Patent Publication Number: US-2021161395-A1

Title: Non-invasive venous waveform analysis for evaluating a subject

Description:
CROSS REFERENCE 
     This application claims priority to U.S. Provisional Patent Application Ser. No. 62/485423 filed Apr. 14, 2017, incorporated by reference herein in its entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     This invention was made with government support under Contract Number 1549576 awarded by the National Science Foundation. The government has certain rights in the invention. 
    
    
     BACKGROUND 
     Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. 
     Acute decompensated heart failure is a common cause of patient hospitalization. Assessing a patient&#39;s pulmonary capillary wedge pressure (PCWP) is a useful tool for assessing vascular volume overload that can lead to such heart failure. PCWP assessment can also be used to assess the severity of heart failure and confirm the diagnosis of heart failure with preserved ejection fractions. When PCWP data is available, clinicians can prevent hospitalizations due to heart failure and can provide improvements in patient quality of life. Obtaining PCWP data is somewhat difficult because the procedure requires invasive placement of a pulmonary artery catheter, and, in some cases, the placement of an expensive invasive permanent device. 
     SUMMARY 
     In one example, a method includes detecting, via a sensor, vibrations originating from a vein of a subject and obtaining an intensity spectrum of the detected vibrations over a range of frequencies. The method further includes using the obtained intensity spectrum to determine a metric selected from a group that includes: a pulmonary capillary wedge pressure (PCWP), a mean pulmonary arterial pressure, a pulmonary artery diastolic pressure, a left ventricular end diastolic pressure, a left ventricular end diastolic volume, a cardiac output, total blood volume, and a volume responsiveness of the subject. 
     In another example, a computing device includes one or more processors, a sensor, and a computer readable medium storing instructions that, when executed by the one or more processors, cause the computing device to perform functions. The functions include detecting, via the sensor, vibrations originating from a vein of a subject and obtaining an intensity spectrum of the detected vibrations over a range of frequencies. The functions further include using the obtained intensity spectrum to determine a metric selected from a group that includes: a pulmonary capillary wedge pressure (PCWP), a mean pulmonary arterial pressure, a pulmonary artery diastolic pressure, a left ventricular end diastolic pressure, a left ventricular end diastolic volume, a cardiac output, total blood volume, and a volume responsiveness of the subject. 
     In yet another example, a non-transitory computer readable medium stores instructions that, when executed by a computing device that includes a sensor, cause the computing device to perform functions. The functions include detecting, via the sensor, vibrations originating from a vein of a subject and obtaining an intensity spectrum of the detected vibrations over a range of frequencies. The functions further include using the obtained intensity spectrum to determine a metric selected from a group that includes: a pulmonary capillary wedge pressure (PCWP), a mean pulmonary arterial pressure, a pulmonary artery diastolic pressure, a left ventricular end diastolic pressure, a left ventricular end diastolic volume, a cardiac output, total blood volume, and a volume responsiveness of the subject. 
     These, as well as other aspects, advantages, and alternatives will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying drawings. Further, it should be understood that this summary and other descriptions and figures provided herein are intended to illustrate the invention by way of example only and, as such, that numerous variations are possible. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of a computing device, according to an example embodiment. 
         FIG. 2  depicts a computing device, including a wireless sensor that is communicatively coupled to the computing device, according to an example embodiment. 
         FIG. 3A  depicts a computing device, according to an example embodiment. 
         FIG. 3B  depicts a sensor, according to an example embodiment. 
         FIG. 4A  is a block diagram depicting a method, according to an example embodiment. 
         FIG. 4B  depicts an intensity spectrum of vibrations originating from a subject&#39;s vein, according to an example embodiment. 
         FIG. 5  depicts a receiver operating curve for prediction of a subject&#39;s PCWP that is greater than 20 mmHg. 
         FIG. 6  depicts a correlation between subject NIVA score and subject volume status. 
         FIG. 7  depicts a correlation between subject NIVA score and subject volume status. 
         FIG. 8  depicts a correlation between PCWP and subject volume status. 
         FIG. 9  depicts a correlation between actual subject PCWP and subject PCWP determined based on subject NIVA score. 
