Source: https://patents.google.com/patent/EP2096995B1/en
Timestamp: 2019-05-19 17:09:36
Document Index: 275459020

Matched Legal Cases: ['Application No. 11', 'arts 904', 'art 904', 'art 906', 'art 900', 'art 932', 'art 932', 'art 1100', 'art 1102', 'art 1104', 'art 1100', 'art 1200', 'art 1200', 'art 1200']

EP2096995B1 - Within-patient algorithm to manage decompensation - Google Patents
Within-patient algorithm to manage decompensation Download PDF
EP2096995B1
EP2096995B1 EP07862949.0A EP07862949A EP2096995B1 EP 2096995 B1 EP2096995 B1 EP 2096995B1 EP 07862949 A EP07862949 A EP 07862949A EP 2096995 B1 EP2096995 B1 EP 2096995B1
EP07862949.0A
EP2096995A1 (en
2006-12-27 Priority to US11/616,450 priority Critical patent/US7629889B2/en
2007-12-17 Priority to PCT/US2007/025667 priority patent/WO2008085309A1/en
2009-09-09 Publication of EP2096995A1 publication Critical patent/EP2096995A1/en
2018-08-22 Publication of EP2096995B1 publication Critical patent/EP2096995B1/en
This patent document pertains generally to implantable medical devices, and more particularly, but not by way of limitation, to systems and methods for managing heart failure decompensation using within-patient diagnostics.
US 6,658,287 B1 describes a method and an apparatus for predicting the onset of seizures based on features derived from signals indicative of brain activity. The apparatus includes one or more sensors, which are usually electrodes configured to detect EEG signals. The apparatus further appears to combine features derived from the EEG signals into a vector. This vector is input into an artificial neural network that appears to act as a means for providing an indication of the onset of a physiological condition. In other words, US 6,658,287 B1 appears to disclose a method for detecting EEG data by deriving features such as autoregressive coefficients, spectral entropy, or the like from an EEG signal, combining different features into a vector, and using this vector as an input for an artificial neural network.
In Example 15, the methods of any one or more of Examples 1-14are optionally performed such that the composite alert score indicates a likelihood of a change in quality of life in a timeframe.
This overview is intended to provide an overview of the 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 subject matter of the present patent application. The subject of the present invention is defined in the claims.
FIG. 20 is a control flow diagram illustrating an example of an interaction between a user-interface system and a control system in accordance with the user-interface illustrated in FIG. 19 .
FIG. 22 is a control flow diagram illustrating an example of an interaction between a user-interface system and a control system in accordance with the user-interface illustrated in FIG. 21 .
FIG. 24 is a control flow diagram illustrating an example of an interaction between a user-interface system and a control system in accordance with the user-interface illustrated in FIG. 23 .
FIG. 26 is a control flow diagram illustrating an example of an interaction between a user-interface system and a control system in accordance with the user-interface illustrated in FIG. 25 .
FIG. 28 is a control flow diagram illustrating an example of an interaction between a user-interface system and a control system in accordance with the user-interface illustrated in FIG. 27 .
FIG. 1 illustrates portions of a system that enables physician-patient communication. In the example of FIG. 1 , a patient 100 is provided with an implantable medical device (IMD) 102. Examples of implantable medical devices include a pacemaker, an implantable cardioverter defibrillator (ICD), a cardiac resynchronization therapy pacemaker (CRT-P), a cardiac resynchronization therapy defibrillator (CRT-D), a neurostimulation device, a deep brain stimulation device, a cochlear implant or a retinal implant. In some examples, the IMD 102 is capable of sensing physiological data and storing such data for later communication. Examples of physiological data include implantable electrograms, surface electrocardiograms, heart rate intervals (e.g., AA, VV, AV or VA intervals), electrogram templates such as for tachyarrhythmia discrimination, pressure (e.g., intracardiac or systemic pressure), oxygen saturation, activity, heart rate variability, heart sounds, impedance, respiration, intrinsic depolarization amplitude, or the like.
Typically, the transceiver 104 is located in close proximity to the patient 100. The transceiver 104 may be included within or attached to a personal computer or a specialized device, such as a medical device programmer. In one example, the transceiver 104 is a hand-held device that is capable of connecting to a local computer 106. Typically, a connection 105 can be made using a hardwired connection (e.g., serial, USB, Firewire) or a wireless connection (e.g., RF, IR). In some examples, the local computer 106 is a specialized device or a personal computer. In certain examples, the local computer 106 is adapted to communicate with a remote server system 108. The communication link between the local computer 106 and the remote server system 108 is typically made through a computer or telecommunications network 110. The network 110 may include, in various examples, one or more wired or wireless networking such as the Internet, satellite telemetry, cellular telemetry, microwave telemetry, or other long-range communication networks.
External sensors 107 can also include devices that measure subjective data from the patient. Subjective data includes information related to a patient's feelings, perceptions, and/or opinions, as opposed to objective physiological data. For example, the "subjective" devices can measure patient responses to inquiries such as "How do you feel?", "How is your pain?" and "Does this taste good?" Such a device may also be adapted to present interrogatory questions related to observational data, such as "What color is the sky?" or "Is it sunny outside?" The device can prompt the patient and record responsive data from the patient using visual and/or audible cues. For example, the patient can press coded response buttons or type an appropriate response on a keypad. Alternatively, responsive data may be collected by allowing the patient to speak into a microphone and using speech recognition software to process the response.
