003e \u003cp\u003eStatistically significant increases in consumption were observed in Hematology (+\u0026thinsp;54.18%, p\u0026thinsp;=\u0026thinsp;0.041) and General Surgery (+\u0026thinsp;22.91%, p\u0026thinsp;=\u0026thinsp;0.048), while Internal Medicine showed a non-significant trend toward reduced consumption (-3.87%, p\u0026thinsp;=\u0026thinsp;0.847) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Hematology contributed 94.36% of the total increase in consumption. The proportion of new beta-lactam/beta-lactamase inhibitors relative to carbapenems increased from 2.20% in 2023 to 13.58% in 2024 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), driving the overall increase.\u003c/p\u003e \u003cp\u003eTotal antibiotic cost increased by 56.12% (from \u0026euro;37,402.94 to \u0026euro;58,393.54), primarily affecting Hematology (+\u0026thinsp;75.32%) and General Surgery (+\u0026thinsp;140.35%) departments, while Internal Medicine saw a 22.42% decrease (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelation analysis revealed a significant positive association between antibiotic consumption and resistance rates, with the strongest correlation observed for carbapenem consumption and NDM-producing \u003cem\u003eKlebsiella\u003c/em\u003e prevalence (r\u0026thinsp;=\u0026thinsp;0.82; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in antibiotic consumption and costs by departments and by molecule. Consumption is expressed by DDD/100 patient-days.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsumption 2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsumption 2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCost 2023 (\u0026euro;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCost 2024 (\u0026euro;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;41.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37,402.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58,393.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;56.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBy department\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,648.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9,036.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-22.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;22.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,465.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,540.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;140.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e222.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e343.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;54.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,288.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33,816.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;75.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBy molecule\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbapenems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e378.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;28.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,651.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,121.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftolozane/tazobactam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1,375.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,617.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10,786.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;566.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeftazidime/avibactam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;278.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6349.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24761.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAZAVI\u0026thinsp;+\u0026thinsp;aztreonam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;502.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,349.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25,485.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u0026thinsp;301.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefiderocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,784.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11,000.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-38.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e(CAZAVI: ceftazidime/avibactam)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure legend: Carbapenems consumption has been separated to allow a better visualisation (ATM: aztreonam; C-T: ceftolozane/tazobactam; CAZAVI: ceftazidime/avibactam; FDC; cefiderocol).\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eForecasting antibiotic consumption\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe line graph in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows a linear regression model using data on overall antibiotic consumption of all analysed antibiotics in the three departments from 2016 to 2024 (shown in \u003cb\u003eSupplemental Table S3\u003c/b\u003e). Although irregular, the average trend over time appears to be upward, with an important increase between 2020 and 2024. Assuming the trend linearity, the consumption will increase further in the coming years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analysed the process and outcomes of a pilot AMS program in a tertiary hospital in Italy, revealing several important findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, overall prescriptive appropriateness was low (~50%), suggesting inadequacy of previous AMS measures. The primary factor of inappropriateness was therapy duration, which generally exceeded guideline recommendations despite evidence supporting shorter antibiotic courses [11,12]. Appropriateness as a measure of antibiotic use aims to assess prescribing quality; however, it appears difficult to define in a reliable, valid, and widely accepted manner [13]. Following other studies, we defined appropriateness as concordance with hospital-endorsed guidelines, considered as proxy for international and evidence-based indications [14, 15], in terms of both the right agent to treat the infection of concern or the pathogen when available, and the right duration of therapy.