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Publication numberUS20050085734 A1
Publication typeApplication
Application numberUS 10/966,487
Publication dateApr 21, 2005
Filing dateOct 15, 2004
Priority dateOct 15, 2003
Also published asDE112004001953T5, DE112004001954T5, DE112004001957T5, US8116872, US8200336, US8255056, US8348941, US8412331, US8467876, US8509901, US20050085865, US20050085866, US20050085867, US20050085868, US20050085869, US20060030894, US20060036294, US20070021795, US20080183239, US20080183240, US20080188903, US20080208281, US20130296964, US20130296973, WO2005037077A2, WO2005037077A3, WO2005037172A2, WO2005037172A3, WO2005037173A2, WO2005037173A3, WO2005037174A2, WO2005037174A3, WO2005037220A2, WO2005037220A3, WO2005037366A1
Publication number10966487, 966487, US 2005/0085734 A1, US 2005/085734 A1, US 20050085734 A1, US 20050085734A1, US 2005085734 A1, US 2005085734A1, US-A1-20050085734, US-A1-2005085734, US2005/0085734A1, US2005/085734A1, US20050085734 A1, US20050085734A1, US2005085734 A1, US2005085734A1
InventorsAmir Tehrani
Original AssigneeTehrani Amir J.
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
Heart failure patient treatment and management device
US 20050085734 A1
Abstract
A device and method to detect and manage heart failure patient symptoms is provided. Respiration and/or cardiac parameters may be sensed or observed to determine the status of a patient's condition. These symptoms may be classified for appropriate patient disease management. A patient's activity level may be monitored in conjunction with respiration and/or cardiac parameters to provide additional patient status information. These symptoms may be classified for appropriate patient disease management. Pulmonary edema is one condition that may be determined to exist when a respiration parameter is out of range for a given sensed activity level. If edema is determined to be present, the device may be configured to respond to treat the edema.
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Claims(46)
1. A patient management system comprising:
an implantable device comprising:
a physiological sensor configured to be coupled to a patient to monitor the patient, and configured to sense at least one cardiac parameter and at least one respiration parameter;
a patient condition status identifier configured to identify a patient condition status based on the at least one cardiac parameter and the at least one respiration parameter.
2. The system of claim 1 wherein the condition status corresponds to a patient's heart failure status.
3. The system of claim 1 wherein the patient status identifier further comprises a severity level sensor configured to determine a patient condition severity level.
4. The system of claim 3 further comprising a responsive element configured to recommend a patient action based on the condition severity level.
5. The system of claim 4 wherein the responsive element is configured to provide a medication intervention instruction based on the condition severity level.
6. The system of claim 4 wherein the responsive element is configured to recommend a lifestyle modification.
7. The system of claim 4 wherein the responsive element is configured to recommend a provider intervention.
8. The system of claim 4 wherein the responsive element is configured to recommend an emergency intervention.
9. The system of claim 1 further comprising a patient status communicator coupled to the patient status identifier and configured to communicate a patient status to a user.
10. The system of claim 9 wherein the user is a patient.
11. The system of claim 9 wherein the user is a health care provider.
12. The system of claim 1 further comprising an activity monitor configured to detect patient activity.
13. The system of claim 12 wherein the physiological sensor is further configured to correlate an activity level sensed by the activity monitor with one or more of the at least one cardiac parameter and the at least one respiration parameter.
14. The system of claim 13 wherein the correlated activity with the at least one parameter further qualifies the patient status.
15. The system of claim 1 wherein the patient status identifier comprises a baseline comparator configured to compare a baseline to the at least one parameter.
16. The system of claim 15 wherein the patient status identifier is configured to identify a patient condition severity level at least in part based on the comparison of the baseline to the at least one parameter.
17. The system of claim 1 wherein the device further comprises a patient compliance monitor.
18. The system of claim 1 wherein the physiological parameter sensor is configured to sense a plurality of parameters and wherein the patient status identifier is configured to identify the patient condition status based the plurality of parameters
19. The system of claim 1, wherein the physiological parameter sensor is configured to sense the cardiac parameter from a location external to a patient's heart.
20. The system of claim 1 wherein the physiological parameter sensor is configured to continuously monitor at least one parameter.
21. The system of claim 1 wherein the patient condition status identifier is configured to identify a patient decompensating status.
22. A method of managing a patient comprising the steps of:
continuously monitoring at least one cardiac parameter and at least one respiration parameter;
sensing the at least one cardiac parameter and at least one respiration parameter; and
identifying a patient status based on the at least one cardiac parameter and at least one respiration parameter.
23. The method of claim 22 further comprising the step of determining a patient condition severity level based on the at least one cardiac parameter and at least one respiration parameter.
24. The method of claim 23 further comprising the step of recommending a patient course of action based on the patient condition severity level.
25. The method of claim 23 further comprising the step of communicating the patient condition severity level to the patient.
26. The method of claim 23 further comprising the step of communicating the patient condition severity level to a healthcare provider.
27. A device for detecting pulmonary edema comprising
a first sensor configured to sense activity of a subject and to generate a first signal corresponding to the activity of a subject;
a second sensor configured to sense at least one respiration parameter of the subject and generate a second signal corresponding to the at least on respiration parameter; and
an edema analyzer coupled to the first sensor and the second sensor, the edema analyzer configured to sense edema based on the first signal and the second signal.
28. The device of claim 27 wherein the first sensor is configured to sense motion of a subject.
29. The device of claim 27 wherein the at least one respiration parameter comprises a respiration rate.
30. The device of claim 27 wherein the at least one respiration parameter comprises a morphology of a respiration waveform.
31. The device of claim 27 wherein the edema detector is configured to detect edema when a sensed respiration rate exceeds a predetermined level of a sensed activity level.
32. A method for detecting pulmonary edema comprising the steps of:
sensing activity of a subject;
sensing at least one respiration parameter of the subject; and
determining a pulmonary edema condition based on the activity and respiration parameter of the subject.
33. The method of claim 32 wherein the step of determining pulmonary edema condition comprises determining a relative severity level of the pulmonary edema condition.
34. A patient management system comprising:
an implantable device comprising:
a physiological sensor configured to be coupled to a patient to continuously monitor the patient, and configured to sense at least one physiological parameter selected from: at least one cardiac parameter sensed from a location external the heart and at least one respiration parameter;
a patient condition status identifier configured to identify a patient condition status based on the at least one physiological parameter.
35. The system of claim 34 wherein the condition status corresponds to a patient's heart failure status.
36. The system of claim 34 wherein the patient status identifier further comprises a severity level sensor configured to determine a patient condition severity level.
37. The system of claim 36 further comprising a responsive element configured to recommend a patient action based on the condition severity level.
38. The system of claim 37 wherein the responsive element is configured to provide a medication intervention instruction based on the condition severity level.
39. The system of claim 37 wherein the responsive element is configured to recommend a lifestyle modification.
40. The system of claim 37 wherein the responsive element is configured to recommend a provider intervention.
41. The system of claim 37 wherein the responsive element is configured to recommend an emergency intervention.
42. A device for sensing cardiac parameters comprising:
at least one electrode configured to be positioned adjacent a diaphragm of a subject and configured to sense an ECG signal.
43. The device of claim 43 wherein the at least one electrode is configured to be positioned on the abdominal surface of a diaphragm.
44. A method for sensing cardiac parameters comprising:
positioning an electrode adjacent a diaphragm of a subject; and
sensing an ECG with the diaphragm.
45. The method of claim 44 wherein the step of positioning the electrode adjacent the diaphragm comprises positioning the electrode at the abdominal side of the diaphragm.
46. The method of claim 44 wherein the step of positioning the electrode adjacent the diaphragm comprises positioning the electrode at the thoracic side of the diaphragm.
Description
RELATED APPLICATION DATA

