Applying Diagnostic Stewardship to Proactively Optimize the Management of Urinary Tract Infections

Morado, Faiza; Wong, Darren

doi:10.3390/antibiotics11030308

Cited by 20 publications

(13 citation statements)

References 91 publications

(152 reference 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%

Evaluation of Two Tests for the Rapid Detection of CTX-M Producers Directly in Urine Samples

Tang,

Lee,

et al. 2023

Antibiotics

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Infections caused by extended-spectrum β-lactamase-producing Enterobacterales have increased rapidly and are mainly attributed to the production of CTX-M enzymes. This study evaluated the NG-Test® CTX-M MULTI lateral flow assay (CTX-M LFA) and the Rapid ESBL NP® test (ESBL NP test) for rapid detection of CTX-M-producing Enterobacterales directly in midstream urine (MSU) samples. Testing was performed on 277 clinical MSU samples in a hospital microbiology laboratory from November 2022 to January 2023; 60 of these samples (30 positive for ESBL producers and 30 positive for non-ESBL producers) were tested retrospectively after the identification and susceptibility results were obtained, and 217 samples were tested prospectively immediately after a Gram stain showing the presence of Gram-negative bacilli. The results were compared against phenotypic detection of ESBL and molecular testing as the reference methods. Overall, 67 of the 277 samples were culture-positive for ESBL-producing Enterobacterales. PCR for the blaCTX-M gene was positive for all ESBL-producing Enterobacterales isolates. All CTX-M LFA results were interpretable, while three of the ESBL NP test results were noninterpretable. The sensitivity of the CTX-M LFA (100%, 95% CI 94.6–100%) was higher than that of the ESBL NP test (86.6%, 95% CI 76.0–93.7%). Both tests had high specificities (CTX-M LFA, 99.1%, 95% CI 96.6–99.9% and ESBL NP test, 100%, 95% CI 98.2–100%). In conclusion, both the CTX-M LFA and the ESBL NP test can deliver rapid results that could improve antimicrobial stewardship for urinary tract infections.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Two Tests for the Rapid Detection of CTX-M Producers Directly in Urine Samples

Tang,

Lee,

et al. 2023

Antibiotics

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“…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 retrospective evaluation of the Euroarray STI-11 multiplex system for the detection of eight STI causing agents

Dichtl¹,

Osterman²,

Förster³

et al. 2023

Sci Rep

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With an incidence of more than > 1,000,000/day, sexually transmitted diseases remain a major challenge for health care systems worldwide. To reduce disease burden, complications, and spread, rapid diagnosis permitting early therapy is pivotal. The range of pathogens is wide and co-infections are common. This complicates pre-analytics, which are based on different laboratory techniques with potentially long turnaround times, e.g., cultivation and multistep serologies. Multiplex PCR provides the opportunity to overcome these limitations. In this study, we evaluated a novel assay, the Euroarray STI-11 microarray (EA; Euroimmun Medizinische Labordiagnostika), for the detection of eight obligate or facultative pathogens. Three-hundred-thirteen clinical specimens, which had been tested and pre-characterized for STI causing agents as part of routine diagnostics, were used as cases and controls in this retrospective study. The EA detected 34/44 Chlamydia trachomatis, 48/50 HSV-1, 50/50 HSV-2, 48/48 Mycoplasma hominis, 45/47 Neisseria gonorrhoeae, 9/11 Treponema pallidum, 46/46 Ureaplasma parvum, and 49/49 Ureaplasma urealyticum infections, respectively. 293 samples were EA positive, with polymicrobial infections (positive for two to six microbial or viral agents) detected in 130/293 cases. Specificities were 100% in the respective control groups (n = 18–48 depending on targeted pathogen) except for N. gonorrhoeae (25/26) and U. urealyticum (44/45). The broad spectrum of obligate and facultative pathogens targeted by the EA makes it a valuable tool in the setting of STI diagnostics and surveillance. The test has the potential to diagnose diseases neglected or overlooked in routine clinical practice. Besides a low sensitivity for C. trachomatis, the EA demonstrated high performance for all analyzed parameters. Further studies are warranted in order to capture a larger variety of the tested pathogens.

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“…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%

Efficacy of Machine Learning Techniques in Diagnosing Urinary Tract Infections: A Study Utilizing a Philippine Clinical Dataset

Airlangga

2024

INFEB

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This research delves into the potential of machine learning models, namely Support Vector Machine (SVM), XGBoost, and LightGBM, to enhance the diagnosis of Urinary Tract Infections (UTIs) based on a comprehensive dataset collected from a local clinic in Northern Mindanao, Philippines, spanning from April 2020 to January 2023. The study integrates clinical variables such as age, gender, and various urine test results including color, transparency, and the presence of substances like glucose, protein, and cells, to determine the most accurate diagnostic model. The dataset presented unique preprocessing challenges, such as converting infant ages into decimal numbers. The SVM with a linear kernel showed remarkable test accuracy of 98.25%, indicating its robustness in handling linear separability in the data. Meanwhile, XGBoost and LightGBM, both with optimal hyperparameter configurations, achieved comparable accuracies of 97.95%. These results underscore the significance of machine learning in medical diagnostics, particularly in settings where swift and reliable decision-making is crucial. Our findings suggest that while ensemble methods like XGBoost and LightGBM are powerful tools for complex datasets, a well-tuned SVM can provide superior accuracy, thus advocating for a data-centric approach in model selection.

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Applying Diagnostic Stewardship to Proactively Optimize the Management of Urinary Tract Infections

Cited by 20 publications

References 91 publications

Evaluation of Two Tests for the Rapid Detection of CTX-M Producers Directly in Urine Samples

Evaluation of Two Tests for the Rapid Detection of CTX-M Producers Directly in Urine Samples

A retrospective evaluation of the Euroarray STI-11 multiplex system for the detection of eight STI causing agents

Efficacy of Machine Learning Techniques in Diagnosing Urinary Tract Infections: A Study Utilizing a Philippine Clinical Dataset

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