Patent Application
20090227883
In spite of rapid technological advances, manual New York Heart Association (NYHA) classification by a physician remains the major gauge of heart function assessment in patients with heart disease. In addition to NYHA classification, American College of Cardiology/American Heart Association (ACC/AHA) classification is another method that physicians use for assessing patient heart function status.
This document describes, among other things, a system and method that automatically classifies a patient's heart function status, such as by using an implantable medical device (IMD) to determine a physiological response to activity, and using that information to perform the classification. For example, a physical activity sensor and a physiological sensor are used to automatically classify patients into heart function status classes, such as NYHA classes or ACC/AHA classes. Changes in a patient's classification can be used to monitor heart function status over time and to monitor therapy responsiveness.
Example 1 describes a system. In this example, the system comprises a physical activity sensor, configured to sense an indication of physical activity of a patient; a physiological sensor, configured to sense a physiological response of a patient corresponding to the sensed indication of the physical activity of the patient; a signal processor circuit, configured to receive the indication of physical activity of the patient from the physical activity sensor, and configured to receive the physiological response of the patient from the physiological sensor, and configured to automatically classify the patient into a classification corresponding to a cardiac function status of the patient, the classification selected from a group of standard diagnostic classes describing different cardiac function statuses, the classes recognized by a medical standard-establishing organization; and a patient classification memory storage location, configured to store an indication of the classification of the patient to be provided to a user or process.
In Example 2, the system of Example 1 optionally includes the signal processor circuit configured to repeat the classifying over a period of time, detect a change in the classification during the period of time, and provide an indication of the change in the classification of the patient to a user or process.
In Example 3, the system of one or more of Examples 1-2 optionally includes the signal processor circuit configured to classify the patient into a NYHA class that is automatically selected from a group of NYHA classes using the physiological response to activity.
In Example 4, the system of one or more of Examples 1-3 optionally includes the signal processor circuit configured to classify the patient into an ACC/AHA class that is automatically selected from a group of ACC/AHA classes using the physiological response to activity.
In Example 5, the system of one or more of Examples 1-4 optionally includes the physiological sensor comprising a pH sensor configured to sense pH from the patient.
In Example 6, the system of one or more of Examples 1-5 optionally includes the signal processor circuit configured to use pH to determine an indication of fatigue, and to use the indication of fatigue to automatically classify the patient into a classification corresponding to a cardiac function status of the patient.
In Example 7, the system of one or more of Examples 1-6 optionally includes the physiological sensor comprising a heart rate sensor configured to sense a heart rate of the patient, wherein the signal processor circuit is coupled to the heart rate sensor to receive and use information about the sensed heart rate to automatically classify the patient into a classification corresponding to the cardiac function status of the patient.
In Example 8, the system of one or more of Examples 1-7 optionally includes the physiological sensor comprising a respiration sensor configured to sense a respiration rate of the patient, wherein the signal processor circuit is coupled to the respiration sensor to receive and use information about the sensed respiration rate to automatically classify the patient into a classification corresponding to the cardiac function status of the patient.
In Example 9, the system of one or more of Examples 1-8 optionally includes the physiological sensor comprising a periodic breathing sensor configured to sense a periodic breathing of the patient, wherein the signal processor circuit is coupled to the periodic breathing sensor to receive and use information about the sensed periodic breathing to automatically classify the patient into a classification corresponding to the cardiac function status of the patient.
In Example 10, the system of one or more of Examples 1-9 optionally includes the signal processor configured to compute an indication of the physiological response to activity by: detecting a first measurement of a physiological parameter corresponding to relatively lower degree of physical activity of the patient; detecting a second measurement of the physiological parameter at a relatively greater degree of physical activity of the patient than that corresponding to the first measurement; and determining the physiological response to activity using a change in the physiological parameter between the first and second measurements of the physiological parameter.
In Example 11, the system of one or more of Examples 1-10 optionally includes the signal processor configured to automatically classify the patient into a classification corresponding to a cardiac function status of a patient by processing the measurement of the physiological response to activity using at least one of: patient medication information, patient co-morbidity information, or physician-provided input.
