Dustin A. Heldman - US grants

[unreadable] DESCRIPTION (provided by applicant): The objective is to develop a multivariate system capable of recording, quantifying, analyzing, and presenting multiple symptoms related to the diagnosis of Parkinson's disease (PD). The clinical system will aid in early screening by objectively quantifying several key PD symptoms including motor features, sense of smell, sleep parameters, voice modulation, and other relevant neurological signs. The system will aid in PD screening and diagnosis, defining diagnostic criteria, and quantifying treatment protocol effects. The objective is to develop a multivariate system capable of recording, quantifying, analyzing, and presenting multiple symptoms related to the diagnosis of Parkinson's disease (PD). No biological markers currently exist for antemortem PD diagnosis. Diagnosis relies upon presence and progression of clinical features and confirmation on neuropathology. Clinicopathologic studies have shown significant false- positive and false-negative diagnosis rates. Several clinical features have been correlated with early PD including motor function, olfaction, heart rate variability and electromyography during sleep, voice modulation, and oculomotor activity. The proposed Multivariate Parkinson's Prediction System (MPPS) will be a non-invasive, easy to use system of small, lightweight, wirelessly networked modules to quantify multiple PD symptoms. Modules will include motor, physiological, speech, and olfaction. The MPPS will aid in PD screening and diagnosis, defining diagnostic criteria, and quantifying treatment protocol effects. The system should allow general practitioners to screen for PD. The MPPS will consist of small, lightweight, telemetry hardware modules and a clinical base station. A motor module will sense three-dimensional motion. A physiologic module will capture standard electrophysiology inputs. A voice module will utilize a wireless microphone to capture quantitative speech features. An olfaction module will integrate a reliable, off the shelf system. The clinical base station will consist of a small, lightweight laptop computer with an integrated radio and clinical interface software. The base station will detect area modules, process data, and report clinical details. The clinical system will aid in early screening by objectively quantifying several key PD symptoms including motor features, olfaction, sleep parameters, voice modulation, and other relevant neurological signs. A patient database will link clinical groups to guide diagnostic criteria and track symptom progression. It will maximize patient safety and comfort through a small, non-invasive, unobtrusive, untethered design that can be used in the clinic or home. It will illustrate through large, well-designed, multi-center clinical trials that the MPPS accurately captures PD symptoms and differentiates between PD and non-PD subjects. Specifically, for Phase I we will integrate prototype hardware, design algorithms for clinical feature extraction, develop a software interface, and conduct a clinical trial with PD and non-PD subjects. We hypothesize that we can accurately record data, extract objective clinical features, and accurately predict between PD and non-PD subjects using multiple objective clinical measures as inputs. The clinical utility of the final Phase I prototype device will also be evaluated by several movement disorder experts. [unreadable] [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): The objective is to design, implement, and clinically assess a portable, user worn, bradykinesia feature extraction system, BradyXplore", for easy integration with CleveMed's existing ParkinSense" technology platform. Idiopathic Parkinson's disease (PD) and other parkinsonian disorders affect millions of people. Three major symptoms responsible for functional disability and reduced quality of life include tremor, bradykinesia, and rigidity. The current standard in evaluating symptoms is the Unified Parkinson's Disease Rating Scale (UPDRS), a subjective, qualitative ranking system. While tremor is often the most visible symptom, bradykinesia can be the most impairing to the patient. Objectively quantifying bradykinesia would aid in evaluating treatment protocol efficacy. More specifically, UPDRS scoring instructions for bradykinesia integrates multiple features into a single score that may cause increased inter-rater variability in scores as well as limit the opportunity to explore if particular bradykinesia features are influenced by specific treatment protocols. CleveMed has previously developed a compact wireless system to quantify movement disorder symptoms called ParkinSense". This previously existing technology will serve as the hardware platform for this proposed program. In a clinical study, this system successfully demonstrated objective quantification of PD motor symptoms. Quantitative variables were processed and applied to algorithms to assess symptom severity. Algorithm outputs were highly correlated with clinicians'qualitative UPDRS