Abstract
Background: The COVID-19 pandemic was associated with extensive empirical antibiotic exposure despite the predominantly viral etiology of the disease. Evaluating patterns of azithromycin use, prescribing determinants, and appropriateness provides insight into real-world antibiotic use under conditions of diagnostic uncertainty. Methods: We performed a retrospective cohort study including 3200 adult patients hospitalized with laboratory-confirmed COVID-19 at the General Hospital in Ferizaj between June 2020 and June 2022. Demographic characteristics, comorbidities, markers of disease severity, laboratory parameters, and antibiotic exposure were extracted from medical records. Azithromycin prescriptions were classified as appropriate, potentially appropriate, or inappropriate based on predefined clinical and laboratory indicators suggestive of bacterial co-infection. Drug utilization patterns, temporal trends, and independent determinants of inappropriate prescribing were assessed using descriptive analyses and multivariable logistic regression modeling. Results: Among 3200 hospitalized patients, 1968 (61.5%) received azithromycin. Of these prescriptions, 612 (31.1%) were classified as appropriate, 418 (21.3%) as potentially appropriate, and 938 (47.6%) as inappropriate. The proportion of inappropriate use decreased over time, from 52.4% in 2020–2021 to 38.7% in 2022 (p < 0.001). However, a substantial proportion of prescriptions remained inappropriate throughout the study period. In multivariable analysis, absence of laboratory markers suggestive of bacterial infection (OR 2.41; 95% CI 1.98–2.93), concomitant use of more than one antibiotic (OR 1.67; 95% CI 1.32–2.11), and lower clinical severity at admission (OR 1.54; 95% CI 1.21–1.95) were independently associated with inappropriate azithromycin prescribing. Conclusions: Azithromycin use was frequent among hospitalized adults with COVID-19, and a considerable proportion of prescriptions lacked clinical or laboratory justification. Although prescribing patterns changed over time, the persistence of inappropriate use highlights ongoing challenges in aligning antibiotic use with emerging evidence. These findings contribute to the understanding of antibiotic utilization patterns in acute care settings and underscore the importance of integrating objective diagnostic indicators into antimicrobial decision-making to strengthen stewardship practice.
1. Introduction
The COVID-19 pandemic substantially influenced drug utilization patterns worldwide, particularly during its early phases when therapeutic uncertainty was prominent. In the absence of established antiviral treatments, empirical antibiotic prescribing became widespread in hospitalized patients, despite the predominantly viral etiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [1,2]. This context created a setting in which prescribing decisions were frequently driven by uncertainty rather than clear diagnostic evidence. This raised concerns regarding appropriateness and potential long-term implications for antimicrobial resistance (AMR) [3,4]. The extensive empirical use of antibiotics during the pandemic has also raised concerns about its potential contribution to accelerating antimicrobial resistance globally, emphasizing the importance of monitoring antibiotic utilization patterns in clinical practice. Azithromycin, a macrolide antibiotic with recognized anti-inflammatory and immunomodulatory properties, was among the most frequently utilized agents in hospitalized patients with COVID-19 during the early phases of the pandemic [5,6,7]. Although early hypotheses suggested potential antiviral or adjunctive benefits, randomized controlled trials and large pragmatic studies did not demonstrate consistent improvements in mortality or disease progression [8,9]. Epidemiologic evidence indicates that bacterial co-infection at hospital admission among patients with COVID-19 is relatively uncommon, whereas secondary infections occur more frequently in critically ill individuals during prolonged hospitalization [10,11,12]. Despite this, empirical antibacterial therapy was often initiated early, highlighting the complexity of distinguishing viral pneumonia from bacterial superinfection in routine clinical practice [11]. Such discrepancies between expected infection prevalence and observed prescribing rates underscore the need for systematic evaluation of prescribing determinants. Laboratory markers such as C-reactive protein (CRP), procalcitonin, leukocyte count, and neutrophil indices have been incorporated into antibiotic decision-making algorithms [13,14,15]. However, these parameters may reflect systemic inflammatory responses to viral infection, potentially contributing to diagnostic ambiguity and variation in prescribing behavior [14]. As a result, reliance on these markers alone may lead to variability in clinical decision-making and potential overuse of antibiotics. Understanding how such clinical and laboratory indicators influence antibiotic utilization is therefore central to this field of research.
