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Twenty-seven percent of pediatric day surgery patients are overweight/obese. These children may be allowed clear liquids 2 h before surgery as GFV(IBW) averages 1 mL/kg regardless of BMI and fasting interval. Rare emetic episodes were not associated with shortened fasting intervals in this population.

The mechanisms underlying latent-virus-mediated heterologous immunity, and subsequent transplant rejection, especially in the setting of T cell costimulation blockade, remain undetermined. To address this, we have utilized MHV68 to develop a rodent model of latent virus-induced heterologous alloimmunity. MHV68 infection was correlated with multimodal immune deviation, which included increased secretion of CXCL9 and CXCL10, and with the expansion of a CD8dim T cell population. CD8dim T cells exhibited decreased expression of multiple costimulation molecules and increased expression of two adhesion molecules, LFA-1 and VLA-4. In the setting of MHV68 latency, recipients demonstrated accelerated costimulation blockade-resistant rejection of skin allografts compared to non-infected animals (MST 13.5 d in infected animals vs 22 d in non-infected animals, p<.0001). In contrast, the duration of graft acceptance was equivalent between non-infected and infected animals when treated with combined anti-LFA-1/anti-VLA-4 adhesion blockade (MST 24 d for non-infected and 27 d for infected, p = n.s.). The combination of CTLA-4-Ig/anti-CD154-based costimulation blockade+anti-LFA-1/anti-VLA-4-based adhesion blockade led to prolonged graft acceptance in both non-infected and infected cohorts (MST>100 d for both, p<.0001 versus costimulation blockade for either). While in the non-infected cohort, either CTLA-4-Ig or anti-CD154 alone could effectively pair with adhesion blockade to prolong allograft acceptance, in infected animals, the prolonged acceptance of skin grafts could only be recapitulated when anti-LFA-1 and anti-VLA-4 antibodies were combined with anti-CD154 (without CTLA-4-Ig, MST>100 d). Graft acceptance was significantly impaired when CTLA-4-Ig alone (no anti-CD154) was combined with adhesion blockade (MST 41 d). These results suggest that in the setting of MHV68 infection, synergy occurs predominantly between adhesion pathways and CD154-based costimulation, and that combined targeting of both pathways may be required to overcome the increased risk of rejection that occurs in the setting of latent-virus-mediated immune deviation.

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

Background Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden. Objective (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics. Materials and Methods We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016–2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric. Results Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden. Conclusion Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.