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Objectives Our understanding of pediatric acute respiratory distress syndrome is based on information from studies reporting intermittent, serial respiratory data. We have analyzed a high-resolution, longitudinal dataset that incorporates measures of hypoxemia severity, metrics of lung mechanics, ventilatory ratio, and mechanical power and examined associations with survival after the onset of pediatric acute respiratory distress syndrome. Design Single-center retrospective cohort, 2013-2018. Setting Tertiary surgical/medical PICU. Patients Seventy-six cases of severe pediatric acute respiratory distress syndrome, determined according to the Pediatric Acute Lung Injury Consensus Conference criteria. Interventions None. Measurements and main results The high-resolution database included continuous monitoring of ventilatory data (0.03 Hz) for up to 14 days after the diagnosis of pediatric acute respiratory distress syndrome or until extubation or death (n = 26). In the 12,128 hours of data during conventional mechanical ventilation, we used generalized estimating equations to compare groups, accounting for any effect of time. We identified an association between survival and faster rate of improvement in delta pressure (peak inspiratory pressure minus positive end-expiratory pressure; p = 0.028). Nonsurvival was associated with higher daily Pediatric Logistic Organ Dysfunction-2 scores (p = 0.005) and more severe hypoxemia metrics (p = 0.005). Mortality was also associated with the following respiratory/pulmonary metrics (mean difference [95% CI]): positive end-expiratory pressure level (+2.0 cm H2O [0.8-3.2 cm H2O]; p = 0.001), peak inspiratory pressure level (+3.0 cm H2O [0.5-5.5 cm H2O]; p = 0.022), respiratory rate (z scores +2.2 [0.9-3.6]; p = 0.003], ventilatory ratio (+0.41 [0.28-0.55]; p = 0.0001], and mechanical power (+5 Joules/min [1-10 Joules/min]; p = 0.013). Based on generalized linear mixed modeling, mechanical power remained associated with mortality after adjustment for normal respiratory rate, age, and daily Pediatric Logistic Organ Dysfunction-2 score (+3 Joules/breath [1-6 Joules/breath]; p = 0.009). Conclusions Mortality after severe pediatric acute respiratory distress syndrome is associated with the severity of organ dysfunction, oxygenation defects, and pulmonary metrics including dead space and theoretical mechanical energy load.

BACKGROUND: Mathematical models based on the physiology when programmed as a software can be used to teach cardiorespiratory physiology and to forecast the effect of various ventilatory support strategies. We developed a cardiorespiratory simulator for children called “SimulResp”. The purpose of this study was to evaluate the quality of SimulResp. METHODS: SimulResp quality was evaluated on accuracy, robustness, repeatability and reproducibility. Blood gas values (pH, PaCO , PaO and SaO ) were simulated for several subjects with different characteristics and in different situations and compared to expected values available as reference. The correlation between reference and simulated data was evaluated by the coefficient of determination and Intraclass correlation coefficient. The agreement was evaluated with the Bland & Altman analysis. RESULTS: SimulResp produced healthy child physiological values within normal range (pH 7.40 +/- 0.5; PaCO2 40 +/- 5 mmHg, PaO2 90 +/- 10 mmHg; SaO2 97% +/- 3%) starting from a weight of 25 to 35 kg, regardless of ventilator support. SimulResp failed to simulate accurate values for subjects under 25 kg and/or affected with pulmonary disease and mechanically ventilated. Based on the repeatability was considered as excellent and the reproducibility as mild to good. SimulResp’s prediction remains stable within time. CONCLUSIONS: The cardiorespiratory simulator SimulResp requires further development before future integration into a clinical decision support system.

