Abstract: A breathing assistance system includes a control unit configured to be connected to a device, the device configured to provide breathing assistance to a patient, the control unit configured to simultaneously control parameters that include a pressure of air supplied to the patient, and at least one of a volume and a flow rate of the air.

Abstract: A ventilation device includes a mechanical ventilator (10), respiratory sensors (30, 32) configured to acquire measurements of respiratory variables including at least measurements of airway pressure and airway flow, and an electronic processor (14) 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 (38) and an exhalation valve (40) 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.

Abstract: A system for evaluating a kidney includes a processing device including an input module configured to acquire patient information, the patient information including at least one of demographic data, diagnostic data, physiological data and intervention data. The processing device also includes an evaluation module, which is configured to input patient class data to an initial kidney model, the initial kidney model configured to simulate a physiological response of a kidney and configured to simulate fluid and solute transport through one or more spatial locations of the kidney. The evaluation module is also configured to input patient data corresponding to an individual patient and calculating a model response, and adjust at least one parameter of the initial kidney model based on a comparison of the patient data and the model response to personalize the initial kidney model for the individual patient.

Abstract: A mechanical ventilation device (10) includes a mechanical ventilator. At least one airway sensor (24, 26) 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 (28, 30) 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 (22) is configured to display an indication of a spontaneous respiration anomaly detected by the anomaly detection component.

Abstract: A respiratory monitoring apparatus (10) includes a central venous pressure sensor (24) configured to measure a central venous pressure (CVP) signal of a patient. At least one processor (32, 34, 36, 38, 40, 42, 44, 58) 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.

Abstract: In respiratory monitoring, a breathing cycle detector (44) detects a breath interval in airway pressure and/or flow data. A respiratory parameters estimator and validator (30) 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.

Abstract: Respiratory variables are estimated on a per-breath basis from airway pressure and flow data acquired by airway pressure and flow sensors (20, 22). A breath detector (28) detects a breath interval. A per-breath respiratory variables estimator (30) 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 (40, 42) 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 (26, 36) or integrated (32) to generate Work of Breathing (WoB) for use in adjusting settings of a ventilator (10).

Abstract: A patient monitoring device includes at least one physiological sensor (32) configured to acquire at least one measured value for a patient of at least one monitored physiological variable. A cardiovascular (CV), pulmonary, or cardiopulmonary (CP) modeling component (42) includes a microprocessor pro-programmed to: receive the measured values of the at least one monitored physiological variable; receive a value for at least one patient-specific medical image parameter generated from at least one medical image of the patient; compute values for the patient of unmonitored physiological variables based on the measured values for the patient of the monitored physiological variables and the patient-specific medical image parameter; and at least one of (1) display the computed values and (2) control a therapy device delivering therapy to the patient based on the computed values.

Abstract: A respiratory monitoring apparatus (10) includes a central venous pressure sensor (24) configured to measure a central venous pressure (CVP) signal of a patient. At least one processor (32, 34, 36, 38, 40, 42, 44, 58) 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.

Abstract: Disclosed herein are approaches for monitoring a patient in real-time for ARDS development and providing a biomarker-driven ventilation therapy recommendation tool based on the correlation of various therapy patterns and an ARDS biomarker score. When the ARDS biomarker indicates that a patient has a high ARDS risk, the recommendation tool suggests possible therapy routes based on clinical practice. In response to a high score output by the ARDS detection model, the tool outputs a recommendation to initiate a lung protective ventilation strategy (Low Tidal Volume, high Positive End-Expiratory Pressure (PEEP)). A high ARDS score is recognized to be predictive of the appropriateness of such therapy up to several hours before such intervention is typically initiated under current clinical practices.