         FIG. 10  depicts a correlation between subject cardiac output and subject volume status. 
         FIG. 11  depicts a correlation between actual change in subject cardiac output and change in subject cardiac output predicted based on subject NIVA score. 
     
    
    
     DETAILED DESCRIPTION 
     As discussed above, direct measurement of PCWP has diagnostic value, but is inherently invasive and can be costly. Methods and systems for using non-invasive venous waveform analysis (NIVA) to indirectly determine PCWP and other subject metrics are disclosed herein. 
     PCWP is considered an important indicator for assessing the volume of blood within a subject&#39;s circulatory system at a particular time, also referred to herein as volume status. In addition to assessing volume status, NIVA can also be used to indirectly determine other useful subject metrics such as mean pulmonary arterial pressure, pulmonary artery diastolic pressure, left ventricular end diastolic pressure, left ventricular end diastolic volume, cardiac output, total blood volume, and volume responsiveness. These determined metrics may then be used to diagnose or treat various disorders that may afflict the subject. 
     More specifically, a sensor may be applied over a peripheral vein of a subject to detect vibrations caused by blood flow within the vein. A computing device may then obtain an intensity spectrum of the detected vibrations over a range of frequencies via signal processing. For instance, the computing device may perform a fast Fourier transform (FFT) upon a signal representing the detected vibrations to yield intensities corresponding to various respective vibration frequencies. The frequencies may represent the subject&#39;s respiratory rate, pulse rate, and various harmonics of the pulse rate. Next, the computing device may use the obtained intensity spectrum to determine a PCWP of the subject, or any other subject metric described herein. For example, the computing device (or a clinician) may determine the PCWP or other metric based on a known correlation between PCWP and the absolute intensities of the vibration frequencies and/or the relative intensity of one or more vibration frequencies compared to one or more other vibration frequencies. 
       FIG. 1  is a simplified block diagram of an example computing device  100  that can perform various acts and/or functions, such as any of those described in this disclosure. The computing device  100  may be a mobile phone, a tablet computer, a laptop computer, a desktop computer, a wearable computing device (e.g., in the form of a wrist band), among other possibilities. 
     The computing device  100  includes one or more processors  102 , a data storage unit  104 , a communication interface  106 , a user interface  108 , a display  110 , and a sensor  112 . These components as well as other possible components can connect to each other (or to another device or system) via a connection mechanism  114 , which represents a mechanism that facilitates communication between two or more devices or systems. As such, the connection mechanism  114  can be a simple mechanism, such as a cable or system bus, or a relatively complex mechanism, such as a packet-based communication network (e.g., the Internet). In some instances, a connection mechanism can include a non-tangible medium (e.g., where the connection is wireless). 
     The processor  102  may include a general-purpose processor (e.g., microprocessor) and/or a special-purpose processor (e.g., a digital signal processor (DSP)). In some instances, the computing device  100  may include more than one processor to perform functionality described herein. 
     The data storage unit  104  may include one or more volatile, non-volatile, removable, and/or non-removable storage components, such as magnetic, optical, or flash storage, and/or can be integrated in whole or in part with the processor  102 . As such, the data storage unit  104  may take the form of a non-transitory computer-readable storage medium, having stored thereon program instructions (e.g., compiled or non-compiled program logic and/or machine code) that, when executed by the processor  102 , cause the computing device  100  to perform one or more acts and/or functions, such as those described in this disclosure. Such program instructions can define and/or be part of a discrete software application. In some instances, the computing device  100  can execute program instructions in response to receiving an input, such as from the communication interface  106  and/or the user interface  108 . The data storage unit  104  may also store other types of data, such as those types described in this disclosure. 
     The communication interface  106  can allow the computing device  100  to connect to and/or communicate with another other device or system according to one or more communication protocols. The communication interface  106  can be a wired interface, such as an Ethernet interface or a high-definition serial-digital-interface (HD-SDI). The communication interface  106  can additionally or alternatively include a wireless interface, such as a cellular or WI-FI interface. A connection provided by the communication interface  106  can be a direct connection or an indirect connection, the latter being a connection that passes through and/or traverses one or more entities, such as such as a router, switcher, or other network device. Likewise, a transmission to or from the communication interface  106  can be a direct transmission or an indirect transmission. 
