Classifications

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  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

• the present invention relates to determining and/or monitoring a state of progression in a subject of a disease or treatment having motion symptoms, by analysing the kinetic state of the subject, and in particular the invention relates to a method and system for monitoring bradykmesia and/or dyskinesia to assess the state of progression of the disease or treatment.
• Motion symptoms include dyskinesia, in which the person is in a
• bradykinesia in which the person is in a hypokinetic state
• Bradykinesia is a symptom of dysfunction of the basal ganglia, and any conditions that can affect this part of the brain will cause bradykinesia. Similarly any condition which has a hyperdopaminergic state or excess activity of the basal ganglia will produce hyperkinetic syndromes that include dyskinesia. Hyperkinetic activity can also be seen in other conditions of basal ganglia overactivity such as Tourettes syndrome and Huntingtons disease.
• bradykinesia is a key manifestation of Parkinson's disease (PD).
• PD Parkinson's disease
• L- Dopa, or Levodopa is often administered to patients having Parkinson's disease, and can have the effect of causing the patient to become dyskinetic for a period of time after administration.
• DBS is an effective treatment of PD, for well-selected PD patients.
• the period of time in the course of a patient's disease when DBS is indicated is relatively constrained: there is a finite window between the onset of fluctuations and the time when DBS is contraindicated and can cause harm.
• accurate patient selection is crucial to ensure positive outcomes.
• Patient selection for DBS usually happens in two steps. First, the general neurologist who provides routine care for a patient, identifies them as a potential DBS candidate and refers him/her to a Movement Disorder Centre experienced in DBS.
• Movement Disorder Specialist provides the second step is regarded as the "gold standard" for identifying DBS candidates.
• general neurologists who are not expert in movement disorders have difficulty in deciding when to refer patients for consideration for DBS surgery and frequently overlook suitable candidates.
• Movement Disorder Specialists also frequently "miss" candidates as candidates are unable to make the specialists aware of their symptoms. This deprives patients who are suitable for DBS of the opportunity of being assessed in the second stage and gaining a benefit from DBS therapy.
• less common cases that are referred are referred too late in the window, described above, or as the window is closing. This imposes an unnecessary burden on both the DBS surgical centres, and the patients and their caregivers who undergo unnecessary visits and tests.
• the second stage of DBS patient selection noted above typically involves a comprehensive selection process requiring considerable resources, the process including levodopa challenge assessment, brain Magnetic Resonance Imaging (MR I ) and evaluation of neuropsychol ogi cal and psychiatric functions.
• MR I brain Magnetic Resonance Imaging
• the present invention provides a method of determining a state of progression in a subject of a disease or treatment having motion symptoms, the method comprising:
• each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia;
• the present invention provides a non-transitory
• each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia;
• the present invention provides a system for determining a state of progression in an subject of a disease or treatment having motion symptoms, the system comprising:
• a motion detector configured to be worn on an extremity of the subject and to output a time series of motion data over an extended period; and a processor configured to receive the motion data and to process the motion data to produce a plurality of measures of kinetic state of the subject at a respective plurality of times throughout the extended period, each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia; the processor further configured to determine a measure of dispersion of the measures of kinetic state; the processor further configured to combine the measure of dispersion with at least one other data characteristic determined from the motion data, to produce a selection score; and the processor further configured to generate an output indicating the selection score.
• the invention is computer implemented such that processing steps are performed by a processor such as may be incorporated into a remote server or central computing facility, client computer, mobile device or other processing means configurable for receiving the time series of motion data and producing the plurality of measures of kinetic state of the subject, determining the measure of dispersion and combining the measure of dispersion with at least one other data characteri stic which may be determined by the processor from the motion data or from other data supplied to the processor, to produce a selection score.
• the processor may also perform a comparison between the selection score and the threshold to generate an output which is indicative of the stage of the motion symptoms.
• the present invention provides a computer implemented method for automated determination of a state of progression in an subject of a disease or treatment having motion symptoms, the method comprising; obtaining at a processor a time series of motion data from a motion detector worn on an extremity of the subject, over an extended period during usual activities of the subject; the processor processing the motion data to produce a plurality of measures of kinetic state of the subject at a respective plurality of times throughout the extended period, each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia, the processor determining a measure of dispersion of the measures of kinetic state; and the processor combining the measure of dispersion with at least one other data characteristic determined from the motion data, to produce a selection score; and generating an output indicating the selection score.
• an output is generated indicating that motion symptoms are at an initial stage if the selection score is less than a threshold, and generating an output indicating that motion symptoms are at an advanced stage if the selection score is greater than the threshold.
• the measure of dispersion could in some embodiments be a measure of the numerical distance between a high and low percentile of the measures of kinetic state, and for example may be a measure of the interquartile range of the measures of kinetic state.
