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Inventory of real world data sources in Parkinson’s disease

BMC NeurologyBMC series – open, inclusive and trusted201717:213

https://doi.org/10.1186/s12883-017-0985-0

Received: 16 February 2017

Accepted: 22 November 2017

Published: 8 December 2017

Abstract

Background

Real world data have an important role to play in the evaluation of epidemiology and burden of disease; and in assisting health-care decision-makers, especially related to coverage and payment decisions. However, there is currently no overview of the existing longitudinal real world data sources in Parkinson’s disease (PD) in the USA. Such an assessment can be very helpful, to support a future effort to harmonize real world data collection and use the available resources in an optimal way.

Methods

The objective of this comprehensive literature review is to systematically identify and describe the longitudinal, real world data sources in PD in the USA, and to provide a summary of their measurements (categorized into 8 main dimensions: motor and neurological functions, cognition, psychiatry, activities of daily living, sleep, quality of life, autonomic symptoms and other). The literature search was performed using MEDLINE, EMBASE and internet key word search.

Results

Of the 53 data sources identified between May and August 2016, 16 were still ongoing. Current medications (81%) and comorbidities (79%) were frequently collected, in comparison to medical imaging (36%), genetic information (30%), caregiver burden (11%) and healthcare costs (2%). Many different measurements (n = 108) were performed and an interesting variability among used measurements was revealed.

Conclusions

Many longitudinal real world data sources on PD exist. Different types of measurements have been performed over time. To allow comparison and pooling of these multiple data sources, it will be essential to harmonize practices in terms of types of measurements.

Keywords

Parkinson diseaseRating scalesLongitudinalCohort studiesReal-world

Background

Parkinson’s disease (PD) is a progressive neurodegenerative disease affecting approximately 630,000 people in the USA and for which no disease-modifying therapy is currently available. With the ever growing ageing population, this number is projected to almost double to 1.1 million by 2030 [1].

The Food and Drug Administration (FDA) defines “real world data” as “all data collected from sources outside of traditional clinical trials” and “real world evidence” as “all evidence derived from aggregation and analysis of real world data” [2]. Such real world evidence reflecting disease progression, treatments and outcomes under conditions of routine clinical practice is a very important resource. It can take a pivotal role to improve the understanding of the underlying disease process [3], optimize currently available therapies and develop new treatment strategies [2, 4].

Although the burden of PD and the interest of real world data are well-known [5, 6], there has not been a literature review to present the overview of longitudinal, real world studies conducted in the USA on PD patients.

There is a need for a comprehensive review to create an integrated view and assist investigators and clinicians to optimize the measurements that best match with their objectives and the already existing data sources [4, 7]. Such an assessment can be very helpful, to support a future effort to harmonize real world data collection and use the available resources in an optimal way.

The objective of this comprehensive literature review is to systematically identify and describe the longitudinal, real world data sources in PD, and to provide a summary of the key characteristics and the measurements assessed in real world studies, as a part of an effort to mobilize a harmonization process, similar to the one that already takes place in Europe.

Methods

Search strategy and literature sources

The search was performed on ProQuest. It was based in MEDLINE on Pubmed, in EMBASE and internet key word search between May and August 2016. Related MeSH, EMTREE and key terms were combined. Articles from peer-reviewed journals, conference abstracts and reviews were screened (AT). The search equation terms are detailed in Appendix 1.

Study screening and selection

We included all studies including patients with a diagnosis of PD based on real world data. We restricted inclusion to only longitudinal, observational cohort studies and registries. The setting was restricted to the USA and the timing of publication in the last 10 years (2006-2016). Cohorts or registries without any publication in the last 10 years were considered as outdated. Exclusion criteria were based on population characteristics: Other diagnosis (e.g. Wolff-Parkinson-White disease or only Parkinsonian syndromes), autopsy data, and studies not focused on patients (e.g. focused on physicians). Moreover, studies without American patients or non-longitudinal studies, such as case-control, were also excluded. Only one main exclusion criterion was reported in the flow chart per excluded study (Fig. 1). No limits were applied for language.
Fig. 1

Flowchart

Data extraction

In a first step, when a publication allowed the identification of a data source of interest, the detailed information available in the publication was extracted. Information on design and setting, funding, population selection, follow-up and measurements were recorded. This was supplemented and updated via information found with an internet search of the study website, registration sites such as clinicaltrials.gov and investigators / funders’ websites. The list of all information captured is available in Appendix 2.

