Practice Model of Unit-based Clinical Pharmacists' Individualized Daily AUD Monitoring Report on Antimicrobial Stewardship in ICU of a tertiary hospital in Guangxi, China: An Interrupted Time Series Analysis

Practice Model of Unit-based Clinical Pharmacists' Individualized Daily AUD Monitoring Report on Antimicrobial Stewardship in ICU of a tertiary hospital in Guangxi, China: An Interrupted Time Series Analysis | 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 Practice Model of Unit-based Clinical Pharmacists' Individualized Daily AUD Monitoring Report on Antimicrobial Stewardship in ICU of a tertiary hospital in Guangxi, China: An Interrupted Time Series Analysis Tianmin Huang, Donglan Zhu, Jun Luo, Hongliang Zhang, Yue Qiu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8726337/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background High antimicrobial resistance and consumption intensity in the Intensive Care Unit (ICU) present critical challenges to patient safety. While the Unit-Based Clinical Pharmacist (UBCP) model is a recommended strategy, the specific impact of combining UBCP with individualized data monitoring tools remains to be fully evaluated. This study aimed to assess the effectiveness of a UBCP model, facilitated by a manual Individualized Daily Antimicrobial Use Density (AUD) Monitoring Report (IAUD-RP), on antimicrobial stewardship in the ICU. Methods A single-center, retrospective, quasi-experimental study using interrupted time series (ITS) analysis was conducted in a 12-bed ICU of a tertiary teaching hospital in Guangxi, China. A total of 657 adult patients admitted between April 1, 2023, and October 31, 2025, were included. The intervention, initiated in August 2024, involved the implementation of a UBCP practice model utilizing manual IAUD-RP for real-time risk stratification and precision intervention. The primary outcome was AUD, measured as defined daily doses (DDDs) per 100 patient-days. Secondary outcomes included average antimicrobial cost per hospitalization (AACPH) and antimicrobial consumption structure. Results Among the 657 included patients (295 pre-intervention; 362 post-intervention), pharmacist interventions achieved a 91.7% acceptance rate. ITS analysis demonstrated a significant immediate reduction in AUD (level change, -29.0 DDDs; P = 0.038) following the intervention, successfully reversing a significant pre-intervention upward trend (slope, + 2.6; P = 0.005). Although the ITS model showed no significant immediate level change for costs, the overall mean AACPH decreased significantly from 25,568 CNY to 14,926 CNY (P < 0.001). The antimicrobial consumption structure improved substantially, characterized by significant reductions in tigecycline (-52.1%), quinolones (-39.7%), and carbapenems (-15.8%), alongside a 99.2% increase in WHO Access group antibiotics. Conclusions The UBCP model, empowered by the visualized real-time feedback of IAUD-RP, effectively curbed the growth of antimicrobial intensity, reduced treatment costs, and optimized prescribing structure by promoting the shift from broad-spectrum empirical use to targeted therapy. antimicrobial stewardship intensive care units clinical pharmacist interrupted time series analysis antibiotic use density Figures Figure 1 Figure 2 Figure 3 1 Introduction Antimicrobial Resistance (AMR) ranks among the top ten global health threats, contributing to around 4.71 million deaths in 2021, with 1.14 million directly attributed to it [ 1 , 2 ]. ICUs are high-risk areas for the spread of multi-drug resistant organisms (MDROs) due to patients' critical conditions and invasive procedures [ 3 , 4 ]. MDROs prevalence in ICUs is consistently high, posing significant treatment challenges and economic burdens [ 5 , 6 ]. The global Antimicrobial Management Strategy (AMS) aims to combat resistance and improve patient outcomes by optimizing antimicrobial use. However, it faces challenges due to delayed data, hindering timely decisions, and lacks real-time interventions [ 7 , 8 ]. China has recently emphasized reforming pharmaceutical management, underscoring the vital role of pharmacists in healthcare teams. In 2024, the National Health Commission initiated pilot projects aimed at integrating unit-based clinical pharmacists (UBCP) within departmental structures, signifying a transition from a "drug-centered" to a "patient-centered" practice model [ 9 ]. This change requires UBCP in key areas like ICUs to join ward rounds and participate drug use comprehensively. This study presents a novel antimicrobial stewardship model, UBCP and individualized daily antimicrobial use density monitoring report (IAUD-DR). The UBCP model enhances drug treatment plans through specialized pharmacists working with physicians, while the IAUD-DR tracks and reports real-time antimicrobial use per patient, prompting prescription reviews. The study uses an interrupted time series (ITS) design, which is considered a more robust causal inference design in quasi-experimental studies [ 10 ], to assess the practice model's effectiveness in ICU antimicrobial management. 2 Methods 2.1 Research design This study employs a single-center, retrospective, quasi-experimental design utilizing ITS, and was conducted in the ICU ward one of the First Affiliated Hospital of Guangxi Medical University (approval No. 2026-E0022). This institution is recognized as the largest tertiary grade A general hospital in the Guangxi Zhuang Autonomous Region, boasting a capacity of 2,850 beds. The ICU ward in question comprises 12 beds and primarily manages a diverse range of acute and critical cases originating from both internal medicine and surgical departments. The inclusion criteria for the study encompassed adult patients (aged 18 years and older) admitted to this ICU ward between April 1, 2023, and October 31, 2025. Patients were excluded from the study if key electronic data, such as records of antimicrobial drug administration and clinical outcomes, were missing. 2.2 Intervention phases The study spans 31 months, with August 2024 marking the intervention point when the UBCP practice model was implemented, as per the "Pilot Work Plan for Resident Pharmacists (Trial)" issued by the First Affiliated Hospital of Guangxi Medical University on August 1, 2024. This plan established the framework and evaluation criteria for UBCP in the ICU, ensuring their integration into the treatment team. The study is split into two phases, Pre-UBCP phase (April 1, 2023 - July 31, 2024, 16 months): Maintaining the original pharmacy service model that pharmacists mainly focused on prescription reviews and consultations. The hospital's quality management office released AUD data quarterly. Post-UBCP phase (August 1, 2024 - October 31, 2025, 15 months): The UBCP practice model will be officially implemented, with IAUD-RP initiated. 2.3 Interventions and data collection 2.3.1 UBCP’ practice model An associate senior clinical pharmacist will be stationed in the ICU. The duties include reviewing medication orders, participating in rounds, collaborating with physicians to optimize drug plans, conducting pharmacological monitoring, and providing medication consultation and education. UBCP will also manage antimicrobial drugs by selecting appropriate medications, optimizing doses, monitoring treatment, and addressing interactions and adverse reactions. 2.3.2 IAUD-RP Data extraction and integration. By 17:00 daily, the ICU UBCP accesses the hospital information system to manually extract patient records. The data is entered into a structured Excel form with 8 columns: patient ID, age/weight, principal and other diagnoses, liver and kidney test results, antimicrobial treatment details, remarks/pharmacist intervention, and defined daily doses (DDDs). Manual calculation and risk stratification. UBCP use Excel formulas to manually calculate daily AUD values and DDDs for each patient. Based on these calculations and clinical data, UBCP assess risk: red marks indicate abnormal test results needing attention for antimicrobial selection, dose adjustment, and monitoring adverse reactions. Tables with DDDs ≥ 2.5 or ≥ 3 antimicrobial are highlighted in yellow for special attention. The "Remarks/Pharmacist Interventions" section is emphasized in bold to highlight key insights and recommendations from the UBCP analysis, based on drug instructions, reference books, treatment guidelines, and drug prices and availability. If no special attention is needed, the remarks section is left blank to ease the clinicians' workload. Information distribution. IAUD-RP is shared with the medical team via the hospital's WeChat group or printed for morning rounds, serving as a key reference. Real-time feedback and adjustment. UBCP advises the medical team on treatment adjustments based on risk labels and monitors patient responses to dynamically update antimicrobial plans. 2.4 Outcome indicators 2.4.1 Primary outcome indicator AUD, measured as DDDs per 100 patient hospital days, calculated using the formula: The DDD value is based on the WHO anatomical therapeutic chemical (ATC)/DDD index (2025 Edition) [ 11 ]. This internationally recognized standard assesses antimicrobial exposure and is essential for hospital management evaluation in China [ 12 ]. 2.4.2 Secondary outcomes Average antimicrobial cost per hospitalization (AACPH): Calculated as the total cost of antimicrobial drugs used during hospital stays divided by the number of discharged patients, measured in CNY. This indicates the economic impact of intervention measures. MDROs detection rate: The percentage of patients testing positive for MDROs among those tested. MDROs include methicillin-resistant Staphylococcus aureu (MRSA), vancomycin-resistant E. faecalis and E. faecium , carbapenem-resistant Acinetobacter baumannii , Klebsiella pneumoniae , Escherichia coli , and Pseudomonas aeruginosa . Antimicrobial use analysis: A three-dimensional approach this evaluation examines shifts in antimicrobial usage proportions based on three classifications, WHO AWaRe classification: Analyzes changes in Access, Watch, and Reserve antimicrobial usage [ 13 ]. ATC classification [ 11 ]: Organizes antimicrobials by the ATC system, including carbapenems, antifungal drugs, tigecycline, linezolid, glycopeptides, quinolones, polymyxins, daptomycin, and others. Beta-lactam and beta-lactamase inhibitors include cefoperazone sodium sulbactam sodium, piperacillin sodium tazobactam sodium, and ceftazidime avibactam. The focus is on changes in DDDs of key drugs like tigecycline, carbapenems, and glycopeptides. Local policy of China Guangxi province classification: Categorizes antimicrobials into special, restricted, and non-restricted use levels [ 14 ]. See Table s1 for a comparison of antibiotic classifications between WHO AWaRe and local policy. UBCP interventions analysis: Intervention scenarios divided into ward rounds (active screening) and on-call consults (passive response via WeChat/phone). The types of UBCP interventions were categorized into dose optimization, adverse drug reaction, therapeutic drug monitoring, de-escalation/streamlining, antimicrobial agent selection, contraindication, interactions, discontinuation/duration. The acceptance rate was calculated as the number of recommendations accepted by physicians divided by the total number of interventions. 