2013 Webinar Series

Technology In Clinical Trials For Parkinson’s Disease

Great Lakes NeuroTechnologies is proud to present the 2013 Webinar Series, “Technology in Clinical Trials for Parkinson’s Disease”. This series is targeted to researchers, clinicians, and product developers who are designing and implementing clinical trials for Parkinson’s disease. Whether you’re designing trials for pharma or deep brain stimulation, our team of scientific investigators will be delivering a wide variety of topics to stimulate creativity and collaboration. Each monthly, 20-minute session will share methods, sample data, and analysis techniques that will drive insight and innovation for designing Parkinson’s trials and integrating automated technology.

2013 Schedule

February 21st Thursday 12:00 to 12:30 EST Measuring Dyskinesia in Parkinson’s Clinical Trials Thomas Mera, MS Download Slides View Abstract
March 28th Thursday 12:00 to 12:30 EST Improving Sensitivity and Reliability in Motor Assessments Dustin Heldman, PhD Download Slides View Abstract
April 25th Thursday
12:00 to 12:30 EST
Motor Fluctuations: Leveraging Telemedicine for PD Trials Joseph Giuffrida, PhD View Abstract
May 23rd Thursday
12:00 to 12:30 EST
Tuning Maps for Deep Brain Stimulation Trials Thomas Mera, MS Download Slides View Abstract
June 20th Thursday
12:00 to 12:30 EST
Continuous Motor Monitoring: Implementation and Value Chris Pulliam, PhD Download Slides View Abstract
July 25th Thursday
12:00 to 12:30 EST
Finger Tapping: Does Parkinson’s Research Deserve Better? Dustin Heldman, PhD View Abstract
September 19th Thursday
12:00 to 12:30 EST
Parkinson’s Gait: Global Efficacy of DBS and Pharma Therapy Elizabeth Brokaw, PhD Download Slides View Abstract
November 21st Thursday
12:00 to 12:30 EST
Patient Acceptance of New Technology to Assess Parkinson’s Dustin Heldman, PhD Download Slides View Abstract
December 12th Thursday
12:00 to 12:30 EST
Intelligent Algorithms to Navigate DBS Response Chris Pulliam, PhD View Abstract

Abstracts

Intelligent Algorithms to Navigate DBS Response

Thursday, 12 December 2013 – 12:00 – 12:30 EST

The clinical utility of deep brain stimulation (DBS) for the treatment of movement disorders such as Parkinson’s disease (PD) has been well established. However, there is a great disparity in outcomes among DBS recipients due to varied postoperative management, particularly concerning DBS programming optimization. Many programmers have only a cursory understanding of electrophysiology and lack the time required to determine an optimal set of DBS parameters (contact, polarity, frequency, pulse width, and amplitude) out of the thousands of possible combinations. In this webinar, we will discuss the feasibility of using the Kinesia motion sensor platform and intelligent algorithms to guide outpatient DBS programming sessions. Software and algorithms have been developed for automated functional mapping of the DBS programming parameter space and outputting settings that reduce symptoms and battery usage. A system designed for use by a general practitioner or nurse rather than by a neurologist or neurophysiologist with years of experience in DBS programming could expand the accessibility of DBS for patients not located near specialized centers where access to this therapy may be hindered by concerns for available post-operative DBS adjustments.

Patient Acceptance of New Technology to Assess Parkinson’s

Thursday, 21 November 2013 – 12:00 – 12:30 EST

New computerized systems that can achieve precise measurement of motion are becoming commonplace as an outcome measure in PD clinical trials. We have previously described how the increased sensitivity and reliability afforded by objective motion sensors can reduce the number of subjects required to detect significant outcomes. However, no matter how technologically advanced any measurement system is, patients must be willing and able to embrace the technology for the benefits to be realized. In this webinar, we will discuss many of the patient considerations that went into the design of our Kinesia HomeView monitoring system. We will share the results from patient focus group discussions as well as give some real-world examples of the data that can be obtained when patients accept new technologies. Keeping the patient in mind during the design process and throughout clinical use greatly improves the user experience and increases the likelihood of patient acceptance.

Parkinson’s Gait: Global Efficacy of DBS and Pharma Therapy

Thursday, 19 September 2013 – 12:00 – 12:30 EST

Balance and gait impairments are a major component of Parkinson’s disease (PD), negatively affect community integration, and are correlated with increased fall risk. Therapeutic interventions that improve gait can significantly improve an individual’s independence and quality of life. The evaluation of gait and balance is often limited to subjective clinical measures with inadequate resolution. Motion sensors have the potential to objectively quantify gait related impairment both during standard evaluation protocols in the clinic and throughout the day during activities of daily living. A clinical study with individuals with Parkinson’s disease was completed in collaboration with University Hospitals Case Medical Center. In this study, Kinesia motion sensor technology was used to capture kinematic features. These features showed good correlation (r > 0.7) with the Unified Parkinson’s Disease Rating Scale for 6 of the 7 gait related items. Kinematic features showed significant improvement from the deep brain stimulation (DBS) off state to the DBS on state for three of the tasks (p<0.05). Additionally, the Kinesia sensors can quantify an individual’s time spent in states like sitting, standing, or walking, which provides general information about activity and general impairment levels in the home environment. This information could be useful in the evaluations of new therapies and help clinicians modulate medication and deep brain stimulation wear off effects. The Kinesia system’s potential for home evaluation of gait could be especially beneficial for tuning of DBS, which has a delayed effect on gait outcomes.

