Optimization of culture read times using full microbiology laboratory automation

Clinical microbiology laboratories are increasingly adopting automation to meet testing demands, improve throughput, and accelerate diagnostic turnaround. This study evaluated optimal culture read times using Copan WaspLab microbiology laboratory automation (MLA) to determine if earlier read times affect accuracy and turnaround times as they pertain to microbial identification and antimicrobial susceptibility testing (AST). A total of 374 patient specimens were cultured simultaneously using a conventional incubation method and MLA, with MLA images captured from 10 to 72 h of incubation. Non-urine specimens were ready for workup in over 50% of cases at 16 h, 92% by 24 h, and 100% by 36 h, while 90% of urine cultures were ready for workup by 16 h. Using frozen isolates (127 gram-positive bacteria, 83 gram-negative bacteria, and 12 yeasts), we further evaluated the accuracy of MALDI-TOF identification and AST results for cultures incubated for 10 h and 16 h in MLA. Identification accuracy remained high across all groups, with gram-negative bacteria achieving 98.8% accuracy at 10 h and 97.6% at 16 h, gram-positive bacteria showing 97.6% accuracy at 10 h and 98.4% at 16 h, and yeasts demonstrating 91.6% accuracy at 10 h and 100% at 16 h of incubation, respectively. AST results using cultures at both time points demonstrated essential agreement above 90% for most drugs on Vitek 2 GN-807N, GP75, and ST02 AST cards. Minor and major errors were rare; however, elevated error rates were observed for specific drugs (e.g., cefazolin, piperacillin/tazobactam, nitrofurantoin, and linezolid), largely due to a limited number of susceptible isolates and breakpoint-related challenges. MLA enables earlier culture reads while maintaining ID and AST accuracy. IMPORTANCE: Microbiology laboratory automation (MLA) is increasingly used to address rising test volumes and workforce limitations; however, optimal incubation durations for automated workflows are not well defined. In this study, we demonstrate that cultures from a range of specimen types can be reliably read as early as 16 h and finalized by 36 h without compromising organism identification or antimicrobial susceptibility testing accuracy compared to conventional incubation. These findings support the use of earlier culture read time points in MLA workflows, enabling faster turnaround of clinically actionable results. Implementation of optimized incubation times has the potential to improve laboratory efficiency and accelerate patient care decisions while maintaining diagnostic reliability.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Microbiology
Published
2026-09-18
DOI
https://doi.org/10.1128/jcm.00232-26
Primary Topic
Bacterial Identification and Susceptibility Testing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Optimization of culture read times using full microbiology laboratory automation

Janiece Glover, Kendall A. Bryant, Bailey B. Demarest, Pam J. Foster
Journal of Clinical Microbiology
Bacterial Identification and Susceptibility Testing
article

Optimization of culture read times using full microbiology laboratory automation

Janiece Glover, Kendall A. Bryant, Bailey B. Demarest, Pam J. Foster
article en

Abstract

Clinical microbiology laboratories are increasingly adopting automation to meet testing demands, improve throughput, and accelerate diagnostic turnaround. This study evaluated optimal culture read times using Copan WaspLab microbiology laboratory automation (MLA) to determine if earlier read times affect accuracy and turnaround times as they pertain to microbial identification and antimicrobial susceptibility testing (AST). A total of 374 patient specimens were cultured simultaneously using a conventional incubation method and MLA, with MLA images captured from 10 to 72 h of incubation. Non-urine specimens were ready for workup in over 50% of cases at 16 h, 92% by 24 h, and 100% by 36 h, while 90% of urine cultures were ready for workup by 16 h. Using frozen isolates (127 gram-positive bacteria, 83 gram-negative bacteria, and 12 yeasts), we further evaluated the accuracy of MALDI-TOF identification and AST results for cultures incubated for 10 h and 16 h in MLA. Identification accuracy remained high across all groups, with gram-negative bacteria achieving 98.8% accuracy at 10 h and 97.6% at 16 h, gram-positive bacteria showing 97.6% accuracy at 10 h and 98.4% at 16 h, and yeasts demonstrating 91.6% accuracy at 10 h and 100% at 16 h of incubation, respectively. AST results using cultures at both time points demonstrated essential agreement above 90% for most drugs on Vitek 2 GN-807N, GP75, and ST02 AST cards. Minor and major errors were rare; however, elevated error rates were observed for specific drugs (e.g., cefazolin, piperacillin/tazobactam, nitrofurantoin, and linezolid), largely due to a limited number of susceptible isolates and breakpoint-related challenges. MLA enables earlier culture reads while maintaining ID and AST accuracy. IMPORTANCE: Microbiology laboratory automation (MLA) is increasingly used to address rising test volumes and workforce limitations; however, optimal incubation durations for automated workflows are not well defined. In this study, we demonstrate that cultures from a range of specimen types can be reliably read as early as 16 h and finalized by 36 h without compromising organism identification or antimicrobial susceptibility testing accuracy compared to conventional incubation. These findings support the use of earlier culture read time points in MLA workflows, enabling faster turnaround of clinically actionable results. Implementation of optimized incubation times has the potential to improve laboratory efficiency and accelerate patient care decisions while maintaining diagnostic reliability.

Journal of Clinical Microbiology
Vanderbilt University Medical Center (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 14%
Bacterial Identification and Susceptibility Testing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.