Abstract: Systems and methods for model-driven system integration on a ventilator comprise a modeled exhalation flow. Ventilator flow sensors contain components that are easily damaged or impacted and have high rates of failure. Knowledge failure frequency and type may help determine when to replace, clean, or calibrate a flow sensor. A modeled exhalation flow may be trained for a ventilator. Additionally, the modeled exhalation flow may be compared to a flow sensor flow to determine a specific failure. Additionally or alternatively, the modeled exhalation flow may supplement, weight, or replace a faulty flow sensor measurement. These systems and methods may assist in identifying an inaccurate flow sensor measurement and/or providing a more accurate modeled flow estimate, thus increasing accuracy in patient-ventilator interactions.

Abstract: A hybrid single-limb patient circuit coupled to an inspiratory port and an expiratory port of a ventilator. The hybrid single-limb patient circuit may include a check valve positioned to direct breathing gases supplied from the inspiratory port in a single direction; a manifold pneumatically coupled to the check valve; a dual-purpose single limb, pneumatically coupled to the manifold and the non-invasive patient interface, to carry breathing gases to the non-invasive patient interface and carry exhaled gases from the non-invasive patient interface; and an exhalation tubing segment, pneumatically coupled to the manifold and the expiratory port, to carry the exhaled gases from the manifold to the expiratory port.

Abstract: Systems and methods for model-driven system integration on a ventilator comprise a modeled exhalation flow. Ventilator flow sensors contain components that are easily damaged or impacted and have high rates of failure. Knowledge failure frequency and type may help determine when to replace, clean, or calibrate a flow sensor. A modeled exhalation flow may be trained for a ventilator. Additionally, the modeled exhalation flow may be compared to a flow sensor flow to determine a specific failure. Additionally or alternatively, the modeled exhalation flow may supplement, weight, or replace a faulty flow sensor measurement. These systems and methods may assist in identifying an inaccurate flow sensor measurement and/or providing a more accurate modeled flow estimate, thus increasing accuracy in patient-ventilator interactions.

Abstract: Systems and methods for lung-protective ventilation are disclosed. In examples, volume-targeted, pressure-controlled ventilation may deliver mandatory breaths to a patient without a rise time setting. Inputs into the ventilation may include a peak inspiratory flow value (Qpeak) and a target tidal volume (VT,set). Respiratory parameters of the patient may be determined based on test breaths. The inputs and the respiratory parameters may be used to calculate a target inspiratory pressure (Pi) and target rise time constant (?). Breaths may then be delivered based on the calculated target inspiratory pressure and target rise time constant. Mechanical power delivered to the patient may also be monitored as an additional measure for patient lung protection.

Abstract: Systems and methods for automatically improving patient-ventilator synchronization, including a method, performed by a ventilator, for automatic synchrony adjustment in medical ventilation. The method may include delivering positive pressure during a first inhalation phase; cycling to a first exhalation phase at an end of the first inhalation phase according to a cycling sensitivity; and at an end of the first exhalation phase, triggering a second inhalation phase. The method may also include during at least one of the first exhalation phase or the second inhalation phase, detecting a cycling-related asynchrony event; in response to the detecting, automatically adjusting the cycling sensitivity without additional user input; delivering positive pressure during the second inhalation phase; and cycling from the second inhalation phase to a second exhalation phase according to the adjusted cycling sensitivity.

Abstract: Systems and methods for increasing accuracy of the fraction of inspired oxygen (FiO2) in delivered breathing gases. In an aspect, the technology relates to a blower-based ventilation system. The system includes a blower; an oxygen flow valve; a processor; and memory storing instructions that, when executed by the processor causes the system to perform a set of operations. The set of operations include, based on a target oxygen concentration level, determining a target ambient air flow rate and a target oxygen flow rate; measuring a flow rate of ambient air generated by a blower; measuring a flow rate of oxygen from an oxygen flow valve; determining a synchronization error; and based on the synchronization error, adjusting operation of at least one of the blower or the oxygen flow valve.

Abstract: Systems and methods for one-touch ventilation mode are disclosed. In examples, settings for a medical ventilator are determined and delivered to a patient with a minimum of one input parameter. The one-touch ventilation mode may reference or apply one or more respiratory mechanics planes to determine desired ventilation parameters. In an example, the input parameter may be mapped to initial ventilation settings on a respiratory mechanics plane. During ventilation delivered according to the initial ventilation settings, ventilation data may be obtained. Based on the ventilation data, one or more ventilation strategies may be implemented, including breath type strategy, alarming strategy, triggering/cycling strategy, and PEEP strategy. Updated ventilation settings may be determined based on the ventilation data and/or the ventilation strategy.