Measuring Dyskinesia in Parkinson’s Clinical Trials

Thursday, 21 February 2013 – 12:00 – 12:30 EST

Chronic use of medication (i.e. levodopa) for treating Parkinson’s disease (PD) can give rise to involuntary motor side effects known as dyskinesias. A significant number of clinical trials currently focus on new drugs and alternative PD therapies designed to minimize the severity and occurrence of dyskinesias. Traditionally, in clinic rating scale assessments and take home diaries have provided standard outcome measures. However, these methods may have limited sensitivity, reliability, and cost effectiveness. Objective monitoring at patient homes using sensors, intelligent algorithms, and broadband tablet PC’s can capture multiple dose-response cycles and distinguish between dyskinesia and other movements. A clinical study completed in collaboration with the University of Rochester captured and quantified dyskinesia severity using patient-worn motion sensor technology and developed a standardized and automated dyskinesia scoring tool highly correlated with expert clinician ratings. Subjects were instrumented with a wireless sensor unit, and data collected over typical levodopa dose-response cycles. Results demonstrated the system effectively assessed global dyskinesia severity compared to clinician ratings. When integrated with home monitoring, this approach may provide a standardized, accurate, and cost effective dyskinesia outcome measure for clinical trials.

Improving Sensitivity and Reliability in Motor Assessments

Thursday, 28 March 2013 – 12:00 – 12:30 EST

Providing the sensitivity and reliability of motor assessments required to demonstrate efficacy is an extremely challenging aspect of clinical trials for Parkinson’s disease (PD) interventions. Traditional assessments in clinical trials rely on subjective clinical rating scales, which can suffer from bias, placebo effects, limited resolution, and poor intra- and inter-rater reliability. This necessitates large numbers of subjects, which can hinder recruitment, increase costs, and extend time of the clinical study. To improve resolution and remove subjectivity from assessments, we have previously developed a motion-sensor monitoring system called Kinesia™. A clinical study was completed with the University of Cincinnati and Henry Ford Health System to determine the sensitivity and test-retest reliability of Kinesia compared to clinical ratings. Eighteen patients with PD and implanted deep brain stimulation (DBS) systems performed tasks to evaluate resting tremor, postural tremor, and bradykinesia while wearing the Kinesia wireless motion-sensor unit on the index finger. The sequence of three tasks was performed three separate times with DBS turned off and at 10 separate stimulation amplitudes as DBS provides a unique opportunity slowly modulate symptom severity. This provided a wide range of symptom severities with relatively few subjects and simulated subtle motor symptom progressions in individual subjects that might otherwise take years to observe. Each task was videoed using a digital camera for subsequent clinical rating. The reliability and sensitivity of Kinesia was comparable to the clinician ratings for rest and postural tremor, but significantly better for finger tapping speed and amplitude. This suggests that Kinesia can provide greater sensitivity and reliability than traditional clinical ratings, which in turn could decrease the number of subjects, costs, and time to detect significant changes in outcomes in clinical drug trials.

Motor Fluctuations: Leveraging Automated Home Assessments

Thursday, 25 April 2013 – 12:00 – 12:30 EST

As new Parkinson’s disease therapies emerge, clinical trials often focus on capturing patient motor fluctuations. Since motor fluctuations are associated with a worsened quality of life, it is important that they are captured accurately. A single office visit cannot capture fluctuations, so patient paper diaries have traditionally been used in clinical trials. However, these diaries can be unreliable and are not electronically time-stamped for accuracy. Objective, automated home assessments include patient-worn motion sensors and a tablet PC for providing task instructions and transmitting data and videos to the web. Automated algorithms use the motion sensor data to score motor symptoms such as tremor, bradykinesia, and dyskinesia. Leveraging automated motor assessment technology can increase sensitivity, reliability, and efficiency, providing several advantages compared to traditional clinical outcome measures in several ways. First, assessments are quantitative and electronically time-stamped when the patient completes the assessment, thereby providing a complete and accurate account of motor fluctuations. Second, there is increased sensitivity of motor symptoms in the time domain which allows complex analyses of motor fluctuations. Next, all data is hosted in a cloud-based server accessible for a secure, web-based application that permits review and analysis, which improves data accessibility. Finally, since patients can be monitored at home, patient travel burden is minimized, which may improve patient recruitment and attrition rates.

