Comparison of antimicrobial consumption indicators in the emergency department

Abstract

Background

Monitoring antimicrobial use in the emergency department is complex due to the wide variety of existing indicators. We evaluated the differences between various indicators used to evaluate antimicrobial use in these units.

Methods

Retrospective analysis of administrative data from all adult patients admitted to an emergency unit (2019–2024). Aggregated quarterly data included the percentage of patients treated, DDDs/100 admissions, DDD/100 patients-day and DOT/100 admissions. An autoregressive integrated moving average (ARIMA) model was used to investigate the association between DDD/100 admissions and the other antimicrobial use indicators.

Findings

Annual median antimicrobial drug use measured by DDDs/100 admissions was 11.7 (IQR 10.8–12.4). Significant differences in antimicrobial consumption correlation were noted when comparing DDD/100 admissions and DDD/100 patient-days for cephalosporins, lincosamides, and carbapenems. Significant differences in the comparison between DDD and DOT were found for imipenem, clindamycin, piperacillin–tazobactam, gentamicin, and vancomycin.

Conclusions

Aggregate antimicrobial use measured by DDDs or DOTs is consistent, though discrepancies in correlation may occur for antibiotics with multiple daily doses. DDD/100 admissions is a suitable indicator, but complementing it with DDD/100 patient-days, DOT, and percentage of patients receiving antibiotics provides valuable information for monitoring antimicrobial use.

Resumen

Antecedentes

La monitorización de uso de antimicrobianos en el servicio de urgencias es compleja, dada la amplia variedad de indicadores existentes. En este estudio evaluamos las diferencias entre varios indicadores utilizados para evaluar el uso de antimicrobianos en estas unidades.

Métodos

Análisis retrospectivo de datos administrativos de pacientes adultos ingresados en un servicio de urgencias (2019-2024). Los datos trimestrales agregados incluyeron el porcentaje de pacientes tratados, DDD/100 admisiones, DDD/100 estancias y DOT/100 admisiones. Se utilizó un modelo autorregresivo integrado de media móvil (ARIMA) para investigar la asociación entre DDD/100 admisiones y los demás indicadores de uso de antimicrobianos.

Resultados

La media anual medida en DDD/100 admisiones fue de 11,7 (RIC 10,8-12,4). Se observó una correlación adecuada para el consumo total de antimicrobianos en todos estos indicadores. Se observaron diferencias significativas en la correlación del consumo al comparar DDD/100 admisiones y DDD/100 estancias para cefalosporinas, lincosamidas y carbapenémicos. Se encontraron diferencias significativas en la comparación entre DDD y DOT para imipenem, clindamicina, piperacilina-tazobactam, gentamicina y vancomicina.

Conclusiones

El uso agregado de antimicrobianos medido por DDD o DOT es consistente, aunque pueden ocurrir discrepancias en la correlación para antibióticos con múltiples dosis diarias. Complementar la información con DDD/100 estancias, DOT y porcentaje de pacientes que reciben antibióticos proporciona información valiosa para monitorizar el uso de antimicrobianos en estas unidades.

Introduction

In recent decades, the rise of multidrug-resistant bacteria has been recognized as a major threat to global public health. The use of antimicrobials has been linked to the emergence of resistant strains of microorganisms.^1,2 Consequently, Antimicrobial Stewardship Programs (ASP) have become a priority for health administrations to minimize the spread and number of infections caused by multidrug-resistant bacteria.^3,4 Knowledge of antibiotic consumption in hospitals is a cornerstone of guidelines aimed at enhancing ASP. Most experiences with these programs have focused on hospitalized patients, particularly critical care patients, and more recently on outpatient settings.⁵ Emergency departments (EDs) are crucial for ASP implementation as they are where the first doses of empiric treatment are prescribed for admitted patients and those returning to primary care. Therefore, antimicrobial prescribing by emergency physicians has a significant impact not only on the patient's clinical outcomes but also on antimicrobial use within the hospital and in the community. On the other hand, recent studies have shown a significant increase in infections caused by multidrug-resistant bacteria in these units, becoming an area of special attention.⁶ Although recent guidelines recognize EDs as ideal for ASP establishment,^7–9 the participation of multidisciplinary teams in these units is still limited. Furthermore, there is a lack of uniformity in the indicators used to monitor antimicrobial use in EDs due to the heterogeneity of infections attended, the different grade of severity of infections, the diverse populations in coming to the EDs and a lack of follow up at the moment of discharge from the EDs.¹⁰

