2022
DOI: 10.3390/antibiotics11030308
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Applying Diagnostic Stewardship to Proactively Optimize the Management of Urinary Tract Infections

Abstract: A urinary tract infection is amongst the most common bacterial infections in the community and hospital setting and accounts for an estimated 1.6 to 2.14 billion in national healthcare expenditure. Despite its financial impact, the diagnosis is challenging with urine cultures and antibiotics often inappropriately ordered for non-specific symptoms or asymptomatic bacteriuria. In an attempt to limit unnecessary laboratory testing and antibiotic overutilization, several diagnostic stewardship initiatives have bee… Show more

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Cited by 20 publications
(13 citation statements)
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“…This is why we used inverted microscopy as our first-tier screening. In many laboratories, urinalysis parameters (e.g., presence of WBC and/or bacteria) are adopted as indicators for reflex urine culture approaches to improve cost-effectiveness and implement antimicrobial stewardship [34,35]. In modelling analysis, absence of pyuria was found to have a very high negative predictive value for significant bacteriuria [36].…”
Section: Discussionmentioning
confidence: 99%
“…This is why we used inverted microscopy as our first-tier screening. In many laboratories, urinalysis parameters (e.g., presence of WBC and/or bacteria) are adopted as indicators for reflex urine culture approaches to improve cost-effectiveness and implement antimicrobial stewardship [34,35]. In modelling analysis, absence of pyuria was found to have a very high negative predictive value for significant bacteriuria [36].…”
Section: Discussionmentioning
confidence: 99%
“…Considering the ongoing developments in NAAT-based diagnostics, which will lead to more and more positive findings, better epidemiological data is needed to assess the medical relevance for the individual patient. Multiplex methods such as the one studied here could be a helpful tool for surveillance, but is also a challenge for the novel field of diagnostic stewardship 29 .…”
Section: Discussionmentioning
confidence: 99%
“…A survey of the literature reveals a burgeoning interest in applying ML to various aspects of UTI management, from identifying risk factors and predicting infection susceptibility to automating urinalysis interpretation [25], [26], [27]. Despite these advances, the integration of ML into routine clinical practice for UTI diagnosis remains limited [28], [29], [30]. Several studies have highlighted the capabilities of ML models to outperform traditional statistical approaches in predicting UTIs, underscoring the feasibility and potential benefits of such technologies [31], [32], [33].…”
Section: Introductionmentioning
confidence: 99%