High rates of antibiotic exposure among hospitalized patients with COVID-19 have been consistently reported across different healthcare settings, despite relatively low rates of confirmed bacterial co-infection at hospital admission [10,11]. Similar concerns regarding antimicrobial stewardship and prescribing variability during the COVID-19 pandemic have also been reported in previous studies [16,17]. In addition, evidence from multinational observational studies indicates that antibiotic prescribing practices vary substantially across healthcare systems, reflecting differences in clinical protocols, resource availability, and antimicrobial stewardship implementation [18]. However, data from routine hospital practice in Southeast Europe remain limited, particularly regarding the appropriateness of antibiotic prescribing and its determinants.
Evidence from real-world hospital settings in this region is scarce, and a more detailed understanding of prescribing patterns may provide valuable insight into health-system responses during large-scale infectious disease emergencies. Furthermore, such analyses may support the development and implementation of targeted antimicrobial stewardship strategies aimed at optimizing antibiotic use and reducing unnecessary antimicrobial exposure in future outbreaks.
Accordingly, the present study aimed to evaluate azithromycin utilization and prescribing appropriateness among hospitalized adults with COVID-19 at the General Hospital in Ferizaj between June 2020 and June 2022. Specifically, we sought to characterize prescribing patterns, assess the appropriateness of azithromycin use based on predefined clinical and laboratory criteria, examine temporal trends over the three-year period, and identify factors independently associated with inappropriate prescribing.
2. Results
Table 1 presents the baseline characteristics of hospitalized COVID-19 patients according to azithromycin use. Patients who received azithromycin differed significantly from those who did not across several baseline characteristics. The azithromycin group was older (mean age 63.1 vs. 58.7 years, p < 0.001) and more frequently male (59.9% vs. 55.0%, p = 0.01). Comorbidities, including hypertension and diabetes mellitus, were more prevalent among patients receiving azithromycin (p < 0.001 for both).
Table 1.
Baseline characteristics of hospitalized COVID-19 patients according to azithromycin use.
Markers of disease severity at admission were also higher in this group. A greater proportion required oxygen therapy (74.0% vs. 55.8%, p < 0.001), had elevated CRP levels (median 82 vs. 41 mg/L, p < 0.001), and were more frequently admitted to the ICU (18.4% vs. 12.2%, p < 0.001). In-hospital mortality was also higher among patients receiving azithromycin (15.1% vs. 12.2%, p = 0.02). These findings indicate that azithromycin was more frequently prescribed to patients presenting with more severe clinical and inflammatory profiles at hospital admission. Table 2 presents the patterns of azithromycin use among hospitalized COVID-19 patients, including timing of initiation, duration of therapy, and use as monotherapy or in combination with other antibiotics.
Table 2.
Patterns of azithromycin use.
Azithromycin therapy was most commonly initiated early during hospitalization, with 53.1% of patients receiving treatment within the first 48 h and an additional 31.1% between days 2 and 3. Only a minority of patients (15.8%) initiated therapy after day 3.
The median duration of treatment was 5 days (IQR 3–7). Most patients received azithromycin in combination with at least one additional antibiotic (73.9%), while monotherapy was less common (26.1%). This pattern reflects predominantly empirical prescribing practices, particularly during the early phase of hospitalization, when bacterial co-infection could not be reliably excluded. Table 3 presents the classification of azithromycin prescribing according to predefined appropriateness criteria.
Table 3.
Appropriateness classification.
Among patients receiving azithromycin, 31.1% of prescriptions were classified as appropriate, 21.3% as potentially appropriate, and 47.6% as inappropriate.
The high proportion of inappropriate use indicates substantial variability in prescribing practices and suggests that azithromycin was frequently administered in the absence of clear clinical or laboratory evidence of bacterial co-infection. Table 4 compares the clinical and laboratory characteristics of patients with appropriate and inappropriate azithromycin prescribing.
