Abstract: A method for identifying physiological states of a patient includes receiving, by a hemodynamic monitor, sensed hemodynamic data representative of an arterial pressure waveform of the patient; performing, by the hemodynamic monitor, waveform analysis of the hemodynamic data to determine a plurality of profiling parameters; extracting, by the hemodynamic monitor, a patient data segment comprising a patient data set for a first profiling parameter of the plurality of profiling parameters; comparing, by the hemodynamic monitor, the patient data segment to a plurality of stored data segments from a database, each of the plurality of stored data segments having an associated stored discrete state data set indicative of whether a clinical intervention was administered and a stored data set for the first profiling parameter; identifying, by the hemodynamic monitor, a plurality of stored data segments satisfying threshold similarity criteria with respect to the patient data segment; and displaying, by the hemodynamic

Abstract: A hemodynamic monitor for detecting nociception of a patient includes a non-invasive blood pressure sensor with an inflatable blood pressure bladder, a pressure controller pneumatically connected to the inflatable blood pressure bladder, and an optical transmitter and an optical receiver that are electrically connected to the pressure controller. The hemodynamic monitor also includes an integrated hardware unit with a system processor, a system memory, and a display with a user interface.

Abstract: A method for determining a post-induction score that represents a prediction that a patient will experience a hypotensive event after beginning an administration of anesthesia is disclosed herein that includes receiving, by a hemodynamic monitor prior to the administration of anesthesia on the patient, sensed hemodynamic data representative of an arterial pressure waveform of the patient. The method further includes extracting, by the hemodynamic monitor, at least one waveform feature from the sensed hemodynamic data. Additionally, the method includes determining, by the hemodynamic monitor based on the at least one waveform feature, the post-induction score that represents the likelihood that the patient will experience a hypotensive event after beginning the administration of anesthesia. Finally, the post-induction score is displayed.

Abstract: A hemodynamic monitoring system monitors arterial blood pressure of a patient and provides a warning to medical personnel of a predicted future hypotension event of the patient. Waveform analysis is performed on sensed hemodynamic data representative of an arterial pressure waveform of the patient to determine a plurality of hypotension profiling parameters predictive of a future hypotension event for the patient. A set of transformed hypotension profiling parameters is generated based on the hypotension profiling parameters and mean and standard deviation values of the hypotension profiling parameters at a standard mean arterial pressure (MAP) threshold for hypotension and an adjusted MAP threshold for hypotension. A risk score representing a probability of a future hypotension event for the patient is determined based on the set of transformed hypotension profiling parameters. A sensory alarm is invoked to produce a sensory signal in response to the risk score satisfying a predetermined risk criterion.

Abstract: A hemodynamic monitoring system monitors arterial blood pressure of a patient and provides a warning to medical personnel of a predicted future hypotension event of the patient. Sensed hemodynamic data representative of an arterial pressure waveform of the patient are received by a hemodynamic monitor. The received hemodynamic data is offset, based on a difference between a standard mean arterial pressure (MAP) threshold for hypotension and an adjusted MAP threshold for hypotension, to produce adjusted hemodynamic data. Waveform analysis of the adjusted hemodynamic data is performed, and a risk score representing a probability of a future hypotension event for the patient is determined based on the waveform analysis. A sensory alarm is invoked to produce a sensory signal in response to the risk score satisfying a predetermined risk criterion.

Abstract: A health monitoring unit includes a hardware processor, a memory, a display, and a graphical user interface (GUI) stored in the memory. The GUI is executed by the processor to provide a selection screen enabling a user to select parameters for viewing on the display from among health parameters of a living subject being tracked by the health monitoring unit. The GUI also presents a main screen showing the parameters selected by the user, the main screen including an icon for communicating a hypotension probability index (HPI) status of the living subject. In addition, the GUI overlays an alarm screen as a pop-up on the display if the HPI of the living subject satisfies a predetermined risk criteria, and enables the user to access an HPI diagnostic screen showing values for a subset of the health parameters identified as predictive of a future hypotension event for the living subject.