• the measure of dispersion could be a measure of the variance of the measures of kinetic state.
• the measure of dispersion could be a measure of the standard deviation of the measures of kinetic state, or other indicator of the variability, scatter, or spread of the measures of kinetic state.
• At least one data characteristic may, in some embodiments comprise a probabilistic measure of bradykinesia.
• the probabilistic measure of bradykinesia could for example comprise one or more of a mean or median value of a time se i es of individual measures of bradykinesia obtained throughout an observation period, referred to as BK 50 , and a 75 th percentile value of a time series of individual measures of bradykinesia, referred to as BK 75 , or any other suitable percentile value BK repeat of the BKS scores.
• At least one other data characteristic may in some embodiments comprise a probabilistic measure of dyskinesia.
• the probabilistic measure of dyskinesia could for example comprise one or more of a mean or median value of a time series of individual measures of dyskinesia obtained throughout an observation period, referred to as DK. 50 , and a 75 th percentile value of a time series of individual measures of dyskinesia, referred to as DK 75 , or any other suitable percentile value DK n of the DK scores.
• At least one other data characteristic may in some embodiments comprise a median or mean DK score specifically in the period where the subject is 'off, that is the period when BK. is high. In some embodiments, the at least one other data characteristic may comprise mean BK.
• the at least one other data characteristic may in some embodiments comprise a dosage measure, such as a number of medication reminders prescribed for that subject during a period of interest, such as reminders per day.
• a dosage measure such as a number of medication reminders prescribed for that subject during a period of interest, such as reminders per day.
• the dosage measure may comprise a binary measure which is 0 for 5 or less doses and 1 for more than 5 daily doses.
• the dosage measure may be zero for subjects prescribed 5 or less doses, and set to [doses - 5] for subjects prescribed more than 5 daily doses.
• the system may in some embodiments deduce the number of prescribed doses from a number of reminders programmed to be delivered by a body-worn device bearing the motion detector.
• the at least one other data characteri tic may in some embodiments comprise the proportion of time immobile (PTI) or amount of time immobile (ATI).
• the PTI / ATI may be deduced from periods when a bradykinesia score (BKS) is very high, such as when BKS exceeds a threshold in the range of 50-100, for example a threshold of 80, during the observation period.
• BKS bradykinesia score
• the PTI/ ATI can be considered a proxy for day time sleep and cognition
• the at least one other data characteristic may in some embodiments comprise a measure of tremor derived from the motion data
• the at least one other data characteri tic may in some embodiments comprise BKS IQR being the interquartile range of BK scores.
• BKS IQR being the interquartile range of BK scores.
• At least one other data characteristic may in some embodiments comprise minutes in bradykinesia, being the number of minutes during an observation period (for example comprising the hours between 09:00 and 18:00) when the subject was bradykinetic.
• the number of minutes in bradykinesia may be deduced e.g. from the number of minutes when the BKS is above a threshold, or from the number of minutes above 75th percentile of the BKS calculated using the binomial theorem.
• this measure may seek windows having a small probability of occurrence and define such windows as contributing to a Minutes_Under measure, which is indicative of the minutes in bradykinesia.
• the window may comprise 7 consecutive BK scores, and assess whether 5 or more of those 7 scores exceed the 75 th percentile of all BK scores, noting that the binomial theorem indicates less than a 5% chance of such an occurrence. Such windows may thus be consistent with under- medi cation of PD.
• the at least one other data characteristic may in some embodiments comprise a measure referred to as Under_Count, comprising a measure of the number of windows of time throughout the observation period in which at least 5 out of 7 BK scores exceed the 75 th percentile, or a comparable low percentage event occurs in the BK scores.
• At least one other data characteri tic may in some embodiments comprise minutes in dyskinesia, being the number of minutes during an observation period (for example comprising the hours between 09:00 and 18:00) when the subject was dyskinetic.
• the number of minutes in dyskinesia may be deduced e.g., from the number of minutes when the DKS is above a threshold, or from the number of minutes above the 75th percentile of the DKS calculated using the binomial theorem.
• this measure may seek windows having a small probability of occurrence and define such windows as contributing to a Minutes Over measure which is indicative of the minutes in dyskinesia.
• This window may comprise 7 consecutive DK scores, and assess whether 5 or more of those 7 scores exceed the 75 th percentile of all DK scores, again noting that the binomial theorem indicates less than a 5% chance of such an occurrence. Such windows may thus be consistent with over- medication of PD.