2.5 Statistical analysis Data were analyzed using SPSS 26.0. Continuous variables were checked for normality with the Shapiro-Wilk test. Normally distributed data were reported as mean ± standard deviation and compared using independent sample t-tests. Skewed data were shown as median (interquartile range) and compared using the Mann-Whitney U test. Categorical variables were presented as frequency and percentage and compared using the chi-square test or Fisher's exact test. P < 0.05 was considered statistically significant. Descriptive statistics were used to summarize the characteristics of UBCP interventions. A Sankey diagram was constructed to visualize the flow of intervention types and their corresponding clinical acceptance outcomes. In the diagram, connection widths represent intervention frequency, and Origin 2025 was used for the drawing. ITS were analyzed using STATA 17. The effects of intervention measures on monthly outcomes (AUD, AACPH, MDROs detection rate) were assessed using a segmented regression model. This model uses time variables to identify the baseline trend before the intervention, the level change at the intervention's start, and the trend change after the intervention. The model equation is: \(\:{\text{Y}}_{\text{t}}\text{=}{\text{β}}_{\text{0}}\text{+}{\text{β}}_{\text{1}}\text{⋅}\text{tim}{\text{e}}_{\text{t}}\text{+}{\text{β}}_{\text{2}}\text{⋅}\text{intervention}\text{+}{\text{β}}_{\text{3}}\text{⋅}\text{tim}{\text{e}}_{\text{post}}\text{+}{\text{ε}}_{\text{t}}\) . Here, \(\:{\text{Y}}_{\text{t}}\) represents the outcome indicator for month t, \(\:\text{tim}{\text{e}}_{\text{t}}\) is the month since the study began, \(\:\text{intervention}\) indicates intervention status (0 before, 1 after), and \(\:\text{tim}{\text{e}}_{\text{post}}\) is the time after intervention. \(\:{\text{β}}_{\text{0}}\) is the initial baseline level, \(\:{\text{β}}_{\text{1}}\) is the pre-intervention trend, \(\:{\text{β}}_{\text{2}}\) is the level change at intervention, and \(\:{\text{β}}_{\text{3}}\) is the post-intervention trend change; \(\:{\text{ε}}_{\text{t}}\) is the error term. To ensure model robustness, the Durbin-Watson statistic tests residual autocorrelation. If significant, the Prais-Winsten method or ARIMA model corrects it. 3 Results 3.1 Patient and ward baseline characteristics and clinical outcomes. The study included 657 discharged patients, with 295 in the pre-UBCP group and 362 in the post-UBCP group. Both groups were comparable in terms of baseline characteristics like gender, age, diagnoses, treatments and Case Mix Index (P > 0.05). The AACPH dropped significantly from 25,568 ± 8,629 CNY pre-UBCP to 14,926 ± 6,560 CNY post-UBCP (P 0.05). However, clinical outcomes failure and others was significantly lower in the post-intervention group (10.5% vs 17.3%, P = 0.016; 9.8% vs 19.3%, P = 0.001). "Other" encompasses patients who left the hospital voluntarily, were transferred, or were discharged for various reasons. 3.2 Analysis of UBCP’s interventions 3.2.1 Implementation of IAUD-RP Figure 1 displays a real-time IAUD-RP for 12 ICU patients, detailing demographics, diagnoses, organ function, support measures, and antimicrobial treatments. Red highlights indicate abnormal liver and kidney indicators, such as low creatinine clearance in patient 4 and high total bilirubin in patient 9. Yellow marks denote intensive antibacterial treatments for patients 3, 5, 6, 7, 9, and 11, suggesting regimen evaluation. On that day, interventions include dose optimization for patient 4 and 10, therapeutic drug monitoring for patient 9 and 10, antimicrobial stewardship (e.g., re-evaluating antifungal therapy for patients 5 and 8), and adverse drug reaction monitoring for patient 9. No special attention is needed for patients 1, 2 and 12. 3.2.2 Visualization of UBCP interventions trajectories The Sankey diagram (Fig. 2 ) highlights the intervention process and outcomes of the UBCP. Interventions originate from 581 ward rounds and 94 on-call consults, totaling 675 cases, showcasing the proactive and real-time aspects of the UBCP practice model. Key interventions cases include 166 dose optimization, 98 adverse reaction, 87 therapeutic drug monitoring, 96 de-escalation/streamlining, 113 antimicrobial agent selection, 14 contraindication, 40 interactions and 61 discontinuation/duration. 91.7% of the UBCP's recommendations are accepted by doctors, with a 100% acceptance for on-call consultations. 3.3 Outcomes of ITS Analysis The ITS model assessed the immediate and long-term impacts of intervention measures on three outcome indicators (Table 2 and Fig. 3 ). 3.3.1 AUD of ITS Before the intervention, the AUD was rising significantly (β1 = 2.6, P = 0.005), suggesting worsening antimicrobial use in the ICU without intervention. Upon implementing the intervention, there was an immediate drop of 29.0 DDDs per 100 patient-days (β2 = -29.0, 95% CI: -56.2 to -1.7, P = 0.038), showing immediate control effects. Post-intervention, the AUD trend reversed with a slope change of -2.6 (β3 = -2.6, 95% CI: -5.7 to 0.5, P = 0.093). Despite P > 0.05, Fig. 3 A shows a significant downward slope post-intervention, contrasting with the pre-intervention upward trend, indicating sustained inhibitory effects. 3.3.2 AACPH of ITS Before the intervention, costs were stable (β1 = -122.5, P = 0.686). Post-intervention, both the level (β2 = -4241.1, P = 0.297) and trend changes (β3 = -410.2, P = 0.326) were not statistically significant, but all coefficients were negative. However, Table 1 shows a highly significant overall difference (P < 0.001). 3.3.3 MDROs detection rate of ITS The ITS analysis showed no significant differences in the baseline trend, level change (β2 = -10.5), or trend change (β3 = -1.0) for the MDROs detection rate (P > 0.05). 3.4 Structure of antimicrobial consumption Table 3 and Table S1 highlight the effects of pharmacist intervention on drug use. Post-intervention, total antimicrobial use dropped by 11.9% (Z = -3.16, P = 0.002). According to WHO AWaRe classification, Access group drug use rose by 99.2% (P < 0.001), while Watch group use fell by 16.6% (P < 0.001). Reserve group use decreased by 14.2%, but this was not significant (P = 0.275). Locally, non-restricted drug use increased by 54.2% (P < 0.001), with restricted and special level drugs showing a non-significant decrease. The intervention measures effectively reduced the use of high-intensity broad-spectrum antimicrobial drugs. Tigecycline usage dropped by 52.1%, quinolones by 39.7%, and carbapenems by 15.8%. However, some drugs saw compensatory growth, with polymyxins increasing by 29.0% and glycopeptides by 7.4%. 4 Discussion The IAUD-DR is a nudge intervention aimed at overcoming clinical inertia. Unlike rigid electronic medical record alerts, it uses visual risk stratification to modify decision-making at the point of care [ 15 ]. The study's IAUD-RP was manually compiled by UBCP, not through an automated IT system. Despite the growing prevalence of AI and intelligent technology, automated systems often experience alarm fatigue due to their inability to assess clinical context, and potentially result in the oversight of critical notifications [ 16 , 17 ]. IAUD-DR offers distinct benefits by requiring UBCP to actively engage with patient infection data, enhancing their ability to detect subtle drug treatment issues beyond automated reports. This model is easy to implement and precise, making it ideal for resource-limited medical institutions without needing complex IT infrastructure. Additionally, this practice model enhances intervention accuracy and doctor compliance [ 18 – 20 ]. A study at a French university hospital revealed that pharmacy residents mainly worked on optimizing prescriptions, achieving an 89.3% acceptance rate, with 44.4% of interventions initiated by the ICU team [ 21 ]. Albayrak A., et al reported that dose changes accounted for 56.79% of planned interventions, with a 90.8% acceptance and full implementation rate [ 22 ]. Our research shows similar findings: a 91.7% adoption rate (619/675) highlights the ICU team's trust in the pharmacist's expertise. Notably, UBCP successfully managed 94 remote On-call Consults via mobile and WeChat, achieving full adoption. This underscores the strong reliance on remote pharmacy services, especially in urgent situations. IAUD-RP intervention offers a distinct advantage over traditional stewardship models by providing immediate feedback, unlike the quarterly updates that cause delays in behavioral change [ 5 ]. By daily visualizing each patient's antibiotic burden, it prompts clinicians to promptly eliminate unnecessary treatments, leading to the rapid decline observed in our model. Before the intervention, there was a significant increase in AUD (slope = 2.6, P = 0.005), indicating a trend towards excessive use without supervision. After implementing the intervention, there was a significant immediate reduction in AUD (level change = -29.0, P = 0.038), highlighting the role of UBCP in reducing unnecessary prescriptions. This result aligns with earlier research demonstrating the link between pharmacist-led multifaceted antimicrobial stewardship programmes and decreased antibiotic usage [ 23 , 24 ]. Figure 3 shows a 1–2-month lag in the intervention effect, likely due to clinical inertia. It takes time for the UBCP to clear old medical orders and alter doctors' prescribing habits [ 25 ]. Although the post-intervention slope change wasn't statistically