Objectives Pediatric tracheostomy has evolved significantly in the past few decades and the optimal timing to perform it in children with respiratory assistance is still debated. The objective of this study was to describe the indications, timing, complications, and outcomes of infants on respiratory support who had a tracheostomy in a tertiary pediatric intensive care unit (PICU). Methods All children younger than 18 months of corrected age requiring respiratory support for at least 1 week and who had a tracheostomy between January 2005 and December 2015 were included. Their demographic and clinical data and their outcomes at 24 months of corrected age were collected and analyzed after approval from the CHU Sainte-Justine ethics committee. Results During the study period, 18 children (14 preterm infants, 4 polymalformative syndromes, and 2 diaphragmatic hernias) were included. The median corrected age at tracheostomy was 97 days (0–289 days) and 94.4% were elective. The indications for tracheostomy were ventilation for more than 7 days with (61.1%) or without (38.9%) orolaryngotracheal anomaly. The median number of consultants involved per patient was 16 consultants (10–23 consultants). The median hospital length of stay was 122 days (8–365 days) before tracheostomy and 235 days (22–891 days) after tracheostomy. The median invasive ventilation time was 68 days (8–168 days) before tracheostomy and 64 days (5–982 days) after tracheostomy. In terms of complications, there were nine cases of tracheitis and five cases of tracheal granulomas. At 24 months of corrected age, 17 of 18 children survived, one of/17 was still hospitalized, three of 17 were decannulated, three of 17 received respiratory support via their tracheostomy, 11 of 17 were fed with a gastrostomy, and all had neurodevelopmental delay. Conclusion Tracheostomy in infants requiring at least 1 week of ventilation is performed for complex cases and is favored for orolaryngotracheal anomalies. Clinicians should anticipate the need for developmental care in this population.

Objectives The Pediatric Logistic Organ Dysfunction-2 is a validated score that quantifies organ dysfunction severity and requires complex data collection that is time-consuming and subject to errors. We hypothesized that a computer algorithm that automatically collects and calculates the Pediatric Logistic Organ Dysfunction-2 (aPELOD-2) score would be valid, fast and at least as accurate as a manual approach (mPELOD-2). Design Retrospective cohort study. Setting Single center tertiary medical and surgical pediatric critical care unit (Sainte-Justine Hospital, Montreal, Canada). Patients Critically ill children participating in four clinical studies between January 2013 and August 2018, a period during which mPELOD-2 data were manually collected. Interventions None. Measurements and main results The aPELOD-2 was calculated for all consecutive admissions between 2013 and 2018 (n = 5,279) and had a good survival discrimination with an area under the receiver operating characteristic curve of 0.84 (95% CI, 0.81-0.88). We also collected data from four single-center studies in which mPELOD-2 was calculated (n = 796, 57% medical, 43% surgical) and compared these measurements to those of the aPELOD-2. For those patients, median age was 15 months (interquartile range, 3-73 mo), median ICU stay was 5 days (interquartile range, 3-9 d), mortality was 3.9% (n = 28). The intraclass correlation coefficient between mPELOD-2 and aPELOD-2 was 0.75 (95% CI, 0.73-0.77). The Bland-Altman showed a bias of 1.9 (95% CI, 1.7-2) and limits of agreement of -3.1 (95% CI, -3.4 to -2.8) to 6.8 (95% CI, 6.5-7.2). The highest agreement (Cohen's Kappa) of the Pediatric Logistic Organ Dysfunction-2 components was noted for lactate level (0.88), invasive ventilation (0.86), and creatinine level (0.82) and the lowest for the Glasgow Coma Scale (0.52). The proportion of patients with multiple organ dysfunction syndrome was higher for aPELOD-2 (78%) than mPELOD-2 (72%; p = 0.002). The aPELOD-2 had a better survival discrimination (area under the receiver operating characteristic curve, 0.81; 95% CI, 0.72-0.90) over mPELOD-2 (area under the receiver operating characteristic curve, 0.70; 95% CI, 0.59-0.82; p = 0.01). Conclusions We successfully created a freely available automatic algorithm to calculate the Pediatric Logistic Organ Dysfunction-2 score that is less labor intensive and has better survival discrimination than the manual calculation. Use of an automated system could greatly facilitate integration of the Pediatric Logistic Organ Dysfunction-2 score at the bedside and within clinical decision support systems.

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.