Antibiotic consumption is usually measured by calculating aggregated ratios to enable comparative evaluations, using either the defined daily dose (DDD) or days of therapy (DOT). The DDD, determined annually by the World Health Organization (WHO),¹¹ represents the assumed average maintenance dose per day for a drug used for its primary indication in adults. DOT represents the days of antibiotic therapy administered to a patient, regardless of dosage strength or number of doses. Using DDD to measure antimicrobial consumption in hospitals has proven problematic, as it often overestimates true prescription practices in several units, including critical care, chronic renal units, and pediatrics, leading to errors in measuring antibiotic consumption in critical care units.¹² However, the appropriateness of antimicrobial consumption indicators in EDs has not been adequately evaluated.

On the other hand, various denominators have been employed to standardize DDDs in hospital settings, including occupied bed days, patient days, and admissions. Conversely, outpatient EDs fall under the category of outpatient acute care settings. As a result, the inpatient denominators cannot be accurately retrieved electronically from hospital databases for these outpatient settings. Furthermore, there are no denominators specifically designed to measure ED and observation unit activity. Alternative metrics, such as the number of prescriptions, have been utilized to assess strategy outcomes,¹³ but standardized indicators specifically designed for ED units have yet to be clearly established.

The purpose of the study is to analyze the different trends of the different indicators and their combinations to determine which is the best method of approximation to better measure the consumption of antibiotics in emergency services.

Material and methods

Study population

A retrospective analysis of administrative data from all adult patients (>18 years) admitted to a general emergency medicine unit at a tertiary university hospital in Spain between 2019 and 2024 was designed. The hospital's ethics committee approved the study and waived the need for informed consent due to the use of aggregate data without patients’ identification (Ref: IIBSP-OAM-2022-86).

Data abstraction and definitions

Trained research physicians collected data form the emergency department's electronic record of the consumption of antibiotics and antifungals and the daily admission and stays from the ED from the institutional administrative o management local database (SAP BusinessObjects). Aggregated quarterly data included the number of patients treated, the number of doses administered, and the total grams administered. No specific adjustments were made to monitor antimicrobial consumption during the COVID-19 pandemic. To estimate antimicrobial use using the DDD method, the total number of grams of each drug used during the period of study were summed and divided by the WHO-assigned DDD. All DDDs were based on the 2016 version of the Anatomical Therapeutic Chemical Classification System and the DDD index.¹¹ To express aggregate use, total DDDs were normalized per 100 patient-day and 100 admissions. To estimate antibiotic use by the DOT method, one DOT represented the administration of a single agent on a given day, regardless of the number of doses administered or dosage strength. A single patient receiving two antimicrobial drugs would be recorded as receiving 2DOTs (1 for each drug administered) and so on according to the number of antimicrobials received daily. To express aggregate use, total DOTs were normalized to 100 patient-days and 100 admissions. Percentage of patients receiving antibiotics was also calculated.

Statistical analysis

We used correlation analysis to graphically examine the relationship between DDDs per 100 patient-days and per 100 admissions, DOTs per 100 patient-days and percentage of patients receiving antibiotics. Linear regression model was used to evaluate trends in antibiotic consumption. An autoregressive integrated moving average (ARIMA) model with quarterly time periods was used to investigate the association between DDD/100 admissions and the other antimicrobial use indicators. A p-value < 0.05 was considerate as statistically significant. Stata v.16 software was used for all statistical analyses.

Results

During the study period, 395,011 adult patients (mean age, 62 ± 15 years) were admitted to the ED, representing 96,006 patients-days. Percentage of patients receiving antibiotic during ED stay was 12.6%. Annual median antimicrobial drug use measured by DDDs/100 admissions was 11.7 (IQR 10.8–12.4) and DOTs/100 admissions, 19.0 (IQR 17.3–20.0) and respectively. The evolution of antimicrobial use among the study period is reflected in Fig. 1. No significant variation in total antimicrobial consumption was observed during the study period. The spike in consumption observed between January and April 2020 corresponds to the surge in azithromycin use during the early months of the SARS-CoV-2 pandemic. This occurred despite a noticeable reduction in the consumption of other major antimicrobial groups, including cephalosporins, penicillins, and carbapenems, during the same period. Notably, the rise in azithromycin use was more prominent when measured by the DDD/100 admissions indicator, whereas it was far less pronounced when using the % of patients with antibiotics indicator. Seasonal patterns in antimicrobial consumption were evident across all the indicators analyzed, with higher usage observed during the winter months. This seasonal variation was particularly pronounced in the DDD/100 admissions and DOT/100 admissions indicators.