significant (P = 0.093), the shift from an upward to a downward trend suggests the model effectively curbs excessive AUD rise. Notably, while the t-test showed a significant reduction in AACPH (25,568 ± 8,629 vs. 14,926 ± 6,560 CNY, P < 0.001), the ITS analysis did not reveal significant immediate level changes. This is attributed to China's National Bulk Procurement (VBP) policy has significantly reduced certain antibiotic prices, increased time series data variability and impacting our economic assessment [ 26 , 27 ]. This increased variance likely enlarged the standard errors in the regression analysis, obscuring the statistical significance of the specific stewardship intervention in the ITS model [ 28 ]. The key finding is the significant decrease in AACPH without affecting patient safety. Treatment failure rates significantly decreased, and all-cause mortality remained unchanged, countering concerns that strict stewardship might lead to under-treatment of critically ill patients [ 29 ]. The ITS findings on MDROs detection rates align with expectations, as improvements in drug resistance rates usually lag [ 30 ]. A 15-month observation period may not adequately capture changes in drug-resistant bacterial populations. Additionally, MDROs spread is heavily influenced by infection control practices like hand hygiene and isolation, making it unlikely that a single drug management strategy will quickly alter the situation [ 31 ]. The study highlights a positive trend in antimicrobial drug use, with a shift towards more optimal usage. According to WHO's AWaRe classification, there was a 99.2% increase in Access category drugs and a 16.6% decrease in Watch category drugs, showing a clear shift in treatment focus that moved from broad-spectrum to narrow-spectrum drugs. For instance, tigecycline and carbapenem saw significant decreases of 52.1% and 15.8% respectively. On the contrary, polymyxins and glycopeptides increased by 29.0% and 7.4%, respectively. This shift may be due to strict restrictions on carbapenems and tigecycline, leading clinicians to use alternatives like polymyxins for carbapenem-resistant infections. Additionally, ICU patients often require targeted treatments for specific resistant bacteria like MRSA. This indicates that control measures should prioritize rational drug selection over merely reducing usage, shifting from empirical to targeted therapy based on drug sensitivity, aligning with MDROs diagnosis and treatment guidelines [ 32 , 33 ]. In local policy classification, UBCP incorporated the approval process for "special use level" drugs into daily routines using the IAUD-DR reporting system. This reduced the use of tightly controlled drugs like tigecycline and meropenem, while "non-restricted use level" drugs increased by 99.2%. This shift matched changes in the WHO AWaRe classification's Watch/Reserve group. UBCP's intervention measures streamlined administrative prescription authority into practical drug selection criteria, optimizing antimicrobial stewardship. However, this single-center retrospective study had a limited sample size, meeting ITS analysis requirements but warranting cautious result extrapolation. The MDROs detection rate didn't significantly decrease, possibly due to the short 15-month observation period. Additionally, the study lacked a detailed analysis of drug resistance changes in specific pathogens. Future research should involve longer follow-ups with comprehensive microbiological data. 5 Conclusion This practice model involving UBCP in the ICU, along with IAUD-RP, effectively halted AUD increase, reduced AACPH, optimized usage, and lowered treatment failure rates. Declarations Acknowledgments The authors are grateful to the patients and families, staff, and clinicians who participated in this study. Authors contributions Tianmin Huang: Data curation, Project administration, Supervision, Writing—review & editing. Donglan Zhu: Software, Data curation. Jun Luo, Hongliang Zhang, Yue Qiu: Methodology, Supervision. Yan Wen, Guoping Liu: Data curation. Hanchun Wen: Resources, Supervision. Taotao Liu: Resources, Supervision, Writing—review & editing. Declaration of conflicting interests The authors declare no conflicts of interest. Ethics approval The study protocol was approved by the Medical Ethics Committee of the First Affiliated Hospital of Guangxi Medical University in Nanning, China (No. 2026-E0022). Given the retrospective nature of the study, informed consent was waived. Supplemental material Supplemental material for this article is available online. Data availability statement The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 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Lancet 2025;405(10474):257-272 Brown NM, Goodman AL, Horner C, et al: Treatment of methicillin-resistant Staphylococcus aureus (MRSA): updated guidelines from the UK. JAC Antimicrob Resist 2021;3(1):dlaa114 Tables Table 1. Patient characteristics during the pre- and post-unit-based clinical pharmacist (UBCP) intervention periods Characteristics Pre-UBCP Post-UBCP P-value April 2023-July 2024 August 2024-October 2025 Discharged patient, n 295 362 Beds, n 12 12 Gender female, n (%) 76 (25.8%) 109 (30.1%) 0.218 Age, years 58.0±16.8 59.1±16.6 0.396 Principal diagnosis (top 3) Severe pneumonia, n (%) 74 (25.1%) 88 (24.3%) 0.890 Sepsis, n (%) 21 (7.1%) 43 (11.8%) 0.056 Sepsis shock, n (%) 17 (5.7%) 19 (5.2%) 0.908 Treatments, n (%) Mechanical ventilation, n (%) 270 (91.5%) 323 (89.2%) 0.392 Continuous renal replacement therapy, n (%) 101 (34.2%) 114 (31.5%) 0.508 Extracorporeal membrane oxygenation, n (%) 21 (7.1%) 34 (9.4%) 0.365 Plasma exchange, n (%) 6 (2.0%) 14 (3.9%) 0.253 Case Mix Index 3.6±0.4 3.8±0.4 0.098 Average length of hospital stays, days 14.7±3.4 12.8±3.1 0.103 Antimicrobial use density 205.1±37.0 195.2±29.9 0.419 Average antimicrobial cost per hospitalization (CNY) 25,568±8,629 14,926±6,560 < 0.001 Multi-drug resistant organisms Detection Rate, n (%) 133 (55.7%) 114 (50.7%) 0.282 Clinical outcomes Cure, n (%) 2 (0.7%) 0, (0%) 0.201 Improvement, n (%) 142 (48.1%) 179 (49.4%) 0.798 Failure, n (%) 51 (17.3%) 38 (10.5%) 0.016 Death, n (%) 71 (24.1%) 75 (20.7%) 0.304 Others, n (%) 29 (9.8%) 70 (19.3%) 0.001 CNY: Chinese yuan. Table 2. Parameter estimates of the interrupted time series analysis for antimicrobial stewardship indicators β1 (95% CI ) 184.2 (163.1 to 205.3) <0.001 2.6 (0.8 to 4.3) 0.005 -29.0 (-56.2 to -1.7) 0.038 -2.6 (-5.7 to 0.5) 0.093 1.82 0.36 26750.9 (22481.1 to 31020.8) <0.001 -122.5 (-736.8 to 491.7) 0.686 -4241.1 (-12430.2 to 3948.1) 0.297 -410.2 (-1251.5 to 431.0) 0.326 1.92 0.45 45.8 (32.2 to 59.4) <0.001 1.1 (-0.4 to 2.6) 0.154 -10.5 (-32.8 to 11.7) 0.339 -1.0 (-3.4 to 1.4) 0.404 1.92 0.06 AUD: Antibiotic use intensity; AACPH: Average antimicrobial cost per hospitalization; MDROs: Multi-drug resistant organisms; CI: Confidence interval; DW: Durbin-Watson. Table 3. Comprehensive analysis of antibiotic consumption across three dimensions (normalized by monthly average) Dimension Category Pre-UBCP (monthly DDDs) Post-UBCP (monthly DDDs) Change(%) Z-valuea P-value Overall Total Antibiotics 744.0 655.7 -11.9% -3.16 0.002 Global Standard Access 26.6 52.9 99.2% 14.3 < 0.001 (WHO AWaRe) Watch 531.9 443.5 -16.6% -6.1 < 0.001 Reserve 185.6 159.3 -14.2% -1.1 0.275 Pharmacological Class Tigecycline 58.0 27.8 -52.1% -10.8 < 0.001 (ATC classification system) Carbapenems 222.2 187.1 -15.8% -2.11 0.035 BL/BLI Combination 113.4 117.0 3.2% 5.14 < 0.001 Polymyxins 24.3 31.3 29.0% 5.66 < 0.001 Glycopeptides 28.9 31.1 7.4% 3.05 0.002 Oxazolidinones 41.5 40.3 -3.1% 1.72 0.086 Quinolones 29.8 18 -39.7% -5.06 < 0.001 Antifungals 151.3 136.1 -10.0% 0.75 0.454 Others 74.6 67.1 -10.1% -1.92 0.055 Local Policy Non-restricted 7.5 11.6 54.2% 4.7 < 0.001 (China Guangxi Province Grade) Restricted 146.0 126.0 -13.7% -0.8 0.45 Special 590.5 518.2 -12.2% -0.6 0.551 UBCP: Unit-based clinical pharmacists; DDDs: Defined daily doses; ATC: Anatomical therapeutic chemical; BL/BLI: Beta-lactam and beta-lactamase inhibitors. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 29 Jan, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 28 Jan, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8726337","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594142274,"identity":"6a9f7371-c2af-417d-a711-90cfee2b2977","order_by":0,"name":"Tianmin Huang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tianmin","middleName":"","lastName":"Huang","suffix":""},{"id":594142277,"identity":"8868918e-d19f-4e6d-b16d-15dfc85ecd78","order_by":1,"name":"Donglan Zhu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Donglan","middleName":"","lastName":"Zhu","suffix":""},{"id":594142280,"identity":"67b9c538-0ef8-401b-bdcd-0a7d8befc129","order_by":2,"name":"Jun Luo","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Luo","suffix":""},{"id":594142283,"identity":"123fe390-3435-4fbe-aec5-47b5514728a6","order_by":3,"name":"Hongliang Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongliang","middleName":"","lastName":"Zhang","suffix":""},{"id":594142284,"identity":"2b1911e5-cf64-4b3c-87a6-4315097ab643","order_by":4,"name":"Yue Qiu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Qiu","suffix":""},{"id":594142286,"identity":"62c1efe5-cd25-41f0-a578-bd840fced689","order_by":5,"name":"Yan Wen","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wen","suffix":""},{"id":594142290,"identity":"4c91d12e-68ff-49e3-bb55-dad1584028d2","order_by":6,"name":"Guoping Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guoping","middleName":"","lastName":"Liu","suffix":""},{"id":594142294,"identity":"105a3d0c-61ab-4af9-972b-c1991cce7b7a","order_by":7,"name":"Hanchun Wen","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanchun","middleName":"","lastName":"Wen","suffix":""},{"id":594142299,"identity":"ebd8890b-7951-4f68-8bc8-0d3c77384a66","order_by":8,"name":"Taotao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACPmYYi70ByjhAQAsbMwMjUK0BAwMPTClBLQwwLRIJxGph5zF/8KPijzy/5PNrUjfbGOT4biQwfi7A6zAew8aeMwaGM2fnFBvntjEYS95IYJaeQUBLA2+bAeOG2zmJj4FaEjfcSAAKErLlb5uB/YabZxIOA7XUE6WlGWgL0HD2gyBbEgwIa2ErnC1zxjh5Zk8Os3HOOQnDmWceNkvj08LPf3jDxzcVcrb97MefSeeU2cjzHU8++BmfFiTAA4wcBgkGSEQRB9gfEKtyFIyCUTAKRhgAABRHRmsV/sXUAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Taotao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-29 02:53:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8726337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8726337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103259244,"identity":"de5a5bda-3f4e-4569-9256-49ec9904e176","added_by":"auto","created_at":"2026-02-23 17:37:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":698140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative example of the individual daily antibiotic use density monitoring report. \u003c/strong\u003eThe report integrates patient demographics, diagnoses, liver and renal function indicators, and antimicrobial regimens to facilitate real-time risk stratification by the Unit-based Clinical Pharmacist (UBCP). Red font indicates abnormal physiological values (e.g., elevated TBIL, ALT, AST, SCr, or low ClCr) that may require dosage adjustment. Yellow highlighting identifies high-intensity antimicrobial regimens (defined as DDDs ≥ 2.5 or complex combinations ≥ 3), serving as a visual trigger for priority stewardship review. “Remarks / Pharmacist Interventions” of this column functions as a comprehensive clinical log for precision stewardship. The bottom row displays the real-time calculation of the point-prevalence AUD for the entire unit on that specific day.