Source: https://patents.google.com/patent/US9757041B2/en
Timestamp: 2018-05-26 00:26:35
Document Index: 673992830

Matched Legal Cases: ['§119', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 2', 'Application No. 2', 'Application No. 13']

US9757041B2 - Hemodynamic reserve monitor and hemodialysis control - Google Patents
Hemodynamic reserve monitor and hemodialysis control Download PDF
US9757041B2
US9757041B2 US13554483 US201213554483A US9757041B2 US 9757041 B2 US9757041 B2 US 9757041B2 US 13554483 US13554483 US 13554483 US 201213554483 A US201213554483 A US 201213554483A US 9757041 B2 US9757041 B2 US 9757041B2
US13554483
US20120330117A1 (en )
This non-provisional application claims the benefit, under 35 U.S.C. §119(e), of co-pending provisional U.S. Patent Application No. 61/510,792, filed Jul. 22, 2011 by Grudic et al. and entitled “Cardiovascular Reserve Monitor”, and co-pending provisional U.S. Patent Application No. 61/614,426, filed Mar. 22, 2012 by Grudic et al. and entitled “Hemodynamic Reserve Monitor and Hemodialysis Control”, both of which are hereby incorporated by reference.
This application is also a continuation-in-part of U.S. patent application Ser. No. 13/041,006 (the “'006 Application”), filed Mar. 4, 2011 by Grudic et al. and entitled “Active Physical Perturbations to Enhance Intelligent Medical Monitoring,” which is hereby incorporated by reference, and which claims the benefit, inter alia, of provisional U.S. Patent Application No. 61/310,583, filed Mar. 4, 2010, which is hereby incorporated by reference. The '006 Application is a continuation-in-part of U.S. patent application Ser No. 13/028,140 (the “'140 Application”), filed Feb. 15, 2011 by Grudic et al. and entitled “Statistical, Noninvasive Measurement of Intracranial Pressure,” which is hereby incorporated by reference, and which claims the benefit of provisional U.S. Patent Application No. 61/305,110, filed Feb. 16, 2010, by Moulton et al. and titled “A Statistical, Noninvasive Method for Measuring Intracranial Pressure,” which is hereby incorporated by reference.
HDRI ⁡ ( t ) = 1 - BLV ⁡ ( t ) BLV HDD ( Eq . ⁢ 1 )
BLV=λ·LBNP (Eq. 2)
HDRI = 1 - BLV ⁡ ( t ) BLV HDD ≈ 1 - λ · LBNP ⁡ ( t ) λ · LBNP HDD = 1 - LBNP ⁡ ( t ) LBNP HDD ( Eq . ⁢ 3 )
Merely by way of example, FIG. 3 illustrates one technique 300 for deriving an estimate of HDRI in accordance with some embodiments. The illustrated technique comprises sampling waveform data (e.g., any of the data described herein and in the Related Applications, including without limitation arterial waveform data, such as continuous noninvasive blood pressure waveforms) for a specified period, such as 32 heartbeats (block 305). That sample is compared with a plurality of waveforms of reference data corresponding to different HDRI values (block 310). (These reference waveforms might be derived using the algorithms described in the Related Applications, might be the result of experimental data, and/or the like). Merely by way of example, the sample might be compared with waveforms corresponding to an HDRI of 1 (block 310 a), an HDRI of 0.5 (block 310 b), and an HDRI of 0 (block 310 c), as illustrated. From the comparison, a similarity coefficient is calculated (e.g., using a least squares or similar analysis) to express the similarity between the sampled waveform and each of the reference waveforms (block 315). These similarity coefficients can be normalized (if appropriate) (block 320), and the normalized coefficients can be summed (block 325) to produce an estimated value of the patient's HDRI.