In some examples, the remote server system 108 comprises one or more computers, such as a database server 114, a network server 116, a file server 118, an application server 120 and a web server 122. In certain examples, one or more terminals 112A, 112B, ..., 112N are locally or remotely connected to the remote server system 108 via network 110. The terminals 112 are communicatively coupled to the remote server system 108 using a wired 124 or a wireless connection 126. Examples of terminals 112 may include personal computers, dedicated terminal consoles, handheld devices (e.g., a personal digital assistant (PDA) or cellular telephone), or other specialized devices. In various examples, one or more users may use a terminal 112 to access the remote server system 108. For example, a customer service professional may use a terminal 112 to access records stored in the remote server system 108 to update patient records. As another example, a physician or clinician may use a terminal 112 to receive or provide patient-related data, such as comments regarding a patient visit, physiological data from a test or collected by a sensor or monitor, therapy history (e.g., IMD shock or pacing therapy), or other physician observations.
In some examples, the IMD 102 is adapted to store patient data and to use the data to provide tailored therapy. For example, using historical physiological data, an IMD 102 may be able to discriminate between lethal and non-lethal heart rhythms and deliver an appropriate therapy. However, it is often desirable to establish a proper baseline of historical data by collecting a sufficient amount of data in the IMD 102. In some examples, a "learning period" of some time (e.g., thirty days) is used to establish the baseline for one or more physiological signals. An IMD 102 may, in an example, store a moving window of data of operation, such as a time period equal to the learning period, and may use the information as a baseline indication of the patient's biorhythms or biological events.
FIG. 2 is a detailed schematic view illustrating portions of a system 200 that measures and detects variance in patient-related data to identify acute changes that may indicate an onset of a physiological condition. In the system 200, two or more detectors 202A, 202B, ..., 202N are connected to one or more sensors 204. Sensors 204 may include implanted or external sensors, such as those described above. Sensors 204 may be configured to automatically collect patient-related data (e.g., a heart rate monitor) or be configured to operate by user commands (e.g., an interrogatory device with a display, or a weight scale). The patient-related data may include sensed physiological data, sensed environmental data, or data collected from a patient in response to a query or request. Examples of the sensors 204 include, without limitation, an electrocardiogram, an accelerometer, a pressure sensor, a cardiac output (CO) detector, a heart rate monitor, an interrogatory device, a weight scale, and a microphone. Examples of sensed value include, without limitation, standard deviation of averaged normal-to-normal (SDANN) cardiac depolarization interval trends, heart rate minimum (HRMin), physical activity, or a patient compliance index (as described below). Each detector 202 may include hardware or software to evaluate the one or more input signals from the one or more sensors 204, such as to determine a value of an alert status associated with the sensor-detector pair.
Each alert status is communicated to a fusion machine 208 using a corresponding data pathway 206A, 206B, ..., 206N. Depending on the configuration of the detectors 202 and the fusion machine 208, one or more of the corresponding data pathways 206 may be wired or wireless. For example, in certain examples, the detectors 202 and the fusion machine 208 are integrated into an IMD. In other examples, one or more detectors 202 may be located separate from the IMD and possibly separate from each other. In this case, the fusion machine 208 may be integrated into one or more detectors 202 or it may comprise a separate machine.
The alert score may be communicated from the first fusion module 210 to the second fusion module 214. In another example, the second fusion module 214 accesses a storage unit, such as the alert score database 212, to obtain the current alert score. The second fusion module 214 also accesses the same or a different storage unit to obtain one or more historical alert scores. In an example, a fixed number of historical alert scores are obtained from the storage unit, such as to obtain a "moving window" of the most recent historical alert score data. For example, when alert scores are calculated and stored weekly, then three prior calculated alert scores may be obtained, along with the current alert score, to effectively view a month's worth of alert score data. The second fusion module 214 uses the one or more historical alert scores in combination with the current alert score to calculate a combined alert score (CAS). In an example, the CAS is a weighted function of the alert scores. In various examples, weights in the weighted function may be equal, unequal, adaptive based on one or more patient characteristics, or based on time (e.g., more recent alert scores are given a higher weight, being considered more relevant, while less recent alert scores are given a lower weight).
Sensitivity generally refers to the ability of the detection scheme to effectively detect a particular result. Sensitivity can be expressed with the formula: sensitivity = (true positives)l(true positives + false negatives). Thus, a higher sensitivity generally indicates that an analysis correctly characterizes more true positives or eliminates false negatives.
Specificity generally refers to the ability of the detection scheme to avoid improper classifications. Specificity can be expressed with the function: specificity = (true negatives)/(true negatives + false positives). Thus, a higher specificity generally reflects more accurate classification of true negatives or reduction of false positives.
In other examples, the threshold CAS value is determined automatically. In an example, the threshold updater module 224 uses one or more input parameters to configure or update the threshold CAS value. Input parameters may include things such as the time, the number of sensors or detectors, one or more patient characteristics, a physician's or clinician's preference, the previous threshold CAS value, or the CAS. The threshold updater module 224 may communicate the current threshold value to the comparator module 216 for use in the comparison. In certain examples, the threshold CAS value is established using a constant false alarm rate (CFAR) technique, such as described in Siejko et al U.S. Patent Application No. 11/276,735 , entitled PHYSIOLOGICAL EVENT DETECTION SYSTEMS AND METHODS, filed on March 13, 2006, which is assigned to the assignee of the present patent application, and which is incorporated herein by reference in its entirety, including its description of CFAR event detection techniques.