In a second step, a classification of measurements was performed for the following dimensions: motor and neurological function, cognition, psychiatric symptoms, activities of daily living, sleep quality, quality of life, autonomic symptoms and other. The “other” dimension gathers some known PD symptoms such as olfaction [8] not included in the previous main dimensions and more general information such as caregivers’ burden measurements. Some dimensions were subdivided in sub dimensions due to their complexity and variety (e.g. Motor and neurological symptoms is sub divided into 4 sub dimensions: global, gait and balance, fine movement and other). This classification was based on the literature [4] with one adaptation: as very few sensory markers were identified, they were gathered in the “other” category.

Data analysis

Data source characteristics were described globally. To address the variability of sources, the description was also performed according to four main characteristics: the completion status (ongoing vs completed); the study population (Parkinson specific data sources vs “generic” data sources including both Parkinsonian patients and patients of other diagnostics); the categories of studies (investigate for motor symptoms, non-motor symptoms, biomarkers, genetics or mixed); and the country (US only vs international sources). Descriptive statistics were reported as absolute frequency and percentages.

Results

Of 1463 records screened, 84% were excluded based on title and abstract, and 7% after review of the full-text (Fig. 1). The most frequent exclusion criterion was that studies were not longitudinal. Only 133 (9%) were included in the qualitative analysis. Of these 133 studies, data from 53 different data sources were extracted [961]. Only one registry was included with 52 cohorts.

Longitudinal real world sources (Table 1)

Forty-two sources (79%) were only in the USA. Three of the 11 international sources were only in North America while the other eight included patients in the USA and Europe, and two also included Asia. Most of the sources included less than 500 PD patients (79%) for more than 5 years (51%). Although most of the sources included information about current medications (81%) and comorbidities (79%); only few collected information on medical imaging (36%), genetics (30%), caregiver’ burden (11%) and healthcare costs (2%).
Table 1

Overview of data sources characteristics (n = 53)

Characteristics

Included

Status

Country

Study population

All (n = 53)

Ongoing (n = 16)

Completed (n = 37)

USA (n = 42)

International (n = 11)

Parkinson cohort

(n = 25)

“Generic” cohort

(n = 28)

Size (number of Parkinsonian patients)

 

0-500

42 (79)

11 (69)

31 (84)

37 (88)

5 (45)

22 (88)

20 (71)

500-1000

7 (13)

4 (25)

3 (8)

3 (7)

4 (36)

3 (12)

4 (14)

>1000

4 (8)

1 (6)

3 (8)

2 (5)

2 (18)

0 (0)

4 (14)

Duration of follow-up (years)

 

<2

6 (11)

0 (0)

6 (16)

4 (10)

2 (18)

4 (16)

2 (7)

2-5

20 (38)

4 (25)

16 (43)

16 (38)

4 (36)

13 (52)

7 (25)

≥5

27 (51)

12 (75)

15 (41)

22 (52)

5 (45)

8 (32)

19 (68)

Dimensions assessed

 

Motor and neurological

46 (87)

12 (75)

34 (92)

36 (86)

10 (91)

25 (100)

21 (75)

Cognition

41 (77)

13 (81)

28 (76)

36 (86)

5 (45)

17 (68)

24 (86)

Psychiatric symptoms

38 (72)

10 (63)

28 (76)

30 (71)

8 (73)

19 (76)

17 (61)

Activities of daily living

22 (42)

6 (38)

16 (43)

15 (36)

7 (64)

12 (48)

10 (36)

Sleep quality

11 (21)

4 (25)

7 (19)

5 (12)

6 (55)

2 (8)

9 (32)

Quality of life

9 (17)

4 (25)

5 (14)

5 (12)

4 (36)

6 (24)

3 (11)

Autonomic symptoms

7 (13)

4 (25)

3 (8)

3 (7)

4 (36)

0 (0)

7 (25)

Other

20 (38)

9 (56)

11 (30)

13 (31)

7 (64)

8 (32)

12 (43)

Other assessments

 

Current medications

43 (81)

13 (81)

30 (81)

32 (76)

11 (100)

22 (88)

21 (75)

Comorbidities

42 (79)

14 (88)

28 (76)

31 (74)

11 (100)

20 (80)

22 (79)

Medical imaging

19 (36)

6 (40)

13 (34)

11 (26)

8 (73)

6 (24)

13 (46)

Genetics

16 (30)

6 (38)

10 (27)

10 (24)

6 (55)

3 (12)

13 (46)