HDRIAdjusted(t)=1−((1−HDRI(t))×Pr_Bleed(t)) (Eq. 4)
As indicated at block 1120, this is achieved by identifying the most predictive set of signals Sk, where Sk contains at least some (and perhaps all) of the derived signals s1, . . . , sD for each outcome ok, where k ε{1, . . . , K}. A probabilistic predictive model ôk=Mk (Sk) is learned at block 1125, where ôk is the prediction of outcome ok derived from the model Mk that uses as inputs values obtained from the set of signals Sk, for all k ε{1, . . . , K}. The method 1100 can learn the predictive models ôk=Mk (Sk) incrementally (block 1130) from data that contains example values of signals s1, . . . , sD, and the corresponding outcomes o1, . . . , oK. As the data become available, the method 1100 loops so that the data are added incrementally to the model for the same or different sets of signals Sk, for all k ε{1, . . . ,K}.
ô k =f k(a 0+Σi=1 d a i s i) (Eq. 5)
In one study, data was collected in Thailand on children that have dengue hemorrhagic fever. The patients were periodically monitored with a NEXFIN continuous non-invasive blood pressure monitor for 10 to 15 min periods each day. Using the NEXFIN signals, the HDRI value was calculated during these monitoring periods. FIG. 13 shows a plot 1300 of HDRI data from one subject. The horizontal axis shows the day and vertical axis shows the estimated HDRI value. The HDRI can clearly be seen tracking resuscitation over a period of 6 days, starting when the patient is the sickest (27Jul. 2011) and treatment begins, and ending on 1 Aug. 2011 when the patient has shown significant recovery. This patient received a blood transfusion on 29 Jul. 2011.
In another study, a subject was monitored by a HDRI monitor (including a NONIN OEM III pulse ox sensor) during a dehydration study with the following protocol. The subjected started well hydrated, jogged for 44 minutes, at an ambient temperature of 30 C, and then rehydrated over a 1 hour period. The HDRI profiles for this are shown in FIG. 14. The subject's weight was recorded before exercise, immediately after exercise and after rehydration. The subject lost 0.7 Kg while exercising and increased 0.9 Kg by consuming 1 of fluids. We assume this 700 g loss represents a 700 ml loss of fluids.
The loss of 700 ml of fluids due to dehydration is clearly observable using HDRI. From the pre-exercise plot 1400 and post-exercise plot 1405, one can see that the subject's HDRI is reduced from about 0.95 to as low as 0.5 after exercise. Note also that the HDRI levels are stable in the pre-exercise plot 1400 and post-exercise plot 1405. We can also see the oral rehydration taking effect in the rehydration plot 1400. After about 35 minute the subject appears fully rehydrated, with an HDRI of about 0.95, similar to the subject's pre-exercise HDRI.
one or more sensors to obtain physiological data from a patient; and
a computer system in communication with the one or more sensors, the computer system comprising:
instructions for receiving the physiological data from the one or more sensors, wherein the physiological data comprises waveform data;
instructions for analyzing the physiological data;
instructions for estimating a hemodynamic reserve index of the patient, based on analysis of the physiological data, by comparing the physiological data to a model constructed using the following formula:
HDRI ⁡ ( t ) = 1 - BLV ⁡ ( t ) BLV HDD
where HDRI(t) is the hemodynamic reserve at time t, BLV(t) is an intravascular volume loss of a test subject at time t, and BLVHDD is an intravascular volume loss at a point of hemodynamic decompensation of the test subject, and wherein the instructions for estimating the hemodynamic reserve index comprise:
instructions for comparing the waveform data with a plurality of sample waveforms, each of the sample waveforms corresponding to a different value of the hemodynamic reserve index, to produce a similarity coefficient expressing a similarity between the waveform data and each of the sample waveforms;
instructions for normalizing the similarity coefficients for each of the sample waveforms; and
instructions for summing the normalized similarity coefficients to produce an estimated hemodynamic reserve index value for the patient; and
instructions for displaying, on a display device, an estimate of the hemodynamic reserve index of the patient.