FIG. 3 illustrates a method 300 of using a composite alert score to detect an increased likelihood of a disease state or onset of a physiological condition. At 302, one or more alert status values are detected. Alert status values may be binary (e.g., on/off, yes/no, high/low), substantially continuous (e.g., 1.4, 2.9, 9.34) or discrete (e.g., 1 out of 5, 2 out of 4). At 304, an alert score is calculated using the alert status values. In an example, the alert score is a weighted function, such that: A l e r t S c o r e A S = A l e r t 1 * w 1 + A l e r t 2 * w 2 + … + A l e r t m w m
where weights w1 , w2 , ..., wm may be modified to weigh one alert value higher or lower than another alert value based on a factor, such as a patient characteristic or a sensor confidence level. In an example, alerts may be temporally related. For example, an alert status may be detected on a periodic or recurrent basis, such as daily, from a particular sensor. In another example, alerts may be otherwise associated. For example, alert statuses may be detected from one or more of similar types of sensors (e.g., implanted and external heart rate monitors), such that if an alert is detected from one sensor, then the alert may be considered to be active for all related or associated sensors. In another example, all related or associated sensors are polled and an alert is detected when some plurality or all concur on an alert state.
At 306, two or more alert scores are combined into a composite alert score (CAS). In an example, the CAS is a weighted function of alert scores, such that: C o m p o s i t e A l e r t S c o r e C A S = A S i * w i + A S i − 1 * w i − 1 + … + A S n * w n
where weights wi , wi-1 , ..., wn may be modified to weigh one alert score higher or lower than another alert score based on a factor, such as time, patient changes over time, or the like. In an example, ASi is the alert score of the current period and ASi-1 is the alert score for the previous period, etc. Periods may be days, weeks, months, or some other regular time interval. At 308, the CAS is compared to a threshold value. In an example, the threshold is fixed, however it may be adapted, such as for particular patients or over time in other examples. When the CAS is over the threshold value in this example, then at 310, an alert state is set. If the CAS does not exceed the threshold value in this example, then at 312, the alert state is not set. In various examples, the alert state may indicate one or more of an onset of a physiological condition, a change in a physiological condition, or a predictive measure of a possibility of an onset of such a physiological condition. For example, the alert state may be used to assist in predicting physiological or patient-related events, such as HF decompensation, lead fracture, sudden cardiac death (SCD), or myocardial infarction (MI). Additionally, the alert state may be indicative of or used for determining a likelihood of a change in a patient's quality of life or a likelihood of patient death in a particular time period or time frame. Portions or all of the method 300 may be executed on different processing machines, for example, method 300 could be executed by a central server, a programmer, or a patient device.
FIG. 4 is a diagram illustrating an example of relationships between alert values, alert scores, and composite alert scores. In an example, alert values 400 are sensed or detected over time and associated with a particular sensor 402. Alert values 400 may be combined first with respect to a particular sensor 402, for example, ASi, ASj, ... ,ASz 404. The alert scores combined with respect to each sensor may then be combined to form the composite alert score, CAS 406. Alternatively, alert values 400 may be combined first with respect to a particular time slice, such that AS1, AS2, ..., ASN 408. Similarly, the alert scores combined with respect to each particular time slice may then be combined into a composite alert score 406. As described above, the calculation of the alert scores, either with respect to a particular sensor or with respect to a particular time slice, may include the use of a weighted function. In addition, the calculation of the combined alert score 406 may include a weighted function.
In other examples, as illustrated in FIGS. 5-6 , alert scores may be calculated using various combinations of alert values. FIG. 5 is a diagram illustrating relationships between alert values, alert scores, and composite alert scores. In FIG. 5 , alert values 500 are used in various combinations to determine alert scores 502A, 502B, 502C. For example, alert score AS 502A is composed of alert values Ai and Aj, alert score AS' 502B is composed of alert values Ay and Az, and alert score AS" 502C is composed of alert values Aj and Ay. Alert scores 502A, 502B, 502C may be combined to form a composite alert score 504. Alert values 500 may be obtained from the same sensor over time or from two or more sensors. In an example, when alert values 500 are obtained from the same sensor, the alert values 500 may be determined at periodic or recurring time intervals, such as daily, hourly, or the like. In another example, when alert values 500 are obtained from two or more sensors, the values 500 may be obtained at approximately the same time.
Several modes of analysis are illustrated in FIGS. 7 and 8. FIG. 7 illustrates an example of a method 700 of using sensed patient actions to determine a level of patient compliance. The method 700 illustrated in FIG. 7 detects and monitors patient actions in response to a request. At 702, one or more occurrences of a patient action are detected. Patient actions may be in response to a request for such an action by a clinician, therapist, or physician. For example, a patient may be requested to log onto a website daily and answer one or more questions, which need not be related to the patient's health or current therapy. When a clinician asks a non-patient health related question, such as "Is it cloudy outside?", the clinician may be more interested in whether the patient responded, and when the patient responded, than whether the response is correct. In another example, a patient may be requested to take and report their blood pressure daily. Such a request may be related to the patient's current therapy or health monitoring, but for the purposes of measuring and determining patient compliance, the value of the blood pressure reading is irrelevant - the requesting physician or clinician may be more interested in the regularity or proper performance of the patient's actions. Some requested actions may be relatively simple, such as pressing a button on a user-interface display daily at a particular time. Other requested actions may be more complex, such as for example, accessing and interacting with a particular website.
At 804, for detected occurrences, the occurrence is analyzed at 806. Analysis of the occurrence may be similar to that described with reference to method 700 in FIG. 7 . For example, one or more aspects of the occurrence may be analyzed, such the time regularity, frequency regularity, or technique correctness.
The third, fourth, and fifth charts 904, 906, 908 illustrated in FIGS. 9C- 9E respectively illustrate a trended time compliance score, a trended frequency compliance score, and a trended quality compliance score (technique compliance). In an example, the trended time compliance score, as illustrated in the third chart 904, is computed using the previous week's worth of recorded patient actions. In an example, the trended time compliance score is normalized, such as from a score of zero to one. Here, the specified time to perform the action is 7:30 AM. Using an allowable time variance of ±30 minutes in this example, when a patient perform the requested action (weighing in) at any time between 7:00 AM and 8:00 AM, the patient is deemed to be in full compliance with respect to time. Using the prior seven day's data, the first value 910 of trended time compliance is a 1.0 because each of the prior seven day's weigh-ins were performed within the 7:00 AM to 8:00 AM allowable time range. When the patient fails to perform the requested action within the allowable range, such as at 912, then the corresponding trended time compliance score falls, such as at group 914.
