{"id":57167,"date":"2024-03-20T10:10:45","date_gmt":"2024-03-20T10:10:45","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=57167"},"modified":"2024-04-02T04:33:24","modified_gmt":"2024-04-02T04:33:24","slug":"prevalence-of-antibiotic-use-in-patients-with-covid-19-in-a-local-hospital-in-kosovo-a-retrospective-descriptive-study","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no1\/prevalence-of-antibiotic-use-in-patients-with-covid-19-in-a-local-hospital-in-kosovo-a-retrospective-descriptive-study\/","title":{"rendered":"Prevalence of Antibiotic use in Patients with COVID -19 in a Local Hospital in Kosovo : A Retrospective Descriptive Study"},"content":{"rendered":"\n

Introduction                          <\/strong><\/p>\n\n\n\n

The use of antibiotics of hospitalized patients during\nthe pandemic has increased all over the world and has caused resistance to them\nto be considered as the main global health challenge 1<\/sup>. In several nations, the use of antibiotics was\noverspread in the early stages of the COVID-19 pandemic. According to a recent\nmeta-analysis, during the first six months of the pandemic, 74.6% of the\npatients with COVID-19 had antibiotic prescriptions written for them 2<\/sup>. This could be explained by the fact that COVID-19\nshares many clinical signs, including fever, tiredness, and cough, with\nbacterial pneumonia. Doctors typically administer empiric or preventative\nantibiotics to patients when these disease diagnoses cannot be made with\nsufficient precision. Antibiotics are massively prescribed due to the lack of\nknowledge about the viruses causing the disease and the lack of guidelines for\nits management. In the early days of the pandemic, a study published in March\n2020 showed antibiotic prescriptions for 95% of cases 3<\/sup>. Over 70% of patients, primarily those with\nbroad-spectrum infections, were estimated by systematic reviews, to have\nreceived antibacterial therapy. Over-prescription of antibiotics is likely due\nto the fear of bacterial co-infections of the respiratory tract 4<\/sup>. There are not many studies assessing how empirical\nantibiotic use affects clinical outcomes in COVID-19 patients who don’t have a\nbacterial infection 5<\/sup>. While some studies have reported on the use of\nantimicrobials, their significance, and the potential for inappropriate or\nindiscriminate use, particularly when combined with broad-spectrum antibiotics,\nthat are not adequately taken into account. In the 71% of patients in a\nresearch done by Chen et al. received antibiotic treatment; of these, 25%\nreceived single antibiotic therapy and 45% received combination medication. The\ncephalosporin, quinolone, carbapenem, and tetracycline classes of antibiotics were\nthe most commonly used ones 6<\/sup>. Additionally, a different study found that the three\nmost commonly used antibiotics during the pandemic were azithromycin (18%),\nceftriaxone (25%) and moxifloxacin (64%)7<\/sup>. Taking into consideration all these data collected\naround the world with the overuse of the antibiotics and its huge impact on the\nresistance of the antibiotics, it is still necessary to properly evaluate the\nimpact of antibiotic use in COVID-19 patients on a larger scale, with as much\nparticipation from other nations as possible.<\/p>\n\n\n\n

Material and Methods <\/strong><\/p>\n\n\n\n

This\ninvestigation constitutes a retrospective observational analysis conducted within\na regional hospital situated in Kosovo. Encompassing patients admitted due to\nCOVID-19 infection exhibiting a spectrum of symptom severity from mild to\nsevere, the study involved the enrollment of 300 individuals. The cohort\ncomprised patients hospitalized between October 2020 and January 2021.\nExclusion criteria were applied to patients who tested positive for COVID-19\nbut were not subsequently admitted to the hospital.<\/p>\n\n\n\n

 Data collection and analysis<\/strong><\/p>\n\n\n\n

The demographic data underwent\nmeticulous scrutiny. Initially, age was scrutinized as a potential determinant,\ndelineating the populace into five distinct cohorts: individuals aged 20 to 30\nyears, 31 to 50 years, 51 to 70 years, 71 to 90 years, and a final cohort\nspanning from 91 to 100 years. Subsequently, an array of variables including\ncomorbidities, smoking status, and vaccination history were scrutinized.\nFollowing this, a comprehensive dataset on antibiotic utilization was amassed,\nelucidating the rationale behind antibiotic selection, a detail expounded upon\nwithin the results section. This dataset encompassed pertinent laboratory\nparameters including C-reactive protein, total leukocyte count, and neutrophil\ncount, each categorized as elevated, diminished, or within normal limits.\nConclusively, an exhaustive compilation of antibiotic regimens administered for\nempirical therapy was undertaken, culminating in the identification of the most\nfrequently employed antibiotic. The ensuing findings are succinctly articulated\nthrough numerical representations and percentage distributions across all\ngraphical and tabular presentations.<\/p>\n\n\n\n

