To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Infection prevention and control (IPC) workflows are often retrospective and manual. New tools, however, have entered the field to facilitate rapid prospective monitoring of infections in hospitals. Although artificial intelligence (AI)–enabled platforms facilitate timely, on-demand integration of clinical data feeds with pathogen whole-genome sequencing (WGS), a standardized workflow to fully harness the power of such tools is lacking. We report a novel, evidence-based workflow that promotes quicker infection surveillance via AI-assisted clinical and WGS data analysis. The algorithm suggests clusters based on a combination of similar minimum inhibitory concentration (MIC) data, timing of sample collection, and shared location stays between patients. It helps to proactively guide IPC professionals during investigation of infectious outbreaks and surveillance of multidrug-resistant organisms and healthcare-acquired infections. Methods: Our team established a 1-year workgroup comprised of IPC practitioners, clinical experts, and scientists in the field. We held weekly roundtables to study lessons learned in an ongoing surveillance effort at a tertiary care hospital—utilizing Philips IntelliSpace Epidemiology (ISEpi), an AI-powered system—to understand how such a tool can enhance practice. Based on real-time case discussions and evidence from the literature, a workflow guidance tool and checklist were codified. Results: In our workflow, data-informed clusters posed by ISEpi underwent triage and expert follow-up analysis to assess: (1) likelihood of transmission(s); (2) potential vector(s) identity; (3) need to request WGS; and (4) intervention(s) to be pursued, if warranted. In a representative sample (spanning October 17, 2019, to November 7, 2019) of 67 total isolates suggested for inclusion in 19 unique cluster investigations, we determined that 9 investigations merited follow-up. Collectively, these 9 investigations involved 21 patients and required 115 minutes to review in ISEpi and an additional 70 minutes of review outside of ISEpi. After review, 6 investigations were deemed unlikely to represent a transmission; the other 3 had potential to represent transmission for which interventions would be performed. Conclusions: This study offers an important framework for adaptation of existing infection control workflow strategies to leverage the utility of rapidly integrated clinical and WGS data. This workflow can also facilitate time-sensitive decisions regarding sequencing of specific pathogens given the preponderance of available clinical data supporting investigations. In this regard, our work sets a new standard of practice: precision infection prevention (PIP). Ongoing effort is aimed at development of AI-powered capabilities for enterprise-level quality and safety improvement initiatives.
Funding: Philips Healthcare provided support for this study.
Disclosures: Alan Doty and Juan Jose Carmona report salary from Philips Healthcare.
To evaluate the use of a perianal swab to detect CDI.
A perianal swab was collected from each inpatient with a positive stool sample for C. difficile (by polymerase chain reaction [PCR] test) and was tested for C. difficile by PCR and by culture. The variables evaluated included demographics, CDI severity, bathing before perianal swab collection, hours between stool sample and perianal swab, cycle threshold (Ct) to PCR positivity, and doses of CDI treatment before stool sample and before perianal swab.
Of 83 perianal swabs, 59 (71.1%) tested positive for C. difficile by PCR when perianal swabs were collected an average of 21 hours after the stool sample. Compared with the respective stool sample, the perianal sample was less likely to grow C. difficile (P=.005) and had a higher PCR Ct (P<.001). A direct, significant but weak correlation was detected between the Ct for a positive perianal sample and the respective stool sample (r=0.36; P=.006). An inverse dose relationship was detected between PCR positivity and CDI treatment doses before perianal swab collection (P=.27).
Perianal swabs are a simple method to detect C. difficile tcdB gene by PCR, with a sensitivity of 71%. These data were limited because stool samples and perianal swabs were not collected simultaneously. Compared with stool samples, the perianal Ct values and culture results were consistent with a lower bacterial load on the perianal sample due to the receipt of more CDI treatment before collection or unknown factors affecting perianal skin colonization.
Infect Control Hosp Epidemiol 2017;38:658–662
Email your librarian or administrator to recommend adding this to your organisation's collection.