scores for rest, postural, and kinetic tremor. While moderate results were also achieved for bradykinesia, this application addresses optimization of bradykinesia feature extraction including speed, amplitude, and rhythm. The UPDRS uses three tasks to evaluate bradykinesia severity on a 0-4 scale: finger taps;hand opening- closing;and pronation-supination. Patients are instructed to repetitively complete each task as fast and with as wide an excursion as they can. Clinician scorers are instructed to account for speed, amplitude, fatiguing, hesitations, arrests in movement, and how these variables change over time. Accounting for all of these variables is challenging, even for the most experienced movement disorder specialist. It is difficult to gauge weights specific clinicians place on different bradykinesia manifestations. Also, it is unclear if underlying neural mechanisms for different manifestations are the same. Objectively quantifying specific bradykinesia features should aid in the development of novel DBS therapies for targeting specific bradykinesia manifestations. Furthermore, a portable platform for more continuous monitoring will allow a clinician to capture complex fluctuation patterns in treatment response. PUBLIC HEALTH RELEVANCE: Parkinson's disease affects more than 1,000,000 people in the United States causing motor symptoms of which one of the most debilitating is bradykinesia (slowed movements). Bradykinesia is currently evaluated using a subjective rating scale that gives a single score taking into account speed, amplitude, fatiguing, hesitations, arrests in movement, and how these variables change over time. The proposed BradyXplore bradykinesia feature extraction system will separately quantify specific features of bradykinesia (speed, amplitude, and rhythm), which should aid in the development of novel therapies to target a patient's specific bradykinesia manifestations.

DESCRIPTION (provided by applicant): The objective is to design, implement, and clinically assess ETSense, an adaptive, compact, portable essential tremor (ET) monitor for optimizing therapeutic interventions. ET is characterized primarily by postural and kinetic (action) tremors of the limbs, which are rated by various subjective tremor rating scales. These scales all provide a discrete, subjective symptom rating at a discrete point in time, require a clinician to visually assess the patient, and cannot capture complex fluctuations that occur throughout the day in response to interventions. Objectively capturing ET symptoms continuously during daily activities and using adaptive algorithms to both classify tremor types and severity will help clinicians better titrate therapy to minimize symptom fluctuations and expand care to rural and underserved populations. The Phase I ETSense effort successfully used kinematic data recorded from a sensor unit placed on the finger of subjects with ET to discriminate tremor from voluntary motion associated with daily activities and objectively quantified tremor severity with scores highly correlated with clinicians' qualitative ratings, providing a standardized platform for continuous ET assessment. Tremor quantification algorithms were extrapolated to non-standardized tasks, suggesting that it is feasible to rate tremor continuously throughout the day during activities of daily living. The three primary innovations of the proposed system include: 1) a compact, portable, user-worn device for continuous monitoring during ADLs, 2) intelligent, adaptive algorithms to continuously classify tremor type and rate severity, and 3) web-based access to symptom response reports. The clinically deployable system will be contained in a lightweight, finger-worn housing for continuous wear while patients perform everyday tasks at home or in public. A push button diary will allow the patient to indicate when medication is taken. All data will be stored in memory for subsequent analysis and report generation detailing symptom fluctuations in response to therapeutic interventions. Adaptive algorithms developed in Phase I will be further optimized to account for voluntary motion that can create tremor false positives or mask over kinematic tremor signals. The system will shift between scoring algorithms (i.e. rest, kinetic) based on any voluntary motion detected. After data collection is complete, clinicians will use a web-interface to view patient reports. These reports will detail tremor type, severity, and fluctuations, as well as when medication was taken to aid clinicians in optimizing existing therapeutic interventions or in the research and development of novel treatment protocols. We hypothesize that the commercial ETSense system will 1) continuously quantify tremor severity throughout the day during activities of daily living, 2) improve patient outcomes with better and/or faster medication optimization, 3) decrease healthcare costs by reducing office visits, and 4) enable the testing and validation of novel therapeutic interventions, facilitated by high-resolution continuous home monitoring.