Ethical\napproval <\/strong><\/p>\n\n\n\n

The\nnumber approved for permission by the Ethical Council of the Chamber of\nPharmacists of Kosovo is 77\/ 14\/03\/2023! <\/p>\n\n\n\n

Results<\/strong><\/p>\n\n\n\n

Demographic\ncharacteristics of the patients<\/strong><\/p>\n\n\n\n

Initially, we analyzed the demographic features of the patients. Out of 300 patients in total, the majority were male with 60% while female with 40% (Figure 1 A). While we were interested to see the ages which were affected the most, we divided them into groups as shown on the Figure 1 B, and accordingly, most of the patients were between the age group of 51 to 70 years old with 44% followed by those between 71 to 90 years old with total 117 patients (39%).<\/p>\n\n\n\n\n\n
\"\"<\/td>\nFigure 1: The demographic data of the patients with COVID-19<\/strong>. <\/strong>

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

 Diagnosis of the disease and other parameters\ncorrelated to it <\/strong><\/p>\n\n\n\n

Furthermore,\nwe have checked the methods by which the disease was diagnosed, whether it is\nby investigating the clinical symptoms or by performing Real Time-Polymerase\nChain Reaction (RT-PCR) test or using both ways of diagnosis. Indeed, most of\nthe cases were confirmed only by the clinical appearance of the disease, with 204\npatients diagnosed this way, while only a small portion, with 5% were diagnosed\nusing both, clinical symptoms and PCR test (Figure 2 A). Moreover, we were\ninterested to know if these patients received the anti-COVID-19 vaccine, and as\nexpected more than the half, 84% respectively, were vaccinated (Figure 2 B). <\/p>\n\n\n\n

Moreover,\nrecognizing that a significant portion of the patient cohort comprised\nindividuals of middle age and considering the multi-organ impact of COVID-19\nbeyond the respiratory system, our aim was to assess the presence of\ncomorbidities potentially prolonging infection duration and exacerbating\nprognostic outcomes. Notably, our observations revealed a prevalence of\nadditional diseases among the patients, including but not limited to diabetes,\narterial hypertension (AHT), cardiac ailments, renal dysfunction, malignancies,\npulmonary disorders, epilepsy, and dementia. Among these, arterial hypertension\nemerged as the most prevalent comorbidity, affecting 132 patients, constituting\n44% of the cohort, followed by diabetes affecting 81 patients, accounting for\n27% (refer to Figure 2C). Subsequently, considering the primary impact of\nsmoking on pulmonary health and its relevance to COVID-19 infection, we\nexamined the smoking status of patients to ascertain its potential influence on\ndisease severity. Surprisingly, the majority of patients were non-smokers,\ncomprising 85% of the cohort, while a mere 7% had a history of smoking but had\nceased prior to contracting the disease (refer to Figure 2D).<\/p>\n\n\n\n\n\n
\"\"<\/td>\nFigure 2: Diagnosis and co-morbidities of patients<\/strong>.<\/strong>

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Antibiotics\nused to treat the COVID-19 patients<\/strong><\/p>\n\n\n\n

Subsequently,\nour investigation focused in the utilization of antibiotics. Initial scrutiny\nof patient health records revealed several indications for antibiotic\nadministration, encompassing the severity of the disease, levels of\ninflammation biomarkers, and discernible alterations noted by clinicians\nthrough radiologic imaging. Our analysis identified three primary parameters\nindicative of inflammation based on laboratory findings: C-reactive protein\n(CRP), total leukocyte count, and neutrophil count. Notably, CRP data were\nunavailable for 42% of patients, with the remaining exhibiting predominantly\nelevated levels (56%), while a mere 2% presented within the normal range.\nConversely, leukocyte counts were elevated in 49% of patients and within normal\nlimits in 51%. Neutrophil counts, however, were elevated in 297 patients,\nrepresenting 99% of the cohort, while only 1% demonstrated a normal count\n(refer to Table 1). Given the established association between heightened white\nblood cell counts and infectious states, the widespread prescription of\nantibiotics to hospitalized patients is explicable. Indeed, all hospitalized\npatients included in the study received antibiotic treatment, with empiric\nregimens featuring prominently, including Levofloxacin, Ceftriaxone, and\nImipenem, as illustrated in Table 2.<\/p>\n\n\n\n