\u003c/p\u003e\n\u003cp\u003eSecond, although proactive prescription review interventions most often confirmed ongoing therapeutic choices, we found a significant association between inappropriate therapies and their modification, suggesting good targeting of interventions. This is relevant since we demonstrated that inappropriateness more frequently leads to an unfavourable outcome and to a longer time to clinical resolution. Particularly, inappropriate targeted therapies were more likely to be modified, and in this case the review prompted a simplification of the antibiotic therapy: this result underlines the importance of microbiological data in therapeutic decisions and advocates the implementation of innovative diagnostic tools or other diagnostic strategies to decrease unnecessary or broad-spectrum antibiotic use, thus promoting antimicrobial stewardship. Examples of successful common infection-based interventions are the use of rapid molecular tests for patients with suspected community-acquired pneumonia [16] or the selective reporting of urine cultures [17].\u003c/p\u003e\n\u003cp\u003eThird, microbiological data showed increased prevalence of multidrug-resistant organisms, particularly NDM-producing \u003cem\u003eKlebsiella\u003c/em\u003e spp., both in colonization and infection contexts, aligning with European surveillance reports showing rising isolations of carbapenemase-producing \u003cem\u003eEnterobacterales\u003c/em\u003e [18]. This increase should be considered within the hospital\u0026apos;s role as a tertiary-level regional hub, receiving complex patients with comorbidities. The epidemiological relationship between the hospital and territorial/long-term care facilities, known reservoirs for resistant bacteria dissemination [19], likely plays a role.\u003c/p\u003e\n\u003cp\u003eRegarding antibiotic consumption data, the implementation of post-prescription review was associated with significantly increased consumption of targeted antibiotics, contrary to expected AMS outcomes. This may reflect the transition from a restrictive to a post-prescription review approach, which can initially increase antibiotic use [20], as observed in our General Surgery department with the introduction of previously unused antibiotics. Finally, we tried to forecast antibiotic consumption in our setting by analysing data from years 2016-2024 and building a linear regression model. In such a long-time frame, we observed a continuous upward trend, with a more pronounced surge in 2024 and the projection of a further increase in subsequent years. Such finding reflects increased circulation of multidrug-resistant pathogens within the hospital, highlighting the importance of complementing AMS with effective infection control practices, also to improve resource distribution. A recent study shows that new anti-infective molecules have a favourable impact on the allocation of resources in the public health system [21].\u003c/p\u003e\n\u003cp\u003eSeveral systematic reviews have demonstrated the efficacy of AMS programs in improving prescriptive compliance, reducing therapy duration and hospitalization without increasing mortality [22,23]. However, determining the impact of individual interventions remains challenging, as they are often combined and their effects may vary across settings and populations [24]. Despite strongly recommended in evidenced-based guidelines, both restrictive and proactive modes of intervention have several limits. The first ones are sometimes poorly effective, while the second ones are resource- and time- consuming and need a dedicated multidisciplinary team. Our intervention based on an electronic alert system showed to be effective in detecting inappropriate prescriptions and may be an interesting way to combine the two principal modes of controlling antibiotic use. The \u0026quot;handshake stewardship\u0026quot; model, which includes face-to-face meetings with ward medical staff, has shown greater effectiveness than traditional approaches [25] and may represent an improvement opportunity for our program.\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. First, we conducted a comprehensive evaluation examining multiple dimensions of the AMS intervention, including process indicators, clinical outcomes, microbiological trends, and economic impacts. In this context, we chose to use appropriateness of empirical and targeted antibiotic therapy as a measure, along with antibiotic consumption. Second, we employed robust statistical methods including multivariate analyses to control for confounding factors. Third, the real-world implementation context provides valuable insights for similar healthcare settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLimitations include the retrospective design, arbitrary time selection, heterogeneous patient sample, and limited sample size affecting statistical power. Interpretation biases include the lack of documentation regarding when treatments were prescribed based on informal infectious disease consultation. Microbiological data collection lacked dedicated software and required post-hoc processing. Antibiotic consumption was calculated based on department deliveries rather than actual patient administration, although this limitation was mitigated by examining sufficiently long periods to average out anomalous orders.