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Rationale: High data quality is essential to ensure the validity of clinical and research inferences based on it. However, these data quality assessments are often missing even though these data are used in daily practice and research. Aims and objectives: Our objective was to evaluate the data quality of our high‐resolution electronic database (HRDB) implemented in our paediatric intensive care unit (PICU). Methods We conducted a prospective validation study of a HRDB in a 32‐bed paediatric medical, surgical, and cardiac PICU in a tertiary care freestanding maternal‐child health centre in Canada. All patients admitted to the PICU with at least one vital sign monitored using a cardiorespiratory monitor connected to the central monitoring station. Results: Between June 2017 and August 2018, data from 295 patient days were recorded from medical devices and 4645 data points were video recorded and compared to the corresponding data collected in the HRDB. Statistical analysis showed an excellent overall correlation (R2 = 1), accuracy (100%), agreement (bias = 0, limits of agreement = 0), completeness (2% missing data), and reliability (ICC = 1) between recorded and collected data within clinically significant pre‐defined limits of agreement. Divergent points could all be explained. Conclusions: This prospective validation of a representative sample showed an excellent overall data quality. [ABSTRACT FROM AUTHOR]

Objective: The main purpose of our study was to subjectively assess the quality of a paediatric intensive care unit (PICU) database according to the Directory of Clinical Databases (DoCDat) criteria. Design and setting: A survey was conducted between April 1 and June 15, 2018, among the Sainte Justine PICU research group. Population: Every member of this group whose research activity required the use of the database and/or who was involved in the development/validation of the database. Interventions: None. Measurements and main results: All 10 research team members (one Information Technology specialist, one junior medical student, and eight clinician researchers) who used the high‐resolution database fulfilled the survey (100% response rate). The median quality level of the Sainte Justine PICU database across all the 10 criteria was 3 (2‐4), rated on a 1 (worst) to 4 (best) numeric scale. When compared with previously assessed databases through the DoCDat criteria, we found that the Sainte Justine PICU database performance was similar. Conclusions: The PICU high‐resolution database appeared of good quality when subjectively assessed by the DoCDat criteria. Further validation procedures are mandatory. We suggest that data quality assessment and validation procedures should be reported when creating a new database. [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. [ABSTRACT FROM AUTHOR]

Purpose: The use of intravenous immunoglobulins (IVIG) has increased significantly in the last decade causing challenges for blood suppliers to respond to the demand. Indications for which IVIG infusion should be given to critically ill children remain unclear. The objective of this study is to characterize the epidemiology of IVIG use in this population. Methods: We performed a single-center retrospective cohort study of all patients aged between 3 days and 18 years who received at least one IVIG infusion while hospitalized in the pediatric intensive care unit of the Centre hospitalier universitaire (CHU) Sainte-Justine, Montréal Quebec (Canada) between January 1, 2013 and December 31, 2018. Results: One hundred and seventy-two patients received a total of 342 IVIG infusions over the study period. Most common indications for IVIG infusions were staphylococcal or streptococcal toxic shock syndrome (n=53/342, 15.5%), immunoglobulin replacement in chylothorax (n=37/342, 10.9%), prophylaxis following bone marrow transplantation (n=31/342, 9.1%), myocarditis (n=25/342, 7.3%) and post-solid organ transplant complications (n=21/342, 6.1%). The median dose of IVIG per infusion was 0.95 g/kg (IQR 0.5-1.0) and median number of IVIG infusions per patient was one (IQR: 1-2). Seventy-nine percent of IVIG infusions given were administrated for off-label indications with regards to Health Canada recommendations. Conclusion: This study identified the most common indications for IVIG infusion in critically ill children in a tertiary care pediatric intensive care unit. Given the costs, the known adverse events associated with IVIG and the pressure that blood suppliers are facing to meet the demands, clinical trials are needed to evaluate the efficacy and safety of IVIG in conditions where use is significant. [ABSTRACT FROM AUTHOR]