Example 12 describes a method. In this example, the method comprises using a medical device, detecting an indication of physical activity of a patient; using the medical device, detecting a measurement of a physiological response of the patient corresponding to the measurement of physical activity of the patient; using the measurement of the physiological response, automatically classifying the patient into a classification corresponding to a cardiac function status of a patient, the classification selected from a group of standard diagnostic classes describing different cardiac function statuses, the group of classes recognized by a medical standard-establishing organization; and providing an indication of the classification of the patient to a user or process.
In Example 13, the method of Example 12 optionally comprises repeating the classifying over a period of time; detecting a change in the classification during the period of time; and providing an indication of the change in the classification of the patient to a user or process.
In Example 14, the method of one or more of Examples 12-13 optionally comprises classifying the patient into a classification corresponding to cardiac function status of the patient by classifying the patient into a NYHA class that is automatically selected from a group of NYHA classes using the measurement of the physiological response to activity.
In Example 15, the method of one or more of Examples 12-14 optionally comprises classifying the patient into a classification corresponding to cardiac function status of the patient by classifying the patient into an ACC/AHA class that is automatically selected from a group of ACC/AHA classes using the measurement of the physiological response to activity.
In Example 16, the method of one or more of Examples 12-15 optionally comprises detecting the measurement of the physiological response corresponding to the measurement of physical activity by measuring pH.
In Example 17, the method of one or more of Examples 12-16 optionally comprises using measured pH for generating an indication of fatigue, and using the generated indication of fatigue for automatically classifying the patient into the classification corresponding to the cardiac function status of the patient.
In Example 18, the method of one or more of Examples 12-17 optionally comprises detecting the measurement of the physiological response corresponding to the measurement of physical activity by measuring heart rate, wherein classifying the patient into the classification corresponding to a cardiac function status of the patient includes using the measured heart rate.
In Example 19, the method of one or more of Examples 12-18 optionally comprises detecting the measurement of the physiological response corresponding to the measurement of physical activity by measuring respiration rate, wherein classifying the patient into the classification corresponding to a cardiac function status of the patient includes using the measured respiration rate.
In Example 20, the method of one or more of Examples 12-19 optionally comprises detecting the measurement of the physiological response corresponding to the measurement of physical activity by measuring periodic breathing, wherein classifying the patient into the classification corresponding to a cardiac function status of the patient includes using the measured periodic breathing.
In Example 21, the method of one or more of Examples 12-20 optionally comprises detecting the measurement of the physiological response corresponding to the measurement of physical activity by: detecting a first measurement of a physiological parameter corresponding to relatively lower degree of physical activity of the patient; detecting a second measurement of the physiological parameter at a relatively greater degree of physical activity of the patient than that corresponding to the first measurement; and determining the physiological response to activity using a change in the physiological parameter between the first and second measurements of the physiological parameter.
In Example 22, the method of one or more of Examples 12-21 optionally comprises determining a measurement of the physiological response to activity by determining at least one degree of physical activity of the patient using at least one of: a six-minute walk, a maximum exercise intensity level, or a maximum exercise duration.
In Example 23, the method of one or more of Examples 12-22 optionally comprises automatically classifying the patient into a classification corresponding to a cardiac function status of a patient by using the measurement of the physiological response to activity, including processing the measurement of the physiological response using at least one of: patient medication information, patient co-morbidity information, or physician-provided input.
This overview is intended to provide an overview of subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the invention. The detailed description is included to provide further information about the present patent application.
In the drawings, which are not necessarily drawn to scale, like numerals can describe substantially similar components throughout the several views. Like numerals having different letter suffixes can represent different instances of substantially similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.
This document describes, among other things, automatic classification of a patient into a heart function status class, such as by using an implantable medical device that measures a physiological response to physical activity. Such information can be used to classify the patient into a medically recognized standardized heart function class.
Table 1 illustrates NYHA classification, a standardized medically-recognized schema that is typically used by doctors for classifying heart status manually, rather than automatically using physiological response to activity information obtained from an implantable medical device, as described below. Advancement to a higher-numbered NYHA class is generally accompanied by increased heart failure mortality of the subpopulation represented by that class. NYHA Class II patients generally exhibit a heart failure mortality rate of 5-10%, Class III patients generally exhibit a heart failure mortality rate of 10-15%, and Class IV patients generally exhibit a heart failure mortality rate of 30-40%.