Abstract: Described herein are thermal mass flow sensors that combine calorimetric and anemometric (e.g., hot-wire) elements to provide a hybrid approach to determining flow rate of a liquid. The flow probes or flow sensors are configured to use a heater to apply heat to a thermally-conducting material in contact with the flowing liquid, to measure a temperature of the thermally-conducting material upstream of the heater and downstream or at the heater, to adjust power to the heater to achieve a targeted temperature difference, and to determine a flow rate based at least in part on the power supplied to the heater and the measured temperatures. This approach provides flow rate due at least in part to the fluid cooling the thermally-conductive material proportionate to flow rate with non-linear effects. This hybrid approach can provide accurate readings of flow rates of liquids delivered through an IV line to a patient.

Abstract: There are provided systems and methods for performing mean arterial pressure (MAP) derived prediction of future hypotension. Such a system includes a hardware unit including a hardware processor and a system memory, a hypotension prediction software code stored in the system memory, and a sensory alarm. The hardware processor is configured to execute the hypotension prediction software code to receive MAP data of the living subject, and to transform the MAP data to one or more parameters predictive of a future hypotension event of the living subject. The hardware processor is further configured to execute the hypotension prediction software code to determine a risk score of the living subject corresponding to the probability of the future hypotension event based on at least some of the one or more parameters, and to invoke the sensory alarm if the risk score of the living subject satisfies a predetermined risk criteria.

Abstract: A system for monitoring arterial pressure of a patient determines a score that is predictive of responsiveness of the patient to a therapy. Sensed hemodynamic data representative of an arterial pressure waveform of the patient are received by a hemodynamic monitor. Magnitude data and trend data are derived from the hemodynamic data. The score that is predictive of the responsiveness of the patient to the therapy is determined based on the magnitude data and the trend data of the hemodynamic parameter. A representation of the score is output.

Abstract: There are provided systems and methods for performing mean arterial pressure (MAP) derived prediction of future hypotension. Such a system includes a hardware unit including a hardware processor and a system memory, a hypotension prediction software code stored in the system memory, and a sensory alarm. The hardware processor is configured to execute the hypotension prediction software code to receive MAP data of the living subject, and to transform the MAP data to one or more parameters predictive of a future hypotension event of the living subject. The hardware processor is further configured to execute the hypotension prediction software code to determine a risk score of the living subject corresponding to the probability of the future hypotension event based on at least some of the one or more parameters, and to invoke the sensory alarm if the risk score of the living subject satisfies a predetermined risk criteria.

Abstract: The present disclosure provides systems that include a flow probe that senses fluid flow or mass flow of fluid delivery to a patient. Based at least in part on the sensed fluid or mass flow, the flow probe provides flow-related data (e.g., volume or mass flow rate) that the system uses to derive a volume of fluid being delivered. The fluid can be delivered from an IV bag, another in-line port, or a combination of the two. The disclosed systems provide fluid volume and/or fluid rate for display to a clinician and an informative understanding of what volume of fluid/mass a patient has received.

Abstract: A system disclosed herein includes a hardware processor and a predictive risk model training software code stored in a system memory. The hardware processor executes the software code to receive vital sign data of a population of subjects including positive and negative subjects with respect to a health state, to define data sets for use in training a predictive risk model, to transform the vital sign data to parameters characterizing the vital sign data, and to obtain differential parameters based on those parameters. The hardware processor executes the software code to further generate combinatorial parameters using the parameters and the differential parameters, to analyze the parameters, the differential parameters, and the combinatorial parameters to identify a reduced set of parameters correlated with the health state, to identify a predictive set of parameters enabling prediction of the health state for a living subject, and to compute predictive risk model coefficients.

Abstract: A system having a processor obtain a digital hemodynamic data from a hemodynamic sensor, obtain one or more vital sign parameters characterizing vital sign data from the digital hemodynamic data, derive differential parameters based on the one or more vital sign parameters, generate combinatorial parameters using the one or more vital sign parameters and the differential parameters, determine a risk score corresponding to a probability of a future hypotension event for the living subject based on a weighted combination of a plurality of hypotension profiling parameters including the one or more vital sign parameters characterizing vital sign data, the differential parameters and the combinatorial parameters, and invoke a sensory alarm if the risk score satisfies a predetermined risk criterion.