• the at least one other data characteristic may in some embodiments comprise a measure referred to as Over Count, comprising a measure of the number of windows of time throughout the observation period in which at least 5 out of 7 DK scores exceed the 75 th percentile, or a comparable low percentage event occurs in the DK scores,
• the at least one other data characteristic may in some embodiments comprise a means of assessing the number of minutes during an observation period when the subject was not dyskinetic or when dyskinesia was below a threshold AND when the subject was not bradykinetic or when bradykinesia was below a threshold.
• At least one other data characteristic may comprise data not derived from the motion data and indicative of other factors such as years with motion disease (such as PD), subject's cognitive state, age blood pressure, impulsivity, apathy and the like.
• PD motion disease
• the observation period could for example comprise the hours between 09:00 and 18:00.
• the extended period may comprise one day, or more than one day, and for example may comprise 6, 7, 8, 9 or 10 days or more.
• the motion data is preferably obtained for the present method only during waking hours, for example during the period between 9:00 AM and 6:00 PM for the or each day during the extended period, or during an adaptive awake period defined by the absence of sleep using an automatic measure of somnolence,
• the present invention recognises that, in Parkinson's disease for example, an increase in a subject's fluctuation between ON, OFF and dyskinetic states (FDS), when further combined with probabilistic bradvkinesia and/or dyskinesia measures, is an improved predictor of the subject needing advanced therapies.
• FDS ON, OFF and dyskinetic states
• monitoring the selection score and in some specific cases, monitoring for elevation of the selection score so produced beyond a threshold provides for an automated and objective method for monitoring progression of the disease and
• each measure of kinetic state comprises both a measure for bradykinesia and a measure for dyskinesia.
• the measure of dispersion may be produced as a weighted sum of a measure of the dispersion of the measures for bradykinesia and a measure of the dispersion of the measures for dyskinesia.
• the weights may each be 0,5, or any other weight in the range of -1 to 1, inclusive although it is contemplated that any weighting scale may be utilised.
• the measure of dispersion may be produced by summing each measure of bradykinesia with a contemporaneous measure of dyskinesia to produce a combined measure of kinetic state, and determining the measure of dispersion from the dispersion of the combined measures of kinetic state.
• the measure of dispersion may be produced by first processing the measure of the dispersion of the measures for bradykinesia and/or the measure of the dispersion of the measures for dyskinesia using a mathematical function or respective mathematical functions.
• the processed measures in some embodiments may then be summed linearly to produce the measure of dispersion.
• the or each mathematical function may comprise applying a weighting as discussed above and/or may comprise applying a logarithmic or exponential to the respective measures, for example,
• Combining the measure of dispersion with the at least one other data characteristic may comprise linearly summing, or weighted summing, to produce the selection score. Additionally or alternatively the measure of dispersion and/or the at least one other data characteristic may be modified by any suitable mathematical function prior to or during the combining step, such as by applying a logarithmic or exponential to the measure of dispersion and/or the at least one other data characteristic. The measure of dispersion combined with the at least one other data characteristic may alternatively/additionally produce the selection score visually as a chart or vector,
• the method of the present invention may simply determine a selection score.
• the invention may determine whether the selection score exceeds the threshold, to give a binary output.
• alternative embodiments may further comprise recording the value of the selection score as determined on different occasions in order to monitor progression of the selection score, for example over the course of hours, days, weeks, months or years.
• Some embodiments may additionally or alternatively monitor a rate of change in the selection score over time, for example to project or predict disease progression towards a threshold at which advanced therapies may become appropriate.
• monitoring the selection score during progression of a disease may in some embodiments be used as a basis to indicate which therapy, of a plurality of available progressions in therapy, is suitable for that particular patient.
• the selection score may take values both above and below a threshold for referral for advanced therapy. Some embodiments may preferentially, or solely, consider selection score values obtained when medication is wearing off or worn off, as a basis for assessing whether advanced therapy is indicated.
• the threshold against which the selection score may be compared in order to determine whether the subject might require advanced therapy may be determined or predefined in any suitable manner.
• the threshold may be predefined as being the median value of the selection score for normal subjects (being subjects not having a neurodegenerative disorder), or the median level of the selection score for subjects having received advanced therapy, or the 75th percentile level of the selection score for subjects having received advanced therapy, or a scalar, logarithmic or exponential variant derived from such values, or the like.
• the threshold may be defined by reference to a score or range of scores corresponding to baseline data obtained from the subject being assessed prior to development of a neurodegenerative disorder.
• a change in score, or rate of change of scores obtained over time may be utilised as at least one other characteristic used to determine the selection score.
• the selection score may be derived from data characteristic of dyskinesia alone, given the importance of dyskinesia to assessing late stage PD or advanced therapy eligibility.
• the dyskinesia data may in some embodiments be scores produced in accordance with the teachings of International Patent Application Publication No. WO 2009/149520, the content of which is incorporated herein by reference.