Table 4.
Comparison of appropriate vs. inappropriate azithromycin use.
Patients classified as having appropriate azithromycin use were older (65.2 vs. 60.4 years, p < 0.001) and had higher inflammatory markers at admission, including CRP levels (median 94 vs. 48 mg/L, p < 0.001).
They were also more likely to require oxygen therapy (81.4% vs. 62.5%, p < 0.001) and to receive combination antibiotic therapy (51.0% vs. 81.5%, p < 0.001).
These findings suggest that appropriate prescribing was more common among patients with clinical and laboratory features consistent with more severe disease and possible bacterial co-infection. Table 5 presents the laboratory parameters according to the appropriateness of azithromycin use among hospitalized COVID-19 patients.
Table 5.
Laboratory parameters according to the appropriateness of azithromycin use.
Inflammatory and infection-related laboratory parameters were significantly higher among patients classified as having appropriate azithromycin use. White blood cell counts, neutrophil levels, and procalcitonin concentrations were all elevated in this group compared to patients with inappropriate use (p < 0.001 for all comparisons).
These laboratory differences support the validity of the classification criteria and indicate that appropriate use was more closely aligned with objective indicators of bacterial infection. Table 6 presents the temporal trends in azithromycin use and inappropriate prescribing during the study period from 2020 to 2022.
Table 6.
Temporal trends in azithromycin use and inappropriate prescribing (2020–2022).
A progressive decline in both overall azithromycin use and inappropriate prescribing was observed over the study period. Azithromycin use decreased from 75.7% in 2020 to 42.7% in 2022, while inappropriate use declined from 54.2% to 32.3%.
Despite this reduction, a considerable proportion of prescriptions remained inappropriate throughout the study period, indicating persistent empirical use even after accumulating evidence on the limited role of azithromycin in COVID-19. Table 7 presents the in-hospital outcomes according to the appropriateness of azithromycin use.
Table 7.
In-hospital outcomes according to the appropriateness of azithromycin use.
Patients in the appropriate-use group had higher rates of ICU admission (24.2% vs. 19.8%, p = 0.04), longer hospital stays (median 11 vs. 8 days, p < 0.001), and higher mortality (18.3% vs. 14.3%, p = 0.03).
These differences likely reflect greater baseline disease severity rather than a direct effect of azithromycin treatment, as patients with more severe clinical presentations were more likely to receive antibiotics appropriately. Table 8 presents the multivariable logistic regression analysis of factors independently associated with inappropriate azithromycin prescribing.
Table 8.
Multivariable logistic regression for inappropriate azithromycin use.
In multivariable analysis, the absence of laboratory markers suggestive of bacterial infection was the strongest independent predictor of inappropriate azithromycin use (OR 2.41; 95% CI 1.98–2.93; p < 0.001).
Additional factors associated with inappropriate prescribing included the use of multiple antibiotics (OR 1.67; 95% CI 1.32–2.11; p < 0.001), lower clinical severity at admission (OR 1.54; 95% CI 1.21–1.95; p < 0.001), and earlier pandemic period (2020–2021 vs. 2022; OR 1.89; 95% CI 1.45–2.47; p < 0.001).
These findings indicate that inappropriate prescribing was primarily driven by diagnostic uncertainty and the absence of objective indicators of bacterial infection.
3. Discussion
In this retrospective cohort, azithromycin was administered to more than half of hospitalized adults with COVID-19. Although prescribing was more frequent among patients with greater inflammatory and clinical severity markers, nearly half of all prescriptions were classified as inappropriate according to predefined criteria. These findings characterize empirical antibiotic utilization within a hospital-based population during a period of substantial therapeutic uncertainty. Because treatment allocation was not randomized, these findings should be interpreted within the context of observational data where prescribing decisions were influenced by clinical severity and inflammatory status at admission.