monitoring, with one or more sensors, physiological data of a patient, wherein the physiological data comprises waveform data;
analyzing, with a computer system, the physiological data;
estimating, with the computer system, a hemodynamic reserve index of the patient, based on analysis of the physiological data, by comparing the physiological data to a model constructed using the following formula:
where HDRI(t) is the hemodynamic reserve at time t, BLV(t) is an intravascular volume loss of a test subject at time t, and BLVHDD is an intravascular volume loss at a point of hemodynamic decompensation of the test subject, and wherein estimating the hemodynamic reserve index comprises:
comparing the waveform data with a plurality of sample waveforms, each of the sample waveforms corresponding to a different value of the hemodynamic reserve index, to produce a similarity coefficient expressing a similarity between the waveform data and each of the sample waveforms;
normalizing the similarity coefficients for each of the sample waveforms; and
summing the normalized similarity coefficients to produce an estimated hemodynamic reserve index value for the patient; and
displaying, with a display device, an estimate of the hemodynamic reserve index of the patient.
estimating a dehydration state of the patient.
predicting, with the computer system, the hemodynamic reserve index of the patient at one or more time points in the future, based on analysis of the physiological data; and
displaying, with the display device, a predicted hemodynamic reserve index of the patient at one or more points in the future.
normalizing the estimate of the hemodynamic reserve index of the patient relative to a normative normal blood volume value corresponding to euvolemia and a normative minimum blood volume value corresponding to cardiovascular collapse;
wherein displaying the estimate of the hemodynamic reserve index of the patient comprises displaying the normalized estimate of the hemodynamic reserve index of the patient.
10. The method of claim 9, wherein the normative normal blood volume value corresponding to euvolemia is 1 and the normative minimum blood volume value corresponding to cardiovascular collapse is 0.
11. The method of claim 9, wherein displaying the normalized estimate of the hemodynamic reserve index of the patient comprises displaying a graphical plot showing the normalized normal blood volume value, the normalized minimum blood volume value, and the normalized estimate of the hemodynamic reserve index relative to the normalized normal blood volume value, the normalized minimum blood volume value.
normalizing the estimate of the hemodynamic reserve index of the patient relative to a normative normal blood volume value corresponding to euvolemia, a normative excess blood volume value corresponding to circulatory overload, and a normative minimum blood volume value corresponding to cardiovascular collapse;
13. The method of claim 12, wherein the normative excess blood volume value corresponding to circulatory overload is 1, the normative normal blood volume value corresponding to euvolemia is 0, and the normative minimum blood volume value corresponding to cardiovascular collapse is −1.
14. The method of claim 12, wherein the normative excess blood volume value corresponding to circulatory overload is >1, the normative normal blood volume value corresponding to euvolemia is 1, and the normative minimum blood volume value corresponding to cardiovascular collapse is 0.
15. The method of claim 12, wherein displaying the normalized estimate of the hemodynamic reserve index of the patient comprises displaying a graphical plot showing the normalized excess blood volume value, the normalized normal blood volume value, the normalized minimum blood volume value, and the normalized estimate of the hemodynamic reserve index relative to the normalized excess blood volume value, the normalized normal blood volume value, the normalized minimum blood volume value.
determining a probability that the patient is bleeding; and
displaying, with the display device, an indication of the probability that the patient is bleeding.
adjusting the estimate of the hemodynamic reserve index of the patient, based on the probability that the patient is bleeding.
selecting, with the computer system, a recommended treatment option for the patient; and
displaying, with the display device, the recommended treatment option.
19. The method of claim 18, wherein the recommended treatment option is selected from the group consisting of: optimizing hemodynamics of the patient, a ventilator adjustment, an intravenous fluid adjustment, transfusion of blood or blood products to the patient, infusion of volume expanders to the patient, a change in medication administered to the patient, a change in patient position, and surgical therapy.
repeating the operations of monitoring physiological data of the patient, analyzing the physiological data, and estimating the hemodynamic reserve index of the patient, to produce a new estimated hemodynamic reserve index of the patient;
wherein displaying the estimate of the hemodynamic reserve index of the patient comprises updating a display of the estimate of the hemodynamic reserve index to show the new estimate of the hemodynamic reserve index, in order to display a plot of the estimated hemodynamic reserve index over time.