The fourth chart 906 in FIG. 9D illustrates a trended frequency compliance score based on the data in the first chart 900. Similar to the time compliance scores, the trended frequency compliance score is based on the previous week's worth of data, in an example. Here, when the patient performs the action, a corresponding daily frequency compliance score is one, and when the patient fails to perform the action, the corresponding daily frequency score is zero. The trended frequency compliance may be calculated as a linear function of the previous week's daily frequency compliance scores, such as ∑ j = 0 6 f c j 7 ,
In some examples, a combined compliance score may be calculated, as illustrated in the sixth chart 932 in FIG. 9F . The combined compliance score may be a weighted function of one or more of the trended time compliance score, the trended frequency compliance score, or the trended quality compliance score. In the example illustrated, the combined compliance score is a weighted linear function of the trended time compliance score, the trended frequency compliance score, and the trended quality compliance score, each with equal weights. In an example, the combined compliance score may also be trended with respect to time. A threshold value may also be provided (illustrated as dashed line 934), such that if the combined compliance score is calculated to be less than the threshold value, an alarm is issued.
As an extension of the example illustrated in FIGS. 9A-9F , two or more requested patient actions may be recorded and analyzed. The combined compliance score, as shown in the sixth chart 932, may be a function of one or more of the time, frequency, or quality compliance scores from each of the two or more requested patient actions. One or more of the requested patient actions may be weighed differently from each other in the combined compliance score. In addition, each element of the combined compliance score (e.g., time, frequency, or quality) may also have an associated weight, which may differ from one another.
Although monitoring a patient's physiological or other health-related indications over time may provide some insight into the patient's health-related trends, analysis may be made more complete by including a between-patient diagnosis technique. Between-patient diagnosis leverages previously recorded and documented patient data for the benefit of a current patient. By comparing the current patient to a group of similarly situated patients, probabilistic determinations may be made. For example, based on comparisons to a reference group or control group of patients, a particular patient may be said to be more similar or less similar to the reference group. As another example, using one or more other comparisons to the reference group, the particular patient may be probabilistically deemed more or less likely to experience a health event in a given amount of time (e.g., a specified "prediction time interval"), relative to the reference group. Using one or more such probabilistic measurements, a physician may change diagnosis or adjust or adapt therapy to increase the quality of life of the particular patient. For example, a physician may increase the number of follow up visits or shorten the length of time between successive follow up visits, tune one or more thresholds on one or more alert methods, or alter medication to be more aggressive or less aggressive.
In an example, a between-patient technique provides a population-based stratification of patients according to their risk of a health condition (e.g., heart failure decompensation) within a particular time frame (e.g., three months). For example, a given patient may be classified as "high," "medium," or "low" risk when compared to a reference patient population. The technique can include comparison of one or more heart rate variability (HRV) diagnostics of a patient with a model of one or more similar diagnostics of a reference population. The reference population may include one or more typically, multiple patients, that may be similar to the current patient, such as being prescribed with similar medical devices or associated with similar therapies. The between-patient technique results in an index value, which may indicate whether (or a degree to which) the patient is similar to the reference population.
Reference group patients may be selected from public or private databases. For example, patients may be selected from a database associated with a remote patient management system, such as LATITUDE® as provided by Boston Scientific Corporation's Cardiac Rhythm Management (CRM) group of St. Paul, Minnesota. In addition, reference groups may be static or dynamic. Static reference groups may be comprised of patients having records that existed in a database or system at the time the current patient enrolled or entered the database or system. Thus, static reference groups may represent a snapshot of patients who existed in the system at a particular time, such as at the time of enrollment of a new patient. Static reference groups may not be updated. For example, for a particular diagnostic technique, a snapshot static reference group of patients is used to satisfy assumptions made in the analysis of the particular diagnostic technique. Changes in the static reference group may invalidate the results of such a strict diagnostic technique.
At 1010, the current patient's received physiological value can be used to determine an index value based on the model of the reference group dataset. The index value may be calculated periodically or recurrently, such as daily, weekly, or monthly, such as by using average values for the periodic or recurrent time interval. In an example, 1-dimensional CDFs can be used as "look up tables" to determine what percentage of reference group patients had physical activity levels less than or equal to the current patient's physical activity level. A similar process may be used with SDANN and Footprint % values. For each percentile, values near 0.5 can indicate that the patient is in the 50th percentile of the reference group (e.g., the patient is similar to the reference group), while values near 0 or 1 indicate that the patient is dissimilar to the reference group. The individual indices may be combined into a composite index, such as, for example, by multiplying, adding, or otherwise mathematically combining the individual indices.
FIGS. 11A-11C illustrate examples of a physical activity cumulative distribution function (CDF) chart 1100 in FIG. 11A , an SDANN CDF chart 1102 in FIG. 11B , and a Footprint % CDF chart 1104 in FIG. 11C . In FIG. 11A , the activity CDF chart 1100 includes an activity value 1106 along the x-axis and an activity index 1108 along the y-axis. The activity value 1106, in an example, represents the percentage of time a patient is considered active using a threshold, which may be based on heart rate, blood pressure, accelerometer, or one or more other indications of physical activity. The activity index 1108 represents the percentile of a particular patient with a particular activity value 1106. For example, a patient with an activity value 1106 of 10 has a corresponding activity index 1108 of approximately 0.62, which indicates that the patient is in the 62nd percentile of active patients, e.g., the patient is more active than 62% of the patients represented.
