Koninklijke Philips Corporation N.v.

Adam Jacob Seiver

Larry James Eshelman

Nicolas Wadih Chbat

Antonio Albanese

Syed Waseem Haider

Peter Doyle

Samir Saleh Ahmad

Bryan Conroy

Minnan Xu

Smita Garde

Francesco Vicario

Fernando Jose Isaza

Nikolaos Karamolegkos

Abigail Acton Flower

Srinivasan Vairavan

Cristhian Potes

Caitlyn Marie Chiofolo

Daniel Van Hise

Peter R Doyle

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 system () for assessing fluid responsiveness includes an infusion pump () in communication with at least one processor (), and a plurality of physiological monitors () operable to receive physiological signals from an associated patient. Physiological signals () acquired from the associated patient () during a fluid challenge are synchronized with a timing signal () of the infusion pump () administering the fluid challenge. One or more dynamic indices and/or features () is calculated from the synchronized physiological signals (), and one or more dynamic indices and/or features () is calculated from baseline physiological signals () acquired from the associated patient () prior to the fluid challenge. A fluid responsiveness probability value () of the patient () is determined based on dynamic indices and/or features () from the synchronized physiological signals () and dynamic indices and/or features () from the baseline physiological signals ().

Method and system for assessing fluid responsiveness using multimodal data

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 medical modeling system and method predict a risk of a physiological condition, such as mortality, for a patient. Measurements of a plurality of predictive variables for the patient are received. The plurality of predictive variables are predictive of the risk of the physiological condition. The risk of the physiological condition is calculated by applying the received measurements to at least one model modeling the risk of the physiological condition using the plurality of predictive variables. The at least one model includes at least one of a hidden Markov model and a logistic regression model. An indication of the risk of the physiological condition is output to a clinician.

Modeling techniques for predicting mortality in intensive care units

A ventilator () that pressurizes gas from a body of gas to provide a flow of gas to an airway of a subject () such that the gas flow mechanically facilitates the respiration of the subject (). In order to enhance the comfort of the subject () during ventilation by the ventilator (), the ventilator () enables the subject () to make unsupervised adjustments to one or more of the properties of the gas flow, within predetermined limits.

Patient control of ventilation properties

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Los Altos Hills, CA, United States of America

Adam Jacob Seiver