Table 2 illustrates ACC/AHA classification based on a patient's symptoms and the physical condition of the patient's heart. The ACC/AHA classification schema is a standardized medically-recognized schema that is typically used by doctors for classifying heart status manually, rather than automatically using physiological response to activity information obtained from an implantable medical device, as described below. At the present time, ACC/AHA stages may be thought of as being less dynamic the NYHA classes. For example, once a patient is classified as ACC/AHA stage B, the patient generally cannot improve to stage A, even if that patient's NYHA classification improves. In the future, however, technology may allow for earlier detection and reversal of heart failure signs, which would permit patients to improve from one ACC/AHA stage to the next. In either case, long-term monitoring of ACC/AHA stages may be useful.
Information about one or more of the physiological parameters measured by one or more of the various sensors can be communicated from the physiological sensor 304 to the signal processor circuit 306. Using the information about the patient's physiological response to physical activity, the signal processor 306 can be configured to automatically classify the patient into a class corresponding to cardiac function status 420. In addition to physiological response to physical activity data from the physiological sensor 306, the signal processor circuit 306 can use patient co-morbidity information 414, patient medication information 416, and physician-provided input 418 to automatically classify the patient into a class corresponding to cardiac function status 420. For example, from the outset, a patient who has chronic obstructive pulmonary disease (COPD), in addition to a heart failure condition, may exhibit, in response to an increase in physical activity, a bigger increase in respiration or heart rate, or a bigger decrease in pH relative to a patient having a heart failure condition without the accompanying COPD co-morbidity. These COPD-related effects can be taken into account by the signal processor circuit 306 in classifying the patient according to heart function status. Furthermore, certain medications can affect a patient's physiologic response to physical activity. For example, patients taking beta blockers generally exhibit a lesser increase in heart rate in response to physical activity compared to patients who are not on beta blockers. Thus, for a patient taking beta-blockers, the signal processor circuit 306 can be programmed to allow for a lower heart rate threshold for placing a patient into a “more compromised” heart status class when classifying the patient according to cardiac function status. In certain examples, a physician can independently classify a patient into a heart status class based on one or more of the patient's symptoms and response to a six-minute walk test, without using the patient's implanted automatic heart function status classification device. In certain examples, the physician's independent classification can be used as an input signal for the signal processor circuit 306, and the automatic classification can be compared to the physician's classification. The physician's independent classification or the results of a patient's six-minute walk test can be used to adjust the automatic classification system for a particular patient, such as to calibrate the automatic classification system or to make the automatic classification system adaptive via a machine learning process, for example. Physician calibration can be performed recurrently or periodically.
Table 3 is an example of an automatic machine-implemented NYHA classification based on patient respiration rate, such as described above. In certain examples, a patient can be automatically classified into one of the four NYHA classes depending on that patient's measured respiration rate during various levels of physical activity. Both the respiration rate and the physical activity level can be measured using an implantable medical device, such as described below. The automatic heart status classification can then be performed using the implantable or an external device, such as described above. The numbers provided in this table are non-limiting illustrative examples.
Table 4 is an example of an automatic machine-implementable NYHA classification based on patient heart rate. In certain examples, a patient can be automatically classified into one of the four NYHA classes depending on that patient's measured heart rate during various levels of physical activity. Both the heart rate and the physical activity level can be measured using an implantable medical device, such as described above. The automatic heart status classification can then be performed using the implantable or an external device, such as described above. The numbers provided in this table are non-limiting illustrative examples.
In this document, certain examples have been described with respect to using a “respiration rate measurement,” for illustrative clarity. However, such examples can also be performed using a “respiration interval measurement” rather than a “respiration rate measurement,” without departing from the scope of the described systems and methods.
The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention can be practiced. These embodiments are also referred to herein as “examples.” All publications, patents, and patent documents referred to in this document are incorporated by reference herein in their entirety, as though individually incorporated by reference. In the event of inconsistent usages between this document and those documents so incorporated by reference, the usage in the incorporated reference(s) should be considered supplementary to that of this document; for irreconcilable inconsistencies, the usage in this document controls.
In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B.” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.
Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, the code may be tangibly stored on one or more volatile or non-volatile computer-readable media during execution or at other times. These computer-readable media may include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAM's), read only memories (ROM's), and the like.
The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to comply with 37 C.F.R. §1.72(b), to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
This application claims the benefit of U.S. Provisional Application No. 61/033,943, filed on Mar. 5, 2008, under 35 U.S.C. §119(e), which is hereby incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 61033943 | Mar 2008 | US |