• the bradykinesia scores may in some embodiments be scores produced in accordance with the teachings of International Patent Application Publication No. WO 2009/149520, the content of which is incorporated herein by reference.
• the bradykinesia scores may in some
• An output indicating that motion symptoms are either at an initial stage or at an advanced stage, may in preferred embodiments be communicated to a physician in order that the physician may consider whether the patient is ready (or not yet ready) to prescribe or administer an altered or advanced therapy based on an objective evaluation of motor criteria.
• the output may be provided to a general neurologist undertaking a first stage of DBS patient selection, and/or to a DBS specialist undertaking a second stage of DBS patient selection.
• Such embodiments may thus utilise the selection score to provide a quantitative, simple, automatic, and accurate patient screening tool to support general neurologists in the referral stage, and/or DBS specialists in the DBS eligibility assessment of the subject,
• the selection score may additionally or alternatively be used to guide refinements to dosages, whether dosages of medicaments, or dosages or characteristics of DBS stimuli for a DBS recipient.
• An advanced stage of motion symptoms identified by the present invention may thus indicate a need for revised dosage, mix or selection of oral medicaments, such as an advance from levodopa to a levodopa-carbidopa mix, and/or may indicate a need for advanced therapies such as deep brain stimulation (DBS), apomorphine by continuous infusion, drug delivery by a patch, and levodopa-carbidopa intestinal gel delivered by pump, and/or may indicate any other suitable progression or change in therapy.
• DBS deep brain stimulation
• apomorphine by continuous infusion
• drug delivery by a patch
• levodopa-carbidopa intestinal gel delivered by pump and/or may indicate any other suitable progression or change in therapy.
• the present invention provides a method of screening an subject having motion symptoms for an advanced therapy, the method comprising:
• the method includes generating an output indicating that motion symptoms are at an initial stage if the selection score is less than a threshold, and generating an output indi cating readiness for an advanced therapy if the selection score is greater than the threshold.
• Particularly preferred embodiments for screening a subject having motion symptoms are directed to determining the subj ect' s suitability for an advanced therapy selected from the group including apomorphine therapy, duodopa therapy, and deep brain stimulation (DBS),
• the present invention provides a method for automated screening of a subject to determine clinical readiness to receive advanced therapy for a disease having motion symptoms, the method comprising:
• the processor calculating from the motion data a plurality of measures of kinetic state of the subject at a respective plurality of times throughout the extended period, each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia;
• the processor determining a measure of dispersion of the measures of kinetic state; and the processor combining the measure of dispersion with at least one other data characteristic determined from the motion data, to produce a selection score;
• the processor generating an output indicating one or more of clinical readiness for advanced therapy when the selection score is greater than a threshold; and clinical unreadiness for advanced therapy when the selection score is less than the threshold.
• the threshold is selected from the group including: (i) a median level of the selection score for subjects having received advanced therapy; (ii) the 75th percentile level of the selection score for subjects having received advanced therapy; and (iii) a scalar, logarithmic or exponential variant derived from such values in (i) or (ii).
• the method further includes the step of automatically determining a subject's readiness to receive an advanced therapy selected from the group including: deep brain stimulation (DBS), apomorphine and levodopa-carbidopa (duodopa).
• an advanced therapy selected from the group including: deep brain stimulation (DBS), apomorphine and levodopa-carbidopa (duodopa).
• the subject's readiness to receive the selected advanced therapy is automatically determined by the processor when the selection score is greater than a threshold determined by a median level of the selection score for subjects having received the selected advanced therapy, or the 75th percentile level of the selection score for subjects having received the selected advanced therapy or an aggregate of these, or a scalar, logarithmic or exponential variant derived from such values.
• the invention further provides for automatically generating a patient specific report based on a report module containing instructions that are executable by a processor receiving at least the motion data, wherein the report module populates fields of a report template with the selection score and clinical observations derived from the motion data.
• the report template may include fields selected from the group including: a subject identifier, referring clinician; duration of data collection; dates of data collection; dosage
• the invention may further comprise aggregating selection scores obtained for a plurality of subjects in order to assess a state or progression of disease or treatment of the group.
• Such embodiments may for example be utilised to assess a geographical region or jurisdiction, a clinic, a clinician, or a class of patients, for example to assess whether a country, a region, a clinic or an individual clinician is over- or under-treating their patients, or is prescribing advanced therapies at an appropriate time as compared to other countries, regions, clinics or clinicians.
• the motion detector may comprise an accelerometer, outputting acceleration data, or a gyroscope or the like.
• each of the various aspects described herein may incorporate features, modifications and alternatives described in the context of one or more other aspects, such as but not limited to the various kinetic states, measures of dispersion and other data characteristics used in the determination of a selection score.