Fig. 1.

Evolution of antimicrobial use during the study period.

The correlation analysis of total antimicrobial drug use, measured as DDD/100 admissions, DDD/100 patient-days, DOTs/100 admissions, and the percentage of patients receiving antimicrobials, can be found in Table 1 and Fig. 2. Significant correlations were observed for total antimicrobial use across all these indicators. The ARIMA model revealed a significant association between DDD/100 admissions and DDD/100 patient-days, as well as DOT/100 admissions and the percentage of patients on antibiotics (p < 0.001 for all comparisons).

Table 1.

Total median antimicrobial drug use measured as defined daily dose (DDDs) per 100 admissions, DDD per 100 patient-days, days of therapy (DOTs) per 100 patient-days and percentage of patients with antimicrobials.

Period	DDD/100 admissions	DDD/100 patient-day	% Patients with antibiotics	DOT/100 admissions
Jan–Mar 19	12.47	54.36	10.84	16.40
Apr–Jun 19	11.83	51.09	11.05	16.94
Jul–Sep 19	10.28	47.15	11.21	17.26
Oct–Dec 19	12.10	48.14	12.43	18.94
Jan–Mar 20	18.91	78.93	14.83	19.92
Apr–Jun 20	12.39	50.87	15.90	24.34
Jul–Sep 20	11.08	45.32	15.65	22.66
Oct–Dec 20	12.56	49.26	17.35	24.95
Jan–Mar 21	12.36	58.86	13.66	20.67
Apr–Jun 21	10.88	46.96	11.79	18.07
Jul–Sep 21	10.43	47.81	11.35	17.47
Oct–Dec 21	11.64	46.30	12.52	19.07
Jan–Mar 22	10.83	44.54	14.79	19.88
Apr–Jun 22	8.72	35.82	9.87	15.11
Jul–Sep 22	10.77	44.07	12.06	17.47
Oct–Dec 22	11.48	45.02	12.75	18.34
Jan–Mar 23	12.61	49.19	13.62	20.10
Apr–Jun 23	10.88	40.78	11.90	17.32
Jul–Sep 23	10.14	40.91	11.08	16.55
Oct–Dec 23	11.17	39.92	11.62	17.81

Fig. 2.

Correlation of total median antimicrobial drug use measured by defined daily dose (DDDs) per 100 admissions and DDD per 100 patient-days, days of therapy (DOTs) per 100 patient-days and percentage of patients with antimicrobials.

The results of the comparison between indicators across therapeutic groups are shown in Table 2. Significant differences in antimicrobial consumption trends in ARIMA models (p > 0.05) were noted when comparing DDD/100 admissions and DDD/100 patient-days for cephalosporins, lincosamides, penicillins/B-lactamase inhibitors and carbapenems. In the case of cephalosporins, this lack of correlation was also seen in the comparison between DDD/100 admissions and DOT/100 admissions, as well as the percentage of patients on antibiotics. Among the 20 most commonly used antibacterial drugs (Table 3), no significant differences were observed in consumption trends between the evaluated indicators over time, except for the comparison between DDD/100 admissions and DDD/100 patient-days for amoxicillin/clavulanate. Regarding the comparison between DDD and DOT, the largest differences were found for imipenem, clindamycin, piperacillin–tazobactam, gentamicin, and vancomycin.

Table 2.

Mean value and correlation between antimicrobial drug use measured as defined daily dose (DDDs) per 100 admissions and DDD per 100 patient-days, days of therapy (DOTs) per 100 patient-days and percentage of patients with antimicrobials.