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe pilot AMS program at Fondazione IRCCS Policlinico San Matteo operated in a context of low prescriptive appropriateness of antibiotic therapies. After six months, it was associated with increased consumption and cost of targeted antibiotics, partly justified by increased circulation of multidrug-resistant bacteria. Based on our findings, we recommend several improvements to the program: increase prescriptive appropriateness through targeted educational interventions on hospital guidelines; enhance proactive reviews based on electronic alerts; implement stronger infection control measures, particularly for screening and containing multidrug-resistant organisms; develop department-specific approaches rather than uniform strategies; consider long-term cost analyses that account for incremental cost and patient quality of life.\u003c/p\u003e\n\u003cp\u003eThis study highlights the challenges of implementing effective AMS programs in the context of rising antimicrobial resistance, suggesting the need for comprehensive, multidisciplinary approaches that combine stewardship with robust infection control practices.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntimicrobial resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntimicrobial Stewardship\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAztreonam\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-T\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCeftolozane/tazobactam\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity-acquired pneumonia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAZAVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCeftazidime/avibactam\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLABSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral line-associated bloodstream infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral nervous system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCarbapenemase-producing \u003cem\u003eEnterobacterales\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRAB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCarbapenem-resistant \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDefined daily dose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDTR-PA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifficult-to-treat \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\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-lactamases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCefiderocol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHospital-acquired pneumonia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInfectious diseases\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\"\u003eKPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e carbapenemase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNew Delhi metallo-beta-lactamase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSTIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSkin and soft tissue infections\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrinary tract infections\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol was approved by the Pavia Ethics Committee (protocol no. 2023311590). All data were anonymised and remained so throughout the study. Given the retrospective and anonymous nature of the analysis, the ethics committee waived the requirement for specific written informed consent. However, upon admission to hospital, all patients signed a consent form regarding the management of sensitive data and their use in clinical studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLMN, MV and PS conceived and designed the analysis. LMN, MV and EA collected the data. PC and MC provided microbiological data. EP and CM analyzed and interpreted data regarding Internal Medicine patients. RL and EC analyzed and interpreted data regarding General Surgery patients. AR and PZ analyzed and interpreted data regarding Hematology patients. LMN, MV and PS performed the analysis. LMN, PS, VZ and RB wrote the paper. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge Professor Lucia Sacchi for the support in the statistical analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGoossens H, Ferech M, Stichele RV, Elseviers M. Outpatient Antibiotic Use in Europe and Association with Resistance: A Cross-National Database Study. The Lancet. 2005;365:579\u0026ndash;587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(05)17907-0\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(05)17907-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 15708101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjulo S, Awosile B, Global Antimicrobial Resistance and Use Surveillance System (GLASS 2022). Investigating the Relationship between Antimicrobial Resistance and Antimicrobial Consumption Data across the Participating Countries. PLoS ONE. 2024;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0297921\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0297921\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 38315668; PMCID: PMC10843100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Antimicrobial Resistance Collaborators. Global Burden of Bacterial Antimicrobial Resistance 1990\u0026ndash;2021: A Systematic Analysis with Forecasts to 2050. The Lancet. 2024;404:1199\u0026ndash;1226. doi: 10.1016/S0140-6736(24)01867-1. Epub 2024 Sep 16. PMID: 39299261; PMCID: PMC11718157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth Emergency Preparedness and Response Authority (HERA). Health Union: Identifying Top 3 Priority Health Targets. