Creating a High-Frequency Electronic Database in the PICU: The Perpetual Patient

Delay Between Actual Occurrence of Patient Vital Sign and the Nominal Appearance in the Electronic Health Record: Single-Center, Retrospective Study of PICU Data, 2014-2018

OBJECTIVES

Patient vital sign data charted in the electronic health record (EHR) are used for time-sensitive decisions, yet little is known about when these data become nominally available compared with when the vital sign was actually measured. The objective of this study was to determine the magnitude of any delay between when a vital sign was actually measured in a patient and when it nominally appears in the EHR.

DESIGN

We performed a single-center retrospective cohort study.

SETTING

Tertiary academic children's hospital.

PATIENTS

A total of 5,458 patients were admitted to a PICU from January 2014 to December 2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We analyzed entry and display times of all vital signs entered in the EHR. The primary outcome measurement was time between vital sign occurrence and nominal timing of the vital sign in the EHR. An additional outcome measurement was the frequency of batch charting. A total of 9,818,901 vital sign recordings occurred during the study period. Across the entire cohort the median (interquartile range [IQR]) difference between time of occurrence and nominal time in the EHR was in hours:minutes:seconds, 00:41:58 (IQR 00:13:42-01:44:10). Lag in the first 24 hours of PICU admission was 00:47:34 (IQR 00:15:23-02:19:00), lag in the last 24 hours was 00:38:49 (IQR 00:13:09-01:29:22; p < 0.001). There were 1,892,143 occurrences of batch charting.

CONCLUSIONS

This retrospective study shows a lag between vital sign occurrence and its appearance in the EHR, as well as a frequent practice of batch charting. The magnitude of the delay-median ~40 minutes-suggests that vital signs available in the EHR for clinical review and incorporation into clinical alerts may be outdated by the time they are available.

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.

Prediction of Return of Spontaneous Circulation in a Pediatric Swine Model of Cardiac Arrest Using Low-Resolution Multimodal Physiological Waveforms

Monitoring physiological waveforms, specifically hemodynamic variables (e.g., blood pressure waveforms) and end-tidal CO2 (EtCO2), during pediatric cardiopulmonary resuscitation (CPR) has been demonstrated to improve survival rates and outcomes when compared to standard depth-guided CPR. However, waveform guidance has largely been based on thresholds for single parameters and therefore does not leverage all the information contained in multimodal data. We hypothesize that the combination of multimodal physiological features improves the prediction of the return of spontaneous circulation (ROSC), the clinical indicator of short-term CPR success. We used machine learning algorithms to evaluate features extracted from eight low-resolution (4 samples per minute) physiological waveforms to predict ROSC. The waveforms were acquired from the 2nd to 10th minute of CPR in pediatric swine models of cardiac arrest (N = 89, 8-12 kg). The waveforms were divided into segments with increasing length (both forward and backward) for feature extraction, and machine learning algorithms were trained for ROSC prediction. For the full CPR period (2nd to 10th minute), the area under the receiver operating characteristics curve (AUC) was 0.93 (95% CI: 0.87-0.99) for the multivariate model, 0.70 (0.55-0.85) for EtCO2 and 0.80 (0.67-0.93) for coronary perfusion pressure. The best prediction performances were achieved when the period from the 6th to the 10th minute was included. Poor predictions were observed for some individual waveforms, e.g., right atrial pressure. In conclusion, multimodal waveform features carry relevant information for ROSC prediction. Using multimodal waveform features in CPR guidance has the potential to improve resuscitation success and reduce mortality.

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.

Exploring the clinical relevance of vital signs statistical calculations from a new-generation clinical information system

New information on the intensive care applications of new generation 'high-density data clinical information systems' (HDDCIS) is increasingly being published in the academic literature. HDDCIS avoid data loss from bedside equipment and some provide vital signs statistical calculations to promote quick and easy evaluation of patient information. Our objective was to study whether manual records of continuously monitored vital signs in the Paediatric Intensive Care Unit could be replaced by these statistical calculations. Here we conducted a prospective observational clinical study in paediatric patients with severe diabetic ketoacidosis, using a Medlinecare® HDDCIS, which collects information from bedside equipment (1 data point per parameter, every 3-5 s) and automatically provides hourly statistical calculations of the central trend and sample dispersion. These calculations were compared with manual hourly nursing records for patient heart and respiratory rates and oxygen saturation. The central tendency calculations showed identical or remarkably similar values and strong correlations with manual nursing records. The sample dispersion calculations differed from the manual references and showed weaker correlations. We concluded that vital signs calculations of central tendency can replace manual records, thereby reducing the bureaucratic burden of staff. The significant sample dispersion calculations variability revealed that automatic random measurements must be supervised by healthcare personnel, making them inefficient.