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CPR, cardiopulmonary resuscitation; MODS, multiple organ dysfunction syndrome; AKI, acute kidney injury; ARDS, acute respiratory distress syndrome; COPD, chronic obstructive pulmonary disease; CAD, coronary artery disease. Cx: Culture; NGS: Next-generation sequencing; BALF: Bronchoalveolar lavage fluid; TBIL: Total bilirubin; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; SCr: Serum creatinine; ClCr: Creatinine clearance; ARC: Augmented renal clearance; CRRT: Continuous renal replacement therapy; TDM: Therapeutic drug monitoring; Ld: Loading dose; Maint: Maintenance dose; Neb: Nebulization; L-AmB: Liposomal Amphotericin B; MDR: Multi-drug resistant; ESBLs: Extended-spectrum beta-lactamases; G test: 1,3-beta-D-glucan test; GM test: Galactomannan test; PCT: Procalcitonin; AUC: Area under the curve; AE: Adverse event. DDDs: Defined Daily Doses; AUD: Antibiotic use intensity.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8726337/v1/ce2ec9fe78f97a3c34be5cfd.png"},{"id":103259243,"identity":"af04d0b3-e309-46c7-85fa-eac6bb82308a","added_by":"auto","created_at":"2026-02-23 17:37:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSankey diagram of unit-based clinical pharmacist interventions.\u003c/strong\u003e The intervention process begins with a \"Ward Rounds/On-call Consults,\" progresses through interventions like \"dose optimization, adverse drug reaction, therapeutic drug monitoring, de-escalation/streamlining, antimicrobial agent selection, contraindication, interactions, discontinuation/duration\" and concludes with outcomes such as \"all acceptance/non-acceptance\" and \"acceptance/non-acceptance of ward rounds/on-call consults.\"\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8726337/v1/03d771feeb432003fc4f943c.png"},{"id":103259245,"identity":"26239479-cecb-4e27-a95e-32aeabd00dc5","added_by":"auto","created_at":"2026-02-23 17:37:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":428428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of unit-based clinical pharmacist intervention on antimicrobial stewardship indicators. \u003c/strong\u003eInterrupted time series analysis of (A) antibiotic use intensity (AUD), (B) average antimicrobial cost per hospitalization, and (C) detection rate of multi-drug resistant organisms (MDROs). The vertical dashed line indicates the implementation of the intervention (August 2024). The solid black line represents the observed monthly data. The solid red and blue lines represent the fitted trends before and after the intervention, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8726337/v1/cb5d3e26048f047c67997c21.png"},{"id":103506077,"identity":"b0a3b982-3505-4966-ae1e-0c7309401e37","added_by":"auto","created_at":"2026-02-26 13:34:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2535162,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8726337/v1/450a7571-42a8-4156-ae31-2cf44ef013e7.pdf"},{"id":103259246,"identity":"6e2c46c6-7c75-41f8-ae09-6309dd57f164","added_by":"auto","created_at":"2026-02-23 17:37:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12471,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8726337/v1/5ba244dc9dd62e5ca30b2ceb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePractice Model of Unit-based Clinical Pharmacists' Individualized Daily AUD Monitoring Report on Antimicrobial Stewardship in ICU of a tertiary hospital in Guangxi, China: An Interrupted Time Series Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAntimicrobial Resistance (AMR) ranks among the top ten global health threats, contributing to around 4.71\u0026nbsp;million deaths in 2021, with 1.14\u0026nbsp;million directly attributed to it [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ICUs are high-risk areas for the spread of multi-drug resistant organisms (MDROs) due to patients' critical conditions and invasive procedures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. MDROs prevalence in ICUs is consistently high, posing significant treatment challenges and economic burdens [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The global Antimicrobial Management Strategy (AMS) aims to combat resistance and improve patient outcomes by optimizing antimicrobial use. However, it faces challenges due to delayed data, hindering timely decisions, and lacks real-time interventions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChina has recently emphasized reforming pharmaceutical management, underscoring the vital role of pharmacists in healthcare teams. In 2024, the National Health Commission initiated pilot projects aimed at integrating unit-based clinical pharmacists (UBCP) within departmental structures, signifying a transition from a \"drug-centered\" to a \"patient-centered\" practice model [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This change requires UBCP in key areas like ICUs to join ward rounds and participate drug use comprehensively.\u003c/p\u003e \u003cp\u003eThis study presents a novel antimicrobial stewardship model, UBCP and individualized daily antimicrobial use density monitoring report (IAUD-DR). The UBCP model enhances drug treatment plans through specialized pharmacists working with physicians, while the IAUD-DR tracks and reports real-time antimicrobial use per patient, prompting prescription reviews. The study uses an interrupted time series (ITS) design, which is considered a more robust causal inference design in quasi-experimental studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], to assess the practice model's effectiveness in ICU antimicrobial management.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Research design\u003c/h2\u003e\n \u003cp\u003eThis study employs a single-center, retrospective, quasi-experimental design utilizing ITS, and was conducted in the ICU ward one of the First Affiliated Hospital of Guangxi Medical University (approval No. 2026-E0022). This institution is recognized as the largest tertiary grade A general hospital in the Guangxi Zhuang Autonomous Region, boasting a capacity of 2,850 beds. The ICU ward in question comprises 12 beds and primarily manages a diverse range of acute and critical cases originating from both internal medicine and surgical departments. The inclusion criteria for the study encompassed adult patients (aged 18 years and older) admitted to this ICU ward between April 1, 2023, and October 31, 2025. Patients were excluded from the study if key electronic data, such as records of antimicrobial drug administration and clinical outcomes, were missing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Intervention phases\u003c/h2\u003e\n \u003cp\u003eThe study spans 31 months, with August 2024 marking the intervention point when the UBCP practice model was implemented, as per the \u0026quot;Pilot Work Plan for Resident Pharmacists (Trial)\u0026quot; issued by the First Affiliated Hospital of Guangxi Medical University on August 1, 2024. This plan established the framework and evaluation criteria for UBCP in the ICU, ensuring their integration into the treatment team. The study is split into two phases, Pre-UBCP phase (April 1, 2023 - July 31, 2024, 16 months): Maintaining the original pharmacy service model that pharmacists mainly focused on prescription reviews and consultations. The hospital\u0026apos;s quality management office released AUD data quarterly. Post-UBCP phase (August 1, 2024 - October 31, 2025, 15 months): The UBCP practice model will be officially implemented, with IAUD-RP initiated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Interventions and data collection\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1 UBCP\u0026rsquo; practice model\u003c/h2\u003e\n \u003cp\u003eAn associate senior clinical pharmacist will be stationed in the ICU. The duties include reviewing medication orders, participating in rounds, collaborating with physicians to optimize drug plans, conducting pharmacological monitoring, and providing medication consultation and education. UBCP will also manage antimicrobial drugs by selecting appropriate medications, optimizing doses, monitoring treatment, and addressing interactions and adverse reactions.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2 IAUD-RP\u003c/h2\u003e\n \u003cp\u003eData extraction and integration. By 17:00 daily, the ICU UBCP accesses the hospital information system to manually extract patient records. The data is entered into a structured Excel form with 8 columns: patient ID, age/weight, principal and other diagnoses, liver and kidney test results, antimicrobial treatment details, remarks/pharmacist intervention, and defined daily doses (DDDs).\u003c/p\u003e\n \u003cp\u003eManual calculation and risk stratification. UBCP use Excel formulas to manually calculate daily AUD values and DDDs for each patient. Based on these calculations and clinical data, UBCP assess risk: red marks indicate abnormal test results needing attention for antimicrobial selection, dose adjustment, and monitoring adverse reactions. Tables with DDDs\u0026thinsp;\u0026ge;\u0026thinsp;2.5 or \u0026ge;\u0026thinsp;3 antimicrobial are highlighted in yellow for special attention. The \u0026quot;Remarks/Pharmacist Interventions\u0026quot; section is emphasized in bold to highlight key insights and recommendations from the UBCP analysis, based on drug instructions, reference books, treatment guidelines, and drug prices and availability. If no special attention is needed, the remarks section is left blank to ease the clinicians\u0026apos; workload.