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Centre for Disease Prevention and Control. Point Prevalence Survey of Healthcare-Associated Infections and Antimicrobial Use in European Acute Care Hospitals 2022\u0026ndash;2023. Stockholm: ECDC; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Centre for Disease Prevention and Control. Assessing the Health Burden of Infections with Antibiotic-Resistant Bacteria in the EU/EEA, 2016\u0026ndash;2020. Stockholm: ECDC; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDyar OJ, Huttner B, Schouten J, Pulcini C, ESGAP (ESCMID Study Group for Antimicrobial stewardshiP). What Is Antimicrobial Stewardship? Clin Microbiol Infect. 2017;23:793\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmi.2017.08.026\u003c/span\u003e\u003cspan address=\"10.1016/j.cmi.2017.08.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2017 Sep 4. PMID: 28882725.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS Department of Health and Human Services. CDC. Core Elements of Hospital Antibiotic Stewardship Programs. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. The WHO AWaRe (Access, Watch, Reserve) Antibiotic Book. 1st ed. Geneva: World Health Organization; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgrinc G, Davies L, Goodman D, Batalden P, Davidoff F, Stevens D. SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): Revised Publication Guidelines from a Detailed Consensus Process. BMJ Qual Saf. 2016;25:986\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjqs-2015-004411\u003c/span\u003e\u003cspan address=\"10.1136/bmjqs-2015-004411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2015 Sep 14. PMID: 26369893; PMCID: PMC5256233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpellberg B. The New Antibiotic Mantra-Shorter Is Better. JAMA Intern Med. 2016;176:1254\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamainternmed.2016.3646\u003c/span\u003e\u003cspan address=\"10.1001/jamainternmed.2016.3646\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 27455385; PMCID: PMC5233409.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitiello A, Sabbatucci M, Salzano A, Zovi A. The Importance of Antibiotic Treatment Duration in Antimicrobial Resistance. Eur J Clin Microbiol Infect Dis. 2024;43:1673\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10096-024-04867-y\u003c/span\u003e\u003cspan address=\"10.1007/s10096-024-04867-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Jun 4. PMID: 38833103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris AM. Antimicrobial Stewardship Programs: Appropriate Measures and Metrics to Study their Impact. Curr Treat Options Infect Dis. 2014;6(2):101\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40506-014-0015-3\u003c/span\u003e\u003cspan address=\"10.1007/s40506-014-0015-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 25999798; PMCID: PMC4431704.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalpaert MJ, Gopal Rao G, Cooper BS, Wade P. Impact of guidelines and enhanced antibiotic stewardship on reducing broad-spectrum antibiotic usage and its effect on incidence of Clostridium difficile infection. J Antimicrob Chemother. 2011;66(9):2168\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jac/dkr253\u003c/span\u003e\u003cspan address=\"10.1093/jac/dkr253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2011 Jun 14. PMID: 21676904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCabe C, Kirchner C, Zhang H, Daley J, Fisman DN. Guideline-concordant therapy and reduced mortality and length of stay in adults with community-acquired pneumonia: playing by the rules. Arch Intern Med. 2009;169(16):1525-31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/archinternmed.2009.259\u003c/span\u003e\u003cspan address=\"10.1001/archinternmed.2009.259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 19752411.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkussen DL, Serigstad S, Ritz C, Knoop ST, Ebbesen MH, Faurholt-Jepsen D, Heggelund L, van Werkhoven CH, Clark TW, Bj\u0026oslash;rneklett RO, Kommedal \u0026Oslash;, Ulvestad E, Grewal HMS. Diagnostic Stewardship in Community-Acquired Pneumonia With Syndromic Molecular Testing: A Randomized Clinical Trial. JAMA Netw Open. 2024;7(3):e240830. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2024.0830\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2024.0830\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaley P, Garcia D, Inayatullah R, Penney C, Boyd S. Modified Reporting of Positive Urine Cultures to Reduce Inappropriate Treatment of Asymptomatic Bacteriuria Among Nonpregnant, Noncatheterized Inpatients: A Randomized Controlled Trial. Infect Control Hosp Epidemiol. 2018;39(7):814\u0026ndash;9. Epub 2018 May 28. PMID: 29804552.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Centre for Disease Prevention and Control and World Health Organization. Antimicrobial Resistance Surveillance in Europe 2023\u0026thinsp;\u0026ndash;\u0026thinsp;2021 Data. Stockholm: ECDC; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeldstein D, Sloane PD, Feltner C. Antibiotic Stewardship Programs in Nursing Homes: A Systematic Review. J Am Med Dir Assoc. 2018;19:110\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jamda.2017.06.019\u003c/span\u003e\u003cspan address=\"10.1016/j.jamda.2017.06.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2017 Aug 7. PMID: 28797590.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta JM, Haynes K, Wileyto EP, et al. Comparison of Prior Authorization and Prospective Audit with Feedback for Antimicrobial Stewardship. Infect Control Hosp Epidemiol. 2014;35:1092\u0026ndash;9. doi: 10.1086/677624. Epub 2014 Jul 23. PMID: 25111916; PMCID: PMC4198070.