The overall utilization rate observed in this study is consistent with findings from large-scale hospital-based investigations and meta-analyses conducted during the early and intermediate phases of the pandemic, where empirical antibiotic exposure was reported to exceed 70% despite a low prevalence of documented bacterial co-infection at admission [2]. This discrepancy between expected bacterial burden and observed prescribing rates reflects patterns of precautionary antibiotic use in the context of limited diagnostic clarity. Such patterns highlight the broader challenges faced by clinicians when managing emerging infectious diseases in the absence of clear diagnostic differentiation between viral and bacterial infections. This prescribing behavior reflects a precautionary clinical approach frequently observed during emerging infectious disease outbreaks, where empirical antibiotic therapy is initiated to mitigate the risk of undetected bacterial co-infection. A considerable proportion of prescriptions in the present cohort were initiated without documented clinical or laboratory indicators suggestive of bacterial involvement. Cohort data have similarly demonstrated that confirmed bacterial co-infection at presentation is uncommon in hospitalized COVID-19 patients [19]. From a pharmacoepidemiologic standpoint, such divergence between infection prevalence and prescribing frequency highlights the influence of contextual and behavioral factors on antimicrobial utilization. Laboratory parameters were strongly associated with prescribing classification. Patients categorized as having appropriate azithromycin use demonstrated higher leukocyte counts, neutrophil levels, and procalcitonin concentrations. Biomarkers such as procalcitonin have been evaluated as tools to guide antibiotic decision-making in patients with suspected respiratory infections; however, their interpretation may vary depending on clinical context [20]. In this cohort, objective laboratory indicators were closely aligned with appropriateness categorization, supporting their relevance in prescribing assessment. The association between objective laboratory markers and prescribing classification further supports the clinical rationale underlying antibiotic initiation in patients with suspected bacterial involvement. Although microbiological confirmation of bacterial co-infection was not systematically available due to the retrospective design, the classification of azithromycin appropriateness was based on predefined clinical and laboratory indicators suggestive of bacterial infection, including elevated inflammatory markers (e.g., CRP, procalcitonin), leukocyte count, and clinical presentation at admission. These surrogate indicators are commonly used in routine clinical practice when microbiological data are limited. A progressive decline in both overall azithromycin utilization and inappropriate prescribing was observed across the three-year study period. This temporal change suggests modification of prescribing behavior over time, potentially associated with the accumulation of clinical evidence, dissemination of emerging data, and updates in treatment guidance discouraging routine empirical antibiotic use. Similar temporal patterns have been reported in other healthcare systems as the pandemic evolved. This trend likely reflects the progressive integration of emerging clinical evidence and international guideline recommendations into routine clinical practice. However, despite this improvement, the persistence of a considerable proportion of inappropriate prescribing across all study years indicates that empirical antibiotic use remained common even after the limited benefit of azithromycin had become widely recognized.
In multivariable analysis, the absence of laboratory markers suggestive of bacterial infection was the strongest independent determinant of inappropriate prescribing. Lower clinical severity and concomitant use of multiple antibiotics were also independently associated with increased odds of inappropriate use. These findings indicate that prescribing decisions were influenced by both objective clinical indicators and broader patterns of empirical antibiotic exposure, reflecting precautionary prescribing behavior observed during the pandemic. From a pharmacoepidemiologic perspective, these results illustrate how antibiotic prescribing behavior during a rapidly evolving public health crisis may be shaped by both diagnostic uncertainty and perceived clinical risk.
This study has several strengths. It includes a large hospital-based cohort covering multiple phases of the COVID-19 pandemic and provides detailed clinical and laboratory data, enabling evaluation of prescribing appropriateness in routine clinical practice. However, the retrospective observational design limits causal inference, and prescribing decisions were inherently influenced by physician judgment and patient clinical severity at the time of hospitalization. These findings should be interpreted within the broader context of concerns regarding antimicrobial resistance and the need to strengthen antimicrobial stewardship strategies during and after the COVID-19 pandemic [16,17,21,22].