     The user interface  108  can facilitate interaction between the computing device  100  and a user of the computing device  100 , if applicable. As such, the user interface  108  can include input components such as a keyboard, a keypad, a mouse, a touch sensitive and/or presence sensitive pad or display, a microphone, a camera, and/or output components such as a display device (which, for example, can be combined with a touch sensitive and/or presence sensitive panel), a speaker, and/or a haptic feedback system. More generally, the user interface  108  can include any hardware and/or software components that facilitate interaction between the computing device  100  and the user of the computing device  100 . 
     In a further aspect, the computing device  100  includes the display  110 . The display  110  may be any type of graphic display. As such, the display  110  may vary in size, shape, and/or resolution. Further, the display  110  may be a color display or a monochrome display. 
     The sensor  112  may take the form of a piezoelectric sensor, a pressure sensor, a force sensor, an optical wavelength selective reflectance or absorbance measurement system, a tonometer, an ultrasound probe, a plethysmograph, or a pressure transducer. Other examples are possible. The sensor  112  may be configured to detect vibrations originating from a vein of a subject as further described herein. 
     As indicated above, the connection mechanism  114  may connect components of the computing device  100 . The connection mechanism  114  is illustrated as a wired connection, but wireless connections may also be used in some implementations. For example, the communication mechanism  112  may be a wired serial bus such as a universal serial bus or a parallel bus. A wired connection may be a proprietary connection as well. Likewise, the communication mechanism  112  may also be a wireless connection using, e.g., Bluetooth® radio technology, communication protocols described in IEEE 802.11 (including any IEEE 802.11 revisions), cellular technology (such as GSM, CDMA, UMTS, EV-DO, WiMAX, or LTE), or Zigbee® technology, among other possibilities. 
       FIG. 2  depicts one embodiment of the computing device  100  and the sensor  112 . In  FIG. 2 , the sensor  112  takes the faun of a wearable wristband that is worn by a human subject and the computing device  100  takes the form of a mobile phone. The sensor  112  may detect vibrations originating from a vein at the subject&#39;s wrist and wirelessly transmit (e.g., via Bluetooth®) a signal representing the detected vibrations. The computing device  100  may receive the signal for further processing as described further herein. 
       FIG. 3A  depicts another embodiment of the computing device  100 . In  FIG. 3A , the computing device  100  is communicatively coupled to the sensor  112  via a wired connection. 
       FIG. 3B  depicts an embodiment of the sensor  112 , taking the form of a wristband. 
       FIG. 4A  is a block diagram of a method  400  that may be performed by andlor via the use of the computing device  100 . 
     At block  402 , the method includes detecting, via a sensor, vibrations originating from a vein of a subject. For example, the computing device  100 , via the sensor  112 , may detect vibrations originating from a vein (e.g., a vein wall) of a subject. In a specific example, the sensor  112  may be secured (e.g., via a Velcro strap) to the subject&#39;s skin above or near the subject&#39;s antebrachial vein. The sensor  112  may detect the vibrations caused by blood flow through the antebrachial vein (or another vein) as the vibrations are conducted through tissues such as the subject&#39;s skin. The subject may be human, but other animals are possible. As the sensor  112  detects the vibrations, the subject may be breathing spontaneously, e.g., without the aid of a mechanical ventilator, or with the aid of a mechanical ventilator. 
     At block  404 , the method includes obtaining an intensity spectrum of the detected vibrations over a range of frequencies (e.g., 0.05 Hz-25 Hz). More specifically, the computing device  100  may perform a fast Fourier transform (FFT) upon a signal representing the detected vibrations that is received from the sensor  112 . Performing the FFT may yield one or more intensities corresponding respectively to one or more frequencies of the detected vibrations. Frequencies of interest such as a subject&#39;s respiratory rate, a pulse rate, and harmonics or multiples of the pulse rate may take the form of “peaks” within the obtained intensity spectrum. Such peaks may take the form of local (or global) maxima of signal intensity with respect to signal frequency. The FFT may be non-linear or any other foul) of FFT. In some examples, the computing device  100  may perform the FFT after the computing device  100  performs an autocorrelation operation, a Hilbert-Huang Transform (HHT), or an empirical mode decomposition (EMD) upon the signal representing the vibrations. 