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaci H, Murphy P, Woods B, Lomas J, Wei J, Papanicolas I. Population-health impact of new drugs recommended by the National Institute for Health and Care Excellence in England during 2000-20: a retrospective analysis. Lancet. 2025;405(10472):50\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(24)02352-3\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(24)02352-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Dec 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavey P, Marwick CA, Scott CL, et al. Interventions to Improve Antibiotic Prescribing Practices for Hospital Inpatients. Cochrane Database Syst Rev. 2017;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/14651858.CD003543.pub4\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD003543.pub4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 28178770; PMCID: PMC6464541.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaur D, Gladstone BP, Burkert F, et al. Effect of Antibiotic Stewardship on the Incidence of Infection and Colonisation with Antibiotic-Resistant Bacteria and Clostridium Difficile Infection: A Systematic Review and Meta-Analysis. Lancet Infect Dis. 2017;17:990\u0026ndash;1001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1473-3099(17)30325-0\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(17)30325-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2017 Jun 16. PMID: 28629876.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuts EC, Hulscher MEJL, Mouton JW, et al. Current Evidence on Hospital Antimicrobial Stewardship Objectives: A Systematic Review and Meta-Analysis. Lancet Infect Dis. 2016;16:847\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1473-3099(16)00065-7\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(16)00065-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2016 Mar 3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacBrayne CE, Williams MC, Levek C et al. Sustainability of Handshake Stewardship: Extending a Hand Is Effective Years Later. Clin Infect Dis. 2020;70:2325\u0026ndash;2332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cid/ciz650\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciz650\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31584641.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial stewardship, Antimicrobial resistance, Post-prescription review, Multidrug-resistant organisms, Carbapenemase-producing Enterobacterales","lastPublishedDoi":"10.21203/rs.3.rs-7236630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7236630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAntimicrobial resistance (AMR) represents a major global health emergency. This study evaluated a pilot Antimicrobial Stewardship (AMS) program based on post-prescription review of selected antibiotics at Fondazione IRCCS Policlinico San Matteo in Pavia, Italy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis quasi-experimental study analysed the first six months (March-August 2024) of an AMS program in Internal Medicine, General Surgery, and Hematology departments. The intervention focused on post-prescription review of carbapenems, new beta-lactam/beta-lactamase inhibitors, and cefiderocol. Multiple outcome measures were assessed, including prescription appropriateness, intervention effects, microbiological patterns, antibiotic consumption, and costs, with comparison to the corresponding period in 2023.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe analysed 210 antibiotic prescriptions (64 in Internal Medicine, 40 in General Surgery, 106 in Hematology). Overall appropriateness was 50.0% (95% CI: 43.2\u0026ndash;56.8%), with therapy duration being the principal factor of inappropriateness (75.0% of inappropriate cases). Inappropriate therapy was associated with a significantly higher risk of unfavourable outcomes (χ2\u0026thinsp;=\u0026thinsp;9.41; p\u0026thinsp;=\u0026thinsp;0.002) and longer hospital stays (median difference: 2 days; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Antibiotic consumption increased significantly (+\u0026thinsp;41.93%), primarily in Hematology (+\u0026thinsp;54.17%), with overall costs rising by 56.12%. The prevalence of multidrug-resistant organisms increased substantially, with NDM-producing \u003cem\u003eKlebsiella\u003c/em\u003e spp. showing increases in both colonization (+\u0026thinsp;77.58%) and infection (+\u0026thinsp;95.10%). There was a significant association between inappropriate prescriptions and subsequent modification (χ\u0026sup2;=6.42; p\u0026thinsp;=\u0026thinsp;0.011), more evident for targeted than for empirical therapies.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe pilot AMS program identified significant challenges in optimizing antibiotic use amid increasing resistance rates. Department-specific responses suggest tailored approaches may be more effective than uniform strategies. Enhanced infection control, targeted economic stewardship, and an improved handshake model of intervention are recommended to improve future outcomes.\u003c/p\u003e","manuscriptTitle":"Implementation and Impact of an Antimicrobial Stewardship Post-Prescription Review Program: Challenges Amid Rising Resistance in a Tertiary Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 06:27:01","doi":"10.21203/rs.3.rs-7236630/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":"d382cd9b-a887-4ddf-9d88-c1b1c44000d0","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-06T15:55:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 06:27:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7236630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7236630","identity":"rs-7236630","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}