Abstract: Systems and methods are disclosed herein for quantitatively identifying a patient's sedation level and predicting adverse events, based on one or more capnograms or outputs from a pharmacokinetic, pharmacodynamic, or ventilatory model. A sensor measures a carbon dioxide concentration of air exhaled by a patient into a breath receiver. A processor processes the sensor data to generate a capnogram including one or more respiratory cycles, computes the outputs of pharmacokinetic, pharmacodynamic, or ventilatory models, and extracts one or more of the resulting features from the capnogram and pharmacokinetic, pharmacodynamic, or ventilatory model outputs. A multi-parameter metric is computed based on the one or more extracted features and estimates the current or predicted sedation level of the patient.

Abstract: Ventricular catheters and their methods of use are disclosed. In some embodiments, the disclosed ventricular catheters may reduce, or substantially prevent, obstruction of the catheter by astrocytes or other brain tissue due to adhesion and/or growth within the catheter. For example, in some embodiments, the holes and internal lumen of a ventricular catheter may be constructed such that the wall shear stresses applied within the holes and internal lumen of the catheter are greater than a threshold shear stress to prevent cell adhesion and growth within the catheter.

Abstract: Techniques for detecting embolic information for a patient. The techniques may include obtaining data identifying an ultrasound signal associated with the patient, identifying a set of candidate embolic regions in the data, identifying a set of embolic regions from among the set of candidate embolic regions, and outputting embolic information corresponding to the set of embolic regions.

Abstract: Techniques for estimating intracranial pressure using arterial blood pressure and cerebral blood flow velocity measurements. The techniques may include obtaining a first set of data identifying arterial blood pressure and cerebral blood flow velocity of a patient during a first period of time and estimating an initial intracranial pressure value for the patient. The techniques further include obtaining a second set of data identifying arterial blood pressure and cerebral blood flow velocity of the patient during a second period of time, estimating an updated intracranial pressure value for the patient by determining a change in intracranial pressure of the patient based on the second set of data and the initial intracranial pressure value, and outputting information indicating the updated intracranial pressure value.

Abstract: Systems and methods are disclosed for sepsis care management. First data regarding a patient and second data regarding a clinician's treatment of a patient are received by at least one processor. The first data regarding the patient is processed to assess a likelihood that the patient would benefit from administration of each of one or more critical actions for treatment of sepsis, wherein at least one of the one or more critical actions relates to a request for at least one additional diagnostic action. A target treatment protocol, comprising a decision for each of the one or more critical actions, is determined based on the assessed likelihoods. The second data regarding a clinician's treatment of the patient is compared to the target treatment protocol and a notification is provided to the clinician if the second data is incompatible with the target treatment protocol.

Abstract: Systems and methods are disclosed herein for quantitatively identifying a patient's sedation level and predicting adverse events, based on one or more capnograms or outputs from a pharmacokinetic, pharmacodynamic, or ventilatory model. A sensor measures a carbon dioxide concentration of air exhaled by a patient into a breath receiver. A processor processes the sensor data to generate a capnogram including one or more respiratory cycles, computes the outputs of pharmacokinetic, pharmacodynamic, or ventilatory models, and extracts one or more of the resulting features from the capnogram and pharmacokinetic, pharmacodynamic, or ventilatory model outputs. A multi-parameter metric is computed based on the one or more extracted features and estimates the current or predicted sedation level of the patient.

Abstract: Systems and methods are disclosed herein for quantitatively identifying a patient's sedation level and predicting adverse events, based on one or more capnograms or outputs from a pharmacokinetic, pharmacodynamic, or ventilatory model. A sensor measures a carbon dioxide concentration of air exhaled by a patient into a breath receiver. A processor processes the sensor data to generate a capnogram including one or more respiratory cycles, computes the outputs of pharmacokinetic, pharmacodynamic, or ventilatory models, and extracts one or more of the resulting features from the capnogram and pharmacokinetic, pharmacodynamic, or ventilatory model outputs. A multi-parameter metric is computed based on the one or more extracted features and estimates the current or predicted sedation level of the patient.

Abstract: A method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient.

Abstract: A method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient.

Abstract: Systems and methods are disclosed herein for quantitatively identifying a patient's sedation level and predicting adverse events, based on one or more capnograms or outputs from a pharmacokinetic, pharmacodynamic, or ventilatory model. A sensor measures a carbon dioxide concentration of air exhaled by a patient into a breath receiver. A processor processes the sensor data to generate a capnogram including one or more respiratory cycles, computes the outputs of pharmacokinetic, pharmacodynamic, or ventilatory models, and extracts one or more of the resulting features from the capnogram and pharmacokinetic, pharmacodynamic, or ventilatory model outputs. A multi-parameter metric is computed based on the one or more extracted features and estimates the current or predicted sedation level of the patient.

Abstract: Systems and methods are disclosed for sepsis care management. First data regarding a patient and second data regarding a clinician's treatment of a patient are received by at least one processor. The first data regarding the patient is processed to assess a likelihood that the patient would benefit from administration of each of one or more critical actions for treatment of sepsis, wherein at least one of the one or more critical actions relates to a request for at least one additional diagnostic action. A target treatment protocol, comprising a decision for each of the one or more critical actions, is determined based on the assessed likelihoods. The second data regarding a clinician's treatment of the patient is compared to the target treatment protocol and a notification is provided to the clinician if the second data is incompatible with the target treatment protocol.