Multimodality Video Acquisition System for the Assessment of Vital Distress in Children

In children, vital distress events, particularly respiratory, go unrecognized. To develop a standard model for automated assessment of vital distress in children, we aimed to construct a prospective high-quality video database for critically ill children in a pediatric intensive care unit (PICU) setting. The videos were acquired automatically through a secure web application with an application programming interface (API). The purpose of this article is to describe the data acquisition process from each PICU room to the research electronic database. Using an Azure Kinect DK and a Flir Lepton 3.5 LWIR attached to a Jetson Xavier NX board and the network architecture of our PICU, we have implemented an ongoing high-fidelity prospectively collected video database for research, monitoring, and diagnostic purposes. This infrastructure offers the opportunity to develop algorithms (including computational models) to quantify vital distress in order to evaluate vital distress events. More than 290 RGB, thermographic, and point cloud videos of each 30 s have been recorded in the database. Each recording is linked to the patient's numerical phenotype, i.e., the electronic medical health record and high-resolution medical database of our research center. The ultimate goal is to develop and validate algorithms to detect vital distress in real time, both for inpatient care and outpatient management.

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.

A Web-Based Platform for the Automatic Stratification of ARDS Severity

Acute respiratory distress syndrome (ARDS), including severe pulmonary COVID infection, is associated with a high mortality rate. It is crucial to detect ARDS early, as a late diagnosis may lead to serious complications in treatment. One of the challenges in ARDS diagnosis is chest X-ray (CXR) interpretation. ARDS causes diffuse infiltrates through the lungs that must be identified using chest radiography. In this paper, we present a web-based platform leveraging artificial intelligence (AI) to automatically assess pediatric ARDS (PARDS) using CXR images. Our system computes a severity score to identify and grade ARDS in CXR images. Moreover, the platform provides an image highlighting the lung fields, which can be utilized for prospective AI-based systems. A deep learning (DL) approach is employed to analyze the input data. A novel DL model, named Dense-Ynet, is trained using a CXR dataset in which clinical specialists previously labelled the two halves (upper and lower) of each lung. The assessment results show that our platform achieves a recall rate of 95.25% and a precision of 88.02%. The web platform, named PARDS-CxR, assigns severity scores to input CXR images that are compatible with current definitions of ARDS and PARDS. Once it has undergone external validation, PARDS-CxR will serve as an essential component in a clinical AI framework for diagnosing ARDS.

Hemodynamic assessment in children after cardiac surgery: A pilot study on the value of infrared thermography

Introduction

Low cardiac output syndrome in the postoperative period after cardiac surgery leads to an increase in tissue oxygen extraction, assessed by the oxygen extraction ratio. Measurement of the oxygen extraction ratio requires blood gases to be taken. However, the temperature of the skin and various parts of the body is a direct result of blood flow distribution and can be monitored using infrared thermography. Thus, we conducted a prospective clinical study to evaluate the correlation between the thermal gradient obtained by infrared thermography and the oxygen extraction ratio in children at risk for low cardiac output after cardiac surgery.

Methods

Children aged 0 to 18 years, having undergone cardiac surgery with cardio-pulmonary bypass in a pediatric intensive care unit were included in the study. One to 4 thermal photos were taken per patient using the FLIR One Pro thermal imaging camera. The thermal gradient between the central temperature of the inner canthus of the eye and the peripheral temperature was compared to the concomitant oxygen extraction ratio calculated from blood gases.

Results

41 patients were included with a median age of 6 months (IQR 3-48) with median Risk Adjustment for Congenital Heart Surgery-1 score was 2 (IQR 2-3). Eighty nine thermal photos were analyzed. The median thermal gradient was 2.5 °C (IQR 1,01-4.04). The median oxygen extraction ratio was 35% (IQR 26-42). Nine patients had an oxygen extraction ratio ≥ 50%. A significant but weak correlation was found between the thermal gradient and the oxygen extraction ratio (Spearman's test p = 0.25, p = 0.016). Thermal gradient was not correlated with any other clinical or biologic markers of low cardiac output. Only young age was an independent factor associated with an increase in the thermal gradient.

