A general two-compartment model for mechanical ventilation

Detection and quantitative analysis of patient-ventilator interactions in ventilated infants by deep learning networks

BACKGROUND

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses.

METHODS

An observational study was conducted in 23 babies randomly selected from 170 neonates who were ventilated with SIPPV-VG, SIMV-VG or PSV-VG mode for at least 12 h. 500 breaths were randomly selected and manually annotated from each recording to train convolutional neural network (CNN) models for PVI classification.

RESULTS

The average asynchrony index (AI) over all recordings was 52.5%. The most frequently occurring PVIs included expiratory work (median: 28.4%, interquartile range: 23.2-40.2%), late cycling (7.6%, 2.8-10.2%), failed triggering (4.6%, 1.2-6.2%) and late triggering (4.4%, 2.8-7.4%). Approximately 25% of breaths with a PVI had two or more PVIs occurring simultaneously. Binary CNN classifiers were developed for PVIs affecting ≥1% of all breaths (n = 7) and they achieved F1 scores of >0.9 on the test set except for early triggering where it was 0.809.

CONCLUSIONS

PVIs occur frequently in neonates undergoing conventional mechanical ventilation with a significant proportion of breaths containing multiple PVIs. We have developed computational models for seven different PVIs to facilitate automated detection and further evaluation of their clinical significance in neonates.

IMPACT

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses. By adapting a recent taxonomy of PVI definitions in adults, we have manually annotated neonatal ventilator waveforms to determine prevalence and co-occurrence of neonatal PVIs. We have also developed binary deep learning classifiers for common PVIs to facilitate their automatic detection and quantification.

Nonlinear N - Compartments model of respiratory mechanics considering viscoelasticity, inertia and surface tension properties

Respiratory biomechanics constitutes an important topic in clinical practice. Different strategies like mathematical models have been implemented to understand and replicate scenarios allowing deeper analysis. In this paper, a nonlinear N - compartments model is presented, allowing to represent the lung in a heterogeneous way. It considers the resistance of each generation of the airway and each alveolar compartment characterized independently. Includes properties of nonlinear elastance, viscoelasticity, inertia, and surface tension. In this work, to show the functionality of the model, a simulation of four alveolar units coupled to the airway model is presented using pressure as input signal simulating mechanical ventilation. However, the model can be used to simulate any desired number of alveolar units. Values at airway output were compared to the linear model, obtaining a correlation close to 1. Also, was compared to a physical test lung using Hamilton - S1 mechanical ventilator obtaining a positive correlation. The model makes it possible to evaluate the effects of different properties during spontaneous respiration or mechanical ventilation, both at the airway opening and alveolar. These properties include viscoelasticity, surface tension, inertia, among others.

Optimal determination of respiratory airflow patterns using a nonlinear multicompartment model for a lung mechanics system

We develop optimal respiratory airflow patterns using a nonlinear multicompartment model for a lung mechanics system. Specifically, we use classical calculus of variations minimization techniques to derive an optimal airflow pattern for inspiratory and expiratory breathing cycles. The physiological interpretation of the optimality criteria used involves the minimization of work of breathing and lung volume acceleration for the inspiratory phase, and the minimization of the elastic potential energy and rapid airflow rate changes for the expiratory phase. Finally, we numerically integrate the resulting nonlinear two-point boundary value problems to determine the optimal airflow patterns over the inspiratory and expiratory breathing cycles.

A NEW LOOK AT THE STRESS INDEX FOR LUNG INJURY

Ranieri and colleagues1,2 have proposed a simple mathematical equation (Paw(t) ≈ atb+c) where a, b, and c are constants that relates airway pressure, Paw(t), over inspiration (0 ≤ t ≤ ti) in constant flow mechanical ventilation to ventilator-induced lung injury. They have correlated b with low-volume stress (b ≤ 0.9), high-volume stress (b ≥ 1.1), and minimal stress (0.9 ≤ b ≤ 1.1) in the lungs. In this paper we examine the theoretical underpinnings of this empirical equation and show that it has a relationship to the upper and lower inflection points of the Pelastic - V curve during inspiration. An alternative equation [Formula: see text] that comes from the theoretical analysis of this type of mechanical ventilation is presented. Both equations are compared using experimental data.

Closed-loop control for intensive care unit sedation

The potential clinical applications of active control for pharmacology in general, and anesthesia and critical care unit medicine in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery and the intensive care unit is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control for drug administration. These models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative and are characterized by conservation laws (e.g., mass, energy, fluid, etc.) capturing the exchange of material between kinetically homogenous entities called compartments. Compartmental models have been particularly important for understanding pharmacokinetics and pharmacodynamics. One of the basic motivations for pharmacokinetic/pharmacodynamic research is to improve drug delivery. In critical care medicine it is current clinical practice to administer potent drugs that profoundly influence levels of consciousness, respiratory, and cardiovascular function by manual control based on the clinician's experience and intuition. Open-loop control (manual control) by clinical personnel can be tedious, imprecise, time-consuming, and sometimes of poor quality, depending on the skills and judgement of the clinician. Closed-loop control based on appropriate dynamical systems models merits investigation as a means of improving drug delivery in the intensive care unit. In this article, we discuss the challenges and opportunities of feedback control using nonnegative and compartmental system theory for the specific problem of closed-loop control of intensive care unit sedation. Several closed-loop control paradigms are investigated including adaptive control, neural network adaptive control, optimal control, and hybrid adaptive control algorithms for intensive care unit sedation.