Pre-analytical delay of blood cultures which hinders the management of sepsis and fosters the emergence of antimicrobial resistance is an adverse effect of laboratory centralisation.
Noone, M. r.
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BackgroundThe clinical diagnosis of sepsis is based on non-specific criteria and blood culture remains the gold standard confirmatory test. While early results are of wide clinical benefit delayed reports lead to prolonged anti-microbial therapy which fosters the emergence of antimicrobial resistance. Pre-analytical delay of a blood culture delays or decreases the chance of a positive report and a maximal four-hour delay between collection and incubation of the specimen is recommended by the United Kingdom Standards for Microbiology Investigations (UK SMI). This retrospective observational study documents compliance with this quality standard by National Health Service (NHS) hospitals across England. An appraisal is undertaken of the policies which governed laboratory centralisation and their impact on the microbiology service. MethodsFreedom of Information (FOI) applications were submitted to 116 NHS Trusts in England requesting retrospective audit data showing compliance with the recommended pre-analytical standard for blood cultures. Information relating to the configuration of microbiology services and global laboratory costs were also requested. ResultsReports were received from 89 Trusts (76.7%) comprising 146 acute hospitals. Only four hospitals (2.7%) showed full compliance with the four-hour pre-analytical standard. Service configurations varied widely. The anticipated savings resulting from centralisation have not been realised. ConclusionsThere was poor compliance with the quality standard for pre-analytical delay of blood cultures. Evidence is presented to show that the poor compliance rates reported are a result of the approach taken and the guiding policies applied when laboratory centralisation was imposed by NHS England. Reversal of these adverse effects will require mandatory implementation of UK SMIs and computing the cost of quality measures in the context of the overall health care benefit to the patient.
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