Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria

This study aimed to validate Metasystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module (Neon Metafer) with assistance on respiratory and pleural samples, compared to conventional manual fluorescence microscopy (MM). Analytical parameters were assessed first, followed by a retrospective validation study. In all, 320 archived auramine-O-stained slides selected non-consecutively [85 originally reported as AFB-smear-positive, 235 AFB-smear-negative slides; with an overall mycobacterial culture positivity rate of 24.1% (77/320)] underwent whole-slide imaging and were analyzed by the Metafer Neon AFB Module (version 4.3.130) using a predetermined probability threshold (PT) for AFB detection of 96%. Digital slides were then examined by a trained reviewer blinded to previous AFB smear and culture results, for the final interpretation of assisted digital microscopy (a-DM). Paired results from both microscopic methods were compared to mycobacterial culture. A scanning failure rate of 10.6% (34/320) was observed, leaving 286 slides for analysis. After discrepant analysis, concordance, positive and negative agreements were 95.5% (95%CI, 92.4%-97.6%), 96.2% (95%CI, 89.2%-99.2%), and 95.2% (95%CI, 91.3%-97.7%), respectively. Using mycobacterial culture as reference standard, a-DM and MM had comparable sensitivities: 90.7% (95%CI, 81.7%-96.2%) versus 92.0% (95%CI, 83.4%-97.0%) (P-value = 1.00); while their specificities differed 91.9% (95%CI, 87.4%-95.2%) versus 95.7% (95%CI, 92.1%-98.0%), respectively (P-value = 0.03). Using a PT of 96%, MetaSystems' platform shows acceptable performance. With a national laboratory staff shortage and a local low mycobacterial infection rate, this instrument when combined with culture, can reliably triage-negative AFB-smear respiratory slides and identify positive slides requiring manual confirmation and semi-quantification.

IMPORTANCE

This manuscript presents a full validation of MetaSystems' automated acid-fast bacilli (AFB) smear microscopy scanning and deep-learning-based image analysis module using a probability threshold of 96% including accuracy, precision studies, and evaluation of limit of AFB detection on respiratory samples when the technology is used with assistance. This study is complementary to the conversation started by Tomasello et al. on the use of image analysis artificial intelligence software in routine mycobacterial diagnostic activities within the context of high-throughput laboratories with low incidence of tuberculosis.

In vitro assessment of antibacterial and antiviral activity of three copper products after 200 rounds of simulated use

Copper has well-documented antibacterial effects but few have evaluated it after prolonged use and against bacteria and viruses. Coupons from three copper formulations (solid, thermal coating, and decal applications) and carbon steel controls were subjected to 200 rounds simulated cleaning using a Wiperator™ and either an accelerated hydrogen peroxide, quaternary ammonium, or artificial sweat products. Antibacterial activity against S. aureus and P. aeruginosa was then evaluated using a modified Environmental Protection Agency protocol. Antiviral activity against coronavirus (229E) and norovirus (MNV-1) surrogates was assessed using the TCID50 method. Results were compared to untreated control coupons. One hour after inoculation, S. aureus exhibited a difference in log kill of 1.16 to 4.87 and P. aeruginosa a log kill difference of 3.39-5.23 (dependent upon copper product and disinfectant) compared to carbon steel. MNV-1 demonstrated an 87-99% reduction on each copper surfaces at 1 h and 99% reduction at 2 h compared to carbon steel. Similarly, coronavirus 229E exhibited a 97-99% reduction after 1 h and 90-99% after 2 h. Simulated use with artificial sweat did not hinder the antiviral nor the antibacterial activity of Cu surfaces. Self-sanitizing copper surfaces maintained antibacterial and antiviral activity after 200 rounds of simulated cleaning.