       FIG. 4B  is a graphical depiction of an arbitrary intensity spectrum yielded by performing an FFT on a signal representing vibrations that are detected from a vein wall. The arbitrary intensity spectrum represents intensities of vein vibrations corresponding to various respective frequencies.  FIG. 4B  shows intensity or amplitude peaks  410 ,  412 ,  414 , and  416  that may represent frequencies of interest for establishing correlations between vein vibration data and various subject metrics discussed below. 
     At block  406 , the method includes using the obtained intensity spectrum to determine a metric selected from a group that includes: a pulmonary capillary wedge pressure (PCWP), a mean pulmonary arterial pressure, a pulmonary artery diastolic pressure, a left ventricular end diastolic pressure, a left ventricular end diastolic volume, a cardiac output, total blood volume, and a volume responsiveness of the subject. More specifically, the computing device  100  or a user may use the obtained intensity spectrum to determine one or more of the aforementioned subject metrics. 
     This process may involve using known statistical correlations between previously collected intensity spectra of subject vein vibrations and the aforementioned subject metrics. For example, vein vibration data may be collected for a number of subjects while one or more of the aforementioned metrics are directly measured for each of the subjects. This data may then be used to determine statistical correlations between the collected vein vibration data and the aforementioned subject metric data. More specifically, such correlations between the vein vibration data and the subject metric data can be approximated as mathematical functions using various statistical analysis or “curve fitting” techniques (e.g., least squares analysis). As such, future subject metrics may be determined indirectly (e.g., without direct measurement) and non-invasively with the sensor  112  by performing the identified mathematical functions upon subsequently collected vein vibration intensity data. 
     In a specific example, PCWP may be determined by using the following derived formula: NIVA score=6.5+4.8(0.92A 0 +2A 1 +0.4A 2 +0.2A 3 )/(A 0 +A 1 +A 2 +A 3 )+44*(A 4 +A 5 +A 6 +A 7 +A 8 )/(A 1 +A 2 +A 3 +A 4 +A 5 +A 6 +A 7 +A 8 )+0.0296(A 0 /A 1 ). In some examples, the determined NIVA score is equal to a value predicted to be equal to the subject&#39;s PCWP. In this example, A 0  is an intensity of the subject&#39;s respiration rate, A 1  is an intensity of the subject&#39;s pulse rate (f 1 ), and A 2 , A 3 , A 4 , A 5 , A 6 , A 7 , and A 8  are respective intensities of  2 f 1 ,  3 f 1 ,  4 f 1 ,  5 f 1 ,  6 f 1 ,  7 f 1 , and  8 f 1 . The respiration rate, pulse rate, and harmonics of the pulse rate may be identified as frequencies at which local or global maxima of intensity occur. 
     The determined PCWP or other determined subject metric may be used to diagnose or treat one or more of the following disorders: hypervolemia, hypovolemia, euvolemia, dehydration, heart failure, tissue hypoperfusion, myocardial infarction, hypotension, valvular heart disease, congenital heart disease, cardiomyopathy, pulmonary disease, arrhythmia, drug effects, hemorrhage, systemic inflammatory response syndrome, infectious disease, sepsis, electrolyte imbalance, acidosis, renal failure, hepatic failure, cerebral injury, thermal injury, cardiac tamponade, preeclampsia/eclampsia, or toxicity. The determined PCWP or other determined subject metric may also be used to diagnose respiratory distress or hypoventilation due to one or more of the following conditions: pneumonia, cardiac disorders, sepsis, asthma, obstructive sleep apnea, hypopnea, anesthesia, pain, or narcotic use. 
     The method  400  may be performed to diagnose or treat a subject that is suffering from increased or decreased cardiac output compared to control or increased or decreased intravascular volume status compared to control. The method  400  may also be performed for subjects that are to undergo cardiac catheterization or have undergone cardiac catheterization. 