• features, modifications and alternatives have not been repetitiousiy disclosed for each and ever ⁇ ' aspect although one of skill in the art wi ll appreciate that such combinations of features, modifications and alternatives disclosed for some aspects apply similarly for other aspects and are within the scope of and form part of the subject matter of this disclosure.
• Figure I is a diagrammatic view of a means for detection of various Parkinsonian clinical states in accordance with an embodiment of the invention.
• Figures 2a - 2c illustrate a system for kinetic state monitoring and reporting in accordance with one embodiment of the invention.
• Figure 2d is an example of a report template that in some embodiments, may be populated automatically with meaningful clinical information and summaries;
• Figure 3 gives an example of DK and BK scores output from one day of wrist-worn data logger motion recording from a patient
• Figures 4-6 illustrate the classification efficacy of a selection score in accordance with one embodiment of the invention
• Figure 7 illustrates the classification efficacy of a selection score in accordance with another embodiment of the invention
• Figure 8 illustrates the classification efficacy of a selection score in accordance with a further embodiment of the invention.
• Figure 9 illustrates steps in a method of determining a state of progression in a subject of a disease or treatment having motion symptoms according to embodiments of the invention.
• Figure 10 illustrates a general-purpose computing device that may be used in an
• FIG. 1 is a diagrammatic view of apparatus for detection of various Parkinsonian or kinetic states in accordance with an embodiment of the invention.
• the apparatus 115 comprises three elements for obtaining movement data of a limb of a person to determine a state of progression of the disease or treatment.
• the apparatus 1 1 5 comprises a motion monitor 121 in the form of an accelerometer, an assessor 122 for analysis of the motion monitor data in a manner that provides an objective determination of bradykinesia and dyskinesia, and an output means 123 for outputting objective determination of bradykinesia or dyskinesia over time periods so as to allow a clinician to prescribe medications or to allow the person to better understand their own kinetic state.
• the objective determination is in the form of a selection score.
• the motion monitor 121 is a light weight device which is intended to be worn on the most affected wrist of the person to provide a sufficiently accurate representation of the kinetic state of the whole body throughout waking hours.
• the device is mounted on an elastic wrist band so as to be firmly supported enough that it does not wobble on the arm and therefore does not exaggerate accelerations.
• the device is configured to rise away from the person's wrist by a minimal amount so as to minimise exaggeration of movements.
• the motion monitor may be disposable,
• the motion monitor 121 records acceleration in three axes X, Y, Z over the bandwidth 0 - lOHz, and stores the three channels of data in memory on -board the device.
• This motion monitor 121 typically has I GB or more of storage so as to allow data collected by the device over an extended period of 6, 7, 8, 9 or 10 or more days to be stored, after which the data can be downloaded and analysed. Data may be downloaded from the motion monitor 121 via a physical connection such as a USB connector or other standard or bespoke means, or by a wireless interface as would be understood by one of skill in the art.
• Data may be collected by the motion monitor 121 for a finite assessment period of e.g. 6, 7, 8, 9 or 10 or more days, or it may be collected for analysis by assessor 122 on an ongoing basis, indefinitely, with the motion data being periodically downloaded for evaluation. Periodical removal of the motion monitor 121 from the wearer for download of data also provides an opportunity for the motion monitor 121 to be recharged.
• the output means 123 is typically remote from the motion monitor 121 and may be provided in concert with the assessor 122 (as shown by broken line 124) to provide evaluation and selection scores and reports to clinicians. It is to be understood, however, that in some embodiments the motion monitor 121, assessor 122 and optionally the output means 123 may be provided in a single body -worn device.
• FIGs 2a-2c illustrate a system 215 for kinetic state monitoring and reporting in accordance with one embodiment of the invention.
• a patient 212 is wearing the motion monitor 121 of Figure I which is typically in the form of an accelerometer data logger.
• the motion monitor 121 logs accelerometer data and communicates it to a central computing facility 214.
• the computing facility 214 analyses the data to produce an output comprising kinetic state reporting, indicating whether motion symptoms are at an initial stage or an advanced stage.
• the output is reported to a neurologist or physician 216 who is typically located remotely from the central computing facility 214.
• the neurologist/physician may receive the kinetic state reporting by email or by being made available on a website or portal on the Internet or other communication network, in a format which can be rapidly interpreted by the neurologist to ensure efficient use of the neurologist's time.
• the neurologist 216 interprets the report and updates the patient's medication or prescription or therapy so that it is optimised according to the objective clinical information contained in the report. Additionally or alternatively the neurologist may make recommendations, based on the report, for further evaluation using the inventive system or method e.g. to determine if the patient 212 is a candidate for a particular advanced therapy.