This application is a continuation-in-part of U.S. patent application Ser. No. 10/686,891, entitled: “BREATHING DISORDER DETECTION AND THERAPY DELIVERY DEVICE AND METHOD”, by Tehrani filed Oct. 15, 2003, and incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to devices and methods for managing heart failure patients by monitoring cardiac and/or respiration parameters and identifying the severity of heart failure/cardiac and/or pulmonary disorders. Another aspect includes monitoring pulmonary edema.

BACKGROUND OF THE INVENTION

A number of cardiac parameters are believed to be predictors of adverse outcomes in heart failure patients. Such parameters may relate, for example, to aspects of the ECG waveform and to heart rate.

Patients with heart failure are prone to a variety of cardiac arrhythmias that may have an important role in cardiac function, symptoms and prognosis.

General cardiac arrhythmias, including atrial fibrillation, ventricular tachycardia, and ventricular fibrillation have been monitored with pacemakers and defibrillators and are known to indicate improving or worsening of the patient's heart rhythms. The monitored information has been downloaded from these devices for such monitoring purposes.

However, such monitoring has not been used to provide comprehensive patient management.

Disordered breathing may contribute to a number of adverse cardiovascular outcomes such as hypertension, stroke, congestive heart failure, and myocardial infarction. Sleep-related breathing disorders, especially central sleep apnea, have been found to have a relatively high prevalence in patients with heart failure and may have a causative or influencing effect on heart failure. In about 50% of patients with stable congestive heart failure, there is an associated sleep disordered breathing, predominantly central sleep apnea, with a minority having obstructive sleep apnea. Furthermore, sleep related breathing disorders are believed to be physiologically linked with heart failure. Central sleep apnea is a known risk factor for diminished life expectancy in heart failure. It is also believed that in view of this link, treatment aimed at relieving sleep related breathing disorders may improve cardiovascular outcomes in patients with heart failure.

Pulmonary edema, a condition in which there is excess fluid in the lungs and often found in heart failure patients, is believed in some circumstances to lead to hyperventilation and hyperoxia or apnea. Most heart failure patients with central sleep apnea, when lying flat, tend to have central fluid accumulation and pulmonary congestion, which may stimulate vagal irritant receptors in the lungs to cause reflex hyperventilation. A severe form of pulmonary edema or fluid accumulation is known as decompensation.

While breathing disorders have been treated a number of ways, a comprehensive patient management with breathing disorder or pulmonary edema monitoring has not been provided.

Heart failure patients are prescribed on average 11 medications. Management of these patients has proven difficult particularly with the complexity of the various medications and patient lifestyle and drug compliance issues. Patient compliance tracking has been shown to decrease hospital rates and improve quality of life leading to reduced healthcare costs. However, this tracking system did not provide comprehensive patient monitoring.

SUMMARY OF THE INVENTION

The present invention provides a device and method for managing heart failure patients that utilizes cardiac and/or respiration monitoring. In addition to monitoring cardiac or respiration parameters, a patient's activity level may be monitored to further discern a level of patient condition severity. One aspect includes monitoring cardiac and/or respiratory conditions or states to detect decompensation. Another aspect includes monitoring cardiac and/or respiratory condition/states to predict, sense or detect decompensation. Decompensation typically requires urgent emergency care. Decompensation typically occurs when cardiac filling pressure increases, lungs fill with fluids, and cardiac output cannot meet the body's needs at rest. Another aspect includes sensing edema.

According to one aspect of the invention, one or more cardiac parameters may be monitored. The cardiac parameters may be monitored with an implanted device and its related electrodes from a location outside of the heart. In one particular example, an ECG may be sensed using electrodes positioned on the diaphragm. The electrodes may be positioned on a surface of the diaphragm such as the abdominal side. The thoracic side may be used as well. The phrenic nerve at or adjacent the diaphragm may also be used to sense ECG.