DESCRIPTION (provided by applicant): The objective is to design, build, and clinically assess Kinesia-HS, an integrated solution to facilitate pharmaceutical development of neuroprotective interventions targeted to Parkinson's disease (PD). The system will include both compact patient-worn instrumentation and web-based infrastructure for home monitoring to provide significantly increased motor symptom resolution in both amplitude and time. There has been tremendous growth and active research into neuroprotective treatments designed to slow the progression PD. Treatment efficacy is judged by the rate at which patient symptoms deteriorate over time. The current standard in evaluating PD motor symptoms is the Unified Parkinson's Disease Rating Scale (UPDRS), a ranking system in which clinicians must be present to provide a subjective integer score to document symptom severity. The discrete nature of the UPDRS renders it profoundly inadequate for measuring the rate of deterioration of motor symptoms in a neuroprotective drug study. These drugs target patients with early PD, when symptoms are barely noticeable. It often takes years or even decades before the discrete UPDRS can detect a significant change in the rate of decline of motor symptom severity. The primary innovations of the proposed system include utilizing DBS in a clinical study to simulate disease progression, using high speed video as the gold standard linked directly back to the UPDRS for sensitivity validation, and a standardized, web-based infrastructure to improve the efficiency of clinical drug trials. We will leverage CleveMed's previously developed Kinesia system, a compact wireless system to quantify PD motor symptoms, which includes user worn motion sensors and interactive software to automate a patient exam. In two large clinical studies, the motion sensing technology successfully demonstrated objective quantification of PD motor symptoms with high correlations to clinical UPDRS motor scores. While previously existing technology will be leveraged to speed development and increase likelihood of project success, significant novel software development, system integration, and evaluation is required for the pharmaceutical application. In order to validate the quantification of very slight changes in symptom severity, the existing algorithms for quantifying tremor and bradykinesia severities will be tested against high-speed, calibrated videos that give precise measures of hand movements. In addition to highly sensitive instrumentation for monitoring PD symptoms in the home, a primary innovation of the proposed system is the infrastructure backbone to enable the straightforward integration of Kinesia-HS outputs into standardized electronic data capture software currently used in clinical trials. This standardized platform for objective home assessments could lead to clinically significant results faster and with improved resolution compared to traditional methods, which could enable breakthrough therapies to get to market faster and lower developmental costs. PUBLIC HEALTH RELEVANCE: Pharmaceutical companies are placing great emphasis on neuroprotective agents designed to slow the progression of Parkinson's disease. The current standard for evaluating motor symptoms in response to therapy is a subjective, integer rating scale that does not provide the resolution necessary to measure the rate at which motor symptoms change during disease progression. The proposed system will include both compact patient-worn instrumentation and web-based infrastructure for home monitoring to provide significantly increased motor symptom resolution in both amplitude and time and easy integration into clinical drug trials to speed the development of PD interventions.

? DESCRIPTION (provided by applicant): The objective is to engineer, build, and clinically validate DBS-Expert, an expert system for optimizing postoperative programming of deep brain stimulation (DBS) in patients with movement disorders such as Parkinson's disease (PD). The clinical utility of DBS for treatment of PD is well established. However, great outcome disparity exists among recipients due to varied postoperative management, particularly concerning DBS programming optimization. Many programmers have only a cursory understanding of electrophysiology and lack expertise and/or time required to determine an optimal set of DBS parameters from thousands of possible combinations. DBS-Expert will improve outcomes and equalize care across the country for patients not in close proximity to DBS specialty centers. The primary innovations include 1) automated functional mapping based on objective motion sensor-based motor assessments that will intelligently navigate the DBS parameter space to guide the programming session and 2) intelligent algorithms that will find a set of parameters that optimize for efficacy while minimizing side effects and battery usage. The clinically deployable DBS-Expert system will include wireless wearable motion sensors, a tablet software app, and secure cloud storage. The app will include a simple interface to guide the programming session, collect all sensor and stimulation data, and adjust DBS settings. For typical use, the system will start by performing automated monopolar survey to determine the patient-specific functional anatomy around the lead site and narrow the search space for determining an optimal set of programming parameters. This therapeutic window will be valuable at the initial postoperative programming session and simplify subsequent adjustment sessions. In Phase I, subjects with PD wore our existing Kinesia motion sensor while prototype software guided an automated monopolar survey. Stimulation was incrementally increased at each contact until symptoms stopped improving or side effects appeared. Search algorithms were successfully developed to automatically identify optimal DBS stimulation parameters. Parameters chosen by the algorithms improved symptoms by nearly 36% or maintained therapeutic benefits while reducing stimulation amplitude to decrease battery usage. Phase II will include 1) developing an app to integrate the successful Phase I prototype functional mapping software with DBS IPG programmer communication protocols to streamline use, 2) a multi-center clinical evaluation to optimize specific functional mapping protocols and parameter space navigation algorithms, and 3) integration of the optimal search algorithm and bidirectional communication protocols into a commercially viable product. We hypothesize DBS-Expert will improve patient outcomes, access to care, clinician and patient experience, battery usage, and frequency and duration of follow-up programming sessions compared to traditional programming practices.