21. The method of claim 2, wherein at least one of the one or more sensors is selected from the group consisting of a blood pressure sensor, an intracranial pressure monitor, a central venous pressure monitoring catheter, an arterial catheter, an electroencephalograph, a cardiac monitor, a transcranial Doppler sensor, a transthoracic impedance plethysmograph, a pulse oximeter, a near infrared spectrometer, a ventilator, an accelerometer, an electrooculogram, a transcutaneous glucometer, an electrolyte sensor, and an electronic stethoscope.
22. The method of claim 2, wherein the physiological data comprises blood pressure waveform data.
23. The method of claim 2, wherein the physiological data comprises plethysmograph waveform data.
24. The method of claim 2, wherein the physiological data comprises photoplethysmograph (PPG) waveform data.
estimating a first value of the hemodynamic reserve index when the patient is in a first position;
estimating a second value of the hemodynamic reserve index when the patient is in a second position; and
estimating a sensitivity of the patient to volume loss based on a difference between the first value and the second value.
26. The method of claim 24, wherein the first position is selected from the group consisting of lying prone and sitting, and wherein the second position is selected from the group consisting of sitting and standing.
27. The method of claim 2, wherein analyzing the physiological data comprises: analyzing the physiological data against a pre-existing model.
29. The method of claim 28, wherein generating the pre-existing model comprises:
receiving data pertaining to one or more physiological parameters of a test subject to obtain a plurality of physiological data sets;
directly measuring one or more physiological states of the test subject with a reference sensor to obtain a plurality of physiological state measurements; and
correlating the plurality of physiological data sets with the plurality of physiological state measurements of the test subject.
30. The method of claim 29, wherein the one or more physiological states comprise reduced circulatory system volume.
inducing the physiological state of reduced circulatory system volume in the test subject.
32. The method of claim 31, wherein inducing the physiological state comprises subjecting the test subject to lower body negative pressure (“LBNP”).
33. The method of claim 31, wherein inducing the physiological state comprises subjecting the test subject to dehydration.
34. The method of claim 29, wherein the one or more physiological states comprise a state of cardiovascular collapse or near-cardiovascular collapse.
35. The method of claim 29, wherein the one or more physiological states comprise a state of euvolemia.
36. The method of claim 29, wherein the one or more physiological states comprise a state of hypervolemia.
37. The method of claim 29, wherein the one or more physiological states comprise a state of dehydration.
38. The method of claim 2, further comprising:
controlling operation of hemodialysis equipment, based at least in part on the estimate of the hemodynamic reserve index of the patient.
39. The method of claim 38, wherein controlling operation of the hemodialysis equipment comprises adjusting an ultra-filtration rate of the hemodialysis equipment.
40. The method of claim 29, wherein correlating the received data with the physiological state measurements of the test subject comprises:
identifying a most predictive set of signals Sk out of a set of signals s1, s2, . . . , SD for each of one or more outcomes ok, wherein the most-predictive set of signals Sk corresponds to a first data set representing a first physiological parameter, and wherein each of the one or more outcomes okrepresents a physiological state measurement;
instructions for receiving physiological data from one or more sensors, wherein the physiological data comprises waveform data;
estimating, with the computer system, a dehydration state of the patient from a hemodynamic reserve index of the patient, based on analysis of the physiological data, by comparing the physiological data to a model constructed using the following formula:
where HDRI(t) is the hemodynamic reserve at time t, BLV(t) is an intravascular volume loss of a test subject at time t, and BLVHDD is an intravascular volume loss at a point of hemodynamic decompensation of the test subject, and wherein estimating the dehydration state comprises:
displaying, on a display device, an estimate of the dehydration state of the patient.
predicting the dehydration state of the patient at one or more future points in time.
44. The method of claim 42, wherein estimating a dehydration state of the patient comprises:
estimating a hemodynamic reserve index of the patient, based on analysis of the physiological data; and
estimating the dehydration state based on the estimated hemodynamic reserve index of the patient.
summing the normalized similarity coefficients to produce an estimated hemodynamic reserve index value for the patient;
displaying, with a display device, an estimate of the hemodynamic reserve index of the patient; and
controlling operation of hemodialysis equipment based on the estimated hemodynamic reserve index.
predicting the hemodynamic reserve index of the patient at one or more future points in time.