Group	DDD/100 admissions	Trend 2019–2023	DDD/100 p.d.	Trend 2019–2023	R/p-value	% Patients with antibiotics	Trend 2019–2023	R/p-value	DOT/100 admissions	Trend 2019–2023	R/p-value
Carbapenem	0.794	0.044	3.257	0.132	r = 0.021 p = 0.948	1.54%	0.092	r = 0.509 p = 0.806	1.582	0.056	r = 0.509 p = 0.021
Cephalosporines	4.263	0.956	17.493	0.479	r = 0.146 p = 0.539	5.06%	0.885	r = 0.44 p = 0.052	5.169	0.614	r = 0.501 p = 0.807
Penicillins/BLI	1.902	0.137	7.804	0.060	r = 0.589 p = 0.051	5.39%	0.046	r = 0.686 p < 0.001	5.477	0.052	r = 0.717 p < 0.001
Glycopepties	0.085	0.007	0.348	0.005	r = 0.877 p < 0.001	0.29%	0.001	r = 0.805 p < 0.001	0.308	0.001	r = 0.830 p < 0.001
Quinolones	1.933	0.052	7.931	0.013	r = 0.790 p < 0.001	2.47%	0.026	r = 0.755 p < 0.001	2.541	0.037	r = 0.768 p < 0.002
Amynoglucosids	0.071	0.075	0.293	0.037	r = 0.847 p < 0.001	0.15%	0.114	r = 0.792 p < 0.001	0.166	0.015	r = 0.927 p < 0.001
Azols	0.129	0.003	0.530	0.009	r = 0.809 p < 0.001	0.16%	<0.001	r = 0.723 p = 0.003	0.175	<0.001	r = 0.739 p = 0.002
Lincosamides	0.192	0.052	0.788	0.004	r = 0.454 p = 0.053	0.71%	0.006	r = 0.733 p < 0.001	0.732	0.003	r = 0.691 p < 0.001
Macrolids	0.678	0.051	2.780	0.122	r = 0.807 p < 0.002	0.74%	0.329	r = 0.523 p = 0.017	0.766	0.241	r = 0.559 p = 0.010
Nitroimidazoles	0.557	0.116	2.284	0.020	r = 0.796 p < 0.002	1.78%	0.021	r = 0.982 p < 0.001	1.805	0.024	r = 0.991 p < 0.001
Penicillins	0.078	0.002	0.319	0.006	r = 0.877 p < 0.001	0.19%	0.074	r = 0.443 p = 0.050	0.216	0.307	r = 0.641 p = 0.020

DDD = daily defined dose, DOT = days of therapy, p.d. = patients days; BLI: B-lactamase inhibitors. The p-value indicates the comparison of consumption trends for each indicator relative to the trend observed in DDD/100 patient-days, as analyzed using the ARIMA model.

Table 3.

Mean value and correlation between main antimicrobial drug used in the Emergency department measured as defined daily dose (DDDs) per 100 admissions and DDD per 100 patient-days, days of therapy (DOTs) per 100 patient-days and percentage of patients with antimicrobials.

	DDD/100 admissions	DDD/100 p.d.	R/p-value	% Patients with antibiotics	R/p-value	DOT/100 admissions	R/p-value
Amikacin	0.04	0.16	r = 0.951 p < 0.001	0.07%	r = 0.963 p < 0.001	0.08	r = 0.877 p < 0.008
Amoxillin Clavulanic	1.83	7.56	r = 0.351 p = 0.148	4.69%	r = 0.965 p < 0.001	4.76	r = 0.974 p < 0.001
Azytromicin	1.06	4.46	r = 0.897 p < 0.001	0.71%	r = 0.970 p < 0.001	0.74	r = 0.983 p < 0.001
Cefepime	0.26	1.08	r = 0.801 p = 0.002	0.31%	r = 0.952 p < 0.001	0.33	r = 0.964 p < 0.001
Ceftriaxone	3.80	15.90	r = 0.454 p = 0.050	4.17%	r = 0.930 p < 0.001	4.21	r = 0.923 p < 0.001
Cefurixime	0.13	0.55	r = 0.791 p < 0.001	0.14%	r = 0.790 p < 0.001	0.15	r = 0.870 p < 0.001
Ciprofloxacin	0.74	0.34	r = 0.740 p < 0.001	0.70%	r = 0.903 p < 0.001	0.01	r = 0.961 p < 0.001
Clindamycin	0.20	0.84	r = 0.901 p < 0.001	0.62%	r = 0.877 p < 0.001	0.65	r = 0.984 p < 0.001
Cotrimoxazole	0.21	0.84	r = 0.877 p < 0.001	0.10%	r = 0.953 p < 0.001	0.11	r = 0.877 p < 0.001
Datopmycin	0.05	0.20	r = 0.909 p < 0.001	0.03%	r = 0.955 p < 0.001	0.03	r = 0.063 p < 0.001
Ertapenem	0.37	1.52	r = 0.849 p < 0.001	0.36%	r = 0.991 p < 0.001	0.37	r = 0.997 p < 0.001
Fluconazole	0.11	0.45	r = 0.857 p < 0.001	0.11%	r = 0.940 p < 0.001	0.12	r = 0.970 p < 0.001
Fosfomycin	0.16	0.67	r = 0.754 p < 0.001	0.15%	r = 0.980 p < 0.001	0.17	r = 0.946 p < 0.001
Genatmycin	0.03	0.12	r = 0.815 p < 0.001	0.08%	r = 0.952 p < 0.001	0.09	r = 0.927 p < 0.001
Imipenem	0.09	0.36	r = 0.756 p = 0.011	0.33%	r = 0.996 p < 0.001	0.35	r = 0.999 p < 0.001
Levofloxacin	1.65	6.74	r = 0.877 p < 0.001	1.61%	r = 0.877 p < 0.001	1.65	r = 0.877 p < 0.001
Meropenem	0.42	1.74	r = 0.512 p = 0.025	0.86%	r = 0.987 p < 0.001	0.89	r = 0.993 p < 0.001
Metronidazole	0.57	2.40	r = 0.742 p = 0.011	1.74%	r = 0.982 p < 0.001	0.77	r = 0.991 p < 0.001
Piperacillin Tazobactam	0.18	0.75	r = 0.787 p < 0.001	0.62%	r = 0.977 p < 0.001	0.64	r = 0.987 p < 0.001
Vancomycin	0.07	0.28	r = 0.893 p < 0.001	0.25%	r = 0.967 p < 0.001	0.27	r = 0.996 p < 0.001