Taken together, these results describe empirical azithromycin utilization within a defined hospital population and identify determinants associated with inappropriate prescribing. The findings contribute to the understanding of antimicrobial use dynamics under conditions of diagnostic uncertainty and may inform the evaluation of antibiotic stewardship practices in comparable settings. Strengthening antimicrobial stewardship strategies and integrating objective diagnostic markers into prescribing decisions may help reduce unnecessary antibiotic exposure in future respiratory viral outbreaks and improve the rational use of antimicrobials in hospital settings. These findings should therefore be interpreted within the broader clinical context of the pandemic, where precautionary prescribing and limited early evidence frequently influenced antimicrobial decision-making in routine hospital practice.
Strengths and Limitations
This study has several methodological strengths. It includes a large cohort of consecutively hospitalized patients observed over a three-year period, enabling assessment of prescribing patterns across different stages of the pandemic. The use of predefined and structured criteria for classification of prescribing appropriateness supports internal consistency and reproducibility. In addition, the evaluation of temporal trends and multivariable determinants allows a comprehensive assessment of factors associated with inappropriate utilization.
Several limitations should be acknowledged. The retrospective design relies on the accuracy and completeness of medical record documentation and does not allow causal inference. Microbiological confirmation of bacterial infection was not uniformly available, which may have affected classification in selected cases. As the study was conducted at a single center, external generalizability to other healthcare settings may be limited. Residual confounding related to unmeasured clinical factors cannot be excluded.
In addition, although predefined clinical and laboratory indicators were used to assess prescribing appropriateness, the absence of routine microbiological confirmation for all hospitalized patients may have influenced prescribing decisions during periods of heightened clinical uncertainty. Furthermore, prescribing behavior during the COVID-19 pandemic was influenced by rapidly evolving treatment recommendations and physician concern regarding potential bacterial superinfection, which should be considered when interpreting the observed patterns of azithromycin utilization.
4. Materials and Methods
4.1. Study Design and Data Source
This retrospective cohort study was conducted at the General Hospital in Ferizaj, a secondary-level referral hospital that provided inpatient care for patients with COVID-19 during the pandemic. The study evaluated azithromycin utilization among adult patients (≥18 years) hospitalized with laboratory-confirmed SARS-CoV-2 infection between June 2020 and June 2022, with diagnosis confirmed by polymerase chain reaction (PCR) testing according to national diagnostic protocols in place at the time of admission.
The General Hospital in Ferizaj functions as a regional secondary-care center, providing multidisciplinary inpatient care, including dedicated COVID-19 wards during the pandemic period, and serving as a referral institution for patients with moderate-to-severe disease from the surrounding region.
Data were obtained from both electronic and paper-based medical records. Extracted variables included demographic characteristics, comorbidities, markers of disease severity at admission, laboratory parameters, details of antibiotic therapy, and in-hospital outcomes. Clinical data collection followed a standardized data abstraction protocol developed specifically for this study. Two trained physician–researchers independently reviewed medical records to ensure accurate extraction of demographic, clinical, laboratory, and treatment-related information, while data consistency was verified through periodic cross-checking of randomly selected records, with discrepancies resolved by consensus.
4.2. Study Population
All consecutively hospitalized adult patients with confirmed COVID-19 during the study period were screened for eligibility. Patients were included if documentation regarding azithromycin exposure during hospitalization was complete, while those with missing key clinical or laboratory information required for classification of prescribing appropriateness were excluded from the analytical dataset.
To minimize potential selection bias, only the first hospitalization episode per patient was included in the analysis, and repeat admissions related to the same disease episode were excluded in order to avoid duplication of clinical data.
4.3. Exposure Definition
The exposure of interest was initiation of azithromycin therapy during hospitalization, with each episode of treatment initiation considered as the unit of analysis. The index date was defined as the calendar day on which azithromycin treatment was started.
Azithromycin use was evaluated as a measure of empirical antibiotic utilization in hospitalized patients with viral respiratory infection. Microbiological investigations, including blood or sputum cultures, were performed according to routine clinical practice and physician judgment rather than a standardized study protocol. As a result, microbiologically confirmed co-infections were not systematically documented for all patients and were more frequently recorded in those with greater clinical severity.
