Simulations for mechanical ventilation in children: review and future prospects

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.

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems.

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.

Basics of Infant Conventional Mechanical Ventilation: An Interactive Animated Teaching Module

INTRODUCTION

While conventional mechanical ventilation is a common therapy in the neonatal intensive care unit (NICU), pediatric residents receive insufficient instruction. This stand-alone computer module provides an interactive method of learning basic infant pulmonary physiology and principles of mechanical ventilation.

METHODS

This module runs offline and is compatible with a variety of operating systems. Participants complete a six-question, case-based pretest. The seven-section instructional module is self-paced, narrated, animated, and interactive. Learners can repeat each section as needed. At the conclusion of the module, participants complete the same six-question test and receive feedback. In total, the module requires 15-20 minutes to complete.

RESULTS

The curriculum has been implemented at the beginning of the NICU rotation over a 2-year period within our pediatric residency program. Participants preferred this interactive module and had higher posttest scores when compared to a PowerPoint presentation. After 4 months, there was evidence of knowledge decay.

DISCUSSION

The interactive module is enjoyable, effective, and convenient. It engages participants in active learning and allows them to control the time and pace of their instruction. We have implemented the curriculum within our residency program and believe it would be useful for a variety of NICU health care providers.

Development and validation of a computational simulator for pediatric acute respiratory distress syndrome patients

This paper presents the adaptation of an advanced cardiorespiratory model of acute respiratory distress syndrome in adult patients to pediatric pathophysiology. We describe how model equations and parameters were revised to represent the physiological characteristics of pediatric Acute Respiratory Distress Syndrome (ARDS) patients. The adapted model was matched to data from twelve mechanically ventilated patients diagnosed with Pediatric Acute Respiratory Distress Syndrome (PARDS), and was shown to reproduce the available clinical data accurately for all patients. This new model constitutes the first detailed computational simulator specifically tailored to PARDS patients, and can be used as an investigational tool for developing and evaluating novel therapeutic strategies.

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.

Computerized Decision Support Systems for Mechanical Ventilation in Children

Mechanical ventilation is an effective treatment in the ICU but can have significant adverse effects. Approaches from adult research have been adopted in pediatric critical care despite known differences in respiratory physiology and ICU processes. There continues to be considerable variation in how ventilators are managed. Computerized decision support systems implement explicit protocols, and are designed to make mechanical ventilation management safer, more consistent, and more lung protective. Variable results and low or unknown compliance with protocols and CDSS tools have been reported. To date, there has been limited research regarding CDSS for mechanical ventilation in children.

Comparative performances analysis of neonatal ventilators

Background

Mechanical ventilation is a therapeutic action for newborns with respiratory diseases but may have side effects. Correct equipment knowledge and training may limit human errors. We aimed to test different neonatal mechanical ventilators’ performances by an acquisition module (a commercial pressure sensor plus an isolated chamber and a dedicated software).

Methods

The differences (ΔP) between peak pressure values and end-expiration pressure were investigated for each ventilator. We focused on discrepancies among measured and imposed pressure data. A statistical analysis was performed.

Results

We investigated the measured/imposed ΔP relation. The ΔP do not reveal univocal trends related to ventilation setting parameters and the data distributions were non-Gaussian.

Conclusions

Measured ΔP represent a significant parameter in newborns’ ventilation, due to the typical small volumes. The investigated ventilators showed different tendencies. Therefore, a deep specific knowledge of the intensive care devices is mandatory for caregivers to correctly exploit their operating principles.

An active one-lobe pulmonary simulator with compliance control for medical training in neonatal mechanical ventilation

Mechanical ventilation is a current support therapy for newborns affected by respiratory diseases. However, several side effects have been observed after treatment, making it mandatory for physicians to determine more suitable approaches. High fidelity simulation is an efficient educational technique that recreates clinical experience. The aim of the present study is the design of an innovative and versatile neonatal respiratory simulator which could be useful in training courses for physicians and nurses as for mechanical ventilation. A single chamber prototype, reproducing a pulmonary lobe both in size and function, was designed and assembled. Volume and pressure within the chamber can be tuned by the operator through the device control system, in order to simulate both spontaneous and assisted breathing. An innovative software-based simulator for training neonatologists and nurses within the continuing medical education program on respiratory disease management was validated. Following the clinical needs, three friendly graphic user interfaces were implemented for simulating three different clinical scenarios (spontaneous breathing, controlled breathing and triggered/assisted ventilation modalities) thus providing physicians with an active experience. The proposed pulmonary simulator has the potential to be included in the range of computer-driven technologies used in medical training, adding novel functions and improving simulation results.