Caregiver burden

6 (11)

4 (27)

2 (5)

5 (12)

1 (9)

4 (16)

2 (7)

Healthcare costs

1 (2)

1 (7)

0 (0)

0 (0)

1 (9)

1 (4)

0 (0)

Data are shown as absolute frequency (percentage)

Among the 53 sources, 16 (30%) are still ongoing. There has been an increased availability of genetic information (38% vs 27%) and caregivers’ burden data (27% vs 5%) in ongoing versus completed sources, respectively. Moreover, there has been a trend toward larger inclusions and longer durations: comparing ongoing versus completed sources, 31% vs 16% included more than 500 patients and 75% vs 41% have a duration of more than 5 years.

Likewise, US sources were smaller and shorter than international sources (88% vs 45% included less than 500 PD patients, and 52% vs 45% have a duration of more than 5 years). US sources reported more caregiver burden data than international sources (12% vs 9%) but less frequently the other assessments such as medical imaging (26% vs 73%) or genetic information (24% vs 55%).

Sources including only Parkinsonian patients were smaller (12% vs 28% included more than 500 patients) and shorter (32% vs 68% had a duration of more than 5 years) than the “generic” cohorts. Medical imaging (24% vs 46%) and genetics (12% vs 46%) were less assessed in Parkinson’s specific than in “generic” cohorts.

The 53 data sources have different objectives. Mainly the sources investigated as their primary objective: non-motor symptoms (32%), then biomarkers (21%), motor symptoms (15%) and genetics (4%). Fifteen sources (28%) investigated several of these points as first objective. The sources investigating the biomarkers as primary objective were large and recent with four sources still ongoing and four sources begun in the last 5 years. In contrast, the sources investigating the motor symptoms as primary objective were small, all with less than 500 patients and with very frequent assessment, on average twice a year.

Measurements in real world studies in PD

The name of each included data source with its main characteristics (Table 2) and its measurements (Table 3) are presented individually. A large number of measurements (n = 108) was identified through this literature review and each of the 53 sources had its own unique range of measurements (Table 4). Most of the measurements were cited only once or twice. The distribution of the number of measurements over the different dimensions was not equal with only 3 different to assess autonomic symptoms and 43 to assess cognition.
Table 2

Overview of data sources characteristics listed in alphabetic order (n = 53)

Nb

Study

Acronym

Individuals included

Follow-up duration (y)

Planned follow-up

Main inclusion criteria

1

A Longitudinal Observational Follow-up of the PRECEPT Study Cohorta

PostCEPT

537

4

 

Post-RCT; under dopaminergic therapy

2

Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson’s disease

 

15

4

Every 2 years

Hemi parkinsonism

3

Amyloid is linked to cognitive decline in patients with Parkinson disease without dementia

 

46

5

Annually

 

4

Arizona Study of Aging and Neurodegenerative Disease

AZSAND

3000

ongoing

  

5

Ashkenazi Jewish LRRK2 consortium cohort

LRRK2

2611

1.5

Every 12-18 months

Ashkenazi Jewish

6

Baltimore Longitudinal Study of Aging

BLSA

10,000?

ongoing

Every few years for life

Healthy

7

Boston university medical center - University of Alabama Birmingham - Washington University in Saint Louis School of medicine

 

80

2

 

>40 years

8

Central Control of Mobility in Aging

CCMA

439

ongoing

Annually

Elderly (>65 years); non demented

9

Cerebral glucose metabolic features of Parkinson disease and incident dementia: longitudinal study

 

50

4

Annually

Levodopa treatment

10

Charting the progression of disability in Parkinson disease

 

171

2

Every 6 months

>40 years; mild to moderate Parkinson’s disease

11

Clinical course in Parkinson’s disease with elevated homocysteine

 

97

2

Every 2 years

35-90 years without brain surgery or neurologic/psychiatric comorbidity

12

Clinical Research in Neurology (CRIN) - Emory center

CRIN

3581

15

  

13

Comparative utility of the BESTest; mini-BESTest; and brief-BESTest for predicting falls in individuals with Parkinson disease: a cohort study

BESTest

80

1

Every 6 months

Without neuropsychiatric comorbidities

14

Comparison of the Agonist Pramipexole With Levodopa on Motor Complications of Parkinson’s Diseasea

CALM-PD follow-up

301

2

Annually

Post-RCT; under dopaminergic therapy; diagnostic < 7 years

15

Contursi kindred

CONTURSI

210

?

