Computational simulation to assess patient safety of uncompensated COVID-19 two-patient ventilator sharing using the Pulse Physiology Engine

IMPLEMENTATION OF A DYNAMIC AND EXTENSIBLE MECHANICAL VENTILATOR MODEL FOR REAL-TIME PHYSIOLOGICAL SIMULATION

We have designed and implemented a generic virtual mechanical ventilator model into the open-source Pulse Physiology Engine for real-time medical simulation. The universal data model is uniquely designed to apply all modes of ventilation and allow for modification of the fluid mechanics circuit parameters. The ventilator methodology provides a connection to the existing Pulse respiratory system for spontaneous breathing and gas/aerosol substance transport. The existing Pulse Explorer application was extended to include a new ventilator monitor screen with variable modes and settings and a dynamic output display. Proper functionality was validated by simulating the same patient pathophysiology and ventilator settings virtually in Pulse as a physical lung simulator and ventilator setup.

Computational Simulation Is a Vital Resource for Navigating the COVID-19 Pandemic

INTRODUCTION

COVID-19 has prompted the extensive use of computational models to understand the trajectory of the pandemic. This article surveys the kinds of dynamic simulation models that have been used as decision support tools and to forecast the potential impacts of nonpharmaceutical interventions (NPIs). We developed the Values in Viral Dispersion model, which emphasizes the role of human factors and social networks in viral spread and presents scenarios to guide policy responses.

METHODS

An agent-based model of COVID-19 was developed with individual agents able to move between 3 states (susceptible, infectious, or recovered), with each agent placed in 1 of 7 social network types and assigned a propensity to comply with NPIs (quarantine, contact tracing, and physical distancing). A series of policy questions were tested to illustrate the impact of social networks and NPI compliance on viral spread among (1) populations, (2) specific at-risk subgroups, and (3) individual trajectories.

RESULTS

Simulation outcomes showed large impacts of physical distancing policies on number of infections, with substantial modification by type of social network and level of compliance. In addition, outcomes on metrics that sought to maximize those never infected (or recovered) and minimize infections and deaths showed significantly different epidemic trajectories by social network type and among higher or lower at-risk age cohorts.

CONCLUSIONS

Although dynamic simulation models have important limitations, which are discussed, these decision support tools should be a key resource for navigating the ongoing impacts of the COVID-19 pandemic and can help local and national decision makers determine where, when, and how to invest resources.

Sharing Mechanical Ventilator: In Vitro Evaluation of Circuit Cross-Flows and Patient Interactions

During the COVID-19 pandemic, a shortage of mechanical ventilators was reported and ventilator sharing between patients was proposed as an ultimate solution. Two lung simulators were ventilated by one anesthesia machine connected through two respiratory circuits and T-pieces. Five different combinations of compliances (30–50 mL × cmH₂O⁻¹) and resistances (5–20 cmH₂O × L⁻¹ × s⁻¹) were tested. The ventilation setting was: pressure-controlled ventilation, positive end-expiratory pressure 15 cmH₂O, inspiratory pressure 10 cmH₂O, respiratory rate 20 bpm. Pressures and flows from all the circuit sections have been recorded and analyzed. Simulated patients with equal compliance and resistance received similar ventilation. Compliance reduction from 50 to 30 mL × cmH₂O⁻¹ decreased the tidal volume (V_T) by 32% (418 ± 49 vs. 285 ± 17 mL). The resistance increase from 5 to 20 cmH₂O × L⁻¹ × s⁻¹ decreased V_T by 22% (425 ± 69 vs. 331 ± 51 mL). The maximal alveolar pressure was lower at higher compliance and resistance values and decreased linearly with the time constant (r² = 0.80, p < 0.001). The minimum alveolar pressure ranged from 15.5 ± 0.04 to 16.57 ± 0.04 cmH₂O. Cross-flows between the simulated patients have been recorded in all the tested combinations, during both the inspiratory and expiratory phases. The simultaneous ventilation of two patients with one ventilator may be unable to match individual patient’s needs and has a high risk of cross-interference.

Multiplex Ventilation: Solutions for Four Main Safety Problems

BACKGROUND

The COVID-19 pandemic has led to an increased demand for mechanical ventilators and concerns of a ventilator shortage. Several groups have advocated for 1 ventilator to ventilate 2 or more patients in the event of such a shortage. However, differences in patient lung mechanics could make sharing a ventilator detrimental to both patients. Our previous study indicated failure to ventilate in 67% of simulations. The safety problems that must be solved include individual control of tidal volume (V_T), individual measurement of V_T, individualization of PEEP settings, and individual PEEP measurement. The purpose of this study was to evaluate potential solutions developed at our institution.

METHODS

Two separate lung simulators were ventilated with a modified multiplex circuit using pressure control ventilation. Parameters of the lung models used for simulations (resistance and compliance) were evidence-based from published studies. Individual circuit-modification devices were first evaluated for accuracy. Devices were an adjustable flow diverter valve, a prototype dual volume display, a PEEP valve, and a disposable PEEP display. Then the full modified multiplex circuit was assessed by ventilating 6 pairs of simulated patients with different lung models and attempting to equalize ventilation. Ventilation was considered equalized when V_T and end-expiratory lung volume were within 10% for each simulation.

RESULTS

The adjustable flow diverter valve allowed volume adjustment to 1 patient without affecting the other. The average error of the dual volume display was -17%. The PEEP valves individualized PEEP, but the PEEP gauge error ranged from 17% to 41%. Using the multiplex circuit, ventilation was equalized regardless of differences in resistance or compliance, reversing the "failure modes" of our previous study.

CONCLUSIONS

The results of this simulation-based study indicate that devices for individual control and display of V_T and PEEP are effective in extending the usability and potential patient safety of multiplex ventilation.

2020 Year in Review: Shared Ventilation for COVID-19

COVID-19 resulting from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a pandemic of respiratory failure previously unencountered. Early in the pandemic, concentrated infections in high-density population cities threatened to overwhelm health systems, and ventilator shortages were predicted. An early proposed solution was the use of shared ventilation, or the use of a single ventilator to support ≥ 2 patients. Spurred by ill-conceived social media posts, the idea spread in the lay press. Prior to 2020, there were 7 publications on this topic. A year later, more than 40 publications have addressed the technical details for shared ventilation, clinical experience with shared ventilation, as well as the numerous limitations and ethics of the technique. This is a review of the literature regarding shared ventilation from peer-reviewed articles published in 2020.