Conclusion

In this pilot study, which included mainly children without severe cardiac output decrease, a significant but weak correlation between thermal gradient by infrared thermography and oxygen extraction ratio after pediatric cardiac surgery was observed. Infrared thermography is a promising non-invasive technology that could be included in multimodal monitoring of postoperative cardiac surgery patients. However, a clinical trial including more severe children is needed.

Oxygenation Defects, Ventilatory Ratio, and Mechanical Power During Severe Pediatric Acute Respiratory Distress Syndrome: Longitudinal Time Sequence Analyses in a Single-Center Retrospective Cohort

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.

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.

Incremental effect of non-invasive oscillating device on chest physiotherapy in critically ill children: a cross-over randomised trial

INTRODUCTION

Chest physiotherapy (CPT) and intrathoracic percussion ventilation have been recognised as to encourage dislodging the secretions; nonetheless, the tolerance to the procedure and its efficiency have not been proven to be sufficient.

METHOD AND ANALYSES

This study aims to examine the tolerance, feasibility and physiological effects in airway clearance by using a novel extrathoracic non-invasive oscillating transducer device (NIOD) in critically ill children. A two-stage cross-over randomised controlled study in a paediatric intensive care unit in a Canadian Academic Children's Hospital will be applied. Children under 24 months old, for whom CPT is prescribed for airway clearance, will be included. The study consists of two stages; (1) Stage 1 'Frequency Level': we will apply two different frequencies of the NIOD (40 Hz vs 60 Hz) for 12 min each, on each patient 3 hours apart, and (2) Stage 2 'NIOD versus CPT': we will implement NIOD and CPT alternatingly for 3 hours apart. The order of the procedures will be randomly allocated for each case. We will compare the average Δchanges of tidal lung volume measured by a 3D imaging system and regional lung functions using electrical impedance tomography, between the two different frequencies and between the NIOD periods and the CPT periods. We will also examine tolerance by seeing COMFORT Scales and related complications during the procedures. We estimate necessary sample size as 6 for each arm (Total 12 cases) for stage 1 and 48 cases for Stage 2, with power of 0.8 and alpha of 0.05.

ETHICS AND DISSEMINATION

This study has been approved by the Health Research Ethics Board of University of Montreal, Canada (REB number: 2020-2471). We will disseminate our findings through peer-reviewed publications and conference presentations in paediatric or/and critical care fields.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT03821389).

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.

A practical approach to storage and retrieval of high-frequency physiological signals

OBJECTIVE

Storage of physiological waveform data for retrospective analysis presents significant challenges. Resultant data can be very large, and therefore becomes expensive to store and complicated to manage. Traditional database approaches are not appropriate for large scale storage of physiological waveforms. Our goal was to apply modern time series compression and indexing techniques to the problem of physiological waveform storage and retrieval.

APPROACH

We deployed a vendor-agnostic data collection system and developed domain-specific compression approaches that allowed long term storage of physiological waveform data and other associated clinical and medical device data. The database (called AtriumDB) also facilitates rapid retrieval of retrospective data for high-performance computing and machine learning applications.

MAIN RESULTS

A prototype system has been recording data in a 42-bed pediatric critical care unit at The Hospital for Sick Children in Toronto, Ontario since February 2016. As of December 2019, the database contains over 720,000 patient-hours of data collected from over 5300 patients, all with complete waveform capture. One year of full resolution physiological waveform storage from this 42-bed unit can be losslessly compressed and stored in less than 300 GB of disk space. Retrospective data can be delivered to analytical applications at a rate of up to 50 million time-value pairs per second.

SIGNIFICANCE

Stored data are not pre-processed or filtered. Having access to a large retrospective dataset with realistic artefacts lends itself to the process of anomaly discovery and understanding. Retrospective data can be replayed to simulate a realistic streaming data environment where analytical tools can be rapidly tested at scale.

Comparison of the Automated Pediatric Logistic Organ Dysfunction-2 Versus Manual Pediatric Logistic Organ Dysfunction-2 Score for Critically Ill Children

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.

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.

Ventilator-Associated Pneumonia and Events in Pediatric Intensive Care: A Single Center Study

OBJECTIVES

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.

DESIGN

Retrospective, observational, single-center.

SETTING

PICU in a tertiary-care university hospital.

PATIENTS

Consecutive critically ill children mechanically ventilated for greater than or equal to 48 hours between November 2013 and November 2015.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

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; p < 0.001) and PICU stay (19 vs 9 d; p < 0.001). 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%).

CONCLUSIONS

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.