47. The method of claim 46, wherein controlling operation of the hemodialysis equipment further comprises controlling operation of the hemodialysis equipment based on the predicted hemodynamic reserve index of the patient at one or more future points in time.
48. The method of claim 45, wherein controlling operation of hemodialysis equipment comprises providing, with the computer system, instructions to a human operator of the hemodialysis equipment.
a hemodialysis machine;
a computer system in communication with the one or more sensors and the hemodialysis machine, the computer system comprising:
instructions for comparing the waveform data with a plurality of sample waveforms, each of the sample waveforms corresponding to a different value of the hemodynamic reserve index to produce a similarity coefficient expressing a similarity between the waveform data and each of the sample waveforms;
instructions for controlling operation of hemodialysis machine based on the estimated hemodynamic reserve index.
50. The system of claim 49, wherein the computer system is incorporated within the hemodialysis machine.
51. The method of claim 2, wherein one or more of the sample waveforms are generated by exposing a test subject to a state of hemodynamic decompensation, near hemodynamic decompensation, or a series of states progressing towards hemodynamic decompensation and monitoring physiological data of the test subject.
52. The system of claim 1, wherein the physiological data comprises waveform data and wherein estimating a hemodynamic reserve index of the patient comprises comparing the waveform data with one or more sample waveforms generated by exposing one or more test subjects to a state of hemodynamic decompensation or near hemodynamic decompensation, or a series of states progressing towards hemodynamic decompensation, and monitoring physiological data of the test subjects.
53. The method of claim 2, wherein the physiological data comprises waveform data and wherein estimating a hemodynamic reserve index of the patient comprises comparing the waveform data with one or more sample waveforms generated by exposing one or more test subjects to state of hemodynamic decompensation or near hemodynamic decompensation, or a series of states progressing towards hemodynamic decompensation, and monitoring physiological data of the test subjects.
54. The method of claim 44, wherein the physiological data comprises waveform data and wherein estimating a hemodynamic reserve index of the patient comprises comparing the waveform data with one or more sample waveforms generated by exposing one or more test subjects to a state of hemodynamic decompensation or near hemodynamic decompensation, or a series of states progressing towards hemodynamic decompensation, and monitoring physiological data of the test subjects.
55. The method of claim 45, wherein the physiological data comprises waveform data and wherein estimating a hemodynamic reserve index of the patient comprises comparing the waveform data with one or more sample waveforms generated by exposing one or more test subjects to a state of hemodynamic decompensation or near hemodynamic decompensation, or a series of states progressing towards hemodynamic decompensation, and monitoring physiological data of the test subjects.
56. The system of claim 49, wherein the physiological data comprises waveform data and wherein estimating a hemodynamic reserve index of the patient comprises comparing the waveform data with one or more sample waveforms generated by exposing one or more test subjects to a state of hemodynamic decompensation or near hemodynamic decompensation, or a series of states progressing towards hemodynamic decompensation, and monitoring physiological data of the test subjects.
57. The method of claim 29, wherein the one or more physiological states is a plurality of physiological states, the plurality of physiological states comprising:
a state of cardiovascular collapse or near-cardiovascular collapse;
a state of euvolemia;
a state of hypervolemia; and
a state of dehydration.
US13554483 2008-10-29 2012-07-20 Hemodynamic reserve monitor and hemodialysis control Active 2033-01-27 US9757041B2 (en)
US201161510792 true 2011-07-22 2011-07-22
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US20120330117A1 true US20120330117A1 (en) 2012-12-27
US9757041B2 true US9757041B2 (en) 2017-09-12
US13554483 Active 2033-01-27 US9757041B2 (en) 2008-10-29 2012-07-20 Hemodynamic reserve monitor and hemodialysis control
US15649411 Pending US20170303799A1 (en) 2008-10-29 2017-07-13 Hemodynamic Reserve Monitor and Hemodialysis Control
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