Additionally or alternatively one or more respiration parameters are monitored.

The observed parameter or parameters are used to determine a level of condition severity. In one particular variation, the shift from a baseline or normal parameter or parameters is used to determine the severity of the patient's condition. Based on that level, a course of action is provided for a patient.

One aspect of the invention provides for decompensation monitoring. As decompensation is developing, the ECG QRS and QT periods increase. The respiratory parameters also change as breathing becomes more rapid and shallow as compared to a baseline. Also, in some situations, pulmonary edema severity may increase.

One aspect of the invention provides for pulmonary edema monitoring, an indicator of heart failure status. According to this aspect of the invention, a pulmonary monitoring device senses a patient activity level and at least one respiration parameter. For example, a low or at rest activity level with a high or out of range respiration rate may indicate existence or a worsening of pulmonary edema. Other respiration parameters relative to a patient's activity level may indicate a worsening pulmonary edema condition. If a patient's respiratory rate increases with an increase in activity and decreases with a decrease in activity, within a normal range, the patient's system will be considered functioning normally. This information may be used in a patient management scheme that also monitors drug titrations, diuretic use, ACE Inhibitors, Beta Blockers and patient compliance.

Based on the observed parameters and/or compliance information a course of action may be provided or suggested. A provider may use the information in developing an optimum treatment plan for the patient including medication management, e.g. of drug titrations, diuretics, ACE inhibitors, and beta blockers. The system may also provide alarms and patient or provider communications if patient is in need of urgent attention possibly leading to hospitalization. This is a frequent occurrence with heart failure patients in decompensation. The patient compliance information may also be used for understanding the drug regimen effectiveness or dosing, whether the patient complies, or it may be used to educate the patient when there is lack of compliance with the therapy plan. If the patient's symptoms are brought more towards a normal range with a drug dose, then the drug treatment would be maintained. If the drug treatment did not affect the patient's symptoms sufficiently then the drug dosage may be increased. Accordingly, the drug dosage may vary with detected cardiac and/or breathing irregularities.

According to an aspect of the invention, the patient monitoring system provides continuous monitoring of physiological parameters to detect baseline shifts in cardiac and respiratory functions. This monitoring assists in supporting the patient with lifestyle and medication compliance. It further allows tiered intervention by providing daily patient self-management feedback, intervention according to programmed parameters by the physician (e.g., by increasing or decreasing medication), providing alerts to make office visits (including office visit scheduling), and providing urgent alerts for emergency room visits.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a heart failure management system in accordance with the invention.

FIG. 2 is a flow chart illustrating a severity classification scheme for an awake patient with a low activity level.

FIG. 3 is a flow chart illustrating a severity classification scheme for an awake patient with a medium activity level.

FIG. 4 is a flow chart illustrating a severity classification scheme for an awake patient with a high activity level.

FIG. 5 is a flow chart illustrating a severity classification scheme for a sleeping patient.

FIG. 6 is a flow chart illustrating the operation of a heart failure management device in accordance with the invention.

FIG. 7 is a chart illustrating an averaging method according to a patient disorder classification scheme in accordance with the invention.

FIG. 8 is a schematic of an external controller that may be used for communications, data transfer and device programming.

DETAILED DESCRIPTION

Referring to FIG. 1, a heart failure management system 10 in accordance with the invention is illustrated. The system includes a sensor assembly 20, which in this particular illustration is coupled to a diaphragm 15 of a subject. More specifically the sensor assembly is located on the abdominal side of the diaphragm. However, one or more sensors that sense cardiac parameters or respiration parameters as may be used according to the invention, and may be positioned elsewhere in, on or adjacent a subject, including without limitation, on the thoracic side of the diaphragm or on the phrenic nerve at or adjacent the diaphragm.

The sensor assembly 20 comprises an electrode sensor 21 configured to sense signals from the phrenic nerve and/or diaphragm muscle. The signals may comprise a diaphragm EMG, phrenic nerve signals corresponding to respiration and/or an ECG signal. The sensor assembly 20 also includes a motion sensor 22 such as, e.g., an accelerometer. The motion sensor 22 may sense movement of the diaphragm 15 corresponding to respiration and/or other movement such as gross movements corresponding to subject activity.

The sensor assembly 20 is in communication with a controller 30 that includes signal processing circuits for processing signals from the sensors 21, 22 of the sensor assemble. The controller 30 also includes RAM memory and ROM memory. The memory devices contain programming information for controlling the device and processing signal information. The memory devices also include recording/data storage for recording data sensed by the sensors. The controller also includes an input/output device for communicating with the sensors and other devices such as external communication and controlling devices. The controller may also receive programming information from an external device.

In general, cardiac parameters that are sensed may indicate a condition of a heart failure patient. Such parameters may include ECG parameters, for example, QRS duration (typically duration of QRS complex in the frontal plane), QT interval (typically measured from the beginning of QRS to the end of T wave in the frontal plane), PR interval (typically measured from the beginning of P to the beginning of QRS in the frontal plane). As the heart failure worsens, the width of the QRS interval increases. Variations in QRS width could also be an indicator of a condition status. Similarly, QT dispersion may indicate an increase in heart failure. PR may indicate atrial conduction disturbances.

Incidences of atrial fibrillation, ventricular arrhythmia, and bradycardia may be detected, recorded or counted by sensing an ECG. Increase in these events may indicate a worsening of the heart failure patient's condition.

Heart rate and/or heart rate variability are cardiac parameters that may be sensed to indicate a condition status of a heart failure patient. Heart rate variability (HRV) for example, is a marker for autonomic tone and a risk predictor of part infarction mortality. Heart failure patients with worsening symptoms often have a decrease in their HRV.