The p-value indicates the comparison of consumption trends for each indicator relative to the trend observed in DDD/100 patient-days, as analyzed using the ARIMA model.

Discussion

To our knowledge, this is one of the first studies comparing different methods for measuring antimicrobial drug use in an ED. The results indicate significant differences in the temporal trends of antibiotic consumption across common antibiotic groups used in these settings, such as carbapenems and cephalosporins, when measured using DDD, DOT or percentage of patients undergoing treatment.

The use of different indicators to monitor antimicrobial consumption in hospitals has been a subject of debate over the past few decades.^13–15 The heterogeneous nature of ED services, where areas of immediate care co-exist with observation units of varying stay lengths, adds complexity to the measurement and interpretation of antimicrobial use. The findings of this study allow for the assessment of the behavior of the indicators commonly used in these units. The six-year data inclusion period for analysis aligns with the timeframe of previous studies on antimicrobial stewardship program (ASP) indicators, enabling meaningful comparisons of the results.^5,16

Firstly, the results show that the denominator used—whether patient admissions to the unit or ED stays—does not significantly affect global antimicrobial use trends, though some groups may present an inadequate correlation. While using admissions or discharges as the denominator tends to better reflect changes in the unit due to the higher absolute values, both indicators are complementary. An increase in the number of admissions with shorter stays would result in a lower DDD/100 admissions ratio, while maintaining the DDD/100 patient-days constant. For EDs with observation units where patients have extended stays (over a day), DDD/100 patient-days would be a more useful indicator. Our results show a poor correlation between DDD/100 admissions and stays for carbapenems, penicillins/B-lactamase inhibitors, cephalosporins, and lincosamides. This may be due to the predominant use of drugs in these families that require multiple daily administrations, where shorter stays have a significant influence. In fact, the lowest correlation was observed for antibiotics typically administered every 8 h (Table 3), with a significant difference found for amoxicillin/clavulanate due to its higher use in the unit.

Secondly, our study found a strong correlation between DDD/100 admissions and the percentage of patients receiving antibiotics. Regarding DDD/100 admissions, the percentage of patients is particularly useful for evaluating the initiation of antibiotic therapy in clinical syndromes where antibiotic use could be avoided, such as asymptomatic bacteriuria, non-infectious cellulitis, chronic obstructive pulmonary disease exacerbation or viral respiratory infections. These data can be difficult to discern through simple DDD monitoring, making them a key element for ASP interventions in EDs.^9,17 Although this was not evaluated in our study, linking antibiotic use to specific diagnoses like urinary or respiratory tract infections of likely viral origin would be particularly valuable. In our analysis, while a strong correlation with DDD/100 admissions was observed, certain periods showed increases not detected by the DDD/100 admissions measure, likely due to the aforementioned conditions. The lowest correlation was observed in therapeutic groups commonly used for these indications in the ED, such as carbapenems and cephalosporins.