Table 1: The laboratory parameters used as a guide for antibiotic prescription (* For 126 patients the values were missing)<\/strong>.<\/p>\n\n\n\n\n\n\n\n\n\n
\n

 <\/em><\/strong><\/p>\n<\/td>\n

\n

CRP*<\/strong><\/p>\n<\/td>\n

\n

Leukocytes<\/strong><\/p>\n<\/td>\n

\n

Neutrophiles<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/em><\/p>\n<\/td>\n

\n

Nr.<\/strong><\/p>\n<\/td>\n

\n

%<\/strong><\/p>\n<\/td>\n

\n

Nr.<\/strong><\/p>\n<\/td>\n

\n

%<\/strong><\/p>\n<\/td>\n

\n

Nr.<\/strong><\/p>\n<\/td>\n

\n

%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Normal<\/em><\/p>\n<\/td>\n

\n

6<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

153<\/p>\n<\/td>\n

\n

51<\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n<\/tr>\n

\n

High<\/em><\/p>\n<\/td>\n

\n

168<\/p>\n<\/td>\n

\n

56<\/p>\n<\/td>\n

\n

147<\/p>\n<\/td>\n

\n

49<\/p>\n<\/td>\n

\n

297<\/p>\n<\/td>\n

\n

99<\/p>\n<\/td>\n<\/tr>\n

\n

Low<\/em><\/p>\n<\/td>\n

\n

\/<\/p>\n<\/td>\n

\n

\/<\/p>\n<\/td>\n

\n

\/<\/p>\n<\/td>\n

\n

\/<\/p>\n<\/td>\n

\n

\/<\/p>\n<\/td>\n

\n

\/<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Table 2: The antibiotics used in all the patients with COVID-19 and their frequencies <\/strong><\/p>\n\n\n\n\n\n\n
\n

Antibiotics<\/em><\/strong><\/p>\n<\/td>\n

\n

Levofloxacin<\/strong><\/p>\n<\/td>\n

\n

Ceftriaxone<\/strong><\/p>\n<\/td>\n

\n

Imipenem<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/em><\/p>\n

Usage<\/em><\/strong><\/p>\n

 <\/em><\/strong><\/p>\n<\/td>\n

\n

Once per day<\/p>\n

Every 24 h<\/p>\n<\/td>\n

\n

Three times per day<\/p>\n

Every 8 h<\/p>\n<\/td>\n

\n

Twice per day<\/p>\n

Every 12 h<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n

Discussion <\/strong><\/p>\n\n\n\n

The use of antibiotics during the pandemic in\nhospitalized patients has increased worldwide. In most of the countries,\nincluding Kosovo, it was a common protocol to be followed. However, despite\nbeing widely used in all the hospitals, some of the records are missing,\nespecially regarding the vaccination status, the blood culture reports,\nlaboratory analysis of the markers and also the specific doses and uses of\nantibiotics. From the data that we collected from the patients that were hospitalized\nwith COVID-19, it turns out that 60% were men, while 40% were women. All were\nadmitted to the ward, while there were none in the intensive and joint care,\naccording to the age group the most affected age group was 51-70 years old with\n44% and with the smallest percentage was the age group between 91-100 years old\nwith only 2%.  <\/p>\n\n\n\n

Out of 300 patients, 261 of them had co-morbidities.\nAmong them the most frequent was Arterial Hypertension which corresponds to\n50,6%, followed by Diabetes with 31% of patients and others such as heart and\npulmonary disease, renal disfunction, malignant disease, epilepsy and dementia.\nSimilarly, other groups  reported that\npatients, especially of older ages were followed by other co-morbidities which\nfurther contributed to the severity of the COVID-19 8<\/sup>. The burden of COVID-19 was even heavier when companied\nwith other diseases, this was also documented in other studies. In China, 75%\nof the patients with COVID-19 who were in critical condition or even died had\nother co-morbidities, more precisely diabetes and cardiac disease 9<\/sup>.  Hence, having\nother diseases had further worsen the symptoms of COVID-19 which made is\nobvious for the physicians to diagnose the patients. In fact, in our study we\nhave observed that most of the patients, 204 out of 300, who were admitted in\nthe hospital ward were diagnosed by clinical appearance of the diseases rather\nthan running a RT-PCR test. Another interesting finding during our research,\nwas that most of the patients who were admitted to the hospital with symptoms\nwere not immunized against COVID-19, 84% of the patients to be precise. This\nfurther hampered the management of the disease. <\/p>\n\n\n\n

