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Objective: To describe antibiotic treatment durations that pediatric infectious diseases (ID) and critical care clinicians usually recommend for bloodstream infections in critically ill children. Design: Anonymous, online practice survey using five common pediatric-based case scenarios of bloodstream infections. Setting: Pediatric intensive care units in Canada, Australia and New Zealand. Participants: Pediatric intensivists, nurse practitioners, ID physicians and pharmacists. Main outcome measures: Recommended treatment durations for common infectious syndromes associated with bloodstream infections and willingness to enrol patients into a trial to study treatment duration. Results: Among 136 survey respondents, most recommended at least 10 days antibiotics for bloodstream infections associated with: pneumonia (65%), skin/soft tissue (74%), urinary tract (64%) and intra-abdominal infections (drained: 90%; undrained: 99%). For central vascular catheter-associated infections without catheter removal, over 90% clinicians recommended at least 10 days antibiotics, except for infections caused by coagulase negative staphylococci (79%). Recommendations for at least 10 days antibiotics were less common with catheter removal. In multivariable linear regression analyses, lack of source control was significantly associated with longer treatment durations (+5.2 days [95% CI: 4.4–6.1 days] for intra-abdominal infections and +4.1 days [95% CI: 3.8–4.4 days] for central vascular catheter-associated infections). Most clinicians (73–95%, depending on the source of bloodstream infection) would be willing to enrol patients into a trial of shorter versus longer antibiotic treatment duration. Conclusions: The majority of clinicians currently recommend at least 10 days of antibiotics for most scenarios of bloodstream infections in critically ill children. There is practice heterogeneity in self-reported treatment duration recommendations among clinicians. Treatment durations were similar across different infectious syndromes. Under appropriate clinical conditions, most clinicians would be willing to enrol patients into a trial of shorter versus longer treatment for common syndromes associated with bloodstream infections. [ABSTRACT FROM AUTHOR]

Background: Preliminary studies suggested that Aboriginal Canadians had disproportionately higher rates of infection, hospitalization, and critical illness due to pandemic Influenza A(H1N1)pdm09. Methods: We used a prospective cohort study of critically ill patients with laboratory confirmed or probable H1N1 infection in Canada between April 16 2009 and April 12 2010. Baseline characteristics, medical interventions, clinical course and outcomes were compared between Aboriginal and non-Aboriginal patients. The primary outcome was hospital mortality. Results: Of 647 critically ill adult patients with known ethnicity, 81 (12.5%) were Aboriginal, 566 (87.5%) were non-Aboriginal. Aboriginal patients were younger (mean [SD] age 40.7[13.7] v. 49.0[14.9] years, p < 0.001) and more frequently female (64.2% v. 51.1%, p = 0.027). Rates of any co-morbid illnesses (Aboriginal v. non-Aboriginal, 92.6% v. 91.0%, p = 0.63), time from symptom onset to hospital admission (median [interquartile range] 4 [2–7] v. 4 [2–7] days, p = 0.84), time to ICU admission (5 [3–8] v.5 [3–8] days, p = 0.91), and severity of illness (mean APACHE II score (19.9 [9.6] v. 21.1 [9.9], p = 0.33) were similar. A similar proportion of Aboriginal patients received antiviral medication before ICU admission than non-Aboriginal patients (91.4% v. 93.8%, p = 0.40). Among Aboriginal versus non-Aboriginal patients, the need for mechanical ventilation (93.8% v. 88.6%, p = 0.15), ventilator-free days (14 [3–23] v. 17 [0–24], p = 0.62), durations of stay in ICU (13[7-19.5] v. 11 [5–8] days, p = 0.05), hospital (19 [12.5-33.5] v. 18 [11-35] days, p = 0.63), and hospital mortality were similar (19.8% v. 22.6%, p = 0.56). In multiple logistic regression analyses, higher APACHE II score (1.06; 1.04-1.09, p<0.001) was independently associated with an increased risk of death; antiviral treatment with a lower risk of death (0.34; 0.15 – 0.78, p = 0.01). Ethnicity was not associated with mortality. Interpretation: During the 2009-2010 Influenza A (H1N1) pandemic, Aboriginal and non-Aboriginal Canadians with H1N1-related critical illness had a similar risk of death, after adjusting for potential confounding factors. [ABSTRACT FROM AUTHOR]

Background: In an intensive care units, experts in mechanical ventilation are not continuously at patient's bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients' data in real time offers an opportunity to fill this gap., Objective: The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change., Data Sources: Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (< 84%, 85% to 91% and c92% to 100%)., Performance Metrics of Predictive Models: Due to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with area under the curves < 0.75., Conclusion: This single center pilot study using machine learning predictive model resulted in an algorithm with poor accuracy. The comparison of machine learning models showed that bagged complex trees was a promising approach. However, there is a need to improve these models before incorporating them into a clinical decision support systems. One potentially solution for improving predictive model, would be to increase the amount of data available to limit over-fitting that is potentially one of the cause for poor classification performances for 2 of the three class labels., Competing Interests: The authors have declared that no competing interests exist.