Abstract: Systems and methods for detecting respiratory mechanics of a patient during mechanical ventilation. An example ventilator implemented method includes receiving pressure and flow measurements of breathing gas. Based on the measurements, the ventilator generates values for a derivative of an estimate of intrapleural pressure (Psync) of the patient, and the ventilator determines a rate of change of the generated Psync values over a portion of an expiratory phase of a breathing cycle. Based on the determined rate of change, the ventilator selects at least one of a triggering mode or a setting for ventilation, and the ventilator controls ventilation of the patient based on the selected triggering mode and/or the setting for ventilation.

Abstract: Systems and methods for model-driven system integration on a ventilator comprise a modeled exhalation flow. Ventilator flow sensors contain components that are easily damaged or impacted and have high rates of failure. Knowledge failure frequency and type may help determine when to replace, clean, or calibrate a flow sensor. A modeled exhalation flow may be trained for a ventilator. Additionally, the modeled exhalation flow may be compared to a flow sensor flow to determine a specific failure. Additionally or alternatively, the modeled exhalation flow may supplement, weight, or replace a faulty flow sensor measurement. These systems and methods may assist in identifying an inaccurate flow sensor measurement and/or providing a more accurate modeled flow estimate, thus increasing accuracy in patient-ventilator interactions.

Abstract: Systems and methods for detecting extubation of an endotracheal tube (ETT) are described. Extubation of the ETT can be identified by comparing to a threshold, a difference between a determined first volume of breathing gas during a first inspiratory period to a determined second volume of breathing gas during a second inspiratory period. If the difference exceeds the threshold, an alarm can be activated indicating extubation. Extubation detection may also be based on a difference between inspiratory pressures during separate inspiratory periods. Partial extubation and full extubation may also be discerned. Further, extubation of an ETT may be detected without the use of an exhalation flow sensor.

Abstract: Systems and methods for detecting extubation of an endotracheal tube (ETT) are described. Extubation of the ETT can be identified by comparing to a threshold, a difference between a determined first volume of breathing gas during a first inspiratory period to a determined second volume of breathing gas during a second inspiratory period. If the difference exceeds the threshold, an alarm can be activated indicating extubation. Extubation detection may also be based on a difference between inspiratory pressures during separate inspiratory periods. Partial extubation and full extubation may also be discerned. Further, extubation of an ETT may be detected without the use of an exhalation flow sensor.

Abstract: This disclosure describes systems and methods for providing novel back-up ventilation that allows the patient to trigger or initiate the delivery of breath. Further, this disclosure describes systems and methods for triggering ventilation when base flow and/or inspiratory flow is unknown or indeterminable by the ventilator.

Abstract: Systems and methods for ventilation that allows the patient to trigger or initiate the delivery of a breath are provided. Further, systems and methods for triggering ventilation when exhalation flow is unknown or unreliable by the ventilator are provided.

Abstract: Systems and methods for detecting extubation of an endotracheal tube (ETT) are described. Extubation of the ETT can be identified by comparing to a threshold, a difference between a determined first volume of breathing gas during a first inspiratory period to a determined second volume of breathing gas during a second inspiratory period. If the difference exceeds the threshold, an alarm can be activated indicating extubation. Extubation detection may also be based on a difference between inspiratory pressures during separate inspiratory periods. Partial extubation and full extubation may also be discerned. Further, extubation of an ETT may be detected without the use of an exhalation flow sensor.

Abstract: Systems and methods for novel ventilation that allows the patient to trigger or initiate the delivery of a breath are provided. Further, systems and methods for triggering ventilation when exhalation flow is unknown or unreliable by the ventilator are provided.

Abstract: The systems and methods include providing a negative proportional assist breath type, a time adjusted negative proportional assist breath type, or a time adjusted proportional assist breath type during ventilation of a patient with a ventilator.

Abstract: This disclosure describes systems and methods for providing optimized adjustment of a ventilator based on an estimated net value of patient effort. The disclosure describes a novel breath type that delivers a target airway pressure calculated based on an estimated patient muscle effort. A net value of patient muscle effort is estimated using a parameter estimate vector update equation to solve for a recursive least squares gain value representing the estimated net value of patient effort. The estimated net value of patient muscle effort is used to determine a target airway pressure, which is then used to determine a target inspiratory pressure to be delivered to a patient. The target inspiratory pressure is used to determine an onset and/or end of a ventilation cycle and therefore improves the synchronization between the ventilator and the patient.

Abstract: This disclosure describes systems and methods for providing novel back-up ventilation that allows the patient to trigger or initiate the delivery of breath. Further, this disclosure describes systems and methods for triggering ventilation when base flow and/or inspiratory flow is unknown or indeterminable by the ventilator.

Abstract: This disclosure describes systems and methods for providing novel back-up ventilation that allows the patient to trigger or initiate the delivery of a breath. Further, this disclosure describes systems and methods for triggering ventilation when exhalation flow and/or exhalation pressure is unknown or unreliable by the ventilator.