Another interesting factor which led to a number of\nstudies, is smoking. Considering the known impact of smoking in the lungs and\nalso knowing that the main target of the COVID-19 was lungs, it raised\nquestions among scientist whether smokers would have more severe outcomes\nduring the pandemic times or not. Indeed, there is a plethora of studies out there,\nwhich might have raised confusion about the smoking. However, most of them are\nsupported evidences that more smokers were admitted to the hospitals compared\nto non-smokers and most of them had severe symptoms of COVID-1910\u201313<\/sup>. Surprisingly, in our case 85% of the patients were\nnon-smokers.  Hence, this made COVID-19 a\nnot so easy problem to solve. <\/p>\n\n\n\n

In fact, even the treatment was a challenge. Considering\nantibiotics as a powerful use in the world of infections, they were one of the\nmain tools used to fight the symptoms of COVID-19. In our case, antibiotics\nwere used based on clinical appearance and the\nseverity of the disease, the values of the inflammation biomarkers and\naccording to the changes the doctors noticed based on the radiologic images.\nConsidering the role of white blood cells in inflammation and infection,\nespecially that of the neutrophiles and also the significant increase of CRP 14\u201316<\/sup>,\nit was the most common laboratory exam performed on patients with COVID-19.\nIndeed, in our case, 99% of the patients had high number of neutrophiles and\n56% of the data collected included high levels of CRP. To be more precise we\nhave to emphasize that the data for 126 patients concerning CRP analyses were\nmissing. Considering these data, it might explain the use of antibiotics by the\nview of physicians. Indeed, all of the patients conducted in the study, which\nwere hospitalized, were prescribed antibiotics. Since the illness was\nrelatively new and there were no established protocols for treating its\nsymptoms, the majority of hospitalized patients worldwide used antibiotics\nfrequently 17<\/sup>. In our case all of the hospitalized COVID-19 patients\nwere prescribed antibiotics, the most common being Levofloxacin, Ceftriaxone\nand Imipenem.  However, different studies\nhave concluded that the use of antibiotics were not necessary at all 4,17<\/sup> 18<\/sup>.\nIn fact, in a meta-analyses\nstudy conducted by Lansbury et al., it was shown that only a small group of\npatients had bacterial co-infections which does not justify the use of\nantibiotics 19<\/sup>. <\/p>\n\n\n\n

Despite subsequent considerations indicating that the\nadministration of antibiotics may not have been warranted and, indeed, could\nhave exacerbated the issue of antibiotic resistance, their usage was deemed\nimperative. Given the absence of a definitive protocol and the necessity to\ncorrelate symptoms with the clinical presentation of the disease alongside\nlaboratory findings, physicians found themselves navigating a precarious path\nwhere antibiotics appeared as the only viable recourse for patient management.<\/p>\n\n\n\n

Taken together, these findings pile up the evidences on\nthe use of antibiotics during the COVID-19 pandemic, however further deeper\nstudies should be conducted in order to better evaluate the impact it had on\nantibiotic resistance. <\/p>\n\n\n\n

 Conclusion <\/strong><\/p>\n\n\n\n

After an unstoppable work, based on the results collected from this research, we saw an increase in the use of antibiotics, during the COVID-19 pandemic, by patients hospitalized in our country, Kosovo, mainly in the Ferizaj General Hospital, where the use of antibiotics was administered from the very beginning of the pandemic. We found that in these patients hospitalized in the internal ward there was a use of antibiotics such as Imipenem, and Meropenem, Levofloxacine, Ceftriaxone which were administered for different durations. The knowledge about the virus and the lack of guidelines for its management, from our results, it is noted that the guideline for the use of antibiotics was higher from the side of the hospitals other than from the primary care. Also, the reasons for the use of antibiotics by the patients were: Disease weight, inflammatory laboratory markers, radiological changes. Most of the respondents in this research were part of the group age that included the older age, where it was proven that most of the patients besides the COVID-19 disease, also had concomitant diseases. We also note that the number of recovered patients was greater than the number of deaths and transferred to the University Clinical Center.<\/p>\n\n\n\n

Acknowledgements<\/strong><\/p>\n\n\n\n

We thank, Dr. Albina Fejza , for the English language editing of the paper.<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

The authors declare no conflict of interest. <\/p>\n\n\n\n

Authors\u2019\nContribution<\/strong><\/p>\n\n\n\n

F,A, A, H, M,A, G,\nH (Conceptualization, formal analysis, data curation, supervision, writing\noriginal draft, review, and editing). (Investigation, patient administration,\nsoftware,<\/p>\n\n\n\n

visualization, validation,\nreview, and editing). All authors declared that they contributed to this\narticle and that they have read and approved the final manuscript.<\/p>\n\n\n\n

Funding Sources<\/strong><\/p>\n\n\n\n

There is no funding Sources<\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

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