     The determined PCWP or other determined subject metric may additionally be used to determine whether intravenously administering a fluid to the subject would increase, decrease, or not significantly affect a cardiac output of the subject. 
     In some examples, the method  400  may be performed a first time prior to treatment or diagnosis of one or more disorders and a second time after carrying out the treatment or determining the diagnosis. 
     The method  400  may involve iterative derivation using leverage plots of the contribution of one or more of f 0 -f 8  to the data collected for pulmonary capillary wedge pressure (PCWP), a mean pulmonary arterial pressure, a pulmonary artery diastolic pressure, a left ventricular end diastolic pressure, a left ventricular end diastolic volume, a cardiac output, total blood volume, or volume responsiveness. The log worth of the values may be used to determine optimal weighting factors and constants to define NIVA volume index or score. In this case, the algorithm may be a ratio of a sum of the higher harmonics of pulse rate to a sum of the amplitude of lower harmonics of pulse rate modified by a constant that normalizes the data to a known clinical output such as a pulmonary capillary wedge pressure (PCWP), a mean pulmonary arterial pressure, a pulmonary artery diastolic pressure, a left ventricular end diastolic pressure, a left ventricular end diastolic volume, a cardiac output, total blood volume, and a volume responsiveness of the subject according to a(f 0 )+b(f 1 )+c(f 2 )+d(f 3 )+e(f 4 )+(f 5 )+h(f 6 )+i(f 7 )+j(f 8 )+(κ) divided by l(f 0 )+m(f 1 )+n(f 2 )+o(f 3 )+p(f 4 )+q(f 5 )+r(f 6 )+s(f 7 )+t(f 8 )+(λ), where f 0 -f 8  are the frequencies derived from a fast Fourier transformation of the venous waveform and κ, λ, a, b, c, d, e, g, h, i, j,  1 , m, n, o, p, q, r, s, t are numerical constants that weight and normalize the algorithm. 
       FIG. 5  depicts a ROC curve comparing vein vibration data to PCWP data. An area under the curve is 0.805, demonstrating the successful use of the method  400  to detect a PCWP above 20 mmHg. Patients who have a PCWP greater than 20 mmHg are not expected to be volume responsive and have an increased intravascular volume status. 
       FIG. 6  depicts a correlation between subject NIVA score and subject volume status. As shown, NIVA score is shown to increase upon the administration of fluids (e.g., a bolus) and the resultant increased intravascular volume. 
       FIG. 7  depicts raw data showing the correlation between subject NIVA score and subject volume status. Eleven patients who had invasive right heart catheterization also had a NIVA measurement taken on them before and after administration of 500 mL of crystalloid. There was a significant (p&lt;0.05) increase in NIVA score with the administration of fluids. 
       FIG. 8  depicts a correlation between PCWP and subject volume status. As shown, PCWP is shown to increase upon the administration of fluids and the resultant increased intravascular volume. NIVA score and PCWP significantly increased by 21.4% (p=0.006) and 33.3% (p&lt;0.001), respectively, after fluid administration. 
       FIG. 9  depicts a correlation between actual subject PCWP and subject PCWP determined based on subject NIVA score. Forty nine patients that had invasive right heart catheterization were equipped with a NIVA device. These patients had PCWP measured which correlated with the NIVA measurement (p&lt;0.05, R=0.71). 
       FIG. 10  depicts a correlation between subject cardiac output and subject volume status. Thirteen patients who had invasive right heart catheterization underwent a fluid administration where cardiac output was measured before and after a  500  mL fluid bolus. 
     There was a significant (p&lt;0.05) increase in in cardiac output with the administration of fluids. 
       FIG. 11  depicts a correlation between actual change in subject cardiac output and change in subject cardiac output predicted based on subject NIVA score. Predicted change in cardiac output (N=9) correlated strongly with thermodilution-based cardiac output measurements with r 2 =0.82. 
     The following includes further details related to the methods and systems described above. 
