No abstract

IMPORTANCE Sepsis and septic shock are common and, at times, fatal in pediatrics. Blood cultures are often obtained when clinicians suspect sepsis, yet are low-yield with a false-positive rate up to 50%. OBJECTIVES To determine whether a novel, 2-part, clinical practice guideline could decrease the rates of total blood cultures and cultures collected from central venous catheters in critically ill children and to examine the effect of the guideline on patient outcomes. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort study was performed to determine the effect of a new clinical practice guideline on blood culture practices in a 36-bed, combined medical/surgical pediatric intensive care unit of an urban, academic, tertiary care center from April 1, 2013, to March 31, 2015. All patients admitted to the pediatric intensive care unit with length of stay of 4 hours or more were evaluated (4560 patient visits: 2204 preintervention, 2356 postintervention visits). INTERVENTIONS Two documents were developed: (1) fever/sepsis screening checklist and (2) blood culture decision algorithm. Clinicians consulted these documents when considering ordering blood cultures and for guidance about the culture source. MAIN OUTCOMES AND MEASURES Primary outcome was the total number of blood cultures collected per 100 patient-days. RESULTS Of the 2204 children evaluated before the intervention, 1215 were male (55.1%); median (interquartile range) age was 5 (1-13) years. Postintervention analysis included 2356 children; 1262 were male (53.6%) and median (interquartile range) age was 6 (1-13) years. A total of 1807 blood cultures were drawn before the intervention during 11 196 patient-days; 984 cultures were drawn after the intervention during 11 204 patient-days (incidence rate, 16.1 vs 8.8 cultures per 100 patient-days). There was a 46.0% reduction after the intervention in the blood culture collection rate (incidence rate ratio, 0.54; 95% CI, 0.50-0.59). After the intervention, there was an immediate 25.0% reduction in the rate of cultures per 100 patient-days (95% CI, 4.2%-39.7%; P = .02) and a sustained 6.6% (95% CI, 4.7%-8.4%; P < .001) monthly decrease in the rate of cultures per 100 patient-days. Significantly fewer cultures were collected from central venous catheters after vs before the intervention (389 [39.5%] vs 1321 [73.1%]; P < .001). Rates of episodes defined as suspected infection and suspected septic shock decreased significantly after the intervention, but patients meeting these criteria underwent cultures at unchanged frequencies before vs after the intervention (52.1% vs 47.0%, P = .09, compared with 56.7% vs 55.0%, P = .75). In-hospital mortality (45 [2.0] vs 37 [1.6]; P = .23) and hospital readmissions (107 [4.9] vs 103 [4.4]; P = .42) were unchanged after the intervention. CONCLUSIONS AND RELEVANCE A systematic approach to blood cultures decreased the total number of cultures and central venous catheter cultures, without an increase in rates of mortality, readmission, or episodes of suspected infection and ...

March through May 2020, a model of novel coronavirus (COVID-19) disease progression and treatment was constructed for the open-source Synthea patient simulation. The model was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known, along with emerging resources, data, publications, and clinical knowledge. The simulation outputs synthetic Electronic Health Records (EHR), including the daily consumption of Personal Protective Equipment (PPE) and other medical devices and supplies. For this simulation, we generated 124,150 synthetic patients, with 88,166 infections and 18,177 hospitalized patients. Patient symptoms, disease severity, and morbidity outcomes were calibrated using clinical data from the peer-reviewed publications. 4.1% of all simulated infected patients died and 20.6% were hospitalized. At peak observation, 548 dialysis machines and 209 mechanical ventilators were needed. This simulation and the resulting data have been used for the development of algorithms and prototypes designed to address the current or future pandemics, and the model can continue to be refined to incorporate emerging COVID-19 knowledge, variations in patterns of care, and improvement in clinical outcomes. The resulting model, data, and analysis are available as open-source code on GitHub and an open-access data set is available for download.

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