Ventilator-associated pneumonia is the second most common nosocomial infection in pediatric intensive care. The Centers for Disease Control and Prevention recently issued diagnosis criteria for pediatric ventilator-associated pneumonia and for ventilator-associated events in adults. The objectives of this pediatric study were to determine the prevalence of ventilator-associated pneumonia using these new Centers for Disease Control and Prevention criteria, to describe the risk factors and management of ventilator-associated pneumonia, and to assess a simpler method to detect ventilator-associated pneumonia with ventilator-associated event in critically ill children.Retrospective, observational, single-center.PICU in a tertiary-care university hospital.Consecutive critically ill children mechanically ventilated for greater than or equal to 48 hours between November 2013 and November 2015.None.Of 304 patients mechanically ventilated for greater than or equal to 48 hours, 284 were included. Among them, 30 (10.6%) met clinical and radiologic Centers for Disease Control and Prevention criteria for ventilator-associated pneumonia, yielding an prevalence of 7/1,000 mechanical ventilation days. Median time from mechanical ventilation onset to ventilator-associated pneumonia diagnosis was 4 days. Semiquantitative culture of tracheal aspirates was the most common microbiological technique. Gram-negative bacteria were found in 60% of patients, with a predominance of Haemophilus influenzae and Pseudomonas aeruginosa. Antibiotic therapy complied with adult guidelines. Compared with patients without ventilator-associated pneumonia, those with ventilator-associated pneumonia had significantly longer median durations of mechanical ventilation (15 vs 6 d; p0.001) and PICU stay (19 vs 9 d; p0.001). By univariate analysis, risk factors for ventilator-associated pneumonia were younger age, reintubation, acute respiratory distress syndrome, and continuous enteral feeding. Among the 30 patients with ventilator-associated pneumonia, 17 met adult ventilator-associated event's criteria (sensitivity, 56%).Ventilator-associated pneumonia is associated with longer times on mechanical ventilation and in the PICU. Using the ventilator-associated event criteria is of interest to rapidly screen for ventilator-associated pneumonia in children. However, sensitivity must be improved by adapting these criteria to children.

Objective: We aim to create a predictive model capable of giving a noninvasive, immediate and reliable estimate of the arterial partial pressure of carbon dioxide (PaCO2) in mechanically ventilated children with a better reliability than its estimation from end-tidal CO2 (PetCO2) and minute ventilation volume (Vmin) evolution. Methods: We collected data from the Intensive Care Unit (ICU) database of Sainte-Justine University Hospital (Montreal, Canada) and used the multilayer perceptron (MLP) to estimate the PaCO2. Input data were (1) Arterial blood gas (ABG) at a previous time to calibrate the model, (2) mechanical ventilator parameters and (3) pulse oximetry. The data were divided into four groups depending on the time gap between previous ABG and its prediction: [0 h, 2 h], [2 h, 6 h], [6 h, 12 h] and [12 h, 24 h]. Results: We included 17,329 ABGs collected from 527 patients between May 2015 and October 2018. Median age was 6.7 months (interquartile range 1-60) and female proportion was 45%. Patients had a median of 13 ABGs per patient (IQR 5-34). The accuracy of the models in the four groups was 18%, 18%, 19% and 25% higher than the minute volume models and the PetCO2 models (4% to 11%, respectively). Conclusion: Our model based on noninvasive parameters was able to better estimate the PaCO2 in mechanically ventilated children when compared to the traditional techniques. Significance: ABG analysis is very important in ICU; it is the gold standard in respiratory and acid-base evaluation. ABG is invasive, painful and risky. Our approach, noninvasive and reliable, is an alternative for optimizing mechanical ventilator settings, thus providing better care for patients.

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.

Our objective was to construct a prospective high-quality and high-frequency database combining patient therapeutics and clinical variables in real time, automatically fed by the information system and network architecture available through fully electronic charting in our PICU. The purpose of this article is to describe the data acquisition process from bedside to the research electronic database.Descriptive report and analysis of a prospective database.A 24-bed PICU, medical ICU, surgical ICU, and cardiac ICU in a tertiary care free-standing maternal child health center in Canada.All patients less than 18 years old were included at admission to the PICU.None.Between May 21, 2015, and December 31, 2016, 1,386 consecutive PICU stays from 1,194 patients were recorded in the database. Data were prospectively collected from admission to discharge, every 5 seconds from monitors and every 30 seconds from mechanical ventilators and infusion pumps. These data were linked to the patient's electronic medical record. The database total volume was 241 GB. The patients' median age was 2.0 years (interquartile range, 0.0-9.0). Data were available for all mechanically ventilated patients (n = 511; recorded duration, 77,678 hr), and respiratory failure was the most frequent reason for admission (n = 360). The complete pharmacologic profile was synched to database for all PICU stays. Following this implementation, a validation phase is in process and several research projects are ongoing using this high-fidelity database.Using the existing bedside information system and network architecture of our PICU, we implemented an ongoing high-fidelity prospectively collected electronic database, preventing the continuous loss of scientific information. This offers the opportunity to develop research on clinical decision support systems and computational models of cardiorespiratory physiology for example.