Similarly, in FIG. 11B , the SDANN CDF 1102 includes a standard deviation value along the x-axis 1110 and a SDANN index 1112 along the y-axis. In an example, the SDANN index 1112 represents the percentage of patients that have a SDANN value equal to or less than the corresponding standard deviation value 1110.
In FIG. 11C , the Footprint % CDF 1104 maps a footprint percentage 1114 against a footprint index 1116. In an example, the footprint index 1116 represents a percentile of patients who have a footprint percentage value equal to or less than the corresponding footprint percentage 1114.
FIG. 12 is an example of a probability distribution function chart 1200 that illustrates reference group patients' physical activity levels. The chart 1200 includes activity values on the x-axis and a percentage of patients who have the corresponding activity on the y-axis. To determine an activity index for a particular patient, the area under the probability distribution function (PDF) curve is calculated. In the example illustrated, by using equations that describe the probability distribution function chart 1200, it can be calculated that a patient with an activity level of 14 corresponds to a point 1202 on the curve. The 1-D activity PDF shown in FIG. 12 identifies a pair of points with equivalent probability density that defines an interval of integration. By analogy, a 2-D density would yield sets of points with equivalent probability densities or contours that would define areas of integration. In the example illustrated in FIG. 12 , point 1202 and point 1204 share a common probability density. Using the two points 1202, 1204, an area 1206 under the PDF is defined. In an example, the activity index is equal to the area 1206 under the PDF. Using the calculated activity index may provide advantages, including easier comparison between several patients or easier communication of a patient status to the patient or other medical professionals.
In an example, a within-patient decompensation detection technique may be enabled or disabled when a low or high index value is returned from a between-patient risk stratification technique. FIG. 13 is a diagram 1300 illustrating an example of control and data flow between patient analysis processes. Sensor data 1302 is received and analyzed by a between-patient diagnostic technique 1304, such as one described above. The between-patient diagnostic technique 1304 outputs an index 1306 indicative of a risk or likelihood of a patient experiencing a disease or other health concern similar to that of the population used in the between-patient diagnostic technique 1304. A control module 1308 receives the index 1306 and compares it to a risk threshold. In an example, the risk comparison results in a tri-state output, such as "low," "medium," and "high" risk in comparison to a threshold value or a range of threshold values. When the index 1306 is associated with a low risk, then a corresponding within-patient alert (WPA) technique is disabled 1308. When the index 1306 is associated with a medium risk, then no change is made-if the WPA technique was enabled, then it remains enabled, and if the WPA technique was disabled, then it remains disabled. When the index 1306 is associated with a high risk, then the WPA technique is enabled. In an example, the WPA technique is enabled or disabled automatically. In another example, a user (e.g., an attending physician) may be notified of the suggested change in WPA state and may then manually or semi-automatically enable or disable the WPA technique.
FIG. 14 is a diagram 1400 illustrating an example of control and data flow between patient analysis processes. Similar to the system described in FIG. 13 , based on an index value 1402, risk can be assessed with a tri-state output. In this illustration, when the risk is considered low, then one or more parameters in the within-patient technique are modified to make the technique more specific and less sensitive 1404. When the risk is considered high, then the technique is made more sensitive and less specific by adjusting the one or more parameters 1406. Finally, when the risk is considered medium, then the one or more parameters are maintained at their current values 1408. Parameters may include weights in a weighted function (weighting factors), models used for patient comparison, one or more threshold values, or the like. Parameters may also include variables that control conditional states (e.g., control flow), sample resolution (timing), frequency of assessment, pattern of assessment (e.g., time of day, sequencing of multiple assessments), or the like. For example, one or more parameters may be automatically determined or provided by a user (e.g., a physician or clinician) to indicate which of one or more analysis processes are evaluated and in which order after a preceding analysis is completed. Controlling the selection and arrangement of the analysis processing may be advantageous to refining the analytical result or reducing processing errors (e.g., false positive or false negative indications).
As described above, sensitivity generally refers to the ability of the detection scheme to effectively detect a particular result. Sensitivity can be expressed with the formula: sensitivity = (true positives)/(true positives + false negatives). Thus, a higher sensitivity generally indicates that an analysis correctly characterizes more true positives or eliminates false negatives.
Positive predictive value (PPV) generally refers to the ability of the detection scheme to accurately produce correct positive results. PPV can be expressed with the function: PPV = (true positive)/(true positives + false positives). Thus, PPV exhibits a ratio of correct positive indications.
Negative predictive value (NPV) generally refers to the ability of the detection scheme to accurately produce correct negative results. NPV can be expressed with the function: NPV = (true negatives)/(true negatives + false negatives). Thus, NPV exhibits a ratio of correct negative indications.
False positives (FP) per year is a ratio of false positive indications over one or more years. False positives per year can be expressed with the function: FP/yr = (FP in one or more years)/(number of years).
In another example, three or more diagnostic techniques are configured to interact with each other. For example, a first between-patient diagnostic technique may be configured to focus on physical activity levels, a second between-patient index may be configured to focus on heart rate variability, and a third within-patient diagnostic technique may also be available. The results of the within-patient diagnostic technique (third technique) may affect one or both of the between-patient techniques (first and second). In other examples, two of the techniques may be configured to affect the third. In other examples, one technique may be used to determine which subsequent technique is used or in what order subsequent techniques are performed. In such a configuration, the collection of techniques may be viewed as a state machine. Creating a matrix or "web" of one or more permutations or combinations of between-patient or within-patient diagnostic techniques may provide higher efficiency in diagnosis or fewer false positive or false negative indications.
