Comparison of the MICs Obtained by Gradient Concentration Strip and EUCAST Methods for Four Azole Drugs and Amphotericin B against Azole-Susceptible and -Resistant Aspergillus Section Fumigati Clinical Isolates.

Reference methods used to assess the drug susceptibilities of Aspergillus fumigatus isolates consisted of EUCAST and CLSI standardized broth microdilution techniques. Considering the increasing rate and the potential impact on the clinical outcome of azole resistance in A. fumigatus, more suitable techniques for routine testing are needed. The gradient concentration strip (GCS) method has been favorably evaluated for yeast testing. The aim of this study was to compare the CGS test with EUCAST broth microdilution for amphotericin B (AMB), posaconazole (PCZ), itraconazole (ITZ), voriconazole (VRZ), and isavuconazole (ISA). A total of 121 Aspergillus section Fumigati strains were collected, including 24 A. fumigatus sensu stricto strains that were resistant to at least one azole drug. MICs were determined using GCS and EUCAST methods. Essential agreement between the 2 methods was considered when MICs fell within ±1 dilution or ±2 dilutions of the 2-fold dilution scale. Categorical agreement was defined as the percentage of strains classified in the same category (susceptible, intermediate, or resistant) with both methods. Essential agreements with ±1 dilution and ±2 dilutions were 96.7, 93.4, 90.0, 89.3, and 95% and 100, 99.2, 100, 97.5, and 100% for AMB, PCZ, ITZ, VRZ, and ISA, respectively. Categorical agreements were 94.3, 86.1, 89.3, and 88.5% for AMB, PCZ, ITZ, and VRZ, respectively. Detection of resistance was missed with the GCS for one strain (4.1%) for PCZ and for 2 strains (8.3%) for ISA. Determination of ITZ MICs using the GCS allowed the detection of 91.7% of azole-resistant strains. The GCS test appears to be a valuable method for screening azole-resistant A. fumigatus clinical isolates.

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

BACKGROUND: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems. AIMS: This narrative review aims to explore the current use of ML In clinical microbiology. SOURCES: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019. CONTENT: We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n = 71, 73%) but a significant number used data from low- and middle-income countries (n = 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes. IMPLICATIONS: In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.

Is transthyretin a good marker of nutritional status?

The assay of plasma transthyretin (TTR), also known as prealbumin, is a key step in the assessment of nutritional status. However, it remains unclear whether it really is a useful nutrition marker, and when and how to use it and interpret TTR levels and variations. Risk of malnutrition, malnutrition severity, prognosis associated with malnutrition and effectiveness of refeeding are four parameters in nutritional assessment, and need clear separation to understand the associated utility of TTR. TTR does not have the same impact and potential on each of these parameters: it can be helpful but not essential for evaluating the risk of malnutrition, and it can diagnose malnutrition and its severity in patients with no inflammation syndrome. TTR is a good marker for prognosis associated with malnutrition, and is even better for monitoring refeeding efficacy despite inflammation. Thresholds depend on the purpose for which it is used. We propose a simple algorithm to guide the interpretation of TTR levels as a helpful tool for day-to-day practice.