Koninklijke Philips Corporation N.v.

Koninklljke Philips N.v.

Antonio Albanese

Nicolas Wadih Chbat

Francesco Vicario

Nikolaos Karamolegkos

Dong Wang

Adam Jacob Seiver

Smita Garde

Fernando Jose Isaza

Roberto Buizza

Limei Cheng

Caitlyn Marie Chiofolo

Syed Waseem Haider

Daniel Van Hise

A ventilation device includes a mechanical ventilator (), respiratory sensors () configured to acquire measurements of respiratory variables including at least measurements of airway pressure and airway flow, and an electronic processor () programmed to perform a ventilation method including: operating the mechanical ventilator to provide mechanical ventilation controlled using measurements acquired by the respiratory sensors; performing a pause maneuver comprising closing at least one of an inhalation valve () and an exhalation valve () for a pause interval and estimating a respiratory mechanics index from one or more respiratory system parameters estimated from measurements acquired by the respiratory sensors during or after the pause interval; and triggering a pause maneuver in response to detecting a change in the estimated respiratory mechanics index.

System and method for adaptive scheduling of pause maneuvers used for estimation of compliance and/or resistance during mechanical ventilation

A mechanical ventilation device () includes a mechanical ventilator. At least one airway sensor () is configured to measure at least one of airway pressure and airway air flow as a function of time for a patient on the mechanical ventilator. At least one microprocessor () is programmed to analyze at least one of airway pressure and airway air flow measured by the airway sensor to detect a spontaneous respiration anomaly in respiratory muscle pressure as a function of time generated by a patient on the mechanical ventilator. A display component () is configured to display an indication of a spontaneous respiration anomaly detected by the anomaly detection component.

Anomaly detection device and method for respiratory mechanics parameter estimation

A respiratory monitoring apparatus () includes a central venous pressure sensor () configured to measure a central venous pressure (CVP) signal of a patient. At least one processor () is programmed to process the CVP signal to generate respiratory information for the patient by operations including: segmenting the CVP signal based on detected breath intervals; calculating a surrogate muscle pressure signal from the segmented CVP signal; and filtering the surrogate muscle pressure signal to remove a cardiac activity component a cardiac activity component of the surrogate respiratory muscle pressure signal.

Enhancement of respiratory parameter estimation and asynchrony detection algorithms via the use of central venous pressure manometry

In respiratory monitoring, a breathing cycle detector () detects a breath interval in airway pressure and/or flow data. A respiratory parameters estimator and validator () asynchronously fits the airway pressure and airway flow data to an equation of motion of the lungs relating airway pressure and airway flow to generate asynchronously estimated respiratory parameters for the breath interval, using a sliding time window that is not synchronized with the breath interval. The asynchronously estimated respiratory parameters for the breath interval are validated using at least one physiological plausibility criterion defined with respect to the breath interval. Responsive to failure of the validation, the airway pressure and airway flow data are synchronously fitted to the equation of motion of the lungs to generate synchronously estimated respiratory parameters for the breath interval. The synchronous fitting is performed in a time window aligned with the breath interval.

Breath by breath reassessment of patient lung parameters to improve estimation performance

Respiratory variables are estimated on a per-breath basis from airway pressure and flow data acquired by airway pressure and flow sensors (). A breath detector () detects a breath interval. A per-breath respiratory variables estimator () fits the airway pressure and flow data over the detected breath interval to an equation of motion of the lungs relating airway pressure, airway flow, and a single-breath parameterized respiratory muscle pressure profile () to generate optimized parameter values for the single-breath parameterized respiratory muscle pressure profile. Respiratory muscle pressure is estimated as a function of time over the detected breath interval as the single-breath parameterized respiratory muscle pressure profile with the optimized parameter values, and may for example be displayed as a trend line on a display device () or integrated () to generate Work of Breathing (WoB) for use in adjusting settings of a ventilator ().

Simultaneous estimation of respiratory mechanics and patient effort via parametric optimization

A method includes obtaining a first physiological parameter indicative of a non-invasively measured airway pressure of a subject, obtaining a second physiological parameter indicative of a non-invasively measured air flow into the lungs of the subject, and estimating a third physiological parameter indicative of an intra-pleural pressure of the subject based on the first and second physiological parameters and generating a signal indicative thereof. An other method includes obtaining a first physiological parameter indicative of a non-invasively estimated intra-pleural pressure of a subject, determining a second physiological parameter indicative of a lung volume of the subject that is based on a third physiological parameter indicative of a non-invasively measured air flow into the lungs of the subject, and determining a work of breathing based on the first and second physiological parameters and generating a signal indicative thereof.

Non-invasive estimation of intra-pleural pressure and/or computation of work of breathing based on a non-invasive estimation of intra-pleural pressure

A mechanical ventilator () is connected with a ventilated patient () to provide ventilation in accordance with ventilator settings of the mechanical ventilator. Physiological values (variables) are acquired for the ventilated patient using physiological sensors (). A ventilated patient cardiopulmonary (CP) model () is fitted to the acquired physiological variables values to generate a fitted ventilated patient CP model by fine-tuning its parameters (). Updated ventilator settings are determined by adjusting model ventilator settings of the fitted ventilated patient CP model to minimize a cost function (). The updated ventilator settings may be displayed on a display component () as recommended ventilator settings for the ventilated patient, or the ventilator settings of the mechanical ventilator may be automatically changed to the updated ventilator settings so as to automatically control the mechanical ventilator.

Systems and methods for model-based optimization of mechanical ventilation

A medical ventilator () performs a method including: receiving measurements of pressure of air inspired by or expired from a ventilated patient () operatively connected with the medical ventilator; receiving measurements of air flow into or out of the ventilated patient operatively connected with the medical ventilator; dividing a breath time interval into a plurality of fitting regions (); and simultaneously estimating respiratory system's resistance and compliance or elastance, and respiratory muscle pressure in each fitting region by fitting to a time series of pressure and air flow samples in that fitting region. In one approach, the fitting includes parameterizing the respiratory muscle pressure by a continuous differentiable function, such as a polynomial function, over the fitting region. In another approach, the fitting is to an equation of motion of the lungs in each fitting region, while monotonicity constraints and inequalities bounding at least the respiratory muscle pressure P(t) and respiratory system's resistence R and compliance C are applied to the respiratory muscle pressure in each region.

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New York, NY, United States of America

Antonio Albanese