Source: http://www.patentsencyclopedia.com/app/20120271183
Timestamp: 2017-12-12 08:02:42
Document Index: 692344787

Matched Legal Cases: ['art 1100', 'art 1102', 'art 1104', 'art 1100', 'art 1200', 'art 1200', 'art 1200']

Patent application number: 20120271183
1. A patient device comprising: an analysis module adapted to: calculate a composite alert score indicative of a likelihood of an onset of a physiological condition, the composite alert score being a combination of multiple alert scores, at least one of the multiple alert scores being a combination of multiple alert statuses, and at least one of the multiple alert statuses being from one of a plurality of detectors and indicative of an occurrence of an event; and compare the composite alert score to a threshold; and a communication module adapted to provide an indication of the likelihood of the onset of the physiological condition using the comparison.
2. The patent device of claim 1, wherein the alert status includes a value that reflects a comparison between a parameter detected at a respective one of the plurality of detectors and a corresponding threshold for the parameter.
9. A non-transitory machine-readable medium comprising instructions, which when executed on a machine, cause the machine to: calculate a composite alert score indicative of a likelihood of an onset of a physiological condition, the composite alert score being a combination of multiple alert scores, at least one of the multiple alert scores being a combination of multiple alert statuses, and at least one of the multiple alert statuses being from one of a plurality of detectors and indicative of an occurrence of an event; compare the composite alert score to a threshold; and provide an indication of the likelihood of the onset of the physiological condition using the comparison.
10. The non-transitory machine-readable medium of claim 9, comprising instructions to receive at least one of the multiple alert scores from at least one of the plurality of detectors.
11. The non-transitory machine-readable medium of claim 9, wherein the alert status includes a value that reflects a comparison between a parameter detected at a respective one of the plurality of detectors and a corresponding threshold for the parameter.
16. A method comprising: calculating, by a computational system, a composite alert score indicative of a likelihood of an onset of a physiological condition, the composite alert score being a combination of multiple alert scores, at least one of the multiple alert scores being a combination of multiple alert statuses, and at least one of the multiple alert statuses being from one of a plurality of detectors and indicative of an occurrence of an event; comparing the composite alert score to a threshold; and providing an indication of the likelihood of the onset of the physiological condition using the comparison.
17. The method of claim 16, comprising receiving at least one of the multiple alert scores from at least one of the plurality of detectors.
18. The method of claim 16, wherein the alert status includes a value that reflects a comparison between a physiological parameter detected at a respective one of the plurality of detectors and a corresponding threshold for the physiological parameter.
21. An apparatus comprising: means for calculating a composite alert score indicative of a likelihood of an onset of a physiological condition, the composite alert score being a combination of multiple alert scores, at least one of the multiple alert scores being a combination of multiple alert statuses, and at least one of the multiple alert statuses being from one of a plurality of detectors and indicative of an occurrence of an event; means for comparing the composite alert score to a threshold; and means for providing an indication of the likelihood of the onset of the physiological condition using the comparison.
[0001] This application is a continuation of U.S. patent application Ser. No. 13/229,110, filed on Sep. 9, 2011, which is a continuation of U.S. patent application Ser. No. 12/613,007, filed on Nov. 5, 2009, now issued as U.S. Pat. No. 8,031,076, which is a continuation of U.S. patent application Ser. No. 11/616,450, filed on Dec. 27, 2006, now issued as U.S. Pat. No. 7,629,889, the specifications of which are hereby incorporated by reference.