DESCRIPTION (provided by applicant): The objective is to design, build, and clinically assess ParkinStim, a home-based, noninvasive brain polarization system used during sleep to treat Parkinson's disease (PD). While current therapeutic standards of drug intervention and deep brain stimulation (DBS) show effective treatment of PD symptoms, transcranial direct current stimulation (tDCS) is a less expensive and less invasive potential alternative/adjunct treatment with few side effects that may help treat PD symptoms, decrease medication usage, and reduce sleep disturbances. Recent studies have demonstrated that noninvasive anodal tDCS applied to the scalp over primary motor cortex (M1) can improve PD symptoms. tDCS provides polarization to the cerebral cortex via painless weak currents transmitted through noninvasive scalp electrodes. Unlike other noninvasive stimulation modalities such as transcranial electrical stimulation (TES) and rapid transcranial magnetic stimulation (rTMS) that can be painful and cause side effects including seizures and psychotic symptoms, tDCS is painless, poses few side effects, and is ideal for home use since it can be provided in an inexpensive and compact package. The primary innovations of ParkinStim include 1) easy-to-don wearable tDCS hardware suitable for home use, 2) a technique for providing tDCS during sleep, and 3) a therapeutic tDCS system to treat PD symptoms and related sleep disturbances. The proposed system will provide a wearable device that patients with PD can easily don before going to sleep and use through the night. Since patients often feel worst in the morning after medication from the previous day has worn off, stimulation during the night may help patients wake up feeling better. Additionally, designing the device for overnight use will make the system convenient and accessible so patients need not worry about using the device in public or during their daily activities. Development will focus on treating the motor symptoms of PD; however, the proposed system may prove beneficial for other PD symptoms or related sleep disturbances. For this Phase I, we aim to demonstrate 1) technical feasibility by safely and effectively using existing stimulation and electrode hardware to provide tDCS to PD patients during sleep and 2) clinical feasibility by demonstrating that tDCS reduces PD symptom severities and decreases symptom fluctuations. Ten PD subjects will participate in a counterbalanced crossover clinical study during which tDCS is applied to M1 while the subject sleeps in a sleep laboratory and standard polysomnography data is collected. Phase I success criteria include safely and effectively administering tDCS to PD patients during sleep without causing waking and demonstrating an acute therapeutic effect of tDCS. While Phase I is designed to evaluate the acute benefits of tDCS, Phase II will investigate the chronic benefits of multiple nights of tDCS used in the home over several weeks. We hypothesize that the final system resulting from Phase I and II development will provide safe and effective tDCS during sleep, decrease PD symptom severities, minimize motor fluctuations, reduce required medication, and improve sleep quality. PUBLIC HEALTH RELEVANCE: Parkinson's disease affects nearly 1.5 million Americans with annual treatment costs approaching $25 billion. While current therapeutic standards of drug intervention and deep brain stimulation (DBS) show effective treatment of PD symptoms, transcranial direct current stimulation (tDCS) during sleep is a less expensive and less invasive potential alternative/adjunct treatment with few side effects that may help treat PD symptoms, decrease medication usage, and reduce sleep disturbances. Successful development will result in a safe, easy-to-use home-based tDCS therapy system PD patients can use during the night to feel better during the day.