\u003c/p\u003e\n \u003cp\u003eInformation distribution. IAUD-RP is shared with the medical team via the hospital\u0026apos;s WeChat group or printed for morning rounds, serving as a key reference.\u003c/p\u003e\n \u003cp\u003eReal-time feedback and adjustment. UBCP advises the medical team on treatment adjustments based on risk labels and monitors patient responses to dynamically update antimicrobial plans.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Outcome indicators\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.1 Primary outcome indicator\u003c/h2\u003e\n \u003cp\u003eAUD, measured as DDDs per 100 patient hospital days, calculated using the formula:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1771847031.png\" width=\"953\" height=\"52\"\u003e\u003c/p\u003e\n \u003cp\u003eThe DDD value is based on the WHO anatomical therapeutic chemical (ATC)/DDD index (2025 Edition) [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. This internationally recognized standard assesses antimicrobial exposure and is essential for hospital management evaluation in China [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.2 Secondary outcomes\u003c/h2\u003e\n \u003cp\u003eAverage antimicrobial cost per hospitalization (AACPH): Calculated as the total cost of antimicrobial drugs used during hospital stays divided by the number of discharged patients, measured in CNY. This indicates the economic impact of intervention measures.\u003c/p\u003e\n \u003cp\u003eMDROs detection rate: The percentage of patients testing positive for MDROs among those tested. MDROs include methicillin-resistant \u003cem\u003eStaphylococcus aureu\u003c/em\u003e (MRSA), vancomycin-resistant \u003cem\u003eE. faecalis and E. faecium\u003c/em\u003e, carbapenem-resistant \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eAntimicrobial use analysis: A three-dimensional approach this evaluation examines shifts in antimicrobial usage proportions based on three classifications, WHO AWaRe classification: Analyzes changes in Access, Watch, and Reserve antimicrobial usage [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. ATC classification [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]: Organizes antimicrobials by the ATC system, including carbapenems, antifungal drugs, tigecycline, linezolid, glycopeptides, quinolones, polymyxins, daptomycin, and others. Beta-lactam and beta-lactamase inhibitors include cefoperazone sodium sulbactam sodium, piperacillin sodium tazobactam sodium, and ceftazidime avibactam. The focus is on changes in DDDs of key drugs like tigecycline, carbapenems, and glycopeptides. Local policy of China Guangxi province classification: Categorizes antimicrobials into special, restricted, and non-restricted use levels [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. See Table \u003cspan class=\"InternalRef\"\u003es1\u003c/span\u003e for a comparison of antibiotic classifications between WHO AWaRe and local policy.\u003c/p\u003e\n \u003cp\u003eUBCP interventions analysis: Intervention scenarios divided into ward rounds (active screening) and on-call consults (passive response via WeChat/phone). The types of UBCP interventions were categorized into dose optimization, adverse drug reaction, therapeutic drug monitoring, de-escalation/streamlining, antimicrobial agent selection, contraindication, interactions, discontinuation/duration. The acceptance rate was calculated as the number of recommendations accepted by physicians divided by the total number of interventions.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eData were analyzed using SPSS 26.0. Continuous variables were checked for normality with the Shapiro-Wilk test. Normally distributed data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using independent sample t-tests. Skewed data were shown as median (interquartile range) and compared using the Mann-Whitney U test. Categorical variables were presented as frequency and percentage and compared using the chi-square test or Fisher\u0026apos;s exact test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n \u003cp\u003eDescriptive statistics were used to summarize the characteristics of UBCP interventions. A Sankey diagram was constructed to visualize the flow of intervention types and their corresponding clinical acceptance outcomes. In the diagram, connection widths represent intervention frequency, and Origin 2025 was used for the drawing.\u003c/p\u003e\n \u003cp\u003eITS were analyzed using STATA 17. The effects of intervention measures on monthly outcomes (AUD, AACPH, MDROs detection rate) were assessed using a segmented regression model. This model uses time variables to identify the baseline trend before the intervention, the level change at the intervention\u0026apos;s start, and the trend change after the intervention. The model equation is: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}}_{\\text{t}}\\text{=}{\\text{\u0026beta;}}_{\\text{0}}\\text{+}{\\text{\u0026beta;}}_{\\text{1}}\\text{\u0026sdot;}\\text{tim}{\\text{e}}_{\\text{t}}\\text{+}{\\text{\u0026beta;}}_{\\text{2}}\\text{\u0026sdot;}\\text{intervention}\\text{+}{\\text{\u0026beta;}}_{\\text{3}}\\text{\u0026sdot;}\\text{tim}{\\text{e}}_{\\text{post}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e. Here, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e represents the outcome indicator for month t, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{tim}{\\text{e}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the month since the study began, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{intervention}\\)\u003c/span\u003e\u003c/span\u003e indicates intervention status (0 before, 1 after), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{tim}{\\text{e}}_{\\text{post}}\\)\u003c/span\u003e\u003c/span\u003e is the time after intervention. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026beta;}}_{\\text{0}}\\)\u003c/span\u003e\u003c/span\u003e is the initial baseline level, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026beta;}}_{\\text{1}}\\)\u003c/span\u003e\u003c/span\u003e is the pre-intervention trend, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026beta;}}_{\\text{2}}\\)\u003c/span\u003e\u003c/span\u003e is the level change at intervention, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026beta;}}_{\\text{3}}\\)\u003c/span\u003e\u003c/span\u003e is the post-intervention trend change; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{\u0026epsilon;}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the error term. To ensure model robustness, the Durbin-Watson statistic tests residual autocorrelation. If significant, the Prais-Winsten method or ARIMA model corrects it.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Patient and ward baseline characteristics and clinical outcomes.\u003c/h2\u003e\n \u003cp\u003eThe study included 657 discharged patients, with 295 in the pre-UBCP group and 362 in the post-UBCP group. Both groups were comparable in terms of baseline characteristics like gender, age, diagnoses, treatments and Case Mix Index (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The AACPH dropped significantly from 25,568\u0026thinsp;\u0026plusmn;\u0026thinsp;8,629 CNY pre-UBCP to 14,926\u0026thinsp;\u0026plusmn;\u0026thinsp;6,560 CNY post-UBCP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although there was a downward trend in average length of hospital stays, AUD and MDROs detection rate post-intervention, these changes were not statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, clinical outcomes failure and others was significantly lower in the post-intervention group (10.5% vs 17.3%, P\u0026thinsp;=\u0026thinsp;0.016; 9.8% vs 19.3%, P\u0026thinsp;=\u0026thinsp;0.001). \u0026quot;Other\u0026quot; encompasses patients who left the hospital voluntarily, were transferred, or were discharged for various reasons.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Analysis of UBCP\u0026rsquo;s interventions\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Implementation of IAUD-RP\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays a real-time IAUD-RP for 12 ICU patients, detailing demographics, diagnoses, organ function, support measures, and antimicrobial treatments. Red highlights indicate abnormal liver and kidney indicators, such as low creatinine clearance in patient 4 and high total bilirubin in patient 9. Yellow marks denote intensive antibacterial treatments for patients 3, 5, 6, 7, 9, and 11, suggesting regimen evaluation. On that day, interventions include dose optimization for patient 4 and 10, therapeutic drug monitoring for patient 9 and 10, antimicrobial stewardship (e.g., re-evaluating antifungal therapy for patients 5 and 8), and adverse drug reaction monitoring for patient 9. No special attention is needed for patients 1, 2 and 12.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Visualization of UBCP interventions trajectories\u003c/h2\u003e\n \u003cp\u003eThe Sankey diagram (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) highlights the intervention process and outcomes of the UBCP. Interventions originate from 581 ward rounds and 94 on-call consults, totaling 675 cases, showcasing the proactive and real-time aspects of the UBCP practice model. Key interventions cases include 166 dose optimization, 98 adverse reaction, 87 therapeutic drug monitoring, 96 de-escalation/streamlining, 113 antimicrobial agent selection, 14 contraindication, 40 interactions and 61 discontinuation/duration. 91.7% of the UBCP\u0026apos;s recommendations are accepted by doctors, with a 100% acceptance for on-call consultations.