[0131] Constructing an appropriate reference group may impact the accuracy or value of any predictive calculations based on comparisons between a patient and the reference group. As such, the reference group may be selected based on one or more similarities with the patient in question. Similar patients may include: [0132] patients who participated in the same controlled study; [0133] patients who are managed by the same or similar health provider, such as the same implant provider or the same therapy provider; [0134] patients who are viewed as stable (e.g., did not die in a particular time, did not decompensate within a particular time, are compliant in their medication or other prescriptions, report a high quality of life, or have not used the health care system in a particular time period); [0135] patients with similar age, gender, ethnicity, geography, clinic, left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) heart failure classification, HF etiology, body mass index (BMI), blood pressure, Six-minute walk test (6MW), quality of life (QoL); [0136] patients who have survived for a particular time frame (e.g., 5 years after implant or 6 months after change of therapy), patients who have not decompensated in a particular time frame (e.g., in the last 9 months); [0137] patients using the same or similar medication; [0138] patients with one or more similar co-morbidities or arrhythmia history; [0139] patients with a similar device implant or device implant history. This list of similarity characteristics is not meant to be exhaustive or complete, but merely illustrative of examples of some characteristics that may be used as parameters to group or associate patients into a reference group.
[0140] Reference group patients may be selected from public or private databases.
[0141] 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, Minn. 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.
[0142] Dynamic reference groups may include dynamically updated static reference groups or true dynamic reference groups. Dynamically updated static reference groups may be updated recurrently or periodically, such as weekly, monthly, seasonally, or annually. Such an update may create a new static reference group, to be used for a period of time. Dynamically updated static reference groups may also be updated at a triggering event. Examples of triggering events include an interrogation of a current patient's implantable device, an implantation of a new patient device, the introduction of a new patient device (e.g., a release of a new model, firmware, software, or other component of a patient device), the introduction of a new drug, or when a new revision of the reference group is approved by an authority, such as the Food and Drug Administration (FDA). Additional examples of triggering events include a detected change in a patient's health condition, a change of a standard of care, a change in a population statistic (e.g., lifestyle, eating habit, education, birth rate, death rate, or habits), or the like. Triggering events may also include one or more user commands to update a reference group. The user commands may include one or more parameters, such as patient age; gender; comorbidity; implant type; or other physiological, environmental, cultural, or patient-related data. In an example, the parameters act as a filter that defines a patient subpopulation, which is used as a dynamically updated patient reference group. In various examples, the parameters may be combined using logical conjunction, disjunction, or both.
[0143] A true dynamic reference group typically includes a patient reference group that modifies its contents automatically, such as in near real-time. For example, a true dynamic reference group may be defined using one or more parameters, such as those described above, to characterize and select a subpopulation of patients. When a patient experiences a change in a physiological, environmental, or other patient-related characteristic, the patient may automatically be added to or removed from the true dynamic reference group. In effect, in an example, a true dynamic reference group may be considered a dynamically updated static reference group that is updated when the reference group statistic (e.g., distribution) is requested or accessed. In another example, a true dynamic reference group may be viewed as a dynamically updated static reference group that is triggered to update at a small increment in time, such as every second, to make the reference group appear as a nearly real-time, dynamic view of a patient subpopulation.
[0144] FIG. 10 illustrates an example of a method 1000 of deriving a probabilistic index based on a particular patient compared to a patient population. At 1002, one or more physiological indications are received. Examples of physiological indications include sensed cardiac signals, physical activity level, and SDANN or Footprint % indices. Footprint % indices may include a measurement of an area under a 2-D histogram of heart rate variability of a patient. Physiological indications may be detected or provided by implanted or external patient monitoring devices. For example, an implanted cardiac rhythm management device may include electronics, memory, or other components to detect or store 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.
[0145] At 1004, a patient reference group is determined or otherwise mapped to the current patient. As described above, the patient reference group may comprise patients from a pool of patients that share one or more similarities with the current patient. Increasing the number of similarities shared between the reference group and the current patient may increase the quality or accuracy of predictive calculations. Determining a relevant reference group may include considering one or more other factors, such as age, gender, medication, medical history, or the like, such as those described above.
[0146] At 1006, a reference group dataset is determined. In an example, the reference group dataset includes patient data of patients in the reference group, where the patient data is substantially similar to the physiological indications received at 1002. For example, if at 1002, a patient's physical activity levels are being monitored and reported by an internal or external patient device, then at 1006, patient data associated with physical activity level from the patient reference group is selected as the reference group dataset.