Abstract: A system having a processor obtain a digital hemodynamic data from a hemodynamic sensor, obtain one or more vital sign parameters characterizing vital sign data from the digital hemodynamic data, derive differential parameters based on the one or more vital sign parameters, generate combinatorial parameters using the one or more vital sign parameters and the differential parameters, determine a risk score corresponding to a probability of a future hypotension event for the living subject based on a weighted combination of a plurality of hypotension profiling parameters including the one or more vital sign parameters characterizing vital sign data, the differential parameters and the combinatorial parameters, and invoke a sensory alarm if the risk score satisfies a predetermined risk criterion.

Abstract: Embodiments of the disclosure are directed to methods, apparatuses, and computer program products for determining a hemodynamic parameter. An exemplary method comprises: receiving data associated with at least one heart beat; calculating a first standard deviation for at least a portion of the data; interpolating a second standard deviation for at least a second portion of the data; and determining the hemodynamic parameter based on the first standard deviation and the second standard deviation.

Abstract: A system for managing healthcare of a living subject includes a computing platform having a hardware processor, a system memory storing a software code, and a display. The hardware processor executes the software code to receive medical history data of the living subject including data entries corresponding to one or more physiological parameter(s), determine respective target range(s) for the physiological parameter(s) based on the medical history data, and store the custom target range(s) in a medical profile of the living subject. The hardware processor further executes the software code to receive sensing signals for the living subject from a device configured to monitor the physiological parameter(s), obtain, from the sensing signals, a present measurement of the physiological parameter(s), retrieve the custom target range(s) from the medical profile of the living subject, compare the present measurement(s) with the custom target range(s), and render the comparison on the display.

Abstract: A cardiac monitoring system includes a display, a blood pressure sensor configured to sense a blood pressure of an artery of a patient, an analog-to-digital converter (ADC) configured to convert a blood pressure signal from the blood pressure sensor to digital blood pressure data, a hardware processor, and a software code. The hardware processor executes the software code to identify cardiovascular metrics of the patient, including one or more of an aortic impedance and an aortic compliance of the patient, based on the blood pressure data, and to determine a stroke volume (SV) of the patient using a subset of the cardiovascular metrics including the aortic impedance and/or aortic compliance, as well as an additive factor. The additive factor is based on a meta-parameter including a weighted sum of combinatorial parameters, each combinatorial parameter including another subset of the cardiovascular metrics.

Abstract: Disclosed is an apparatus, system, and method for compensating for hydrostatic pressure offset in transducer-based pressure measurements. The system may comprise: a measurement pressure transducer to measure an apparent fluid pressure at a measurement site, a reference pressure transducer to measure a hydrostatic pressure caused by a level difference between the measurement pressure transducer and the measurement site, and a controller to generate a corrected fluid pressure measurement based on the apparent fluid pressure and the hydrostatic pressure, wherein the measurement pressure transducer and the reference pressure transducer are placed at a same first level, and the measurement site and an end of a fluid-filled tube connected to the reference pressure transducer are at a same second level.

Abstract: Disclosed herein are devices, systems, and methods that automate the identification of the start and end of a fluid bolus and calculate the volume of fluid given during the challenge. A flow measurement device is used to measure fluid flow in a conduit (e.g., IV tubing) and an algorithm is used to determine a start time and the stop time of a fluid bolus. In addition, the volume of fluid given during the bolus is calculated. Advantageously, this provides accurate and automatic collection of the start time of the fluid bolus, the income of the fluid bolus, and the volume of the fluid bolus. Historically, these data are estimated by the clinician used to assess the hemodynamic response to the fluid bolus.

Abstract: The present disclosure provides systems that include a flow probe that senses fluid flow or mass flow of fluid delivery to a patient. Based at least in part on the sensed fluid or mass flow, the flow probe provides flow-related data (e.g., volume or mass flow rate) that the system uses to derive a volume of fluid being delivered. The fluid can be delivered from an IV bag, another in-line port, or a combination of the two. The disclosed systems provide fluid volume and/or fluid rate for display to a clinician and an informative understanding of what volume of fluid/mass a patient has received.