Summary The objective is to design, build, and clinically assess ParkinPal, an interactive patient-centered system for individuals with Parkinson?s disease (PD). The app-based platform will enable data-driven decisions to optimize treatment using a personalized interface with intelligent algorithms based on quantitative symptom assessment. The system will include a wrist-worn wearable device that communicates wirelessly with an engaging smartphone software application that provides actionable feedback for treatment optimization. When optimizing PD therapy, the patient is in the middle of a complex system where drug types, doses, and times interact to create fluctuating patterns of motor symptoms and side effects. Tools for patients to monitor (let alone act on) these temporal patterns are severely lacking. Clinical rating scales provide a limited snapshot of symptom severity in the clinic. Likewise, handwritten diaries can be burdensome, leading to inaccurate entries and poor compliance. These limitations can make decisions about medication adjustments challenging and require a costly trial-and-error process. Wearable technology has shown great promise for providing an objective evidence base for clinical decision making. We have previously commercialized Kinesia, a clinically validated system to quantify motor symptoms that is being used to measure outcomes in clinical trials and help clinicians with patient care. Kinesia, however, generates reports that require interpretation by a neurologist and does not provide patient-facing feedback ? something patients greatly want. While some existing smartphone apps provide tracking of symptoms, none have been clinically validated and it is likely that these simple trackers will see a big drop off in usage as there is no incentive to stay engaged. ParkinPal will address this major limitation by providing patients with visual feedback and actionable suggestions to discuss with their doctor to optimize treatment. The primary innovations of ParkinPal include an interactive smartphone app that: 1) uses data mining algorithms to analyze temporal patterns and identify clinical indicators of sub-optimal treatment and 2) provides actionable suggestions for changes that patients can discuss with their doctors to improve treatment. Treatment regimen recommendation algorithms will be based on expert clinician suggestions and adapt based on a knowledgebase of actual outcomes as more and more patients use the system. Clinicians will view treatment change recommendations and monitor their patient?s progress via a secure web interface. In Phase I, we will use an interactive design process including PD patient focus groups to develop the user interface and validate algorithms for identification of indicators of sub-optimal treatment. We will also work with a clinical consultant to develop an initial knowledgebase of best practices for managing motor complications. Finally, we will leverage the system in a data collection study to demonstrate feasibility. The final ParkinPal system will empower patients with PD to become more involved in their disease management and allow a personalized approach to treatment, which will ultimately improve symptomatic control and quality of life.

Summary The objective is to design, develop, and clinically assess DiSCERN, a standardized telemedicine tool for identifying patients with Parkinson?s disease (PD) who would benefit from advanced therapies (AT) and determining when AT recipients need therapy adjustments. Once chronic PD medication usage results in motor fluctuations and dyskinesias and all non-invasive therapies have been exhausted, AT (e.g., deep brain stimulation, drug pumps) is often recommended. While experts at academic medical centers may appropriately identify AT candidates, AT is underutilized due to limited access and inequitable utilization of limited evaluative resources for a sizable subset of the PD population. Remote screening and monitoring with DiSCERN will improve patient selection, reduce disparities, and expand access for rural populations and disadvantaged communities. The system will engage and empower patients, providers, and healthcare institutions and lead to improved health, healthcare delivery, and the reduction of health disparities. This mobile health technology will include a patient friendly smartphone app, non-motor assessments, and wireless wearable sensors for continuously monitoring PD motor symptoms, complications, and quality of life (QoL). We have previously commercialized wearables and mobile apps for remote monitoring of PD motor symptoms and side effects, which will significantly de-risk the project. Still, novel development and validation efforts are required to commercialize this new technology. Innovations include: 1) integration of PD monitoring algorithms with context aware activity detection for improved PD motor assessment and QoL quantification; 2) implementation of the algorithms on a smartphone and wearable device; 3) development of a predictive model that uses motor and non-motor features to accurately identify PD patients who would be good candidates for AT; and 4) implementation of a model that alerts clinicians when an AT recipient needs a therapy adjustment. Through integration with AT systems, DiSCERN will improve the clinician experience and allow the limited availability of specialists to scale care to a diverse and growing PD population, who may not otherwise have access to AT. Phase I includes: 1) validation of context aware activity detection algorithms on PD patient data; 2) determining the extent specific activities or activity levels correlate with PD QoL; 3) using clinician feedback to identify collected data features that are useful in informing AT clinical decisions; and 4) identification of wearables to be used in the final system. Phase II includes: 1) transition of context aware activity detection and PD symptom quantification algorithms onto a smartphone and wearable chips; 2) development of a smartphone app that integrates data collection, non-motor assessment, and data-transfer to the cloud; and 3) collecting data from AT candidates in the months before and after AT is initiated to develop models that accurately identify AT candidates and when AT adjustments are needed. DiSCERN will improve therapy efficiency, expand access, and result in more patients opting for AT.