[0147] At 1008, a model of the reference group dataset is determined. In an example, the model is a probabilistic model and calculated using a probability function. In a further example, the probability function includes a cumulative distribution function (CDF). For example, the model may include a series of 1-dimensional (1D) empirical cumulative distribution functions of the reference group's weekly-averaged activity, SDANN, and Footprint % values. As another example, the CDF may include a single joint multivariable CDF with either a diagonal or full covariance matrix. In another example, the probability function includes a probability distribution function (PDF). In an example, a probabilistic model may include a series of 1-D probability distribution functions (PDF), where a particular PDF models a distinct parameter. In another example, the model may include a single joint multi-dimensional PDF, where each dimension models a distinct parameter. For example, a PDF may include a joint multivariable PDF with either a diagonal or full covariance and may be estimated over the reference group patients' weekly-averaged activity, SDANN, and Footprint % values. Other physiological parameters may be used in the modeling and comparison, such as average heart rate, maximum heart rate, minimum heart rate, respiration rate, amplitude of S3 heart sound, or pulmonary artery pressure.
[0148] 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.
[0149] In another example, a probability distribution function (PDF) can be used to model the reference group dataset. For example, a PDF may be constructed using the reference patients' activity, SDANN, and Footprint % values. The current patient's physiological values can be compared to an estimated PDF to determine the patient's index value. The index value may include the negative log-likelihood that the current patient's set of activity, SDANN, and Footprint % values belong to the PDF. In certain examples, the index value may also be the area under the PDF enclosed by (or outside of) an equiprobable contour that represents the probability that the current patient's set of values belong to the estimated PDF. In either case, a low (or high) index value indicates how similar (or different from) the current patient is compared to the reference group.
[0150] The index value may be advantageous to provide easier comparison between patients, provide a reference value that is easy to interpret, provide easier identification of any outlier values, or provide more insight into one or more correlations between patient physiological indications and probabilistic diagnoses. In some examples, the index value may indicate how likely a patient is to enter or recover from a disease state in a particular amount of time. As an illustration, the index value may be interpreted to indicate the likelihood of a patient to experience heart failure decompensation in the next six months, such as relative to other patients in the patient reference group. For example, Hazard ratios or Cox Proportional Models may be used to determine such a likelihood. In other examples, the index may be used to indicate how likely a patient is to experience a change in health, such as an increase or decrease in quality of life, or a likelihood of death in a particular timeframe.
[0151] 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.
[0152] 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.
[0153] 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.
[0154] 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.
[0155] A between-patient analysis may provide a more long-term indication of a patient's risk compared to a population. In contrast, a within-patient analysis may provide a more short-term indication of acute changes in a patient's health. Thus, it may be advantageous to use one analysis to tune performance of another analysis. For example, a between-patient analysis that includes a large number of patients in the population may provide a sufficient confidence that a particular patient is high or low risk for the occurrence of a particular physiological condition. If the patient is considered high-risk, then one or more parameters of a within-patient analysis may be modified. For example, sampling timing intervals may be shortened to detect acute changes quicker, threshold values may be revised, or a probability distribution model may be selected based on the type or severity of the population-based risk. In contrast, if the patient is considered low-risk or lower risk, then a within-patient analysis may not be considered necessary. Alternatively, the within-patient analysis in such a situation may be revised to less be invasive or have reduced sensitivity and increased specificity (e.g., to reduce false alarms). Such a system may allow physicians to stratify patients according to their long-term risk using the between-patient technique and keep a closer watch for acute changes in patients with higher risk using the within-patient technique.
[0156] 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. [0157] Example: After a hospitalization, cardiac diagnostics may stabilize due to the effect of a drug therapy resulting in a lower index value (result of a between-patient diagnostic technique). In light of the lower index value, the within-patient technique may no longer be considered necessary. Thus, the within-patient technique may be disabled automatically or manually to reduce false alarms that may result from acute changes in patient data. [0158] Example: After an implant procedure, if the index value from a between-patient technique is high enough (e.g., greater than a threshold value), it may imply that the patient is sufficiently different from a reference group comprising stable CRT-D patients that a physician may choose to maintain a closer watch on the patient. To do so, the physician may enable within-patient technique to alert the physician of acute changes in diagnostic parameters.