FIG. 16 is a dataflow diagram illustrating an example of a physician feedback process. Patient data 1600 is communicated to a control system 1602. Patient data 1600 may include physiological data, environmental data, or subjective patient responses, in various examples. In an example, the control system 1602 includes some or all of the components described in 108 ( FIG. 1 ). In the example illustrated in FIG. 16 , the control system includes a storage device 1604 and an operating device 1606. The storage device 1604 may be configured as a database, a file structure, or other storage means. The storage device 1604 typically includes a patient data file 1608, a physician data file 1610, and patient diagnostic routine file 1612.
At some time, a physician or other medical professional may access the control system 1602 and receive patient-related data 1616. Patient-related data 1616 may include physiological data, test results, summary data, patient diagnostic parameters, patient therapies, or other patient data stored in the patient data file 1608 or the patient diagnostic routine file 1612. The physician may have an opportunity to interview or examine the patient, such as during a patient visit 1618. Using the observation, interview, or other information, the physician may provide feedback 1620 to the control system 1602. In an example, the physician may provide physician input (e.g., feedback 1620) to the control system 1602 using an observation, interview, examination, or evaluation of a patient or patient-related data. Such input may be independent from a contemporaneous result generated at the control system 1602, such that the physician may not have reviewed test results or may not have been provided with test results in the patient-related data 1616. An independent evaluation of a patient, not biased by a result generated by the control system 1602, may advantageously provide a "gold standard" or truth standard, by which the control system 1602 may adapt its methods or processes to be more accurate when compared to the physician's assessment.
In some examples, a physician or clinician may provide input or feedback using a terminal, for example as illustrated at 112 ( FIG. 1 ). In some examples, a physician or clinician may provide input to an electronic medical records system 1622. Some or all of an electronic medical record 1624 (EMR) stored at the electronic medical records system 1622 may then be imported to control system 1602. Portions or all of physician feedback 1620 may be stored in the physician data file 1610. In an example, the operating device 1606 may use physician feedback 1620 to alter or adjust the execution of one or more patient diagnostic routines 1614.
FIG. 19 is an example of a user-interface to allow a medical professional to submit input or feedback to a control system. In the example illustrated, a medical professional may provide an indication of whether a heart failure patient is decompensating. Such an indication is provided independent from any result calculated from the control system. For example, a physician may independently examine or interview a patient and derive a diagnosis without referring to a diagnosis generated by the control system. The indication need not be tied to a particular diagnostic analysis. For example, the physician may provide an indication that may be related to one or more within-patient diagnostic techniques and/or one or more between-patient diagnostic techniques. In various examples, the medical professional may be presented an input to provide one or more health characterizations (e.g., aspects of decompensation, arrhythmia, weight gain, blood pressure), some of which may be used by the control system (e.g., 1602 in FIG. 16 ) to modify a parameter or other aspect of a patient diagnostic technique, or a sensor's detection process.
FIG. 20 is a control flow diagram illustrating an example of an interaction between a user-interface system 2002 and a control system 2004 in accordance with the user-interface illustrated in FIG. 19 . In an example, the user-interface system 2002 is incorporated into a user terminal, such as illustrated in FIG. 1 at 112. In an example, the control system 2004 is incorporated into a remote server system, such as 108 in FIG. 1 . In the example illustrated in FIG. 20 , data 2006 is received by the control system 2004 and analyzed by a within-patient analysis 2008, such as an analysis described herein. A composite alert score is evaluated and compared to a threshold value (Th). If the composite alert score is greater than the threshold (Th), then the status is presented to a physician interface 2010, such as for display. In examples, the physician interface 2010 may include a computer terminal, an electronic medical records system, or other input mechanism. A physician may make an independent determination of the patient's status, for example during an office visit or during a telephonic patient interview. The physician may then provide the independent determination using the interface, such as an interface illustrated in FIG. 19 . The independent determination may be performed asynchronously with contemporaneous evaluations performed by the control system 2004 or other systems, such that, for example, the independent determination may occur before, during, or after a particular within-patient analysis 2008 has been evaluated. The independent evaluation may rely on, at least in part, data similar to that received by the control system 2004, such as data 2006, or may use independently obtained data, such as data obtained during a patient examination, or may use a combination of data sources. Whatever the source of data, the independent evaluation is typically made without reference to automatically determined results, such as results of within-patient analysis 2008. In an example, the independent evaluation is stored at an electronic medical records store and later communicated to the control system 2004 in the form of an assessment message.
In a similar fashion, if the composite alert score does not exceed the threshold, then that result may also be presented to the physician interface 2010. The physician may make a similar independent evaluation of the patient's status and submit an assessment message 2012to the verification module 2014 in the control system 2004. The verification module 2014 then compares the physician's independent evaluation, contained in the assessment message 2012 with one or more aspects of the result of the analysis and generates a verification message 2026. The verification message 2026 is then communicated to the control message module 2020 and a control message 2028, 2030 is generated. The control system 2004 may use the control message 2028, 2030 to decrease the threshold 2032 or keep the same threshold 2034, in certain examples. For example, if the physician indicates that the patient is not decompensating, then the verification module 2014 confirms that the physician's diagnosis is consistent with the result of the analysis 2008 and no change is made 2034 to the threshold value. However, if the physician determines that the patient is decompensating, then the verification module 2014 may communicate a verification message 2026 indicating that the analysis was incorrect and the threshold value may be decreased 2032 to increase the sensitivity of the analysis in later evaluations. By increasing the threshold value and making analysis more sensitive, the physician may affect the analysis to reduce false negatives in later evaluations. As with previously described case, the control message module 2020 may determine that decreasing the threshold is either impossible (e.g., due to a lower limit of an analytical technique or a sensor's particular capabilities) or impracticable, and in such a case, the control message module 2020 may generate a "No Change" message 2030.