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Outcomes of ITS Analysis\u003c/h2\u003e\n \u003cp\u003eThe ITS model assessed the immediate and long-term impacts of intervention measures on three outcome indicators (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ch2\u003e3.3.1 AUD of ITS\u003c/h2\u003e\n \u003cp\u003eBefore the intervention, the AUD was rising significantly (\u0026beta;1\u0026thinsp;=\u0026thinsp;2.6, P\u0026thinsp;=\u0026thinsp;0.005), suggesting worsening antimicrobial use in the ICU without intervention. Upon implementing the intervention, there was an immediate drop of 29.0 DDDs per 100 patient-days (\u0026beta;2 = -29.0, 95% CI: -56.2 to -1.7, P\u0026thinsp;=\u0026thinsp;0.038), showing immediate control effects. Post-intervention, the AUD trend reversed with a slope change of -2.6 (\u0026beta;3 = -2.6, 95% CI: -5.7 to 0.5, P\u0026thinsp;=\u0026thinsp;0.093). Despite P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA shows a significant downward slope post-intervention, contrasting with the pre-intervention upward trend, indicating sustained inhibitory effects.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 AACPH of ITS\u003c/h2\u003e\n \u003cp\u003eBefore the intervention, costs were stable (\u0026beta;1 = -122.5, P\u0026thinsp;=\u0026thinsp;0.686). Post-intervention, both the level (\u0026beta;2 = -4241.1, P\u0026thinsp;=\u0026thinsp;0.297) and trend changes (\u0026beta;3 = -410.2, P\u0026thinsp;=\u0026thinsp;0.326) were not statistically significant, but all coefficients were negative. However, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows a highly significant overall difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 MDROs detection rate of ITS\u003c/h2\u003e\n \u003cp\u003eThe ITS analysis showed no significant differences in the baseline trend, level change (\u0026beta;2 = -10.5), or trend change (\u0026beta;3 = -1.0) for the MDROs detection rate (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Structure of antimicrobial consumption\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e highlight the effects of pharmacist intervention on drug use. Post-intervention, total antimicrobial use dropped by 11.9% (Z = -3.16, P\u0026thinsp;=\u0026thinsp;0.002). According to WHO AWaRe classification, Access group drug use rose by 99.2% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Watch group use fell by 16.6% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Reserve group use decreased by 14.2%, but this was not significant (P\u0026thinsp;=\u0026thinsp;0.275). Locally, non-restricted drug use increased by 54.2% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with restricted and special level drugs showing a non-significant decrease. The intervention measures effectively reduced the use of high-intensity broad-spectrum antimicrobial drugs. Tigecycline usage dropped by 52.1%, quinolones by 39.7%, and carbapenems by 15.8%. However, some drugs saw compensatory growth, with polymyxins increasing by 29.0% and glycopeptides by 7.4%.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe IAUD-DR is a nudge intervention aimed at overcoming clinical inertia. Unlike rigid electronic medical record alerts, it uses visual risk stratification to modify decision-making at the point of care [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The study's IAUD-RP was manually compiled by UBCP, not through an automated IT system. Despite the growing prevalence of AI and intelligent technology, automated systems often experience alarm fatigue due to their inability to assess clinical context, and potentially result in the oversight of critical notifications [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. IAUD-DR offers distinct benefits by requiring UBCP to actively engage with patient infection data, enhancing their ability to detect subtle drug treatment issues beyond automated reports. This model is easy to implement and precise, making it ideal for resource-limited medical institutions without needing complex IT infrastructure.\u003c/p\u003e \u003cp\u003eAdditionally, this practice model enhances intervention accuracy and doctor compliance [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A study at a French university hospital revealed that pharmacy residents mainly worked on optimizing prescriptions, achieving an 89.3% acceptance rate, with 44.4% of interventions initiated by the ICU team [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Albayrak A., et al reported that dose changes accounted for 56.79% of planned interventions, with a 90.8% acceptance and full implementation rate [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our research shows similar findings: a 91.7% adoption rate (619/675) highlights the ICU team's trust in the pharmacist's expertise. Notably, UBCP successfully managed 94 remote On-call Consults via mobile and WeChat, achieving full adoption. This underscores the strong reliance on remote pharmacy services, especially in urgent situations.\u003c/p\u003e \u003cp\u003eIAUD-RP intervention offers a distinct advantage over traditional stewardship models by providing immediate feedback, unlike the quarterly updates that cause delays in behavioral change [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. By daily visualizing each patient's antibiotic burden, it prompts clinicians to promptly eliminate unnecessary treatments, leading to the rapid decline observed in our model. Before the intervention, there was a significant increase in AUD (slope\u0026thinsp;=\u0026thinsp;2.6, P\u0026thinsp;=\u0026thinsp;0.005), indicating a trend towards excessive use without supervision. After implementing the intervention, there was a significant immediate reduction in AUD (level change = -29.0, P\u0026thinsp;=\u0026thinsp;0.038), highlighting the role of UBCP in reducing unnecessary prescriptions. This result aligns with earlier research demonstrating the link between pharmacist-led multifaceted antimicrobial stewardship programmes and decreased antibiotic usage [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a 1\u0026ndash;2-month lag in the intervention effect, likely due to clinical inertia. It takes time for the UBCP to clear old medical orders and alter doctors' prescribing habits [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although the post-intervention slope change wasn't statistically significant (P\u0026thinsp;=\u0026thinsp;0.093), the shift from an upward to a downward trend suggests the model effectively curbs excessive AUD rise.\u003c/p\u003e \u003cp\u003eNotably, while the t-test showed a significant reduction in AACPH (25,568\u0026thinsp;\u0026plusmn;\u0026thinsp;8,629 vs. 14,926\u0026thinsp;\u0026plusmn;\u0026thinsp;6,560 CNY, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the ITS analysis did not reveal significant immediate level changes. This is attributed to China's National Bulk Procurement (VBP) policy has significantly reduced certain antibiotic prices, increased time series data variability and impacting our economic assessment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This increased variance likely enlarged the standard errors in the regression analysis, obscuring the statistical significance of the specific stewardship intervention in the ITS model [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The key finding is the significant decrease in AACPH without affecting patient safety. Treatment failure rates significantly decreased, and all-cause mortality remained unchanged, countering concerns that strict stewardship might lead to under-treatment of critically ill patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The ITS findings on MDROs detection rates align with expectations, as improvements in drug resistance rates usually lag [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A 15-month observation period may not adequately capture changes in drug-resistant bacterial populations. Additionally, MDROs spread is heavily influenced by infection control practices like hand hygiene and isolation, making it unlikely that a single drug management strategy will quickly alter the situation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study highlights a positive trend in antimicrobial drug use, with a shift towards more optimal usage. According to WHO's AWaRe classification, there was a 99.2% increase in Access category drugs and a 16.6% decrease in Watch category drugs, showing a clear shift in treatment focus that moved from broad-spectrum to narrow-spectrum drugs. For instance, tigecycline and carbapenem saw significant decreases of 52.1% and 15.8% respectively. On the contrary, polymyxins and glycopeptides increased by 29.0% and 7.4%, respectively. This shift may be due to strict restrictions on carbapenems and tigecycline, leading clinicians to use alternatives like polymyxins for carbapenem-resistant infections. Additionally, ICU patients often require targeted treatments for specific resistant bacteria like MRSA. This indicates that control measures should prioritize rational drug selection over merely reducing usage, shifting from empirical to targeted therapy based on drug sensitivity, aligning with MDROs diagnosis and treatment guidelines [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In local policy classification, UBCP incorporated the approval process for \"special use level\" drugs into daily routines using the IAUD-DR reporting system. This reduced the use of tightly controlled drugs like tigecycline and meropenem, while \"non-restricted use level\" drugs increased by 99.2%. This shift matched changes in the WHO AWaRe classification's Watch/Reserve group. UBCP's intervention measures streamlined administrative prescription authority into practical drug selection criteria, optimizing antimicrobial stewardship.\u003c/p\u003e \u003cp\u003eHowever, this single-center retrospective study had a limited sample size, meeting ITS analysis requirements but warranting cautious result extrapolation. The MDROs detection rate didn't significantly decrease, possibly due to the short 15-month observation period. Additionally, the study lacked a detailed analysis of drug resistance changes in specific pathogens. Future research should involve longer follow-ups with comprehensive microbiological data.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis practice model involving UBCP in the ICU, along with IAUD-RP, effectively halted AUD increase, reduced AACPH, optimized usage, and lowered treatment failure rates.