In general respiratory parameters that are sensed may indicate a worsening of a heart failure patient's condition. Such worsening may include, but is not limited to, a worsening of pulmonary edema or decompensation.

Some of the respiratory parameters to monitor in awake patients in accordance with activity may include but are not limited to: respiratory rate and depth (may be categorized, e.g., as normal, rapid and shallow, rapid and deep; respiratory rate and depth correlation to minute ventilation; hyperventilation; incidence of periodic breathing and duration of periodic breathing; incidence of Cheyne-Stokes breathing; and the duration of the Cheyne Stoke hyperventilation and apnea portions.

High left-sided filling pressures and pulmonary congestion can cause rapid shallow breathing indicating a worsened condition. Rapid and deep respiration may indicate low SaO2 where a patient is compensating. Incidence of periodic breathing and duration of periodic breathing may indicate breathing patterns associated with a heart failure condition. Periodic breathing and Cheyne-Stokes breathing have higher rates of ventricular tachycardia than chronic heart failure patients without breathing disorders.

Some of the respiratory parameters to monitor during sleep may include but are not limited to: periodic breathing episodes; apnea episodes or Cheyne-Stokes episodes; information for calculation the AHI (Apnea Hyponea Index, a standardized sleep disorder measurement), Cheyne-Stokes patterns, number of events per period of time, episode durations, and peak diaphragm EMG/tidal volume. Average and standard deviation, averages and standard deviations of these parameters may also be measured or calculated as well.

Information that may be sensed for sensing decompensation may include any parameters that indicate an increased cardiac filling pressure and a cardiac output unable to meet the demands of the patient's physiology at rest. Such information may include, for example, an increased QRS or QT length as well as increased breathing rate, typically with shorten breathing amplitudes. These parameters may be compared to measured or otherwise set baseline parameters.

The information that may be sensed for pulmonary edema management (e.g., of hyperventilation rate and frequency of occurrence) may include the detections rate, detection amplitude, ventilation waveform morphology including slopes and surface of inspiration waveform, slopes and surface area of exhalation waveform, recorded respiratory waveform information in conjunction with activity and position sensor information.

The respiration information used may include all or part of a respiration waveform (e.g., length of cycle or a portion of the cycle, cycle frequency, amplitude, slope of the waveform or a portion of the waveform, waveform morphology), flow, tidal volume, peak tidal volume, averages and standard deviations breathing rate, etc. These parameters may be sensed using one or more respiration sensors, such as, e.g., EMG sensors, diaphragm, chest or other movement sensors, airway flow meters (pneumotachometer), and pressure sensors at one or more of various locations.

The sensors may also be coupled to a processor that may recognize certain breathing patterns such as periodic breathing, Cheyne-Stokes breathing patterns. These and other types of breathing patterns may be used as precursors to other breathing disorders such as described in U.S. patent application Ser. No. ______ entitled “Breathing Disorder and Precursor Predictor and Therapy Delivery Device and Method” filed on even date herewith and incorporated in its entirety herein. The processor may compare sensed breathing patterns to baseline breathing patterns and may determine what degree of correlation there is to the baseline breathing pattern. The baseline may be a normal breathing pattern where a strong correlation indicates normal breathing and a weak correlation indicates a likely breathing disorder. The baseline may be a disordered breathing pattern where a strong correlation indicates a likely breathing disorder. The sensor or processor may identify a general rate of hyperventilation or may count the number of identified episodes, e.g., in a particular time period.

The episodes may be correlated with an activity sensor and/or a real time clock that indicates whether the episodes are awake episodes or sleeping episodes and the level of activity of the patient, e.g., as high, medium or low, no activity.

The movement sensor 22 may sense acceleration and/or movement of the patient, and provide a corresponding signal to the controller 30. The sensor 22 measures the activity levels of the patient and provides the signal to the controller for use in further analysis. The movement signal from the sensor is processed to provide the information indicating the activity level of the patient in conjunction with cardiac and/or respiration parameters. The sensor senses activity threshold as no activity, low, medium or high depending on the programmed threshold value. Such value may be programmed or determined for a specific patient. Using the activity sensor value and cardiac or respiratory information, the health of the cardiac and/or respiratory system may be evaluated and monitored, for example as described with reference to FIGS. 2-7.

FIG. 6 is a flow chart illustrating a patient management device and method in accordance with the invention. At step 100 the patient state is determined in real time. This may be done using an activity sensor to determine the patient's activity level. The activity sensor may indicate sleep, low activity level, medium activity level or high activity level. The invention is not limited to these activity levels. Other gradations of activity levels may be used as well.

At step 101 which occurs in conjunction with step 110, cardiac and/or respiration parameters are determined, i.e., either sensed or determined from sensed information. The determined parameters and activity level as are stored for a period of time, for example for a 24 hour period. The parameters used may be selected from patient to patient. The baseline information used for comparison may be preset, programmed by a provider, and/or determined on a patient by patient basis by recording non-disordered patient parameters.

The cardiac parameters may include but are not limited to the ECG deviation from a baseline for a particular activity level, QRS duration deviation for a particular activity level, atrial fibrillation incidence, ventricular tachycardia incidence, ventricular fibrillation incidence, bradycardia incidence, and QT interval deviation from baseline.

The respiration parameters for an awake state may include, but are not limited to periodic breathing as a percentage of the total breathing during the particular state and rate of Cheyne-Stokes respiration or hyperventilation as a percentage of the total breathing during the particular state.

The respiration parameters for a sleeping state may include but are not limited to Cheyne-Stokes duration, Cheyne-Stokes maximum amplitudes, AHI (apnea-hypopnea index) frequency of breathing, and frequency of occurrence of non-apnea hyperventilation during the particular state.

At steps 102 a-102 d, overall condition classifications for the patient at each of the defined activity levels is determined.

At step 102 a illustrated in more detail in FIG. 2, an overall condition classification of the patient in an awake state at a low activity level is determined.

