Study setting

The present, retrospective, observational study was conducted at Tokyo Metropolitan Tama Medical Center, a 790-bed tertiary-care center in Japan. All inpatients who received at least 1 intravenous antimicrobial agent were included, and facility-wide, intravenous antimicrobial consumption data were obtained from monthly barcode medication administration records from April 2014 to March 2022. Monthly DOT and DP were calculated and DOT was expressed per 1,000 DP using the NHSN AUR module definition. ⁵ Monthly DASC was calculated by summing the ASC score for all the categories in a previous study. ^{Reference Kakiuchi, Livorsi and Perencevich7} Antimicrobials with no established ASC score in the previous literature (ie, cefoperazone-sulbactam, cefmetazole, flomoxef, arbekacin, teicoplanin, and pazufloxacin) were scored by an ASP clinical pharmacist (S.M.) and an infectious disease physician (H.H.) after discussion. ^{Reference Muratani, Inoue and Mitsuhashi11–Reference Bennett, Dolin and Blaser16} The ASC score for amikacin was also revised from 8 to 9 by modifying the CRE section using the process described above (Supplementary Table 1 online).

Antimicrobial stewardship at the study center

At the study center, once-weekly prospective audit and feedback (PAF) was implemented in April 2014 as part of an ASP targeting inpatients receiving carbapenems and piperacillin-tazobactam for >72 hours. It was managed by a multidisciplinary team and continued throughout the study period, including during the COVID-19 pandemic. Monthly data on the proportion of patients for whom ASP recommendations were accepted for inappropriate antimicrobial use were collected as a representative index of ASP efficacy. During the PAF encounter, the appropriateness of carbapenems and piperacillin-tazobactam for inpatients receiving these antimicrobials was evaluated, and the primary care teams were contacted via telephone to modify or discontinue antimicrobial therapy in case inappropriate use was detected. An ASP-related practice was considered accepted if the primary care teams made the necessary modifications within 72 hours. A clinical pharmacist (S.M.) prospectively recorded this information on a data collection form. ^{Reference Honda, Murakami and Tagashira17}

Data collection

Data on monthly incidence density, including Clostridioides difficile infections (CDI) per 10,000 PD, extended-spectrum β-lactamase (ESBL)–producing Enterobacterales detected by phenotypic testing (disk diffusion or broth microdilution) per 1,000 PD, and methicillin-resistant Staphylococcus aureus (MRSA) per 1,000 PD, were collected as microbiological data from all cultures. Moreover, because few species of multidrug-resistant bacteria have been detected at the study center, guidelines were used to determine the incidence density of 2 species of drug-resistant, gram-negative bacteria from all the cultures according to the following definitions. ^{Reference Tacconelli, Mazzaferri and de Smet18,Reference Magiorakos, Srinivasan and Carey19} First, drug-resistant Pseudomonas aeruginosa was defined by nonsusceptibility to at least 1 antimicrobial in 2 or more of the following categories: (1) antipseudomonal cephalosporins (ceftazidime and cefepime), (2) piperacillin and piperacillin/tazobactam, (3) aztreonam, (4) fluoroquinolones (ciprofloxacin and levofloxacin), (5) aminoglycosides (amikacin, tobramycin, and gentamicin), and (6) carbapenems (imipenem and meropenem). Second, certain drug-resistant Enterobacterales, excluding ESBL and carbapenem-resistant organisms, were defined by nonsusceptibility to at least 1 antimicrobial in 2 or more of the following categories: (1) extended-spectrum cephalosporins (third- and fourth-generation cephalosporins: ceftriaxone, cefotaxime, ceftazidime and cefepime), (2) piperacillin-tazobactam, (3) aztreonam, (4) fluoroquinolones (ciprofloxacin and levofloxacin), and (5) aminoglycosides (amikacin, tobramycin, and gentamicin).

Vector autoregressive (VAR) analysis

With the VAR analysis, we assessed the dynamic correlation between multiple variables using the Granger causality test, impulse-response functions (IRFs), and forecast error variance decompositions (FEVDs). ^{Reference Sims8,Reference Stock and Watson9,Reference Abrigo and Love20} The Granger causality test qualitatively determines whether incorporating lagged values of one variable improves the predictive accuracy of other variables of interest without relying on the order of the variables. IRFs quantitatively demonstrate how a 1-unit shock in 1 variable influences the dynamic impact of each variable over time. FEVDs quantify the contribution of each variable to the specific shock of 1 variable.