FIG. 21 is an example of a user-interface to allow a medical professional to submit input or feedback to a control system. In the example illustrated, a medical professional may provide an indication of whether a particular result of a diagnostic analysis is correct. In an example, a user is provided with the results of a particular analysis (e.g., heart failure decompensation risk) along with one or more patient physiological indications (e.g., heart rate intervals, implantable electrograms, electrogram templates for tachyarrhythmia detection or rhythm discrimination, pressure (e.g., intracardiac or systemic pressure), oxygen saturation, physical activity, heart rate variability, heart sounds, thoracic or intracardiac or other impedance, respiration, intrinsic depolarization amplitude, heart rate, data related to tachyarrhythmia episodes, hemodynamic stability, therapy history, autonomic balance, heart rate variability trends or templates, or trends, templates, or abstractions derived from sensed physiological data). The user may then evaluate the patient's condition and determine whether the results of the analysis are correct. Conceptually, in an example, the user takes the place of the verification module 2014 in FIG. 20 .
FIG. 22 is a control flow diagram illustrating an example of an interaction between a user-interface system 2202 and a control system 2204 in accordance with the user-interface illustrated in FIG. 21 . In an example, the user-interface system 2202 is incorporated into a user terminal, such as illustrated in FIG. 1 at 112. In an example, the control system 2204 is incorporated into a remote server system, such as 108 in FIG. 1 . In the example illustrated in FIG. 22 , data 2206 is received by the control system 2204 and analyzed by a within-patient analysis 2208, such as within-patient analysis described herein. A composite alert score is evaluated and compared to a threshold value (Th). If the composite alert score is greater than the threshold (Th), then the status is presented to a physician interface 2210, such as for display. In examples, the physician interface 2210 may include a computer terminal, an electronic medical records system, or other input mechanism. A physician may use the provided information to confirm the results of the analysis. Unlike the situation illustrated in FIG. 20 , the physician has foreknowledge of a result of the automated analysis, such that a patient evaluation is performed in response to the result and furthermore, to confirm the result. The physician may then provide the confirmation determination using the interface, such as an interface illustrated in FIG. 21 . The physician's determination is communicated using a verification message 2212 in certain examples. In various examples, the verification message 2012 may be formatted using a standardized interface language, such as XML, or in a standard file format, such as comma-separated values (csv) or a tab delimited format. Similar to the operation illustrated in FIG. 20 , the control system 2204 can use the verification message 2212 to generate one or more control messages 2214, which may direct the control system 2204 to modify the execution of the analysis 2208.
FIG. 23 is another example of a user-interface 2300 to allow a medical professional to submit feedback to a control system. In FIG. 23 , the physician is provided controls 2302, 2304, 2306 to adjust the sensitivity of a patient analysis. When a physician activates one of the controls 2302, 2304, 2306, a control message is generated and communicated to the control system, in an example. The user-interface may be accessed, for example, during a patient evaluation where a physician has made an independent determination of the patient's status. If the physician concurs with the automatic patient analysis, then the physician may activate the "No Change" control 2306. If the physician believes that the patient analysis is incorrect and indicating a false positive, then the physician may decide to reduce the sensitivity of the analysis and activate the "Less Sensitive" control 2302. On the other hand, if the physician believes that the patient analysis is incorrect and indicating a false negative, then the physician may wish to increase the sensitivity of the analysis and active the "More Sensitive" control 2304. In other examples where multiple patient analysis techniques are used, a separate set of controls may be associated with each patient analysis technique and presented to the physician. In such a configuration, the physician may then have control over each analysis. In other examples, a single set of controls, such as those illustrated, are presented and may control multiple patient analysis techniques in an aggregate configuration. In addition, while controls that may be used to modify an algorithms sensitivity are illustrated in FIG. 23 , in other examples, other controls may be provided to a user to control aspects of performance measures such as a false positive rate, a positive predictive value, a negative predictive value, or the like.
FIG. 24 is a control flow diagram illustrating an example of an interaction between a user-interface system 2402 and a control system 2404 in accordance with the user-interface illustrated in FIG. 23 . Based on the result of the within-patient analysis 2406, the physician may determine that the result is incorrect and lower the sensitivity 2408 or raise the sensitivity 2410, depending on whether the incorrect result is perceived as a false positive of false negative, respectively. If the physician agrees with the within-patient analysis, then no change is indicated, such as in control messages 2412 and 2414. Control messages 2412, 2414 may be formatted using a standardized interface language, such as XML, or in a standard file format, such as comma-separated values (csv) or a tab delimited format.
FIG. 25 is another example of a user-interface 2500. In FIG. 25 , a user is provided one or more controls 2504 to activate or deactivate one or more sensors associated with a patient analysis technique. In the example illustrated, one or more sensors are associated with a heart failure decompensation evaluation. A user (e.g., a physician or clinician) may use the controls 2504 to manage whether each sensor result is used in the patient analysis (e.g., within-patient analysis). Controlling such aspects of the patient evaluation may be advantageous for physicians that wish to dismiss particularly unfavorable sensors or emphasize particularly favorable sensors for a particular patient. For example, a physician may have determined during their practice that a particular sensor is less determinative or less accurate when used in a particular patient's evaluation. Using controls illustrated in FIG. 25 would allow such a physician to remove such a sensor from the calculus of such a patient's status.
FIG. 26 is a control flow diagram illustrating an example of an interaction between a user-interface system 2602 and a control system 2604 in accordance with the user-interface illustrated in FIG. 25 . The user may send one or more control messages 2606A, 2606B to change sensor weights or activate or deactivate particular sensors associated with a patient analysis.