• the system 215 is shown in more detail in Figure 2b.
• a nurse 210 or clinician may pre-screen candidates with reference to whether age is appropriate, cognitive impairment, and whether oral therapy (levodopa) is optimised.
• the nurse uses a tablet or similar device 220 having a processor executing a suitable application to configure the wrist worn motion monitor 121.
• a suitable application to configure the wrist worn motion monitor 121.
• this involves the application executed by the device 220 receiving as inputs one or more of: a patient identifier, that patient's medication type and times, session coding details, and the like.
• the application creates a session key for encryption and decryption of data generated and transmitted through the system.
• the wrist worn motion monitor 121 is worn during typical daily activities of that patient 212, and during this period reminds the patient when to take medication, and in a preferred embodiment, receives patient input indicating when doses are taken.
• the motion monitor 15 may be removed when bathing (although a water resistant device is contemplated and within the scope of this disclosure) or sleeping or for recharging, then use may continue.
• the motion monitor 1 15 is coupled with dock/charge station 222 and interfaced with a tablet or similar device 220 executing a suitable application for data retrieval from the docked motion monitor.
• the data is secured passed and (via wired or wireless means) to a clinic server or equivalently to the central computing facility 214 where patient specific data is retrieved by a processor of the central computing facility and analysed for calculation by the processor of a selection score according to embodiments of the present invention.
• FIG. 2c An example of the process 240 of data analysis undertaken by the inventive system 215 to produce the selection score (as may be included in a work product 225) is illustrated in Figure 2c, As shown, an acceleration time series is used to derive DK and BK scores as described in WO 2009/ 49520.
• the central computing facility 214 further generates reports providing a more detailed interpretation of the retrieved and analysed data that is specific to the patient and provides clinical detail as to the factors underpinning the selection score as determined by the inventive system and method.
• the work product 225 generated by a processor of the central computing facility 214 or equivalent processer/server may include e.g. a P G 226 and report 228 in PDF or other suitable file format readable by the clinician 216.
• An example of a work product 225 populated with meaningful clinical information is provided in Figure 2d.
• the work product template 225 includes a field for a PKG 226 of the patient (being a chart of data extracted from the motion monitor) and a report 228 including fi elds automatically populated by the system.
• Such fields may include but are not limited to: a patient identifier; referring clinician; duration of data collection; dates of data collection; dosage reminders provided to the subject; dosage acknowledgements (of medication dosages consumed) by the subject; therapies prescribed to the subject; summary of kinetic behaviour during data collection (including one or more of bradykinetic, dyskinetic and tremor motion); selection score as calculated by the system; summary of kinetic behaviour response to medication and in particular, evidence from the motion monitor data that the subject did or did not respond to medications such as levodopa based medications (or other medications); summary of clinical findings based on at least one of the motion data and measures of dispersion and selection score calculated by the processor.
• summary of kinetic behaviour response to medication including specific evidence from the motion monitor data that the subject did or did not respond to medications such as levodopa based medications (or other medications) is clinically valuable since levodopa responsiveness is a criteria for selection for advanced therapy.
• the system is configurable to collect data and/or populate fields in the work product template 225 representing metrics for clinical scales collected through the inventive system. These may include for example, cognitive measures or other measures including blood pressure, age, duration of disease, impulsivity or apathy. Such information may be collected via a patient portal provided by a device such as a tablet or mobile phone or other device operated by the patient or carer. Typically, the inventive system prompts the patient or carer to provide such information to the device. In some embodiments, the system is
• Figure 3 gives an example of the output from one day of wrist-worn motion monitor recording from a patient who was prescribed 6 doses of levodopa per day.
• the upper set of data points 306 represent the dyskinesia scores (DK scores) produced from each 2 minute window of data
• the lower set of data points 308 represent the bradvkinesia scores (BK scores) produced from each 2 minute window of data.
• DK scores are plotted only on or above the midline 300 of Figure 3, while BK scores are plotted only on or below the midline 300 of Figure 3.
• Greater severity of dyskinesia is represented by increasing distance of the DK scores 306 upwards from the midline, while greater severity of bradvkinesia is represented by increasing distance of the BK scores 308 downwards from the midline.
• the horizontal lines indicate the respective median, 75th percentile and 90th percentile of controls, for both DK and BK scores, controls being subjects not having a neurodegenerative disorder.
• the six vertical lines, of which two are indicated at 302, indicate the times at which medications were prescribed, and the diamonds 304 represent when the taking of medication was acknowledged by the patient.
• the present embodiment recognises that dispersion, or greater fluctuations, in the DK scores 306 and/or BK scores 308 over an extended period, is a useful predictor of whether motion symptoms have progressed to an advanced stage.