BackgroundIn 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.ObjectiveThe 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 sourcesData 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 modelsDue 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.ConclusionThis 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.

As a result of innovations in informatics over the last decades, physiologic models elaborated in the second half of the 20th century could be transformed into specific virtual patients called computational models. These models, developed initially for teaching purposes, are of great potential interest in responding to current concerns about improving patient care and safety. However, even if there are obvious advantages to using computational models in cardiorespiratory management, major concerns persist as to their reliability and their ability to recreate real patient physiologic evolution over time. Once developed, these models require complex validation and configuration phases prior to implementation in daily practice. This article focuses on the development of computational models, and reviews the methodologies to clinically validate the models including specific patient databases (perpetual patients) and the use in clinical practice including very high fidelity simulation.

A 2005 consensus conference led by the National Association for Medical Direction of Respiratory Care (NAMDRC) defined prolonged mechanical ventilation (PMV) for adults as invasive and/or noninvasive mechanical ventilation (NIV) for ≥ 21 consecutive days for ≥ 6 h/d. In children, no such consensus definition exists. This results in substantial variability in definitional criteria, making study of the impact and outcomes of PMV across and within settings problematic. The objective of this work was to identify how PMV for children and neonates is described in the literature and to outline pediatric/neonatal considerations related to PMV, with the goal of proposing a pediatric/neonatal adaptation to the NAMDRC definition.We searched electronic databases for studies describing PMV in children. We extracted definitional criteria and developed recommendations based on the literature review and our clinical experience.Of the 416 citations obtained, 87 met inclusion criteria, totaling 34,255 subjects. Identified criteria for the pediatric PMV definition included: number of consecutive days of mechanical ventilation (ranging from 6 h to 3 months), inclusion of NIV, time spent off the ventilator during weaning (considered as same ventilation episode), and importance of chronological age (term neonates) and postmenstrual age for preterm neonates. We considered high-flow nasal cannula; however, we determined that its current role as a weaning adjunct is unclear.Therefore, we developed the following recommendations for the pediatric PMV definition: ≥ 21 consecutive days (after 37 weeks postmenstrual age) of ventilation for ≥ 6 h/d considering invasive ventilation and NIV and including short interruptions (48 h) of ventilation during the weaning process as the same episode of ventilation. We propose a definition of pediatric PMV that incorporates the number of consecutive days of mechanical ventilation while taking into account use of NIV and lung maturity and including short interruptions during the weaning process.

ObjectiveTo create an easy-to-use dynamic database designed specifically for the Quebec Trophoblastic Disease Registry (RMTQ).IntroductionIt is now well established that much of the success in managing trophoblastic diseases comes from the development of national and regional reference centers. Computerized databases allow the optimal use of data stored in these centers.MethodsWe have created an electronic data registration system by producing a database using FileMaker Pro 12. It uses 11 external tables associated with a unique identification number for each patient. Each table allows specific data to be recorded, incorporating demographics, diagnosis, automated staging, laboratory values, pathological diagnosis, and imaging parameters.ResultsFrom January 1, 2009, to December 31, 2013, we used our database to register 311 patients with 380 diseases and have seen a 39.2% increase in registrations each year between 2009 and 2012. This database allows the automatic generation of semilogarithmic curves, which take into account β-hCG values as a function of time, complete with graphic markers for applied treatments (chemotherapy, radiotherapy, or surgery). It generates a summary sheet for a synthetic vision in real time.ConclusionsWe have created, at a low cost, an easy-to-use database specific to trophoblastic diseases that dynamically integrates staging and monitoring. We propose a 10-step procedure for a successful trophoblastic database. It improves patient care, research, and education on trophoblastic diseases in Quebec and leads to an opportunity for collaboration on a national Canadian registry.