A study of antimicrobial misuse in a university hospital

Maki, Dennis G.; Schuna, Arthur A.

doi:10.1097/00000441-197805000-00005

Cited by 160 publications

(57 citation statements)

References 19 publications

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“…However, the proportion of inappropriate antibiotic treatments was similar to ours in hospitals with medium [6,7,9] and very low [22] percentages of resistant bacteria; and a DSS improved accuracy in both these settings [9,22].…”

Section: Discussionsupporting

confidence: 63%

Improving empirical antibiotic treatment: prospective, nonintervention testing of a decision support system

Leibovici

Gitelman

Yehezkelli

et al. 1997

Journal of Internal Medicine

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Objectives. Develop a problem-orientated and databased decision support system (DSS) to improve empirical antibiotic treatment, and compare the performance of the system to that of the physician.Design. The DSS was tested in a prospective, noninterventional, comparative cohort study. Setting. University hospital in Israel. Subjects. Consecutive patients (n ϭ 496) in four departments of internal medicine suspected of harboring a moderate to severe bacterial infection. Interventions. None. Main outcome measures. The percentage of appropriate empirical antibiotic treatments. Results. Out of 496 patients included in the study, 219 had positive cultures or serological tests. The physicians prescribed inappropriate empirical antibiotic treatment in 91 of 219 patients (42%); whilst the recommendations of the system were inappropriate in 50 patients (23%) (P Ͻ 0.05). Superfluous treatment was prescribed in 15% of patients by the physician, and in 11% by the system. Out of the 91 patients given inappropriate treatment by the physician, the DSS advised treatment to which the pathogens were susceptible in 61 patients. The advantage of the DSS over the physician was most evident in multiresistant gramnegative isolates, enterococci and Staphylococcus aureus.Out of the 277 patients with negative cultures, the DSS advised narrower-spectrum antibiotic treatment than prescribed by the physicians in 27% of patients, and broader-spectrum in 13%. Conclusion. A problem-orientated, data-based DSS outperformed physicians in the choice of appropriate empirical antibiotic treatment, and recommended less broad-spectrum antibiotics.

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Section: Discussionsupporting

confidence: 63%

Improving empirical antibiotic treatment: prospective, nonintervention testing of a decision support system

Leibovici

Gitelman

Yehezkelli

et al. 1997

Journal of Internal Medicine

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“…Factors contributing to antibiotic misuse are known to include use of antibiotics in animal husbandry 8 , unguided or misguided prophylactic use of antibiotics, and unnecessary extensive use of antibiotics which may be as a result of noncompliance or self-medication 6,9 . Antibiotics are ideally to be prescribed after due diligence has been done to ensure the sensitivity of the implicated microorganism(s) to the activity of the particular antimicrobial agent 10 .…”

Section: Introductionmentioning

confidence: 99%

Use of antibiotics among non-medical students in a Nigerian university

Sanya¹,

Titilayo²,

Adisa³

et al. 2014

Afr H. Sci.

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Background: Antibiotic misuse is a major contributory factor to treatment failure, antibiotic resistance and high healthcare costs. Objectives: To evaluate level of self-reported antibiotic misuse among non-medical undergraduate students of a Nigerian university. Methods: Respondents' knowledge of antibiotics and disposal system for left-over antibiotics were explored using a structured questionnaire. Data were summarized with descriptive statistics. Chi square was used to evaluate relationship between specific categorical variables and respondents' opinions with p<0.05. Results: More than half the respondents obtained their antibiotics through doctor's prescriptions (273; 68.3%). The study revealed gross antibiotic misuse with majority, (298;74.5%) either by keeping left-over antibiotics for future use or throwing it away with refuse. Respondents (289; 72.3%) sometimes forgot to take the antibiotics. Financial constraints (73; 18.3%), long duration of treatment (70; 17.5%), side effects experienced (60;15.0%), polypharmacy (56;14.0%), tablet size (45;11.3%), and perceived low level of confidence in the prescriber (11; 2.8%) were major reasons for non-adherence. Course of study of respondents had no significant effect on respondents' knowledge or adherence (p>0.05). Conclusion: Misuse of antibiotics among non-medical undergraduate students in a Nigerian university setting is pervasive suggesting an urgent need for enlightenment on rational use and disposal of antibiotics.

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“…About a third of patients with severe bacterial infections are given inappropriate empirical antibiotic treatment and 20 percent are prescribed superfluous drugs [8], [12], [13]. Moreover, the overall balance of antibiotic treatment vs. pathogens is tipping worldwide to the wrong side.…”

Section: Introductionmentioning

confidence: 99%

A causal probabilistic network for optimal treatment of bacterial infections

Leibovici

Fishman

Schønheyder

et al. 2000

IEEE Trans. Knowl. Data Eng.

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AbstractÐThe fatality rate associated with severe bacterial infections is about 30 percent and appropriate antibiotic treatment reduces it by half. Unfortunately, about a third of antibiotic treatments prescribed by physicians are inappropriate. We have built a causal probabilistic network (CPN) for treatment of severe bacterial infections. The net is based on modules, each module representing a site of infection. The general configuration of a module is as follows: Major distribution factors define groups of patients, each of them with a definite prevalence of infection caused by a given pathogen. Minor distribution factors multiply the likelihood of one pathogen, without changing much of the prevalence of infection. Infection caused by a pathogen causes local and generalized signs and symptoms. Antibiotic treatment is appropriate if it matches the susceptibility of the pathogens in vitro and appropriate treatment is associated with a gain in life expectancy. This is balanced against the cost of the drug, side effects, and ecological damage, to reach the most cost effective treatment. The net was constructed in such a way that the data for the conditional probability tables will be available, even if it meant sometimes giving up on fine modeling details. For data, we used large databases collected by us in the last 10 years and data from the literature. The CPN was a convenient way to combine data from databases collected at different locations and times with published information. Although the net is based on detailed and large databases, its calibration to new sites requires data that is available in most modern hospitals.

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A study of antimicrobial misuse in a university hospital

Cited by 160 publications

References 19 publications

Improving empirical antibiotic treatment: prospective, nonintervention testing of a decision support system

Improving empirical antibiotic treatment: prospective, nonintervention testing of a decision support system

Use of antibiotics among non-medical students in a Nigerian university

A causal probabilistic network for optimal treatment of bacterial infections

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