Correlation of Quantitative Motor State Assessment Using a Kinetograph and Patient Diaries in Advanced PD: Data from an Observational Study

<div><p>Introduction</p><p>Effective management and development of new treatment strategies for response fluctuations in advanced Parkinson’s disease (PD) largely depends on clinical rating instruments such as the PD home diary. The Parkinson’s kinetigraph (PKG) measures movement accelerations and analyzes the spectral power of the low frequencies of the accelerometer data. New algorithms convert each hour of continuous PKG data into one of the three motor categories used in the PD home diary, namely motor Off state and On state with and without dyskinesia.</p><p>Objective</p><p>To compare quantitative motor state assessment in fluctuating PD patients using the PKG with motor state ratings from PD home diaries.</p><p>Methods</p><p>Observational cohort study on 24 in-patients with documented motor fluctuations who completed diaries by rating motor Off, On without dyskinesia, On with dyskinesia, and asleep for every hour for 5 consecutive days. Simultaneously collected PKG data (recorded between 6 am and 10 pm) were analyzed and calibrated to the patient’s individual thresholds for Off and dyskinetic state by novel algorithms classifying the continuous accelerometer data into these motor states for every hour between 6 am and 10 pm.</p><p>Results</p><p>From a total of 2,040 hours, 1,752 hours (87.4%) were available for analyses from calibrated PKG data (7.5% sleeping time and 5.1% unclassified motor state time were excluded from analyses). Distributions of total motor state hours per day measured by PKG showed moderate-to-strong correlation to those assessed by diaries for the different motor states (Pearson’s correlations coefficients: 0.404–0.658), but inter-rating method agreements on the single-hour-level were only low-to-moderate (Cohen’s κ: 0.215–0.324).</p><p>Conclusion</p><p>The PKG has been shown to capture motor fluctuations in patients with advanced PD. The limited correlation of hour-to-hour diary and PKG recordings should be addressed in further studies.</p></div

Correlations on the total-hours-per-day-level of diary data with calibrated PKG data with respect to the 5 consecutive days of recording.

<p><b>A-D)</b> Displayed are Pearson’s correlation coefficients for correlations on the total-hours-per-day-level for each of the 5 consecutive days of recordings and for the days 1 to 4 for motor Off state <b>(A)</b>, motor On state without dyskinesia <b>(B)</b>, dyskinetic state <b>(C)</b> and for motor state switches <b>(D)</b>.</p

Comparisons of diary data with PKG results.

<p>Comparisons of diary data with PKG results.</p

Frequencies of motor states from diaries, raw PKG and calibrated PKG data.

<p><b>(A)</b> Distributions of motor states from diaries, raw PKG data and calibrated PKG data recorded over 5 days in 24 patients (120 days). Numbers above bars are total hours available (6 am to 10 pm without sleeping/PKG off time and hours with unclassified motor states). <b>(B-D)</b> Displayed are total hours per day in motor Off state (B), motor On state without dyskinesia (C) and dyskinetic state (D) as per the diaries, PKG raw and calibrated data.</p

Ancillary analyses of factors that influenced calibration.

<p><b>A)</b> Number of minutes that diaries were shifted forwards (+) or backwards (-) relative to the PKG time (see text): 12/24 required no shift in time and 9/12 of the remainder required shifting the PKG forward in time. <b>B)</b> Extent to which the threshold for “Off” was increased or decreased from 26 BKS units and 4 DKS units. Note that a reduction in 8 BKS units brings the threshold to the mean of normal subjects. It was necessary to increase the DKS threshold in most subjects. <b>C)</b> Value of the regression coefficient (Y axis) which is the difference in the median BKS (green symbols) or median DKS (red symbols) when the diary has been scored as On compared to when the diary scores were Off or dyskinetic (respectively). The bars show the median and interquartile range (IQR).</p

Data_Sheet_1_Do We Need to Rethink the Epidemiology and Healthcare Utilization of Parkinson's Disease in Germany?.pdf

<p>Epidemiological aspects of Parkinson's disease (PD), co-occurring diseases and medical healthcare utilization of PD patients are still largely elusive. Based on claims data of 3.7 million statutory insurance members in Germany in 2015 the prevalence and incidence of PD was determined. PD cases had at least one main hospital discharge diagnosis of PD, or one physician diagnosis confirmed by a subsequent or independent diagnosis or by PD medication in 2015. Prevalence of (co-)occurring diseases, mortality, and healthcare measures in PD cases and matched controls were compared. In 2015, 21,714 prevalent PD cases (standardized prevalence: 511.4/100,000 persons) and 3,541 incident PD cases (standardized incidence: 84.1/100,000 persons) were identified. Prevalence of several (co-)occurring diseases/complications, e.g., dementia (PD/controls: 39/13%), depression (45/22%), bladder dysfunction (46/22%), and diabetes (35/31%), as well as mortality (10.7/5.8%) differed between PD cases and controls. The annual healthcare utilization was increased in PD cases compared to controls, e.g., regarding mean ± SD physician contacts (15.2 ± 7.6/12.2 ± 7.3), hospitalizations (1.3 ± 1.8/0.7 ± 1.4), drug prescriptions (overall: 37.7 ± 24.2/21.7 ± 19.6; anti-PD medication: 7.4 ± 7.4/0.1 ± 0.7), assistive/therapeutic devices (47/30%), and therapeutic remedies (57/16%). The standardized prevalence and incidence of PD in Germany as well as mortality in PD may be substantially higher than reported previously. While frequently diagnosed with co-occurring diseases/complications, such as dementia, depression, bladder dysfunction and diabetes, the degree of healthcare utilization shows large variability between PD patients. These findings encourage a rethinking of the epidemiology and healthcare utilization in PD, at least in Germany. Longitudinal studies of insurance claims data should further investigate the individual and epidemiological progression and healthcare demands in PD.</p