FIG. 27 is another example of a user-interface 2700 to control one or more sensors. For example, one or more controls may be provided to modify threshold values, modify sensitivity using general labels (e.g., "More Sensitive" or "Less Sensitive"), change the type of threshold computation used (e.g., an absolute value or a percent change from a baseline), or change a detection technique used by a particular sensor. In the example illustrated in FIG. 27 , threshold controls 2702 are provided to a user to set threshold values, such as a function of a percent change from a particular value (e.g., a baseline value or an arbitrary initial value). In addition, sensitivity controls 2704 are provided so that a user may generally set a particular sensor to be more or less sensitive. The sensitivity controls 2704 may be configured to indicate a current setting to the user, such as using bold face, coloring, or other graphical or textual details that display to the user the current setting. In the example shown, when a user changes a threshold value to be higher than the current setting, thus decreasing the sensitivity, the general sensitivity control 2704 associated with the changed threshold control 2702 has its presentation altered to reflect the reduced sensitivity. Similarly, when a user selects a general sensitivity control 2704, a corresponding threshold value may be indicated in the associated threshold control 2702.
FIG. 28 is a control flow diagram illustrating an example of an interaction between a user-interface system 2802 and a control system 2804 in accordance with the user-interface illustrated in FIG. 27 . The user of the user-interface system 2802 may send one or more control messages 2806 to the control system 2804 to change one or more threshold values associated with one or more sensors, change the sensitivity of one or more sensors, manage the detection techniques used on one or more sensors, or perform other management tasks as described with regard to the user-interface in FIG. 27 . In an example, the control system 2804 may receive unmodified, sensed data 2808 from one or more sensors 2810. The control system 2804 may then analyze the data 2808 and set one or more alerts using the modified threshold values, sensitivity levels, or other user-provided inputs, and ultimately derive the composite alert score. In other words, the control system 2804 may retain the user-provided information and manage the alerts local to the control system 2804. In another example, the control system 2804 may communicate the threshold values, sensitivity levels, or other user-provided information to one or more sensors 2810 corresponding to the sensors presented in a user-interface, such as in FIG. 27 . In such an example, each sensor 2810 may then modify its own internal detection algorithm and provide appropriate alerts using the new threshold values, for example.
Some of all of the user-interfaces described in FIGS. 19 , 21 , 23 , 25 , 27 may be combined in various combinations or permutations to grant differing scopes of control to a user. Additionally, other user-interfaces not illustrated may be provided to a user to control other aspects of patient analysis techniques, such as analysis blending, sensor blending, timing intervals of sensor fusion over time, sensor settings, detection thresholds, selected population groups, or the like.
It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. For example, although the description describes a particular example in which information is provided to a medical practice, in other examples, one or more other users obtain such information using the present systems and methods. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. 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.
For the purposes of this specification, the term "machine-readable medium" or "computer-readable medium" shall be taken to include any medium which is capable of storing or encoding a sequence of instructions for execution by the machine and that cause the machine to perform any one of the methodologies of the inventive subject matter. The terms "machine-readable medium" or "computer-readable medium" shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic disks, and other temporary, transient, or permanent storage means, such an executable streaming downloadable program. Further, it will be appreciated that the software could be distributed across multiple machines or storage media, which may include the machine-readable medium.
An apparatus for detecting acute changes in physiologic sensor values comprising:
means for detecting an alert status (202A-202N) of each of one or more sensors (204), where the alert status is reflective of whether an event occurred and detecting the alert status includes detecting a discrete value or a binary value, and the alert status being computed using a threshold associated with each of the one or more sensors; means for calculating an alert score (210) by combining the alert statuses;
means for storing an alert score (212);
means for calculating a composite alert score (214), the composite alert score being indicative of a physiological condition and comprising a combination of two or more alert scores, the two or more alert scores representing alert scores from different time periods;
means for comparing the composite alert score to a composite alert score threshold (216) to obtain a result; and
means for providing an indication of the likelihood of one of death, a change in a patient's quality of life in a particular time frame or the onset of a physiological condition when the result indicates that the composite alert score violates the composite alert score threshold.
The apparatus of claim 1, wherein calculating the alert score includes combining detected alerts occurring over time.
The apparatus of claim 1, comprising a sensor adapted to output the binary value; the binary value being indicative of a heart failure decompensation condition or a non-heart failure decompensation condition.
The apparatus of claim 1, wherein the composite alert score indicates a likelihood of heart failure decompensation.
detecting an alert status of each of one or more sensors (302), where the alert status is reflective of whether an event occurred and detecting the alert status includes detecting a discrete value or a binary value, the alert status being computed using a threshold associated with each of the one or more sensors;
calculating an alert score at the fusion machine by combining the alert status of each of the one or more sensors (304);
storing at least one alert score;
calculating a composite alert score (306), the composite alert score being indicative of a physiological condition and comprising a combination of two or more alert scores, the two or more alert scores representing alert scores from different time periods; and
comparing the composite alert score to a composite alert score threshold (308) to obtain a result.
The method of claim 5, wherein calculating the alert score includes combining detected alerts occurring over time.
choosing an initial value for the composite alert score threshold; and dynamically adjusting the composite alert score threshold to improve one or more performance measures related to false positives or false negatives for a particular patient.
The method of claim 7, wherein choosing the initial value includes using a value determined during a learning period.
EP07862949.0A 2006-12-27 2007-12-17 Within-patient algorithm to manage decompensation Active EP2096995B1 (en)
US11/616,450 US7629889B2 (en) 2006-12-27 2006-12-27 Within-patient algorithm to predict heart failure decompensation
PCT/US2007/025667 WO2008085309A1 (en) 2006-12-27 2007-12-17 Within-patient algorithm to manage decompensation
EP12174214.2A EP2505132B1 (en) 2006-12-27 2007-12-17 Within-patient algorithm to manage decompensation
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