The first VAR model included DASC per DOT and DOT per 1,000 DP. Then, 10 VAR models incorporated 3 variables: PAF acceptance, antimicrobial consumption (DOT or DASC), and microbiological data (CDI, ESBL, drug-resistant P. aeruginosa, drug-resistant Enterobacterales, or MRSA). Recursive VAR models were adopted using Cholesky decomposition. ^{Reference Sims8,Reference Abrigo and Love20} In a bivariate equation with lag-1 autocorrelation (equation 1.1) based on this method, the order of variables is crucial and should be arranged so that y_t is causally prior to z_t, especially when evaluating IRFs and FEVDs. ^{Reference Sims8,Reference Abrigo and Love20} In this situation, while the impulse of y_t in the same month influences z_t, the reverse is not true. This model can be expanded into a multivariate and multilag model. Therefore, the variables were placed in the 2 patterns in the first VAR model incorporating DASC per DOT and DOT per 1,000 DP, because this relationship was unclear. In the next 10 VAR models, the variables were placed in the order of PAF acceptance, antimicrobial consumption, and microbiological data because PAF affects antimicrobial consumption and because antimicrobial consumption may result in the emergence of resistance (Fig. 1). ^{Reference Barlam, Cosgrove and Abbo3,Reference Toth, Fesus and Kungler-Goracz10,Reference Swingler, Song and Moore21,Reference Karanika, Paudel, Grigoras, Kalbasi and Mylonakis22}

(1.1) $$\left\{ {\matrix{ {{{\rm{y}}_{\rm{t}}} = {{\rm{a}}_{10}} + {{\rm{a}}_{11}}{{\rm{y}}_{{\rm{t}} - 1}} + {{\rm{a}}_{12}}{{\rm{z}}_{{\rm{t}} - 1}} + {{\rm{e}}_{1{\rm{t}}}}} \cr {{{\rm{z}}_{\rm{t}}} = {{\rm{a}}_{20}} + {{\rm{a}}_{21}}{{\rm{y}}_{{\rm{t}} - 1}} + {{\rm{a}}_{22}}{{\rm{z}}_{{\rm{t}} - 1}} + {{\rm{e}}_{2{\rm{t}}}}} \cr } } \right. \cdots $$

(1.2) $$\matrix{ { \Leftrightarrow \left[ {\matrix{ {{{\rm{y}}_{\rm{t}}}} \cr {{{\rm{z}}_{\rm{t}}}} \cr } } \right] = \left[ {\matrix{ {{{\rm{a}}_{10}}} \cr {{{\rm{a}}_{20}}} \cr } } \right] + \left[ {\matrix{ {{{\rm{a}}_{11}}} & {{{\rm{a}}_{12}}} \cr {{{\rm{a}}_{21}}} & {{{\rm{a}}_{22}}} \cr } } \right]\left[ {\matrix{ {{{\rm{y}}_{{\rm{t}} - 1}}} \cr {{{\rm{z}}_{{\rm{t}} - 1}}} \cr } } \right] + \left[ {\matrix{ {{{\rm{e}}_{1{\rm{t}}}}} \cr {{{\rm{e}}_{2{\rm{t}}}}} \cr } } \right]} \hfill \cr {\quad \quad \quad \,\,\, \Leftrightarrow {{\bf{x}}_{\bf{t}}} = {{\bf{A}}_0} + {{\bf{A}}_1}{{\bf{x}}_{{\bf{t}} - 1}} + {{\bf{e}}_{\bf{t}}} \cdots } \hfill \cr } $$

Figure 1.

A diagram illustrating the causality assumed to exist between prospective audit and feedback (PAF) acceptance, antimicrobial consumption, and microbiological data in this study.

The Akaike information criterion (AIC) was used to estimate the length of lags in the VAR models and to assess whether each variable had a stationarity process using these lags. A diagnostic test of the VAR models, including residual autocorrelation and stability tests, was performed. After confirming the adequacy of the recursive VAR models, the Granger causality test, IRFs, and FEVDs were performed. In the VAR models incorporating the above 3 variables, the relationship between 2 adjacent, unidirectional variables (ie, PAF acceptance to antimicrobial consumption and antimicrobial consumption to microbiological data) was evaluated to avoid investigating reverse and distant causation. Moreover, to establish whether DOT or DASC was the better indicator, these 2 metrics in each VAR model were compared to determine which had a greater number of confirmed Granger-causality relationships and more appropriate IRF and FEVD dynamics.

Other statistical analyses

DASC per DOT indicates the average ASC score of the antimicrobials used in hospitals. ^{Reference Kakiuchi, Livorsi and Perencevich7} To investigate how DASC per DOT and DOT per 1,000 DP affect each other over time, Spearman rank-order correlation analysis was used to assess their correlation with each other. These results were compared with those of the VAR analysis. For all statistical analyses, P < .05 was considered to indicate statistical significance. All the data were analyzed using Stata/SE version 17.0 software (StataCorp, College Station, TX). The Institutional Review Board of Tokyo Metropolitan Tama Medical Center approved this study (approval no. 3-190).