At substep 120 each of the cardiac parameters measured and recorded over the period of time of recording are weighted. Each of the parameters are weighted differently. These weight factors may be preprogrammed into the device. The weight factors may also be programmed in on a patient by patient basis by a provider and are adjustable. As shown in step 120: the ECG deviation from a baseline is weighted by factor A1; QRS duration deviation is weighted by a factor B1; atrial fibrillation incidence is weighted by a factor C1; ventricular tachycardia incidence is weighted by a factor D1; ventricular fibrillation incidence is weighted by a factor E1; bradycardia incidence is weighted by a factor F1; and QT interval deviation from baseline is weighted by a factor G1. The weighted factors are these summed.

At substep 121 each of the respiration parameters measured and recorded over the period of time of recording are weighted. Each of the parameters are weighted differently. These weight factors may be preprogrammed into the device. The weight factors may also be programmed in on a patient by patient basis by a provider and are adjustable. As shown in step 121: periodic breathing as a percentage of the total breathing is weighted by a factor of H1 and the rate of Cheyne-Stokes respiration or hyperventilation as a percentage of the total breathing is weighted by a factor of I1.

At substep 122 following step 120 the summed weighted parameters A1-G1 are compared to a baseline for the summed parameters. This baseline also may be preset or programmed into the device on a patient by patient basis. This baseline is also adjustable by a provider. A cardiac classification is then determined as follows: a 10% or less base line shift is classified as normal; a baseline shift between 10% and 20% is classified as a mild deviation; a baseline shift from 20% to 30% is classified as a medium deviation; and above a 30% baseline shift is classified as a severe deviation. These baseline shifts are exemplary and are not intended to limit the classification scheme according to the invention.

Similarly at substep 123 following step 121 the summed weighted parameters H1-I1 are compared to a baseline for the summed parameters. This baseline also may be preset or programmed into the device on a patient by patient basis. This baseline is also adjustable by a provider. A respiration classification is then determined as follows: a 10% or less base line shift is classified as normal; a baseline shift between 10% and 20% is classified as a mild deviation; a baseline shift from 20% to 30% is classified as a medium deviation; and above a 30% baseline shift is classified as a severe deviation.

At substep 124 following step 122 the cardiac classification is weighted as a percentage of the total overall classification with the respiration classification being weighted as the remainder percentage of the total overall classification at substep 125 following step 123.

At substep 126, the overall classification is finally determined for step 102 a by combining the weighted cardiac classification with the weighted respiration classification.

At step 102 b illustrated in more detail in FIG. 5, an overall condition classification of the patient in an awake state at patient sleep state is determined.

At substep 150 each of the cardiac parameters measured and recorded over the period of time of recording are weighted. Each of the parameters are weighted differently. These weight factors may be preprogrammed into the device. The weight factors may also be programmed in on a patient by patient basis by a provider and are adjustable. As shown in step 150: the ECG deviation from a baseline is weighted by factor A2; QRS duration deviation is weighted by a factor B2; atrial fibrillation incidence is weighted by a factor C2; ventricular tachycardia incidence is weighted by a factor D2; ventricular fibrillation incidence is weighted by a factor E2; bradycardia incidence is weighted by a factor F2; and QT interval deviation from baseline is weighted by a factor G2. The weighted factors are then summed.

At substep 151 each of the respiration parameters measured and recorded over the period of time of recording are weighted. Each of the parameters are weighted differently. These weight factors may be preprogrammed into the device. The weight factors may also be programmed in on a patient by patient basis by a provider and are adjustable. As shown in step 151: Cheyne-Stokes duration average during the observation period of time is weighted by a factor of J; the average of the maximum Cheyne-Stokes amplitude per occurrence during the time period is weighted by a factor of K; the AHI for the time period (the nighttime period) is weighted by a factor of L; and the frequency of occurrence of non apnea hyperventilation is weighted by a factor of M.

At substep 152 following step 120 the summed weighted parameters A2-G2 are compared to a baseline for the summed parameters. This baseline also may be preset or programmed into the device on a patient by patient basis. This baseline is also adjustable by a provider. A cardiac classification is then determined as follows: a 10% or less base line shift is classified as normal; a baseline shift between 10% and 20% is classified as a mild deviation; a baseline shift from 20% to 30% is classified as a medium deviation; and above a 30% baseline shift is classified as a severe deviation. These baseline shifts are exemplary and are not intended to limit the classification scheme according to the invention.

Similarly at substep 153 following step 151 the summed weighted parameters J-M are compared to a baseline for the summed parameters. This baseline also may be preset or programmed into the device on a patient by patient basis. This baseline is also adjustable by a provider. A respiration classification is then determined as follows: a 10% or less base line shift is classified as normal; a baseline shift between 10% and 20% is classified as a mild deviation; a baseline shift from 20% to 30% is classified as a medium deviation; and above a 30% baseline shift is classified as a severe deviation.

At substep 154 following step 152 the cardiac classification is weighted as a percentage of the total overall classification with the respiration classification being weighted as the remainder percentage of the total overall classification at substep 155 following step 153.

At substep 156, the overall classification is finally determined for step 102 b by combining the weighted cardiac classification with the weighted respiration classification.

At step 102 c illustrated in more detail in FIG. 3, an overall condition classification of the patient in an awake state at a medium activity level is determined.

At substep 130 each of the cardiac parameters measured and recorded over the period of time of recording are weighted. Each of the parameters are weighted differently. These weight factors may be preprogrammed into the device. The weight factors may also be programmed in on a patient by patient basis by a provider and are adjustable. As shown in step 130: the ECG deviation from a baseline is weighted by factor A3; QRS duration deviation is weighted by a factor B3; atrial fibrillation incidence is weighted by a factor C3; ventricular tachycardia incidence is weighted by a factor D3; ventricular fibrillation incidence is weighted by a factor E3; bradycardia incidence is weighted by a factor F3; and QT interval deviation from baseline is weighted by a factor G3. The weighted factors are then summed.

