Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units
Background Clinical metagenomics (CMg) has the potential to be translated from a research tool into routine service to improve antimicrobial treatment and infection control decisions. The SARS-CoV-2 pandemic provides added impetus to realise these benefits, given the increased risk of secondary infection and nosocomial transmission of multi-drug-resistant (MDR) pathogens linked with the expansion of critical care capacity. Methods CMg using nanopore sequencing was evaluated in a proof-of-concept study on 43 respiratory samples from 34 intubated patients across seven intensive care units (ICUs) over a 9-week period during the first COVID-19 pandemic wave. Results An 8-h CMg workflow was 92% sensitive (95% CI, 75–99%) and 82% specific (95% CI, 57–96%) for bacterial identification based on culture-positive and culture-negative samples, respectively. CMg sequencing reported the presence or absence of β-lactam-resistant genes carried by Enterobacterales that would modify the initial guideline-recommended antibiotics in every case. CMg was also 100% concordant with quantitative PCR for detecting Aspergillus fumigatus from 4 positive and 39 negative samples. Molecular typing using 24-h sequencing data identified an MDR-K. pneumoniae ST307 outbreak involving 4 patients and an MDR-C. striatum outbreak involving 14 patients across three ICUs. Conclusion CMg testing provides accurate pathogen detection and antibiotic resistance prediction in a same-day laboratory workflow, with assembled genomes available the next day for genomic surveillance. The provision of this technology in a service setting could fundamentally change the multi-disciplinary team approach to managing ICU infections. The potential to improve the initial targeted treatment and rapidly detect unsuspected outbreaks of MDR-pathogens justifies further expedited clinical assessment of CMg..
Medienart: |
E-Artikel |
---|
Erscheinungsjahr: |
2021 |
---|---|
Erschienen: |
2021 |
Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
---|---|
Enthalten in: |
Genome medicine - 13(2021), 1 vom: 17. Nov. |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Charalampous, Themoula [VerfasserIn] |
---|
Links: |
Volltext [kostenfrei] |
---|
Anmerkungen: |
© The Author(s) 2021 |
---|
doi: |
10.1186/s13073-021-00991-y |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
OLC2128489113 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | OLC2128489113 | ||
003 | DE-627 | ||
005 | 20240326184938.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230505s2021 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s13073-021-00991-y |2 doi | |
035 | |a (DE-627)OLC2128489113 | ||
035 | |a (DE-He213)s13073-021-00991-y-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 610 |q VZ |
100 | 1 | |a Charalampous, Themoula |e verfasserin |0 (orcid)0000-0002-8914-5868 |4 aut | |
245 | 1 | 0 | |a Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units |
264 | 1 | |c 2021 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a © The Author(s) 2021 | ||
520 | |a Background Clinical metagenomics (CMg) has the potential to be translated from a research tool into routine service to improve antimicrobial treatment and infection control decisions. The SARS-CoV-2 pandemic provides added impetus to realise these benefits, given the increased risk of secondary infection and nosocomial transmission of multi-drug-resistant (MDR) pathogens linked with the expansion of critical care capacity. Methods CMg using nanopore sequencing was evaluated in a proof-of-concept study on 43 respiratory samples from 34 intubated patients across seven intensive care units (ICUs) over a 9-week period during the first COVID-19 pandemic wave. Results An 8-h CMg workflow was 92% sensitive (95% CI, 75–99%) and 82% specific (95% CI, 57–96%) for bacterial identification based on culture-positive and culture-negative samples, respectively. CMg sequencing reported the presence or absence of β-lactam-resistant genes carried by Enterobacterales that would modify the initial guideline-recommended antibiotics in every case. CMg was also 100% concordant with quantitative PCR for detecting Aspergillus fumigatus from 4 positive and 39 negative samples. Molecular typing using 24-h sequencing data identified an MDR-K. pneumoniae ST307 outbreak involving 4 patients and an MDR-C. striatum outbreak involving 14 patients across three ICUs. Conclusion CMg testing provides accurate pathogen detection and antibiotic resistance prediction in a same-day laboratory workflow, with assembled genomes available the next day for genomic surveillance. The provision of this technology in a service setting could fundamentally change the multi-disciplinary team approach to managing ICU infections. The potential to improve the initial targeted treatment and rapidly detect unsuspected outbreaks of MDR-pathogens justifies further expedited clinical assessment of CMg. | ||
700 | 1 | |a Alcolea-Medina, Adela |4 aut | |
700 | 1 | |a Snell, Luke B. |4 aut | |
700 | 1 | |a Williams, Tom G. S. |4 aut | |
700 | 1 | |a Batra, Rahul |4 aut | |
700 | 1 | |a Alder, Christopher |4 aut | |
700 | 1 | |a Telatin, Andrea |4 aut | |
700 | 1 | |a Camporota, Luigi |4 aut | |
700 | 1 | |a Meadows, Christopher I. S. |4 aut | |
700 | 1 | |a Wyncoll, Duncan |4 aut | |
700 | 1 | |a Barrett, Nicholas A. |4 aut | |
700 | 1 | |a Hemsley, Carolyn J. |4 aut | |
700 | 1 | |a Bryan, Lisa |4 aut | |
700 | 1 | |a Newsholme, William |4 aut | |
700 | 1 | |a Boyd, Sara E. |4 aut | |
700 | 1 | |a Green, Anna |4 aut | |
700 | 1 | |a Mahadeva, Ula |4 aut | |
700 | 1 | |a Patel, Amita |4 aut | |
700 | 1 | |a Cliff, Penelope R. |4 aut | |
700 | 1 | |a Page, Andrew J. |4 aut | |
700 | 1 | |a O’Grady, Justin |4 aut | |
700 | 1 | |a Edgeworth, Jonathan D. |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Genome medicine |d BioMed Central, 2009 |g 13(2021), 1 vom: 17. Nov. |h Online-Ressource |w (DE-627)594424275 |w (DE-600)2484394-5 |w (DE-576)304547956 |x 1756-994X |7 nnns |
773 | 1 | 8 | |g volume:13 |g year:2021 |g number:1 |g day:17 |g month:11 |
856 | 4 | 0 | |u https://dx.doi.org/10.1186/s13073-021-00991-y |z kostenfrei |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2446 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 13 |j 2021 |e 1 |b 17 |c 11 |