To evaluate the feasibility of continuous determination of the optimal mean arterial blood pressure (opt-MAP) according to cerebral autoregulation and to describe the opt-MAP, the autoregulation limits, and the time spent outside these limits in children within 48 h of cardiac surgery. Cerebral autoregulation was assessed using the correlation coefficient (COx) between cerebral oxygenation and MAP in children following cardiac surgery. Plots depicting the COx according to the MAP were used to determine the opt-MAP using weighted multiple time windows. For each patient, we estimated (1) the time spent with MAP outside the autoregulation limits and (2) the burden of deviation, defined as the area between the MAP curve and the autoregulation limits when the MAP was outside these limits. Fifty-one patients with a median age of 7.1 (IQR 0.7–52.0) months old were included. The opt-MAP was calculated for 94% (IQR 90–96) of the monitored time. The opt-MAP was significantly lower in neonates < 1 month old. The patients spent 24% (18–31) of the time outside of the autoregulation limits, with no significant differences between age groups. Continuous determination of the opt-MAP is feasible in children within the first 48 h following cardiac surgery. [ABSTRACT FROM AUTHOR]

Objectives: Ventilator-associated pneumonia (VAP) is a severe care-related disease. The Centers for Disease Control defined the diagnosis criteria; however, the pediatric criteria are mainly subjective and retrospective. Clinical decision support systems have recently been developed in healthcare to help the physician to be more accurate for the early detection of severe pathology. We aimed at developing a predictive model to provide early diagnosis of VAP at the bedside in a pediatric intensive care unit (PICU). Methods: We performed a retrospective single-center study at a tertiary-care pediatric teaching hospital. All patients treated by invasive mechanical ventilation between September 2013 and October 2019 were included. Data were collected in the PICU electronic medical record and high-resolution research database. Development of the clinical decision support was then performed using open-access R software (Version 3.6.1®). Measurements and main results: In total, 2077 children were mechanically ventilated. We identified 827 episodes with almost 48 h of mechanical invasive ventilation and 77 patients who suffered from at least one VAP event. We split our database at the patient level in a training set of 461 patients free of VAP and 45 patients with VAP and in a testing set of 199 patients free of VAP and 20 patients with VAP. The Imbalanced Random Forest model was considered as the best fit with an area under the ROC curve from fitting the Imbalanced Random Forest model on the testing set being 0.82 (95% CI: (0.71, 0.93)). An optimal threshold of 0.41 gave a sensitivity of 79.7% and a specificity of 72.7%, with a positive predictive value (PPV) of 9% and a negative predictive value of 99%, and with an accuracy of 79.5% (95% CI: (0.77, 0.82)). Conclusions: Using machine learning, we developed a clinical predictive algorithm based on clinical data stored prospectively in a database. The next step will be to implement the algorithm in PICUs to provide early, automatic detection of ventilator-associated pneumonia. [ABSTRACT FROM AUTHOR]

The occurrence of tonic Edi episodes was independently associated with a diagnosis of bronchiolitis (intubated patients: aOR=2.81 (1.13-7.13), p=0,03, NIV patients: aOR=2.68 (1.23-5.90), p=0,01) and a higher respiratory rate (intubated patients: aOR=1.06 (1.00-1.13), p=0,045, NIV: aOR= 1,10 (1.05-1.15), p< 0,001). Sixty three percent of patients on non-invasive ventilation (139/222 patients) and 31% of intubated patients (62/200 patients) presented at least one episode of high tonic Edi. B Introduction: b Tonic diaphragmatic activity (Tonic Edi) represents sustained activation of the diaphragm throughout expiration and reflects patients' efforts to increase lung recruitment. [Extracted from the article]

B Conclusions: b A third of children on general wards received at least one transfusion; 18.5% of these children developed a SCDE after their first transfusion that lead to a PICU admission. Little is known about the epidemiology of transfusion-related adverse events in children hospitalized outside intensive care units(ICU). B Introduction/Hypothesis: b EPOCH(JAMA2018;317:1002-12), an international cluster randomized controlled trial (RCT), investigated the incidence of significant clinical deterioration events(SCDE) leading to PICU admission in children on general wards. [Extracted from the article]