At substep 131 each of the respiration parameters measured and recorded over the period of time of recording are weighted. Each of the parameters are weighted differently. These weight factors may be preprogrammed into the device. The weight factors may also be programmed in on a patient by patient basis by a provider and are adjustable. As shown in step 131: periodic breathing as a percentage of the total breathing is weighted by a factor of H3 and the rate of Cheyne-Stokes respiration or hyperventilation as a percentage of the total breathing is weighted by a factor of I3.

At substep 132 following step 130 the summed weighted parameters A3-G3 are compared to a baseline for the summed parameters. This baseline also may be preset or programmed into the device on a patient by patient basis. This baseline is also adjustable by a provider. A cardiac classification is then determined as follows: a 10% or less base line shift is classified as normal; a baseline shift between 10% and 20% is classified as a mild deviation; a baseline shift from 20% to 30% is classified as a medium deviation; and above a 30% baseline shift is classified as a severe deviation. These baseline shifts are exemplary and are not intended to limit the classification scheme according to the invention.

Similarly at substep 133 following step 131 the summed weighted parameters H3-I3 are compared to a baseline for the summed parameters. This baseline also may be preset or programmed into the device on a patient by patient basis. This baseline is also adjustable by a provider. A respiration classification is then determined as follows: a 10% or less base line shift is classified as normal; a baseline shift between 10% and 20% is classified as a mild deviation; a baseline shift from 20% to 30% is classified as a medium deviation; and above a 30% baseline shift is classified as a severe deviation.

At substep 134 following step 132 the cardiac classification is weighted as a percentage of the total overall classification with the respiration classification being weighted as the remainder percentage of the total overall classification at substep 135 following step 133.

At substep 136, the overall classification is finally determined for step 102 c by combining the weighted cardiac classification with the weighted respiration classification.

At step 102 d illustrated in more detail in FIG. 4, an overall condition classification of the patient in an awake state at a high activity level is determined.

At substep 140 each of the cardiac parameters measured and recorded over the period of time of recording are weighted. Each of the parameters are weighted differently. These weight factors may be preprogrammed into the device. The weight factors may also be programmed in on a patient by patient basis by a provider and are adjustable. As shown in step 140: the ECG deviation from a baseline is weighted by factor 41; QRS duration deviation is weighted by a factor B4; atrial fibrillation incidence is weighted by a factor C4; ventricular tachycardia incidence is weighted by a factor D4; ventricular fibrillation incidence is weighted by a factor E4; bradycardia incidence is weighted by a factor F4; and QT interval deviation from baseline is weighted by a factor G4. The weighted factors are then summed.

At substep 141 each of the respiration parameters measured and recorded over the period of time of recording are weighted. Each of the parameters are weighted differently. These weight factors may be preprogrammed into the device. The weight factors may also be programmed in on a patient by patient basis by a provider and are adjustable. As shown in step 141: periodic breathing as a percentage of the total breathing is weighted by a factor of H4 and the rate of Cheyne-Stokes respiration or hyperventilation as a percentage of the total breathing is weighted by a factor of I4.

At substep 142 following step 140 the summed weighted parameters A4-G4 are compared to a baseline for the summed parameters. This baseline also may be preset or programmed into the device on a patient by patient basis. This baseline is also adjustable by a provider. A cardiac classification is then determined as follows: a 10% or less base line shift is classified as normal; a baseline shift between 10% and 20% is classified as a mild deviation; a baseline shift from 20% to 30% is classified as a medium deviation; and above a 30% baseline shift is classified as a severe deviation. These baseline shifts are exemplary and are not intended to limit the classification scheme according to the invention.

Similarly at substep 143 following step 141 the summed weighted parameters H4-I4 are compared to a baseline for the summed parameters. This baseline also may be preset or programmed into the device on a patient by patient basis. This baseline is also adjustable by a provider. A respiration classification is then determined as follows: a 10% or less base line shift is classified as normal; a baseline shift between 10% and 20% is classified as a mild deviation; a baseline shift from 20% to 30% is classified as a medium deviation; and above a 30% baseline shift is classified as a severe deviation.

At substep 144 following step 142 the cardiac classification is weighted as a percentage of the total overall classification with the respiration classification being weighted as the remainder percentage of the total overall classification at substep 145 following step 143.

At substep 146, the overall classification is finally determined for step 102 d by combining the weighted cardiac classification with the weighted respiration classification.

Referring again to FIG. 6, after overall Awake low classification has been determined in step 102 a and the overall sleep classification has been determined in step 102 b, if either of these classifications is severe 103 then in step 104, the physician is informed for an office visit and/or ER admission is prepared through communication with the physician and/or hospital using a communication device such as described with respect to FIG. 8.

Otherwise in step 105, an average of the overall classifications values for the four conditions determined in steps 1-4 is calculated in accordance with the chart illustrated in FIG. 7. As shown in FIG. 7, a number 0, 1, 2, or 3 is assigned for each overall classification for a given condition.

If the average is less than 1, the average is considered normal 106 and no further course of action is suggested. If the average is greater than or equal to 1 and less than 2, the average is considered mild 108 and a suggestion is made for lifestyle change and compliance with medicine 109. Such lifestyle change may also include a suggestion for exercise. This communication may be made via a patient self management module or communication device as described with reference to FIG. 8. If the average is greater than or equal to 2 and less than 2.5 the average is considered medium 110. The patient self management module as described in FIG. 8 accordingly provides instructions to change medication according to a physician programmed value and an office visit is scheduled 111. Alternatively, a physician may be notified and the medication dosage changed by a physician before or after an office visit. If the average is greater than or equal to 2.5, then the average is considered severe 112. Then in step 113, the physician is informed for an office visit and/or ER admission is prepared through communication with the physician and/or hospital using a communication device such as described with respect to FIG. 8.