Finally, our study allowed us to evaluate the correlation between DDDs and DOTs in an ED setting. As previous studies in hospital and critically ill populations have shown, the DDD system of measuring antimicrobial use overestimates actual consumption for most antibiotics. The use of loading doses, both for critically ill patients and for antibiotics with a high volume of distribution, can create the appearance of higher antibiotic consumption in EDs compared to other hospital units when using DDD values. This factor should be taken into account when assessing the consumption of drugs such as carbapenems, glycopeptides, voriconazole, or tigecycline. DOT is especially useful for populations where dosing regimens differ significantly from standard DDDs, such as pediatric or critically ill patients. Recent IDSA/SHEA guidelines for implementing ASP recommend using DOT over DDD to monitor antibiotic use.¹⁸ In our ED study, global antimicrobial use was 31.5% higher when measured by DDDs compared to DOTs, with the largest discrepancies found for cephalosporins, carbapenems, and penicillins combined with β-lactamase inhibitors. Higher initial doses used in severely ill patients may explain this difference.

Previous studies have explored the correlation between different indicators used to monitor antimicrobial consumption outside the ED setting. Vallès et al.,¹⁶ in a study of critically ill patients, found that for most antimicrobials used in the ICU, total consumption measured by DDD/100 bed-days and DOT/100 bed-days showed significant discrepancies due to the administered dose differing from the standard DDD. Similar to our findings, larger discrepancies were observed for carbapenems and cephalosporins, which require multiple daily administrations. This phenomenon is further exacerbated in units such as the ED, where patient stays are typically less than 24 h, highlighting the necessity of combining multiple indicators for accurate monitoring. Deshwal et al.¹⁹ used data from a cohort of hospitalized patients to analyze all four commonly used indicators for quantifying antimicrobial use. Their study revealed stronger correlations between DDD and DOT compared to DDD and DOT and DDD and length of therapy (LOT). Notably, weaker correlation strengths were observed for cephalosporins, carbapenems, and quinolones. Regarding the denominator used, several previous studies have demonstrated that trends in antibiotic use over time vary depending on whether DDD/100 admissions or DDD/100 patient-days are used. Reported differences range from 5% to 30%, depending on the area and therapeutic group evaluated.^20,21 Our study corroborates these findings within the ED setting.

The use of multiple indicators to monitor antimicrobial consumption is particularly relevant in specific scenarios, such as the COVID-19 pandemic, where both the patient profile and the types of antibiotics utilized differ from baseline conditions. Our data during the peak of the pandemic align with findings from other studies, demonstrating a significant increase in antibiotic consumption.^22,23 In this context, incorporating the percentage of patients receiving antibiotics alongside the DDD indicator provides a more comprehensive assessment of excessive antibiotic use in patients with viral infections—a phenomenon widely documented during the pandemic.

The primary limitations of our study are its retrospective design and the fact that it was conducted in a single center with an elderly population, so the results should not be extrapolated to other ED units with different characteristics. Nonetheless, while recommended daily doses may vary between hospitals, our local practice guidelines are similar to those used in most EDs. Moreover, antibiotic prescriptions at discharge were not evaluated, though this is a key quality indicator for antibiotic prescribing in these units. On the other hand, the analyzed indicators were not correlated with the clinical outcomes of patients in the unit. Nevertheless, the heterogeneity of clinical syndromes treated in these units presents a significant challenge in establishing such correlations. Similarly, our study did not assess the behavior of the indicators in patients admitted to the hospital compared to those discharged directly from the ED. Future research should explore whether the behavior of these indicators is consistent across both patient groups.

In conclusion, for most antibacterial drugs used in ED patients, aggregate antimicrobial use measured by DDDs or DOTs per 100 admissions or patient-days is consistent, though discrepancies in correlation may occur for antibiotics with multiple daily doses. DDD/100 admissions is a suitable indicator for evaluating antibiotic consumption in EDs, but complementing it with DDD/100 patient-days, DOT, or the percentage of patients receiving antibiotics provides complementary and very valuable information for monitoring antimicrobial use in these settings. The combination of different indicators enables the effective identification of consumption trends within therapeutic groups potentially associated with inappropriate antimicrobial use. This facilitates the implementation of antimicrobial stewardship targeted strategies to optimize antimicrobial consumption, enhance clinical outcomes, and minimize the risk of resistance development.

Authors’ approval

All the authors carefully read the manuscript and fully approve of it. We confirm that the manuscript has not been submitted or published anywhere else.

Funding

This research has not received any financial support.

Conflicts of interest

None of the authors has any potential financial conflict of interest.

Availability of data and material

Not applicable.

Code availability (software application or custom code)

Not applicable.