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the patients and families, staff, and clinicians who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTianmin Huang: Data curation, Project administration, Supervision, Writing\u0026mdash;review \u0026amp; editing. Donglan Zhu: Software, Data curation. Jun Luo, Hongliang Zhang, Yue Qiu: Methodology, Supervision. Yan Wen, Guoping Liu: Data curation. Hanchun Wen: Resources, \u0026nbsp;Supervision. Taotao Liu: Resources, Supervision, Writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Medical Ethics Committee of the First Affiliated Hospital of Guangxi Medical University in Nanning, China (No. 2026-E0022). Given the retrospective nature of the study, informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplemental material for this article is available online.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\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"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAntimicrobial Resistance Collaborators: Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. \u003cem\u003eLancet\u003c/em\u003e 2022;399(10325):629-655\u003c/li\u003e\n \u003cli\u003eGlobal burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050\u003cem\u003e. 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Accessed July 1, 2025\u003c/li\u003e\n \u003cli\u003eXia H, Li J, Yang X, et al: Impacts of pharmacist-led multi-faceted antimicrobial stewardship on antibiotic use and clinical outcomes in urology department of a tertiary hospital in Guangzhou, China: an interrupted time-series study\u003cem\u003e. J Hosp Infect\u003c/em\u003e 2024;151:148-160\u003c/li\u003e\n \u003cli\u003eAdekoya I, Maraj D, Steiner L, et al: Comparison of antibiotics included in national essential medicines lis ts of 138 countries using the WHO Access, Watch, Reserve (AWaRe) class ification: a cross-sectional study\u003cem\u003e. Lancet Infect Dis\u003c/em\u003e 2021; 21(10):1429-1440\u003c/li\u003e\n \u003cli\u003eHealth Commission of Guangxi Zhuang Autonomous Region: Classification Management Catalogue of Antimicrobial Drug Clinical Application in Guangxi Zhuang Autonomous Region (2023 Edition). Available at: https://wsjkw.gxzf.gov.cn/xxgk_49493/fdzdgk/wsjszh/yzyg/t17578385.shtml. Accessed July 1, 2025\u003c/li\u003e\n \u003cli\u003eHallsworth M, Chadborn T, Sallis A, et al: Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial\u003cem\u003e. Lancet\u003c/em\u003e 2016;387(10029):1743-1752\u003c/li\u003e\n \u003cli\u003eFernandes CO, Miles S, Lucena CJP, et al: Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation\u003cem\u003e. J Med Internet Res\u003c/em\u003e 2019;21(11):e15406\u003c/li\u003e\n \u003cli\u003eEscobar GJ, Schuler A, Lee C Automated alerts in obstetrics\u003cem\u003e. Am J Obstet Gynecol\u003c/em\u003e 2021;225(2):208\u003c/li\u003e\n \u003cli\u003eNampoothiri V, Hisham M, Mbamalu O, et al: Evolution of pharmacist roles in antimicrobial stewardship: A 20-year systematic review\u003cem\u003e. Int J Infect Dis\u003c/em\u003e 2025;151:107306\u003c/li\u003e\n \u003cli\u003eOtieno PA, Campbell S, Maley S, et al: A Systematic Review of Pharmacist-Led Antimicrobial Stewardship Programs in Sub-Saharan Africa\u003cem\u003e. Int J Clin Pract\u003c/em\u003e 2022;2022:3639943\u003c/li\u003e\n \u003cli\u003eGebretekle GB, Haile Mariam D, Abebe Taye W, et al: Half of Prescribed Antibiotics Are Not Needed: A Pharmacist-Led Antimicrobial Stewardship Intervention and Clinical Outcomes in a Referral Hospital in Ethiopia\u003cem\u003e. Front Public Health\u003c/em\u003e 2020;8:109\u003c/li\u003e\n \u003cli\u003ePlesa A, Razazi K, Viault L, et al: Implementation of clinical pharmacy services in an adult medical intensive care unit at a French university hospital and the ICU team\u0026apos;s perception of the pharmacist\u0026apos;s role\u003cem\u003e. Int J Pharm Pract\u003c/em\u003e. 2025;11:115\u003c/li\u003e\n \u003cli\u003eMeng H, Ji Z, Zhang Z, et al: Retrospective evaluation of resident pharmacists\u0026apos; services in a liver intensive care unit: a single-center experience\u003cem\u003e. Front Pharmacol\u003c/em\u003e 2025;16:1583818\u003c/li\u003e\n \u003cli\u003eDu Y, Li J, Wang X, et al: Impact of a Multifaceted Pharmacist-Led Intervention on Antimicrobial Stewardship in a Gastroenterology Ward: A Segmented Regression Analysis\u003cem\u003e. Front Pharmacol\u003c/em\u003e 2020;11:442\u003c/li\u003e\n \u003cli\u003eWang H, Wang H, Yu X, et al: Impact of antimicrobial stewardship managed by clinical pharmacists on antibiotic use and drug resistance in a Chinese hospital, 2010-2016: a retrospective observational study\u003cem\u003e. BMJ Open\u003c/em\u003e 2019;9(8):e026072\u003c/li\u003e\n \u003cli\u003eIshii M, Ozone S, Masumoto S, et al: Relationship between assertiveness in community pharmacists and pharmacist-initiated prescription changes\u003cem\u003e. Res Social Adm Pharm\u003c/em\u003e 2023;19(10):1380-1385\u003c/li\u003e\n \u003cli\u003eZhu Z, Wang Q, Sun Q, et al: Improving access to medicines and beyond: the national volume-based procurement policy in China\u003cem\u003e. BMJ Glob Health\u003c/em\u003e 2023;8(7)\u003c/li\u003e\n \u003cli\u003eZhao B, Wu J Impact of China\u0026apos;s National Volume-Based Procurement on Drug Procurement Price, Volume, and Expenditure: An Interrupted Time Series Analysis in Tianjin\u003cem\u003e. Int J Health Policy Manag\u003c/em\u003e 2023;12:7724\u003c/li\u003e\n \u003cli\u003eWagner AK, Soumerai SB, Zhang F, et al: Segmented regression analysis of interrupted time series studies in medication use research\u003cem\u003e. J Clin Pharm Ther\u003c/em\u003e 2002;27(4):299-309\u003c/li\u003e\n \u003cli\u003eTimsit JF, Ling L, de Montmollin E, et al: Antibiotic therapy for severe bacterial infections\u003cem\u003e. Intensive Care Med\u003c/em\u003e 2025;51(10):1867-1885\u003c/li\u003e\n \u003cli\u003eNaddaf M 40 million deaths by 2050: toll of drug-resistant infections to rise by 70\u003cem\u003e. Nature\u003c/em\u003e 2024;633(8031):747-748\u003c/li\u003e\n \u003cli\u003eGarcia-Parejo Y, Gonzalez-Rubio J, Garcia Guerrero J, et al: Risk factors for colonisation by Multidrug-Resistant bacteria in critical care units\u003cem\u003e. Intensive Crit Care Nurs\u003c/em\u003e 2025;86:103760\u003c/li\u003e\n \u003cli\u003eMacesic N, Uhlemann AC, Peleg AY Multidrug-resistant Gram-negative bacterial infections\u003cem\u003e. Lancet\u003c/em\u003e 2025;405(10474):257-272\u003c/li\u003e\n \u003cli\u003eBrown NM, Goodman AL, Horner C, et al: Treatment of methicillin-resistant Staphylococcus aureus (MRSA): updated guidelines from the UK. \u003cem\u003eJAC Antimicrob Resist\u003c/em\u003e 2021;3(1):dlaa114\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Patient characteristics during the pre- and post-unit-based clinical pharmacist (UBCP) intervention periods\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"571\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-UBCP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-UBCP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eApril 2023-July 2024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAugust 2024-October 2025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDischarged patient, n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeds, n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender female, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e76 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e109 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e58.0\u0026plusmn;16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e59.1\u0026plusmn;16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrincipal diagnosis (top 3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eSevere pneumonia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e74 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e88 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eSepsis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e21 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e43 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eSepsis shock, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e17 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e19 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatments, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e270 (91.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e323 (89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eContinuous renal replacement therapy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e101 (34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e114 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eExtracorporeal membrane oxygenation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e21 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e34 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003ePlasma exchange, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e14 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase Mix Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.6\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3.8\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage length of hospital stays, days\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e14.7\u0026plusmn;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12.8\u0026plusmn;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntimicrobial use density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e205.1\u0026plusmn;37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e195.2\u0026plusmn;29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage antimicrobial cost per hospitalization (CNY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e25,568\u0026plusmn;8,629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e14,926\u0026plusmn;6,560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMulti-drug resistant organisms Detection Rate, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e133 (55.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e114 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eCure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0, (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eImprovement, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e142 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e179 (49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eFailure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e51 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e38 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eDeath, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e71 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e75 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003eOthers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e29 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e70 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCNY: Chinese yuan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Parameter estimates of the interrupted time series analysis for antimicrobial stewardship indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI )\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e184.2 (163.1 to 205.