FIG. 8 is a schematic of an external controller device 40 that may be used for communications, data transfer and device programming. The external device 140 comprises a processor 45 for controlling the operations of the external device. The processor 45 and other electrical components of the external device 40 are coordinated by an internal clock 50 and a power source 51. The processor 45 is coupled to a telemetry circuit 46 that includes a telemetry coil 47, a receiver circuit 48 for receiving and processing a telemetry signal that is converted to a digital signal and communicated to the processor 45, and a transmitter circuit 49 for processing and delivering a signal from the processor 45 to the telemetry coil 46. The telemetry coil 47 is an RF coil or alternatively may be a magnetic coil depending on what type of coil a telemetry coil of the implanted controller 30 may be.

The telemetry circuit 46 is configured to transmit to the implanted controller 30, signals containing, e.g., programming or other instructions or information, programmed stimulation rates and pulse widths, electrode configurations, and other device performance details.

The telemetry circuit 46 is also configured to receive telemetry signals from the implanted controller 30 that may contain, e.g., sensed and/or accumulated data such as the sensed information corresponding to the cardiac and respiration parameters described herein. Such sensed data may include sensed ECG, heart rate data, sensed EMG activity, sensed nerve activity, sensed movement information whether corresponding to breathing or to patient activity. The information may contain raw or processed data or signals. The patient management system may be contained in part or in whole in the implanted sensor 30 or the external controller 40. Other information such as frequency and time of cardiac or respiratory events, number of cardiac or respiratory events detected in a time interval or during a sleep cycle may be included in the uploaded information as well as the correlated recorded activity level.

With respect to pulmonary edema monitoring, related parameters such as frequency of hyperventilation including time and patient activity level may be recorded and uploaded to the external device 40.

The uploaded information may be stored in RAM event memory 58 or may be uploaded and through an external port 53 to a computer, or processor, either directly or through a phone line or other communication device that may be coupled to the processor 45 through the external port 53. The external device 40 also includes ROM memory 57 for storing and providing operating instructions to the external device 40 and processor 45. The external device also includes RAM event memory 58 for storing uploaded event information such as sensed information and data from the control unit 30, and RAM program memory 59 for system operations and future upgrades. The external device also includes a buffer 54 coupled to or that can be coupled through a port to a user-operated device 55 such as a keypad input or other operation devices. Finally, the external device 40 includes a display device 56 (or a port where such device can be connected), e.g., for display visual, audible or tactile information, alarms or pages.

The external device 40 is configured to communicate patient disease status classifications to a handheld device, computer, or provider/hospital communications interface as described in steps 104, 107, 109, 111, 113. In addition to communicating the patient disease status classifications described in steps 104, 107, 109, 111, 113, the device may also manage a patient's diuretic or other medication level in relationship to breathing frequency and character. The device may monitor the response of the treatment from measured parameters provided by the control unit 100 in response to diuretic or other medication usage that e.g., may be input by the patient. The system could also direct the patient to rest in different positions to alleviate the present problem until help arrives.

The external device may be equipped with a palm pilot type device that connects to the phone line for downloading the patient specific information regarding patient's cardiac, respiratory or pulmonary edema status, and whether the parameters are programmed correctly. This device may allow for remote follow-up, continuous monitoring of the patient's status, effectiveness of the drug regime for a heart failure patient. The device may provide for management of medications (including, e.g., diuretics ACE inhibitors, or beta blockers) for such heart failure related conditions such as pulmonary edema. The information may be viewed by the clinician using a web browser anywhere in the world of the handheld can send a fax or notice to the physician's office once the parameters of interest are outside the programmed range. The physician may then request an office visit. The system also can send a summarized report on weekly, biweekly, or monthly as routine update based on the decision of the physician programmed in the handheld device. Medication adjustment/drug titration may be accomplished remotely. Hand-held communication protocol/technology may be magnetic or RF.

The patient management device may be used in conjunction or incorporated into various therapy devices such as, e.g., diaphragm stimulation devices as described, for example in U.S. application Ser. No. 10/686,891, entitled: “BREATHING DISORDER DETECTION AND THERAPY DELIVERY DEVICE AND METHOD”, by Tehrani filed Oct. 15, 2003, and incorporated herein by reference.

Safety mechanisms may be incorporated into any stimulation device in accordance with the invention. The safety feature disables the device under certain conditions. Such safety features may include a patient or provider operated switch, e.g. a magnetic switch. In addition a safety mechanism may be included that determines when patient intervention is being provided. For example, the device will turn off if there is diaphragm movement sensed without an EMG as the case would be where a ventilator is being used.

While the invention has been described in detail with reference to preferred embodiments thereof, it will be apparent to one skilled in the art that various changes can be made, and equivalents employed, without departing from the scope of the invention.