4.4. Classification of Prescribing Appropriateness
Prescribing appropriateness was assessed using predefined clinical and laboratory criteria documented at the time of azithromycin initiation, based on indicators suggestive of bacterial co-infection. Prescriptions were categorized into three groups.
Azithromycin use was considered appropriate when there was clear documentation supporting bacterial involvement at the time of treatment initiation, including clinical suspicion of bacterial pneumonia or superinfection, laboratory evidence such as leukocytosis or marked neutrophilia, elevated C-reactive protein accompanied by clinical deterioration, elevated procalcitonin levels when available, or microbiological or radiological findings consistent with bacterial infection.
Prescriptions were classified as potentially appropriate in situations where clinical suspicion of bacterial co-infection was present but diagnostic confirmation was incomplete or unavailable, where laboratory findings were inconclusive in differentiating viral from bacterial infection, or where treatment was initiated based on clinical judgment in the absence of confirmatory evidence.
Azithromycin use was considered inappropriate when no clinical, laboratory, or microbiological indicators of bacterial co-infection were documented; when therapy was initiated in patients with mild disease without features suggestive of bacterial involvement; when treatment was continued beyond the recommended duration without justification; or when azithromycin was administered concurrently with other antibiotics providing overlapping coverage without a clearly stated indication.
In cases of ambiguous documentation, prescriptions were conservatively categorized as potentially appropriate in order to minimize the risk of misclassification.
4.5. Outcomes
The primary outcome of interest was inappropriate azithromycin prescribing, while secondary outcomes included overall azithromycin utilization and temporal changes in prescribing patterns across the three study years.
Although microbiological confirmation of bacterial co-infection was not systematically available due to the retrospective design, the classification of azithromycin appropriateness was based on predefined clinical and laboratory indicators suggestive of bacterial infection, including elevated inflammatory markers (e.g., CRP, procalcitonin), leukocyte count, and clinical presentation at admission. These surrogate indicators are commonly used in routine clinical practice when microbiological data are limited.
4.6. Statistical Analysis
Statistical analyses were performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA). Continuous variables were assessed for normality using the Kolmogorov–Smirnov test and are presented as mean ± standard deviation or median with interquartile range, as appropriate, while categorical variables are reported as frequencies and percentages.
Comparisons between patients who received azithromycin and those who did not were conducted using Student’s t-test or Mann–Whitney U test for continuous variables and the χ2 test for categorical variables, with similar approaches applied for comparisons between appropriate and inappropriate prescribing groups. Temporal trends in azithromycin utilization and inappropriate prescribing were evaluated using χ2 tests for trend.
Multivariable logistic regression analysis was used to identify independent determinants of inappropriate prescribing. Variables included in the final model were selected a priori based on clinical relevance and univariable associations, including markers of bacterial infection, concomitant antibiotic use, disease severity at admission, and year of hospitalization. Multicollinearity was assessed using variance inflation factors, and no significant multicollinearity was detected. Results are reported as odds ratios with 95% confidence intervals, with statistical significance defined as a two-sided p-value < 0.05.
4.7. Ethical Considerations
The study was conducted in accordance with the Declaration of Helsinki (2013 revision). Written authorization to access medical records for research purposes was obtained from the management of the General Hospital in Ferizaj. Ethical approval to conduct this study was waived by the management of the General Hospital in Ferizaj, in accordance with institutional practices and the principles of the Declaration of Helsinki. Due to the retrospective design and use of anonymized data, the requirement for individual informed consent was waived. All data were de-identified prior to analysis.
5. Conclusions
In this hospital-based cohort, azithromycin utilization was frequent among adults hospitalized with COVID-19, particularly during earlier phases of the pandemic. Although prescribing was more common in patients with higher inflammatory and clinical severity markers, a substantial proportion of prescriptions did not meet predefined clinical or laboratory criteria suggestive of bacterial co-infection. Inappropriate prescribing was independently associated with the absence of infection-related laboratory indicators, lower disease severity, and earlier hospitalization periods.