     EXAMPLE 1 
     Clinical Study of Non-Invasive Venous Waveform Analysis (NIVA) for Prediction of a High Pulmonary Capillary Wedge Pressure 
     Acute decompensated heart failure is the leading cause of hospitalization in patients over the age of 65. Pulmonary capillary wedge pressures (PCWP) have been considered the gold standard for assessing volume overload. PCWP have also been used to gauge the severity of heart failure and confirm the diagnosis of heart failure with preserved ejection fractions. When continuous pulmonary artery pressure readings are available to clinicians, a reduction in heart failure hospitalizations and an improvement in quality of life have been demonstrated. Limitations to pulmonary capillary wedge pressures are that they require an invasive placement of a pulmonary artery catheter, and, in some cases, the placement of an expensive invasive permanent device. We hypothesize that non-invasive venous waveform analysis (NIVA) that utilizes piezoelectric sensors to detect vascular harmonics can predict high (&gt;20 mmHg) pulmonary capillary wedge pressures without the need for an invasive procedure. 
     Methods: 
     Patients (n=43) undergoing cardiac catheterization were enrolled in this Vanderbilt University Institutional Review Board approved protocol. Prior to the patient undergoing their cardiac catheterization, the NIVA device was placed over the median antebrachial vein. Over the course of the procedure, continuous, non-invasive, real-time data of the vascular harmonics were obtained. Upon completion of the procedure, the piezoelectric sensors were removed from the patient and the data were imported into LabChart software (ADInstruments, Colorado Springs, Colo., USA). The data were transformed into the frequency domain using Fourier transformations to display the patient signal as a function of sine waves and their corresponding power. The peaks corresponding to the patients&#39; heart rate (f 1 -f 8 ) were measured as a function of power and inputted into our “NIVA signal” algorithm (see description above relating to at least block  406  of the method  400 ). The PCWP was obtained from the pulmonary artery catheter used during the cardiac catheterization, per routine. To determine NIVA signal&#39;s ability to predict an elevated PCWP (above 20 mmHg) a receiver operator characteristic (ROC) curve was used. 
     Results: 
     The ROC curve comparing the NIVA signal against the PCWP revealed an area under the curve of 0.805, demonstrating NIVA&#39;s ability to detect a wedge pressure above 20 mmHg (See  FIG. 5 ). 
     Conclusion: 
     In patients undergoing cardiac catheterizations, a patient&#39;s NIVA signal was able to detect high pulmonary capillary wedge pressures. This non-invasive method can provide a real-time assessment of a patient&#39;s cardiac condition by informing a clinician when the pulmonary capillary wedge pressure is high. 
     EXAMPLE 2 
     Clinical Study of Non-Invasive Venous Waveform Analysis (NIVA) for Prediction of Fluid Responsiveness in Spontaneously Breathing Subjects 
     In this study, we evaluated the correlation of Non-invasive venous waveform analysis (NIVA) with fluid responsiveness, as defined by the change in cardiac output in response to a crystalloid fluid bolus. 
     Methods 
     Eleven patients undergoing elective right heart catheterization were included in this study that was approved by the Vanderbilt University Medical Center Institutional Review Board. Mechanically ventilated patients were excluded. NIVA sensors were applied over median antebrachial vein and data was collected immediately pre- and post-infusion of a 500-mL bolus of crystalloid solution. Pulmonary capillary wedge pressure (PCWP) and, if available, cardiac output (CO) was also recorded pre- and post-infusion. NIVA score was calculated using a linear regression model with covariates including the 1 st  through 4 th  harmonics of pulse rate. Predicted change in cardiac output was calculated as a simple linear model including the calculated NIVA score and a regression coefficient. Data were analyzed using paired Student&#39;s t-tests. 
     Results 
     Pre- to post-bolus NIVA score and PCWP were significantly increased by 21.4% (p=0.006) and 33.3% (p&lt;0.001), respectively. See  FIGS. 6 and 8 . Predicted change in cardiac output (N=9) correlated strongly with thermodilution-based cardiac output measurements with r 2 =0.82. See  FIG. 11 . 
     Conclusions 
     In spontaneously breathing patients undergoing right heart catheterization, NIVA correlated strongly with changes in cardiac output as measured by thermodilution. NIVA is a promising non-invasive modality for measurement of fluid responsiveness in spontaneously breathing individuals. 
     While various example aspects and example embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various example aspects and example embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.