[0159] In an example, one or more parameters of a within-patient technique may be enabled, disabled, or modified based on the result of a between-patient technique.
[0160] For example, an acute detection threshold may be adjusted based on one or more population-based risk assessments. As another example, a measurement probability distribution function (PDF) model may be selected based on the population-based result (e.g., using a Gaussian or lognormal PDF model).
[0161] 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).
[0162] By automatically or manually adjusting the parameters of the within-patient technique, false alerts may be reduced or minimized, which may allow patients to be managed more efficiently. In an example, some parameters are adjusted automatically. In another example, one or more proposed changes to parameters are presented to a user, for example, an attending physician, who then may either permit or deny changes to the parameters. [0163] Example: If a between-patient stratifier technique indicates that SDANN has a higher sensitivity for a particular patient compared to minimum heart rate (HRMin), then a within-patient technique may be modified to assign a higher weight to an SDANN parameter in a weighted function.
[0164] In certain examples, one or more performance parameters of a first technique, such as a between-patient stratifier, may be adjusted to affect the false positives, false negatives, specificity, sensitivity, positive predictive value, negative predictive value, number of false positives per year of a second technique, such as a within-patient technique.
[0165] 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.
[0166] 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.
[0167] 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.
[0168] 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.
[0169] 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).
[0170] In an example, a within-patient technique may be used to influence a between-patient technique. For example, the between-patient technique may be enabled, disabled, or have one or more parameters modified or enabled based on the results of the within-patient technique.
[0171] FIG. 15 illustrates a cross-feedback configuration of patient analysis processes. Patient data 1500 is received at an analysis system 1502. In an example, the analysis system includes a remote patient management system, such as LATITUDE®. A between-patient index technique 1504 or a within-patient technique 1506 may use the received patient data 1500 to calculate an index 1508 or an alert 1510, respectively. In an example, the index 1508 indicates how similar a patient is to a patient population (e.g., reference group). In an example, the alert 1510 indicates an acute change in patient physiological parameters. The index 1508 and the alert 1510 are received at a control system 1516. In an example, the control system 1516 is part of the same system as the analysis system 1502, e.g., LATITUDE®. In other examples, the control system 1516 and the analysis system 1502 are in separate devices. For example, the analysis system 1502 may be located in a programmer, while the control system 1516 may be located at a centralized patient management server. A first module 1512 in the control system 1516 determines whether to modify the within-patient technique 1506 based on the calculated index 1508. A second module 1514 in the control system 1516 determines whether to modify the between-patient index technique 1504 based on the alert 1510. In either case, examples of the modifications may include enabling, disabling, initializing, or modifying one or more parameters of the corresponding technique.
[0172] 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.
[0173] In some situations, diagnostic techniques, such as those described herein, may result in false positive or false negative indications. For example, false indications may occur when a technique is first initialized to a general state before the technique has been revised or tuned for a particular patient. To reduce the number of false indications and improve accuracy, it may be advantageous to allow a medical professional to monitor and control such diagnostic techniques.
[0174] 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.
[0175] The patient data file 1608 may include historical physiological data such as in raw or summarized format, historical subjective responsive patient data, one or more alerts generated from one or more patient detection techniques, trending data, extrapolated data (e.g., minimum, maximum, or median patient-related values for a particular timeframe), or other patient-related information (e.g., patient identification information, hospitalization information, historical automatic or physician diagnoses, etc.).
[0176] The physician data file 1610 may include physician notes or comments related to a particular patient, physician input (as described in further detail below), prescribed therapies, or other physician-related information. Patient diagnostic routine file 1612 may include programmatic code or other structures that control or enable the decisional process of an automated patient evaluation. Patient diagnostic routine file 1612 may also include variables, such as threshold values, weighting factors, or other parameters used during the execution of patient diagnostic routines.