These findings characterize patterns and determinants of empirical macrolide use within a defined population and demonstrate measurable changes in prescribing behavior over time. The results contribute to the pharmacoepidemiologic understanding of antibiotic utilization dynamics during periods of diagnostic uncertainty. Because this study was observational, the findings should be interpreted as describing prescribing patterns rather than the causal effects of azithromycin therapy.
From a clinical and public health perspective, strengthening antimicrobial stewardship strategies and promoting the use of objective diagnostic markers may help reduce unnecessary antibiotic exposure and support more rational antimicrobial use in future respiratory viral outbreaks.
Author Contributions
F.A. and M.S.A. conceptualized the study. Methodology was developed by F.A. and A.H.A. Formal analysis was performed by F.A. and M.S.A. Investigation was conducted by F.A. and A.A. Data curation was carried out by M.S.A. The original draft of the manuscript was prepared by F.A. and S.S.S. Review and editing of the manuscript were undertaken by F.A., A.H.A., and M.S.A. Supervision was provided by A.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki (2013). Ethical approval to conduct this study was waived by the management of the General Hospital in Ferizaj, in accordance with institutional practices and the principles of the Declaration of Helsinki. Retrospective studies using existing anonymized clinical data are permitted without requiring a separate formal approval procedure from an ethics committee.
Informed Consent Statement
Patient consent was waived due to the retrospective nature of the study and the use of anonymized clinical data.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Rawson, T.M.; Moore, L.S.P.; Zhu, N.; Ranganathan, N.; Skolimowska, K.; Gilchrist, M.; Satta, G.; Cooke, G.; Holmes, A.H. Bacterial and fungal co-infection in individuals with coronavirus: A rapid review to support COVID-19 antimicrobial prescribing. Clin. Infect. Dis. 2020, 71, 2459–2468. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Langford, B.J.; So, M.; Raybardhan, S.; Leung, V.; Soucy, J.-P.R.; Westwood, D.; Daneman, N.; MacFadden, D.R. Antibiotic prescribing in patients with COVID-19: Rapid review and meta-analysis. Clin. Microbiol. Infect. 2021, 27, 520–531. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- World Health Organization. Clinical Management of COVID-19: Interim Guidance; WHO: Geneva, Switzerland, 2020. [Google Scholar]
- Getahun, H.; Smith, I.; Trivedi, K.; Paulin, S.; Balkhy, H.H. Tackling antimicrobial resistance in the COVID-19 pandemic. Bull. World Health Organ. 2020, 98, 442. [Google Scholar] [CrossRef] [Scilit]
- Pani, A.; Lauriola, M.; Romandini, A.; Scaglione, F. Macrolides and viral infections: Focus on azithromycin in COVID-19 pathology. Int. J. Antimicrob. Agents 2020, 56, 106053. [Google Scholar] [CrossRef] [Scilit]
- Echeverría-Esnal, D.; Martin-Ontiyuelo, C.; Navarrete-Rouco, M.E.; Cuscó, M.D.-A.; Ferrández, O.; Horcajada, J.P.; Grau, S. Azithromycin in the treatment of COVID-19: A review. Expert Rev. Anti Infect. Ther. 2021, 19, 147–163. [Google Scholar] [CrossRef] [Scilit]
- Oldenburg, C.E.; Doan, T. Azithromycin for severe COVID-19. Lancet 2020, 396, 936–937. [Google Scholar] [CrossRef] [Scilit]
- RECOVERY Collaborative Group. Azithromycin in patients admitted to hospital with COVID-19. Lancet 2021, 397, 605–612. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- PRINCIPLE Trial Collaborative Group. Azithromycin for community treatment of suspected COVID-19. Lancet 2021, 397, 1063–1074. [Google Scholar] [CrossRef] [Scilit]