Automated 16S Sequencing Using an R-Based Analysis Module for Bacterial Identification

Sanger sequencing of the 16S rRNA gene is routinely used for the identification of bacterial isolates. However, this method is still performed mostly in more-specialized reference laboratories, and traditional protocols can be labor intensive. In this study, 99 clinical bacterial isolates were used to validate a fast, simplified, and largely automated protocol for 16S sequencing. The workflow combines real-time PCR of the first 500 bp of the bacterial 16S rRNA gene and amplicon sequencing on an automated, cartridge-based sequence analyzer. Sequence analysis, NCBI BLAST search, and result interpretation were performed using an automated R-based script. The automated workflow and R analysis described here produced results equal to those of manual sequence analysis. Of the 96 sequences with adequate quality, 90 were concordantly identified to the genus (n = 62) or species level (n = 28) compared with routine laboratory identification of the organism. One organism identification was discordant, and 5 resulted in an inconclusive identification. For sequences that gave a valid result, the overall accuracy of identification to at least the genus level was 98.9%. This simplified sequencing protocol provides a standardized approach to clinical 16S sequencing, analysis, and quality control that would be suited to frontline clinical microbiology laboratories with minimal experience. IMPORTANCE Sanger sequencing of the 16S rRNA gene is widely used as a diagnostic tool for bacterial identification, especially in cases where routine diagnostic methods fail to provide an identification, for organisms that are difficult to culture, or from specimens where cultures remain negative. Our simplified protocol is tailored toward use in frontline laboratories with little to no experience with sequencing. It provides a highly automated workflow that can deliver fast results with little hands-on time. Implementing 16S sequencing in-house saves additional time that is otherwise required to send out isolates/specimens for identification to reference laboratories. This makes results available much faster to physicians who can in turn initiate or adjust patient treatment accordingly.

Potential influence of rapid diagnostics on timeliness of management decisions for patients with positive blood cultures

BACKGROUND

The Accelerate Pheno system (AXDX) provides rapid identification (ID; 90 minutes) and antimicrobial susceptibility testing (AST; approximately 7 hours) from positive blood culture (BC) bottles. We assessed the potential of AXDX results to influence more timely antibiotic interventions with a convenience sample of 158 positive BCs.

METHODS

BCs with a mono-microbial Gram stain likely to be on the AXDX panel were run in parallel with the standard of care (SOC). Using results from the SOC, the medical microbiologist on call (MMOC) noted interventions made at the time of BC Gram stain and when ID and AST results were available. The timing of MMOC intervention was noted and compared with fastest potential SOC time and AXDX time.

RESULTS

Of 158 specimens selected for analysis, 144 were evaluable. ID was available 11.9 hours and AST 27.7 hours faster than SOC. Correct ID was provided for 85.2% of specimens and AST for 59.0% of specimens, with 97.5% essential agreement compared with the SOC. One hundred and thirteen clinical interventions were made on 100 specimens: 54.9% were narrowing; 33.6%, escalation; 6.2%, consultation with ID; and 3.5%, further investigation. If AXDX data had been used immediately once available, interventions would have been possible 24 hours earlier for ID interventions and 39 hours earlier for AST results.

CONCLUSIONS

Results from rapid diagnostic panels such as AXDX have the potential to support timely antimicrobial de-escalation and other decisions to benefit patients, especially if paired with stewardship interventions.

Detecting Clostridioides (Clostridium) difficile using canine teams: What does the nose know?

Background

Trained canines are capable of detecting Clostridioides (Clostridium) difficile (CD) in the environment; however, the primary odour of interest on which the dogs alert is unclear.

Aim

To evaluate the inter-rater reliability of two canine detection teams for their ability to discriminate between scent pads containing CD-toxin-positive and -negative odours and their ability to discriminate between clostridial strains.

Methods

During a six-month period, two canine teams were tested weekly for their ability to detect CD-toxin-positive odours and discriminate between these and -negative odours. To further determine the canines' discrimination capability, scent pads impregnated with odours from reference isolates representing common CD toxin types (including toxin-negative CD isolates) or from clinical isolates representing other clostridial species were used.

Results

A total of 264 samples were tested with an overall sensitivity of 94.7% (Team A) and 86.8% (Team B) and specificities of 96.9% and 98.7%, respectively. Inter-rater reliability was very good (Cohen's kappa 0.87). When challenged with toxin- and non-toxin-producing strains, the teams alerted on 96.3% of all CD isolate odours (including nontoxigenic strains) and 46.7% of closely related species.

Conclusions

The canine teams exhibited strong inter-rater reliability on both clinical faecal specimens and reference CD isolates (both toxin and non-toxin producing) but were challenged to discriminate between CD and closely related clostridial species. These findings strongly support the development of scent detection programmes provided dogs and their handlers are properly trained and used in the right context.