Referenced by
Citing PatentFiling datePublication dateApplicantTitle
US7206635 *Dec 15, 2003Apr 17, 2007Medtronic, Inc.Method and apparatus for modifying delivery of a therapy in response to onset of sleep
US7630763Apr 20, 2005Dec 8, 2009Cardiac Pacemakers, Inc.Thoracic or intracardiac impedance detection with automatic vector selection
US7704211 *Mar 21, 2005Apr 27, 2010Pacesetter, Inc.Method and apparatus for assessing fluid level in lungs
US7869877Sep 10, 2007Jan 11, 2011Cardiac Pacemakers, Inc.Cardiopulmonary functional status assessment via heart rate response detection by implantable cardiac device
US7917194 *Nov 15, 2006Mar 29, 2011Pacesetter, Inc.Method and apparatus for detecting pulmonary edema
US7962215Mar 9, 2007Jun 14, 2011Synapse Biomedical, Inc.Ventilatory assist system and methods to improve respiratory function
US7974691Sep 21, 2005Jul 5, 2011Cardiac Pacemakers, Inc.Method and apparatus for controlling cardiac resynchronization therapy using cardiac impedance
US8014860Nov 4, 2009Sep 6, 2011Cardiac Pacemakers, Inc.Thoracic or intracardiac impedance detection with automatic vector selection
US8047999Oct 31, 2008Nov 1, 2011Medtronic, Inc.Filtering of a physiologic signal in a medical device
US8126548Jan 18, 2008Feb 28, 2012Cardiac Pacemakers, Inc.Closed loop impedance-based cardiac resynchronization therapy systems, devices, and methods
US8135471Aug 28, 2007Mar 13, 2012Cardiac Pacemakers, Inc.Method and apparatus for inspiratory muscle stimulation using implantable device
US8202223Oct 31, 2008Jun 19, 2012Medtronic, Inc.Method and apparatus for determining respiratory effort in a medical device
US8231536Oct 31, 2008Jul 31, 2012Medtronic, Inc.Method and apparatus for detecting respiratory effort in a medical device
US8233987Sep 10, 2009Jul 31, 2012Respicardia, Inc.Respiratory rectification
US8244359Nov 17, 2006Aug 14, 2012Respicardia, Inc.System and method to modulate phrenic nerve to prevent sleep apnea
US8295927Oct 20, 2008Oct 23, 2012Cardiac Pacemakers, Inc.Closed loop impedance-based cardiac resynchronization therapy systems, devices, and methods
US8340746Jul 16, 2009Dec 25, 2012Massachusetts Institute Of TechnologyMotif discovery in physiological datasets: a methodology for inferring predictive elements
US8346349 *Jan 15, 2009Jan 1, 2013Massachusetts Institute Of TechnologyMethod and apparatus for predicting patient outcomes from a physiological segmentable patient signal
US8372012Apr 28, 2010Feb 12, 2013Cardiac Pacemakers, Inc.System and method for generating a trend parameter based on respiration rate distribution
US8372013Oct 31, 2008Feb 12, 2013Medtronic, Inc.Method and apparatus for determining a respiration parameter in a medical device
US8406885Oct 14, 2010Mar 26, 2013Synapse Biomedical, Inc.System and method for conditioning a diaphragm of a patient
US8428711Oct 10, 2008Apr 23, 2013Cardiac Pacemakers, Inc.Respiratory stimulation for treating periodic breathing
US8428726Jan 20, 2010Apr 23, 2013Synapse Biomedical, Inc.Device and method of neuromodulation to effect a functionally restorative adaption of the neuromuscular system
US8433412Feb 6, 2009Apr 30, 2013Respicardia, Inc.Muscle and nerve stimulation
US8467876 *Oct 15, 2003Jun 18, 2013Rmx, LlcBreathing disorder detection and therapy delivery device and method
US8473050Jul 19, 2011Jun 25, 2013Cardiac Pacemakers, Inc.Thoracic or intracardiac impedance detection with automatic vector selection
US8478412Oct 30, 2008Jul 2, 2013Synapse Biomedical, Inc.Method of improving sleep disordered breathing
US8494618 *Aug 22, 2005Jul 23, 2013Cardiac Pacemakers, Inc.Intracardiac impedance and its applications
US8509902Jul 28, 2011Aug 13, 2013Medtronic, Inc.Medical device to provide breathing therapy
US8538525Feb 13, 2012Sep 17, 2013Cardiac Pacemakers, Inc.Cardiopulmonary functional status assessment via metabolic response detection by implantable cardiac device
US8585604Oct 29, 2010Nov 19, 2013Medtronic, Inc.Integrated patient care
US8676323Mar 9, 2007Mar 18, 2014Synapse Biomedical, Inc.Ventilatory assist system and methods to improve respiratory function
US8706236Mar 25, 2013Apr 22, 2014Synapse Biomedical, Inc.System and method for conditioning a diaphragm of a patient
US8712521Jun 29, 2011Apr 29, 2014Cardiac Pacemakers, Inc.Method and apparatus for controlling cardiac resynchronization therapy using cardiac impedance
US8761876Jun 24, 2013Jun 24, 2014Cardiac Pacemakers, Inc.Thoracic or intracardiac impedance detection with automatic vector selection
US8823490Dec 15, 2009Sep 2, 2014Corventis, Inc.Patient monitoring systems and methods
US8838245Apr 22, 2013Sep 16, 2014Cardiac Pacemakers, Inc.Respiratory stimulation for treating periodic breathing
US20090192394 *Jan 15, 2009Jul 30, 2009Guttag John VMethod and apparatus for predicting patient outcomes from a physiological segmentable patient signal
US20120157799 *Dec 20, 2011Jun 21, 2012Abhilash PatangayUsing device based sensors to classify events and generate alerts
WO2009114548A1 *Mar 10, 2009Sep 17, 2009Corventis, Inc.Heart failure decompensation prediction based on cardiac rhythm
Classifications
U.S. Classification600/484, 600/509
International ClassificationA61B5/0488, A61B5/08, A61N1/36
Cooperative ClassificationA61B5/7264, A61B5/4818, A61N1/3601, A61B5/0488, A61B5/08, A61N1/36132
European ClassificationA61N1/36C
Legal Events
DateCodeEventDescription
Dec 14, 2007ASAssignment
Owner name: RMX, L.L.C., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:INSPIRATION MEDICAL, INC.;REEL/FRAME:020266/0533
Effective date: 20070831
Feb 18, 2005ASAssignment
Owner name: INSPIRATION MEDICAL, INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:TEHRANI, AMIR J.;REEL/FRAME:015749/0058
Effective date: 20050110