Tuning Maps for Deep Brain Stimulation Trials

Thursday, 23 May 2013 – 12:00 – 12:30 EST

Deep brain stimulation (DBS) is a treatment option typically considered in advanced stages of Parkinson’s disease (PD) as an alternative or adjunct therapy to medication. Patients must return to the clinic post-surgery for programming sessions to maximize improvement in motor symptom severity while minimizing side effects. Challenges with DBS programming include inconsistent training and experience across clinical sites and reliance on subjective rating scales. As new surgical targets and stimulation parameters are explored in clinical trials and further validated in post-market analysis, using a standardized and objective programming tool is critical for evaluating DBS therapeutic benefit and selecting optimal settings. Clinical studies completed in collaboration with the Cleveland Clinic and University of Minnesota tested PD patients during their post-surgery routine DBS programming sessions. Patients were equipped with a wireless finger-worn motion sensor while performing specific motor tasks to evaluate tremor and bradykinesia at different combinations of DBS settings. At each setting, symptom severity was rated both by a trained clinician using the Unified Parkinson’s Disease Rating Scale (UPDRS) and by the motion sensor system using mathematical models previously validated with the UPDRS. Tuning maps, or graphical displays of stimulation response, demonstrated that different PD motor symptoms responded uniquely to DBS. Tuning maps can provide key advantages in clinical trials for quantitatively evaluating motor symptom response to stimulation brain targets and parameters.

Continuous Motor Monitoring: Implementation and Value

Thursday, 20 June 2013 – 12:00 – 12:30 EST

Accurate assessment of involuntary motor symptoms (e.g. tremor and dyskinesias) in patients with movement disorders is crucial for optimizing and evaluating new therapies in clinical trials. Both classifying the symptom type and quantifying the severity in the context of daily life has important implications for clinical trials. Subjective clinical rating scales are commonly used to rate symptoms, but since this type of evaluation requires the presence of a clinician, the frequency of assessments is limited. To compensate, patients are often asked to complete diaries detailing their symptoms and side effects throughout the day. In practice, however, diaries are notoriously unreliable. Motion sensors have the potential to address these limitations by providing an objective and comprehensive evaluation of motor symptoms. Furthermore, monitoring patients continuously during daily activities captures wherever and whenever they occur. Kinesia technology, which includes quantitative motor assessment and cloud-based data transfer and reporting, was recently evaluated for continuously quantifying tremor during the performance of unconstrained activities in the home environment. In a clinical study completed in collaboration with Rush University Medical Center and Baylor College of Medicine, 20 subjects diagnosed with essential tremor wore a wireless motion sensor on the finger continuously for up to 10 hours per day on two consecutive days and completed standardized motion sensor tremor assessments at one-hour intervals to serve as checkpoints. High patient compliance was demonstrated, with most patients wearing the motion sensor for at least eight hours each day. Tremor scores from hourly checkpoints were consistent with the tremor scores during unconstrained activities that immediately followed. This suggests that a motion sensor system can accurately capture tremor severity on a continuous basis in the context of daily life and could be a useful tool to capture motor fluctuations in clinical trials.

Finger Tapping: Does Parkinson’s Research Deserve Better?

July 25th Thursday – 12:00 to 12:30 EST

While tremor is the most recognized Parkinson’s disease (PD) symptom, bradykinesia is often most troubling to the patient and is routinely used as a clinical trial endpoint. Technically, “bradykinesia” only refers to slowness of movement; however, difficulty initiating movement (akinesia) and smaller than desired movements (hypokinesia) are often grouped together as “bradykinesia”. The repetitive finger-tapping from the Unified Parkinson’s Disease Rating Scale (UPDRS) is the most commonly used method to evaluate bradykinesia in clinical trials. Patients are instructed to tap their index finger and thumb together as quickly and with as wide an excursion as possible while clinicians assess speed, amplitude, hesitations, halts, and decrementing amplitude – all with a single score. Finger-tapping is advantageous in that it easy for patients to perform and provides a standardized, known task that can be replicated at multiple study sites. However, finger-tapping as a proxy for bradykinesia does have several pitfalls. Finger-tapping is highly dependent on patient effort that may be influenced by an examiner. Additionally, combining all biomechanical features of bradykinesia in a single score dilutes the power of finding significant changes or a differential response in any one feature. Also, it is difficult to gauge weights that specific clinicians place on different bradykinesia manifestations. And although multiple studies have demonstrated high intra- and inter-rater reliability of various PD motor assessments, these results do not hold true for the sections designed to evaluate bradykinesia. Automated, computerized systems may improve the sensitivity and reliability of finger-tapping evaluations by independently quantifying the bradykinesia manifestations of speed, amplitude, and rhythm using a finger-worn motion sensor unit. Additionally, technology can be more sensitive than clinician ratings in capturing improvements of finger-tapping bradykinesia features on new therapies and may provide a more robust endpoint in PD clinical trials.