Abstract: A mechanical ventilator (10) provides pressure support ventilation (PSV) to a patient (12). A power of breathing (PoB) or work of breathing (WoB) estimator (30) generates a PoB or WoB signal (34) for the patient. An error calculator (36) computes an error signal as a difference between the PoB or WoB signal and a set point PoB or WoB value (22). A controller (20) inputs a PSV control signal (24) equal to the product of the controller transfer function and the error signal to the mechanical ventilator. A patient adaptation component (52, 54, 56, 60) fits parameters of a model of a controlled mechanical ventilation system comprising the mechanical ventilator and the patient to data comprising the PoB or WoB signal and the PSV control signal generated by the operating closed loop controller, and adjusts parameters of the controller transfer function to maintain stability of the operating closed loop controller.

Abstract: 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.

Abstract: A mechanical ventilator (10) is connected with a ventilated patient (12) to provide ventilation in accordance with ventilator settings of the mechanical ventilator. Physiological values (variables) are acquired for the ventilated patient using physiological sensors (32). A ventilated patient cardiopulmonary (CP) model (40) is fitted to the acquired physiological variables values to generate a fitted ventilated patient CP model by fine-tuning its parameters (50). Updated ventilator settings are determined by adjusting model ventilator settings of the fitted ventilated patient CP model to minimize a cost function (60). The updated ventilator settings may be displayed on a display component (22) 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.

Abstract: A medical ventilator (10) performs a method including: receiving measurements of pressure of air inspired by or expired from a ventilated patient (12) 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 (60); 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.

Abstract: A system for assessing kidney health includes a processing device including an input module configured to receive input values related to kidney function of a patient, and a prediction module having a computation algorithm and/or a model configured to predict a kidney condition and calculate a kidney health score related to at least one of a severity and a probability of the predicted kidney condition, the kidney health score calculated based on the one or more input values. The system also includes an output module configured to present the predicted kidney condition and the kidney health score to a medical professional.

Abstract: A ventilation device includes a mechanical ventilator (10), respiratory sensors (30, 32) configured to acquire measurements of respiratory variables including at least measurements of airway pressure and airway flow, and an electronic processor (14) 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 (38) and an exhalation valve (40) 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.

Abstract: A process and system for determining a minimal, ‘pruned’ version of the known ARDS model is provided that quantifies the risk of ARDS in terms of physiologic response of the patient, eliminating the more subjective and/or therapeutic features currently used by the conventional ARDS models. This approach provides an accurate tracking of ARDS risk modeled only on the patient's physiological response and observable reactions, and the decision criteria are selected to provide a positive prediction as soon as possible before an onset of ARDS. In addition, the pruning process also allows the ARDS model to be customized for different medical facility sites using selective combinations of risk factors and rules that yield optimized performance. Additionally, predictions may be provided in cases with missing or outdated data by providing estimates of the missing data, and confidence bounds about the predictions based on the variance of the estimates.

Abstract: In respiratory monitoring, a breathing cycle detector (44) detects a breath interval in airway pressure and/or flow data. A respiratory parameters estimator and validator (30) 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.

Abstract: A mechanical ventilation device (10) includes a mechanical ventilator. At least one airway sensor (24, 26) 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 (28, 30) 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 (22) is configured to display an indication of a spontaneous respiration anomaly detected by the anomaly detection component.

Abstract: A mechanical ventilator (10) provides pressure support ventilation (PSV) to a patient (12). A power of breathing (PoB) or work of breathing (WoB) estimator (30) generates a PoB or WoB signal (34) for the patient. An error calculator (36) computes an error signal as a difference between the PoB or WoB signal and a set point PoB or WoB value (22). A controller (20) inputs a PSV control signal (24) equal to the product of the controller transfer function and the error signal to the mechanical ventilator. A patient adaptation component (52, 54, 56, 60) fits parameters of a model of a controlled mechanical ventilation system comprising the mechanical ventilator and the patient to data comprising the PoB or WoB signal and the PSV control signal generated by the operating closed loop controller, and adjusts parameters of the controller transfer function to maintain stability of the operating closed loop controller.