• the median BK scores correlate with contemporaneously obtained clinical ratings using the Unified Parkinson's rating Scale part III (UPDRS3), and the median DK scores correlate with clinically obtained ratings using the modified Abnormal Involuntary Movement Score (AIM S) rating,
• Figure 4 illustrates classification efficacy of a selection score in accordance with one embodiment of the present invention.
• the Selection Score was derived from the following inputs:
• Selection Score 5.86*FDS + 0.981 + 7.1 *BK50 + 8.9* DK 50 + 6.9*BKS IQR + 8.8* Minutes_Under + 4.04 * Minutes_Over + 7.7*Reminder_Count>5 + 0.4 * over count + -0.07 * under count + 0.4 * tremor + -0.8 * PTI + 0.5 * BK75,
• bin_score(n, bins) findfirst(x-> n ⁇ x, [bins...,Infj) - 1
• dbss_a(x) 5.863 * bin_score(x[:FDS], [ 7.7, 9.4, 1 1.7]) +
• the first cluster of dots (I) represents patients who have a selection score above the threshold designated by line A and so have motion symptoms that are at an advanced stage.
• line A is the best separation using ROC (other horizontal bars represent median, 25 m and 75 th percentiles for each group).
• the inventive system may therefore designate patients in the first cluster I as "ready" for an advanced therapy or as candidates for selection for advanced therapy.
• the second cluster of dots (II) represents patients who have a selection score near or below the threshold designated by line A and so have motion symptoms that are at an initial stage.
• the inventive system may therefore designate these patients as "not ready” for advanced therapy.
• This embodiment achieves high Sensitivity of 95% indicating that this selection score and system should identity most DBS candidates.
• This embodiment also provides high
• the Selection Score was calculated on motion monitor data obtained from 33 people with Parkinson's (PwP) from four Australian centres, before (“pre DBS”) and six months after DBS ("post DBS”) as shown in clusters III and IV.
• PwP Parkinson's
• post DBS six months after DBS
• the Specificity in the cohort of cluster III was 90% and the Selectivity was 87.5%.
• MSA multiple system atrophy
• the mean Selection Score fell by 25 points (pO.0001, t-test) following DBS. As well there was broad tendency for those with the highest Selection Score to have the biggest improvement (Figure 5).
• Table I summarises the improved results delivered by the Selection Score as compared to two versions of the earlier Fluctuation Score (FS).
• the next step was to use the study data set (FDS, BKS, DKS scores etc.) to train a decision system to predict which of the Australian PwP should have DBS.
• the system was again about 90%s accurate (very similar to the Selection Score).
• Figure 6 further illustrates performance of the embodiment of Figures 4-5.
• FIG. 7 illustrates an alternative embodiment of the invention, in which the Selection Score is determined from the following inputs:
• dbss_b(x) 7.071 * bin_score(x[:BK_50], [22.0, 25.0, 31.0]) +
• FIG. 8 illustrates another alternative embodiment of the invention, in which the Selection Score is determined from the following inputs:
• Figure 9 illustrates schematically steps in a method 900 for method of determining a state of progression in an subject of a disease or treatment having motion symptoms
• a processor obtains a time series of motion data from a motion detector worn on an extremity of the subject, over an extended period during usual activities of the subject.
• the processor processes the motion data to produce a plurality of measures of kinetic state (905) of the subject at a respective plurality of times throughout the extended period, each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia.
• the processor also determines at least one other data characteristic (907) determined from the motion data.
• a step 908 the processor determines a measure of dispersion of the measures of kinetic state and in a step 908 combines the measure of dispersion with the one or more other data characteristic to produce a selection score (909). In some embodiments, the processor generates an output indicative of the selection score (909). In some embodiments, in a step 910 the processor generates an output indicating that motion symptoms are at an initial stage if the selection score is less than a threshold, and generating an output indicating that motion symptoms are at an advanced stage if the selection score is greater than the threshold.
• a Selection Score derived from dispersion, and also from other data characteristics of a movement symptoms data series has the potential to be used as an improved tool for choosing and optimising therapies for patients with motion disorders including PD.
• This invention recognises that in the conventional approach to advanced therapy, an experienced clinician assesses several symptoms to recognise a suitable DBS candidate based on their experience and knowledge. These symptoms might include overall level of bradykinesia and dyskinesia, the amount of time spent in dyskinesia and bradykinesia, the number of medication doses, cognition and the variability of kinetic state.
• the present invention provides for collection and machine analysis of one or more of several other data characteristics, such as number of minutes during an observation period when the patient was not dyskinetic or when dyskinesia was below a threshold and when the subject was not bradykinetic or when bradykinesia was below a threshold, as well as other patient data such as years with PD or other motion disorder, cognitive state, blood pressure, impulsivity, apathy and the like to produce a robust selection score indicative of the stage of progression of the disease or therapy.