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e2.6 (0.8 to 4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-29.0 (-56.2 to -1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-2.6 (-5.7 to 0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e26750.9 (22481.1 to 31020.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-122.5 (-736.8 to 491.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-4241.1 (-12430.2 to 3948.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-410.2 (-1251.5 to 431.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e45.8 (32.2 to 59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e1.1 (-0.4 to 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-10.5 (-32.8 to 11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-1.0 (-3.4 to 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAUD: Antibiotic use intensity; AACPH: Average antimicrobial cost per hospitalization; MDROs: Multi-drug resistant organisms; CI: Confidence interval; DW: Durbin-Watson.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comprehensive analysis of antibiotic consumption across three dimensions (normalized by monthly average)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-UBCP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(monthly DDDs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-UBCP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(monthly DDDs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-valuea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTotal Antibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e744.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e655.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eGlobal Standard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eAccess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e52.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e99.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e(WHO AWaRe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eWatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e531.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e443.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-16.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eReserve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e185.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e159.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-14.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003ePharmacological Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTigecycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-52.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e(ATC classification system)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCarbapenems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e222.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e187.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-15.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eBL/BLI Combination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e113.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e117.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt; 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0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eAntifungals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e151.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e136.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-10.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e74.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-10.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eLocal Policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNon-restricted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e54.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e(China Guangxi Province Grade)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eRestricted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e146.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e126.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSpecial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e590.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e518.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-12.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eUBCP: Unit-based clinical pharmacists; DDDs: Defined daily doses; ATC: Anatomical therapeutic chemical; BL/BLI: Beta-lactam and beta-lactamase inhibitors.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"antimicrobial-resistance-and-infection-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aric","sideBox":"Learn more about [Antimicrobial Resistance and Infection Control](http://aricjournal.biomedcentral.com/)","snPcode":"13756","submissionUrl":"https://submission.nature.com/new-submission/13756/3","title":"Antimicrobial Resistance \u0026 Infection Control","twitterHandle":"@ARICJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"antimicrobial stewardship, intensive care units, clinical pharmacist, interrupted time series analysis, antibiotic use density","lastPublishedDoi":"10.21203/rs.3.rs-8726337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8726337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHigh antimicrobial resistance and consumption intensity in the Intensive Care Unit (ICU) present critical challenges to patient safety. While the Unit-Based Clinical Pharmacist (UBCP) model is a recommended strategy, the specific impact of combining UBCP with individualized data monitoring tools remains to be fully evaluated. This study aimed to assess the effectiveness of a UBCP model, facilitated by a manual Individualized Daily Antimicrobial Use Density (AUD) Monitoring Report (IAUD-RP), on antimicrobial stewardship in the ICU.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA single-center, retrospective, quasi-experimental study using interrupted time series (ITS) analysis was conducted in a 12-bed ICU of a tertiary teaching hospital in Guangxi, China. A total of 657 adult patients admitted between April 1, 2023, and October 31, 2025, were included. The intervention, initiated in August 2024, involved the implementation of a UBCP practice model utilizing manual IAUD-RP for real-time risk stratification and precision intervention. The primary outcome was AUD, measured as defined daily doses (DDDs) per 100 patient-days. Secondary outcomes included average antimicrobial cost per hospitalization (AACPH) and antimicrobial consumption structure.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 657 included patients (295 pre-intervention; 362 post-intervention), pharmacist interventions achieved a 91.7% acceptance rate. ITS analysis demonstrated a significant immediate reduction in AUD (level change, -29.0 DDDs; P\u0026thinsp;=\u0026thinsp;0.038) following the intervention, successfully reversing a significant pre-intervention upward trend (slope, +\u0026thinsp;2.6; P\u0026thinsp;=\u0026thinsp;0.005). Although the ITS model showed no significant immediate level change for costs, the overall mean AACPH decreased significantly from 25,568 CNY to 14,926 CNY (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The antimicrobial consumption structure improved substantially, characterized by significant reductions in tigecycline (-52.1%), quinolones (-39.7%), and carbapenems (-15.8%), alongside a 99.2% increase in WHO Access group antibiotics.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe UBCP model, empowered by the visualized real-time feedback of IAUD-RP, effectively curbed the growth of antimicrobial intensity, reduced treatment costs, and optimized prescribing structure by promoting the shift from broad-spectrum empirical use to targeted therapy.\u003c/p\u003e","manuscriptTitle":"Practice Model of Unit-based Clinical Pharmacists' Individualized Daily AUD Monitoring Report on Antimicrobial Stewardship in ICU of a tertiary hospital in Guangxi, China: An Interrupted Time Series Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 17:37:14","doi":"10.21203/rs.3.rs-8726337/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-24T07:32:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-21T22:57:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T08:27:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T11:10:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12857544018412180603443955652331698247","date":"2026-02-23T13:41:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205875146918308082083594357168398251596","date":"2026-02-18T02:51:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96566103934350779657938048597500251594","date":"2026-02-17T08:04:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315601993456556777743315180844065355645","date":"2026-02-17T08:00:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T07:40:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-30T04:38:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T04:36:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Antimicrobial Resistance \u0026 Infection Control","date":"2026-01-29T02:39:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"antimicrobial-resistance-and-infection-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aric","sideBox":"Learn more about [Antimicrobial Resistance and Infection Control](http://aricjournal.biomedcentral.com/)","snPcode":"13756","submissionUrl":"https://submission.nature.com/new-submission/13756/3","title":"Antimicrobial Resistance \u0026 Infection Control","twitterHandle":"@ARICJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a269ede5-3776-4d90-84a5-31390a98fd40","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T05:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 17:37:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8726337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8726337","identity":"rs-8726337","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}