Determining Optimal Mean Arterial Blood Pressure Based on Cerebral Autoregulation in Children after Cardiac Surgery

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.

Evaluation of the SIMULRESP: A simulation software of child and teenager cardiorespiratory physiology

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, PaCO2 , PaO2,  and SaO2 ) 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 25-35 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.

Clinical Decision Support System to Detect the Occurrence of Ventilator-Associated Pneumonia in Pediatric Intensive Care

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.

Designing a national pediatric critical care database: a Delphi consensus study

PURPOSE

We sought to describe the processes undertaken for the systematic selection and consensus determination of the common data elements for inclusion in a national pediatric critical care database in Canada.

METHODS

We conducted a multicentre Delphi consensus study of Canadian pediatric intensive care units (PICUs) participating in the creation of a national database. Participants were PICU health care professionals, allied health professionals, caregivers, and other stakeholders. A dedicated panel group created a baseline survey of data elements based on literature, current PICU databases, and expertise in the field. The survey was then used for a Delphi iterative consensus process over three rounds, conducted from March to June 2021.

RESULTS

Of 86 invited participants, 68 (79%) engaged and agreed to participate as part of an expert panel. Panel participants were sent three rounds of the survey with response rates of 62 (91%), 61 (90%) and 55 (81%), respectively. After three rounds, 72 data elements were included from six domains, mostly reflecting clinical status and complex medical interventions received in the PICU. While race, gender, and home region were included by consensus, variables such as minority status, indigenous status, primary language, and ethnicity were not.

CONCLUSION

We present the methodological framework used to select data elements by consensus for a national pediatric critical care database, with participation from a diverse stakeholder group of experts and caregivers from all PICUs in Canada. The selected core data elements will provide standardized and synthesized data for research, benchmarking, and quality improvement initiatives of critically ill children.

Elevated Diaphragmatic Tonic Activity in PICU Patients: Age-Specific Definitions, Prevalence, and Associations

OBJECTIVES

Tonic diaphragmatic activity (tonic Edi, i.e., sustained diaphragm activation throughout expiration) reflects diaphragmatic effort to defend end-expiratory lung volumes. Detection of such elevated tonic Edi may be useful in identifying patients who need increased positive end-expiratory pressure. We aimed to: 1) identify age-specific definitions for elevated tonic Edi in ventilated PICU patients and 2) describe the prevalence and factors associated with sustained episodes of high tonic Edi.

DESIGN

Retrospective study using a high-resolution database.

SETTING

Single-center tertiary PICU.

PATIENTS

Four hundred thirty-one children admitted between 2015 and 2020 with continuous Edi monitoring.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We characterized our definition of tonic Edi using data from the recovery phase of respiratory illness (i.e., final 3 hr of Edi monitoring, excluding patients with significant persistent disease or with diaphragm pathology). High tonic Edi was defined as population data exceeding the 97.5th percentile, which for infants younger than 1 year was greater than 3.2 μV and for older children as greater than 1.9 μV. These thresholds were then used to identify patients with episodes of sustained elevated tonic Edi in the first 48 hours of ventilation (acute phase). Overall, 62 of 200 (31%) of intubated patients and 138 of 222 (62%) of patients on noninvasive ventilation (NIV) had at least one episode of high tonic Edi. These episodes were independently associated with the diagnosis of bronchiolitis (intubated patients: adjusted odds [aOR], 2.79 [95% CI, 1.12-7.11]); NIV patients: aOR, 2.71 [1.24-6.0]). There was also an association with tachypnea and, in NIV patients, more severe hypoxemia.

CONCLUSIONS

Our proposed definition of elevated tonic Edi quantifies abnormal diaphragmatic activity during expiration. Such a definition may help clinicians to identify those patients using abnormal effort to defend end-expiratory lung volume. In our experience, high tonic Edi episodes are frequent, especially during NIV and in patients with bronchiolitis.

Executive Summary of the Second International Guidelines for the Diagnosis and Management of Pediatric Acute Respiratory Distress Syndrome (PALICC-2)

OBJECTIVES

We sought to update our 2015 work in the Second Pediatric Acute Lung Injury Consensus Conference (PALICC-2) guidelines for the diagnosis and management of pediatric acute respiratory distress syndrome (PARDS), considering new evidence and topic areas that were not previously addressed.

DESIGN

International consensus conference series involving 52 multidisciplinary international content experts in PARDS and four methodology experts from 15 countries, using consensus conference methodology, and implementation science.

SETTING

Not applicable.

PATIENTS

Patients with or at risk for PARDS.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Eleven subgroups conducted systematic or scoping reviews addressing 11 topic areas: 1) definition, incidence, and epidemiology; 2) pathobiology, severity, and risk stratification; 3) ventilatory support; 4) pulmonary-specific ancillary treatment; 5) nonpulmonary treatment; 6) monitoring; 7) noninvasive respiratory support; 8) extracorporeal support; 9) morbidity and long-term outcomes; 10) clinical informatics and data science; and 11) resource-limited settings. The search included MEDLINE, EMBASE, and CINAHL Complete (EBSCOhost) and was updated in March 2022. Grading of Recommendations, Assessment, Development, and Evaluation methodology was used to summarize evidence and develop the recommendations, which were discussed and voted on by all PALICC-2 experts. There were 146 recommendations and statements, including: 34 recommendations for clinical practice; 112 consensus-based statements with 18 on PARDS definition, 55 on good practice, seven on policy, and 32 on research. All recommendations and statements had agreement greater than 80%.

CONCLUSIONS

PALICC-2 recommendations and consensus-based statements should facilitate the implementation and adherence to the best clinical practice in patients with PARDS. These results will also inform the development of future programs of research that are crucially needed to provide stronger evidence to guide the pediatric critical care teams managing these patients.

Intravenous Immunoglobulin Use In Critically Ill Children

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.

Validation process of a high-resolution database in a paediatric intensive care unit-Describing the perpetual patient's validation

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.

Arterial Partial Pressures of Carbon Dioxide Estimation Using Non-Invasive Parameters in Mechanically Ventilated 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.

Tracheostomy, respiratory support, and developmental outcomes in neonates with severe lung diseases: Retrospective study in one center

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.

Qualitative subjective assessment of a high-resolution database in a paediatric intensive care unit-Elaborating the perpetual patient's ID card

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.

Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study

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.

Perpetual and Virtual Patients for Cardiorespiratory Physiological Studies

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.