• the selection score of the present invention is particularly effective as it objective and accommodates more than one kinetic symptom, and gathers much more detailed and accurate kinetic state data than patient diaries.
• the present invention also relates to apparatus for performing the operations herein.
• This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer.
• a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
• a machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer).
• a machine- readable medium includes read only memory ("ROM”); random access memory ("RAM");
• magnetic disk storage media optical storage media; flash memor devices; electrical, optical, acoustical or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.); etc.
• propagated signals e.g., carrier waves, infrared signals, digital signals, etc.
• FIG. 10 the invention is illustrated as being implemented in a suitable computing environment.
• the invention wil l be described in the general context of computer-executable instructions, such as program modules, being executed by a personal computer.
• program modules include routines, programs, objects,
• FIG. 10 a general purpose computing device is shown in the form of a
• the system bus 23 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures.
• the system memory includes read only memory (ROM) 24 and random access memory (RAM) 25.
• ROM read only memory
• RAM random access memory
• BIOS basic input/output system
• BIOS basic routines that help to transfer information between elements within the personal computer 20, such as during start-up, is stored in ROM 24.
• the personal computer 20 further includes a hard disk drive 27 for reading from and writing to a hard disk 60, a magnetic disk drive 28 for reading from or writing to a removable magnetic disk 29, and an optical disk drive 30 for reading from or writing to a removable optical disk 31 such as a CD ROM or other optical media.
• the hard disk drive 27, magnetic disk drive 28, and optical disk drive 30 are connected to the system bus 23 by a hard disk drive interface 32, a magnetic disk drive interface 33, and an optical disk drive interface 34, respectively.
• the drives and their associated computer- readable media provide nonvolatile storage of computer readable instructions, data structures, program modules and other data for the personal computer 20.
• a number of program modules may be stored on the hard disk 60, magnetic disk 29, optical disk 31, ROM 24 or RAM 25, including an operating system 35, one or more applications programs 36, other program modules 37, and program data 38.
• a user may enter commands and information into the personal computer 20 through input devices such as a keyboard 40 and a pointing device 42.
• Other input devices may include a microphone, joystick, game pad, satellite dish, scanner, or the like.
• These and other input devices are often connected to the processing unit 21 through a serial port interface 46 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port, or a universal serial bus (USB) or a network interface card.
• a monitor 47 or other type of display device is also connected to the system bus 23 via an interface, such as a video adapter 48.
• personal computers typically include other peripheral output devices, not shown, such as speakers and printers.
• the personal computer 20 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 49.
• the remote computer 49 may be another personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the personal computer 20, although only a memory storage device 50 has been illustrated.
• the logical connections depicted include a local area network (LAN) 51 and a wide area network (WAN) 52, Such networking environments are commonplace in offices, enterprise- wide computer networks, intranets and, inter alia, the Internet.
• LAN local area network
• WAN wide area network
• Such networking environments are commonplace in offices, enterprise- wide computer networks, intranets and, inter alia, the Internet.
• the personal computer 20 When used in a LAN networking environment, the personal computer 20 is connected to local network 5 1 through network interface or adapter 53.
• the personal computer 20 When used in a WAN networking environment, the personal computer 20 typically includes modem 54 or other means for establishing communications over WAN 52.
• the modem 54 which may be internal or external, is connected to system bus 23 via the serial port interface 46.
• program modules depicted relative to the personal computer 20, or portions thereof may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.
• Also described herein is a method of determining a state of progression in a subject of a disease or treatment having motion symptoms, the method comprising:
• each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia;
• non-transitory computer readable medium for determining a state of progression in a subject of a disease or treatment having motion symptoms, comprising instructions which, when executed by one or more processors, causes performance of the following:
• each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia;
• Also described herein is a system for determining a state of progression in a subject of a disease or treatment having motion symptoms, the system comprising:
• a motion detector configured to be worn on an extremity of the subject and to output a time series of motion data over an extended period
• a processor configured to receive the motion data and to process the motion data to produce a plurality of measures of kinetic state of the subject at a respective plurality of times throughout the extended period, each measure of kinetic state comprising at least one of: a measure for bradykinesia, and a measure for dyskinesia; the processor further configured to determine a measure of dispersion of the measures of kinetic state; the processor further configured to combine the measure of dispersion with at least one other data characteristic determined from the motion data, to produce a selection score, and the processor further configured to generate an output indicating that motion symptoms are at an initial stage if the selection score is less than a threshold, and to generate an output indicating that motion symptoms are at an advanced stage if the selection score is greater than the threshold.

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