Abstract: The methods and systems for estimating cardiac output and total peripheral resistance include observing arterial blood pressure waveforms to determine intra-beat and inter-beat variability in arterial blood pressure and estimating from the variability a time constant for a lumped parameter beat-to-beat averaged Windkessel model of the arterial tree. Uncalibrated cardiac output and uncalibrated total peripheral resistance may then be calculated from the time constant. Calibrated cardiac output and calibrated total peripheral resistance may be computed using calibration data, assuming an arterial compliance that is either constant or dependent on mean arterial blood pressure. The parameters of the arterial compliance may be estimated in a least-squares manner.

Abstract: Systems and methods for prediction and detection of circulatory shock using estimates or measurements of arterial blood pressure, heart rate, stroke volume, cardiac output, total peripheral resistance, cardiac ejection fraction, cardiac contractility and ventricular end-diastolic volume are provided. These estimates and measurements are used to determine a type of circulatory shock. In some embodiments, the type of circulatory shock is determined to be one of septic shock, hypovolemic shock, anaphylactic shock, hemorrhagic shock, and cardiogenic shock.

Abstract: The systems, devices, and methods described herein provide for the estimation and monitoring of cerebrovascular system properties and intracranial pressure (ICP) from one or more measurements or measured signals. These measured signals may include central or peripheral arterial blood pressure (ABP), and cerebral blood flow (CBF) or cerebral blood flow velocity (CBFV). The measured signals may be acquired noninvasively or minimally-invasively. The measured signals may be used to estimate parameters and variables of a computational model that is representative of the physiological relationships among the cerebral flows and pressures. The computational model may include at least one resistive element, at least one compliance element, and a representation of ICP.

Abstract: Systems and methods are disclosed herein for quantitatively identifying a patient's physiological state based on one or more capnograms. One or more capnograms are acquired, each capnogram being associated with a patient and including one or more respiratory cycles, and one or more features from the one or more respiratory cycles are extracted. One or more classifiers are provided based on the one or more extracted features, and each classifier is used to select a physiological state from one or more candidate physiological states for each of the one or more respiratory cycles. For each of the selected physiological states, a likelihood value is determined, and a physiological state of the patient is determined based on the likelihood values.

Abstract: The systems, devices, and methods described herein provide for the estimation and monitoring of cerebrovascular system properties and intracranial pressure (ICP) from one or more measurements or measured signals. These measured signals may include central or peripheral arterial blood pressure (ABP), and cerebral blood flow (CBF) or cerebral blood flow velocity (CBFV). The measured signals may be acquired noninvasively or minimally-invasively. The measured signals may be used to estimate parameters and variables of a computational model that is representative of the physiological relationships among the cerebral flows and pressures. The computational model may include at least one resistive element, at least one compliance element, and a representation of ICP.

Abstract: The systems, devices, and methods described herein provide for the estimation and monitoring of cerebrovascular system properties and intracranial pressure (ICP) from one or more measurements or measured signals. These measured signals may include central or peripheral arterial blood pressure (ABP), and cerebral blood flow (CBF) or cerebral blood flow velocity (CBFV). The measured signals may be acquired noninvasively or minimally-invasively. The measured signals may be used to estimate parameters and variables of a computational model that is representative of the physiological relationships among the cerebral flows and pressures. The computational model may include at least one resistive element, at least one compliance element, and a representation of ICP.

Abstract: The methods and systems for estimating cardiac output and total peripheral resistance include observing arterial blood pressure waveforms to determine intra-beat and inter-beat variability in arterial blood pressure and estimating from the variability a time constant for a lumped parameter beat-to-beat averaged Windkessel model of the arterial tree. Uncalibrated cardiac output and uncalibrated total peripheral resistance may then be calculated from the time constant. Calibrated cardiac output and calibrated total peripheral resistance may be computed using calibration data, assuming an arterial compliance that is either constant or dependent on mean arterial blood pressure. The parameters of the arterial compliance may be estimated in a least-squares manner.

Abstract: Systems and methods for prediction and detection of circulatory shock using estimates or measurements of arterial blood pressure, heart rate, stroke volume, cardiac output, total peripheral resistance, cardiac ejection fraction, cardiac contractility and ventricular end-diastolic volume are provided. These estimates and measurements are used to determine a type of circulatory shock. In some embodiments, the type of circulatory shock is determined to be one of septic shock, hypovolernic shock, anaphylactic shock, hemorrhagic shock, and cardiogenic shock.

Abstract: The methods and systems for estimating cardiac output and total peripheral resistance include observing arterial blood pressure waveforms to determine intra-beat and inter-beat variability in arterial blood pressure and estimating from the variability a time constant for a lumped parameter beat-to-beat averaged Windkessel model of the arterial tree. Uncalibrated cardiac output and uncalibrated total peripheral resistance may then be calculated from the time constant. Calibrated cardiac output and calibrated total peripheral resistance may be computed using calibration data, assuming an arterial compliance that is either constant or dependent on mean arterial blood pressure. The parameters of the arterial compliance may be estimated in a least-squares manner.