- Lansbury, L.; Lim, B.; Baskaran, V.; Lim, W.S. Co-infections in people with COVID-19: A systematic review and meta-analysis. J. Infect. 2020, 81, 266–275. [Google Scholar] [CrossRef] [Scilit]
- Vaughn, V.M.; Gandhi, T.N.; Petty, L.A.; Patel, P.K.; Prescott, H.C.; Malani, A.N.; Ratz, D.; McLaughlin, E.; Chopra, V.; Flanders, S.A. Empiric antibacterial therapy and community-onset bacterial coinfection in patients hospitalized with COVID-19. Clin. Infect. Dis. 2021, 72, e533–e541. [Google Scholar] [CrossRef] [Scilit]
- Russell, C.D.; Fairfield, C.J.; Drake, T.M.; Turtle, L.; Seaton, R.A.; Sigfrid, L.; Harrison, E.M.; Docherty, A.B.; I de Silva, T.; Egan, C.; et al. Co-infections, secondary infections, and antimicrobial use in patients hospitalised with COVID-19 during the first pandemic wave from the ISARIC WHO CCP-UK study: A multicentre, prospective cohort study. Lancet Microbe 2021, 2, e354–e365. [Google Scholar] [CrossRef] [Scilit]
- Lippi, G.; Plebani, M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. Clin. Chim. Acta 2020, 505, 190–191. [Google Scholar] [CrossRef] [Scilit]
- Koozi, H.; Lengquist, M.; Frigyesi, A. C-reactive protein as a prognostic factor in intensive care admissions for sepsis: A Swedish multicenter study. J. Crit. Care 2020, 56, 73–79. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Qin, C.; Zhou, L.; Hu, Z.; Zhang, S.; Yang, S.; Tao, Y.; Xie, C.; Ma, K.; Shang, K.; Wang, W.; et al. Dysregulation of immune response in patients with COVID-19. Clin. Infect. Dis. 2020, 71, 762–768. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Hsu, J. How COVID-19 is accelerating the threat of antimicrobial resistance. BMJ 2020, 369, m1983. [Google Scholar] [CrossRef] [Scilit]
- Nieuwlaat, R.; Mbuagbaw, L.; Mertz, D.; Burrows, L.L.; E Bowdish, D.M.; Moja, L.; Wright, G.D.; Schünemann, H.J. Coronavirus disease 2019 and antimicrobial resistance: Parallel and interacting health emergencies. Clin. Infect. Dis. 2021, 72, 1657–1659. [Google Scholar] [CrossRef] [Scilit]
- Beović, B.; Doušak, M.; Ferreira-Coimbra, J.; Nadrah, K.; Rubulotta, F.; Belliato, M.; Berger-Estilita, J.; Ayoade, F.; Rello, J.; Erdem, H. Antibiotic use in patients with COVID-19: A multinational observational study. J. Antimicrob. Chemother. 2020, 75, 3386–3395. [Google Scholar] [CrossRef] [Scilit]
- Garcia-Vidal, C.; Sanjuan, G.; Moreno-García, E.; Puerta-Alcalde, P.; Garcia-Pouton, N.; Chumbita, M.; Fernandez-Pittol, M.; Pitart, C.; Inciarte, A.; Bodro, M.; et al. Incidence of co-infections and superinfections in hospitalized patients with COVID-19: A retrospective cohort study. Clin. Microbiol. Infect. 2021, 27, 83–88. [Google Scholar] [CrossRef] [Scilit]
- Kamat, I.S.; Ramachandran, V.; Eswaran, H.; Guffey, D.; Musher, D.M. Procalcitonin to distinguish viral from bacterial pneumonia: A systematic review and meta-analysis. Clin. Infect. Dis. 2020, 70, 538–542. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Fabre, V.; Karaba, S.; Amoah, J.; Robinson, M.; Jones, G.; Dzintars, K.; Katz, M.; Landrum, B.M.; Qasba, S.; Gupta, P.; et al. The role of procalcitonin results in antibiotic decision-making in coronavirus disease 2019 (COVID-19). Infect. Control Hosp. Epidemiol. 2022, 43, 570–575. [Google Scholar] [CrossRef] [Scilit]
- Monnet, D.L.; Harbarth, S. Will coronavirus disease (COVID-19) have an impact on antimicrobial resistance? Eurosurveillance 2020, 25, 2001886. [Google Scholar] [CrossRef] [Scilit]
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