Increasing the reliability of fully automated surveillance for central line–associated bloodstream infections

OBJECTIVETo increase reliability of the algorithm used in our fully automated electronic surveillance system by adding rules to better identify bloodstream infections secondary to other hospital-acquired infections.METHODSIntensive care unit (ICU) patients with positive blood cultures were reviewed. Central line–associated bloodstream infection (CLABSI) determinations were based on 2 sources: routine surveillance by infection preventionists, and fully automated surveillance. Discrepancies between the 2 sources were evaluated to determine root causes. Secondary infection sites were identified in most discrepant cases. New rules to identify secondary sites were added to the algorithm and applied to this ICU population and a non-ICU population. Sensitivity, specificity, predictive values, and kappa were calculated for the new models.RESULTSOf 643 positive ICU blood cultures reviewed, 68 (10.6%) were identified as central line–associated bloodstream infections by fully automated electronic surveillance, whereas 38 (5.9%) were confirmed by routine surveillance. New rules were tested to identify organisms as central line–associated bloodstream infections if they did not meet one, or a combination of, the following: (I) matching organisms (by genus and species) cultured from any other site; (II) any organisms cultured from sterile site; (III) any organisms cultured from skin/wound; (IV) any organisms cultured from respiratory tract. The best-fit model included new rules I and II when applied to positive blood cultures in an ICU population. However, they didn’t improve performance of the algorithm when applied to positive blood cultures in a non-ICU population.CONCLUSIONElectronic surveillance system algorithms may need adjustment for specific populations.Infect. Control Hosp. Epidemiol. 2015;36(12):1396–1400</jats:sec

Mast cell activation symptoms are prevalent in Long-COVID

OBJECTIVES: Hyper-inflammation caused by COVID-19 may be mediated by mast cell activation (MCA) which has also been hypothesized to cause Long-COVID (LC) symptoms. We determined prevalence/severity of MCA symptoms in LC. METHODS: Adults in LC-focused Facebook support groups were recruited for online assessment of symptoms before and after COVID-19. Questions included presence and severity of known MCA and LC symptoms and validated assessments of fatigue and quality of life. General population controls and mast cell activation syndrome (MCAS) patients were recruited for comparison if they were ≥18 years of age and never had overt COVID-19 symptoms. RESULTS: There were 136 LC subjects (89.7% females, age 46.9 ±12.9 years), 136 controls (65.4% females, age 49.2 ±15.5), and 80 MCAS patients (85.0% females, age 47.7 ±16.4). Pre-COVID-19 LC subjects and controls had virtually identical MCA symptom and severity analysis. Post-COVID-19 LC subjects and MCAS patients prior to treatment had virtually identical MCA symptom and severity analysis. CONCLUSIONS: MCA symptoms were increased in LC and mimicked the symptoms and severity reported by patients who have MCAS. Increased activation of aberrant mast cells induced by SARS-CoV-2 infection by various mechanisms may underlie part of the pathophysiology of LC, possibly suggesting routes to effective therapy