  

16

Deprenyl and Tocopherol Antioxidative Therapy of Parkinsonisma

DATATOP

403

6

Every 3 months

Early phase; postRCT; 30-79 years

17

Depression in Parkinson’s disease

 

685

3.9

Annually

 

18

Dopamine agonist withdrawal syndrome in Parkinson diseasea

DAWS

93

0.25

Annually

Non demented

19

Einstein Aging Study (Bronx Aging Study)

EAS

791

ongoing

Every 12 to 18 months

Elderly (>70 years)

20

Emergence and evolution of social self-management of Parkinson’s disease

 

120

3

Every 6 months

Non demented

21

Hallucinations and sleep disorders in PD: ten-year prospective longitudinal study

 

89

10

0; 6 months; 18 months; 4 years; 6 years; 10 years

24-h caregiver; without neuroleptic treatment; without some comorbidities

22

Harvard Alumni Health Study

 

500,002

77

1962; 1966; 1972; 1988; 1993

Harvard students

23

Health Professionals Follow-up Study

HPFS

51,529

ongoing

Biannually

Men; healthy; 40-75 years

24

Honolulu Asia Aging Study

HAAS

3741

15

3 times between 1994 and 2001

Elderly Japanese-American men

25

Longitudinal study of normal cognition in Parkinson disease

 

141

6

Biannual for 4 years and annual after

Normal cognition at baseline

26

Long-term outcomes of bilateral subthalamic nucleus stimulation in patients with advanced Parkinson’s diseasea

 

33

2

0 –3 –6 –12 –18 – 24 months

Advanced phase with deep brain stimulation

27

Loss of ability to work and ability to live independently in Parkinson’s disease

 

495

10

  

28

Major life events and development of major depression in Parkinson’s disease patients

PEG study

221

4

Annually

New onset (within 3 years)

29

Mayo Clinic cohort study of Personality and Aging (including Rochester Epidemiology project)

 

7216

29.2

Historically for life

20-69 years

30

Mayo clinic study of aging (Olmsted county resident) - Rochester Epidemiology project indexing system

MCSA

2739

ongoing

  

31

Molecular Epidemiology of Parkinson’s Disease

MEPD

1600

ongoing

 

>40 years

32

Mood and motor trajectories in Parkinson’s disease: multivariate latent growth curve modeling

 

186

1.5

6 months; 18 months

 

33

Mood and Subthalamic Nucleus Deep Brain Stimulationa

MOST

91

1

 

Deep brain stimulation eligible; not demented

34

Morris K Udall Parkinson’s Disease Research Center of Excellence cohort - Veteran affair

Udall

314

ongoing

 

Elderly (>60 years)

35

National Parkinson Foundation Quality Improvement Initiative

NPF-QII

10,000

on going

  

36

NeuroGenetics Research Consortium

NGRC

3072

>10

  

37

Nurses’ Health Study

NHS

280,000

ongoing

Every 2 years

Women; healthy; 19-51 years

38

Oxford Parkinson’s Disease Centre

OPDC

1500

1.5

18 months

 

39

Parkinson’s Associated Risk Study

PARS

10,000

ongoing

 

Elderly (>60 years)

40

Parkinson’s Disease Biomarkers Program

PDBP

1436

ongoing

 

Evidence of response to dopaminergic medication

41

Parkinson’s Disease Research Education and Clinical Center - Parkinson’s Genetic Research Study

PADRECCS - PaGeR

1880

ongoing

  

42

Parkinson’s disease: increased motor network activity in the absence of movement

NMRP

12

4.4

Every 2 years

Non demented; tremor-dominant clinical manifestations; without some comorbidities

43

Parkinson’s Progression bioMarkers Initiative

PPMI

748

ongoing

Every 3 months the first year then every 6 months

Untreated recently diagnosed

44

Prospective cohort study of impulse control disorders in Parkinson’s disease

ICD-PD

164

4

 

Non demented

45

Rate of 6-18Ffluorodopa uptake decline in striatal subregions in Parkinson’s disease

 

37

4

Every 1 to 2 years

 

46

Religious Order Study

ROS

>1100

>7

Annually

Elderly; religious clergy

47

Rush Memory and Aging Project

RMAP

1556

5

Annually

Elderly without know dementia

48

Study of Osteoporotic Fractures (SOF) Research Group

SOF

9704

>6

Tri-annually

Women; Elderly (>65 years)

49

The effect of age of onset of PD on risk of dementia

 

440

4

Annually

Elderly (>65 years)

50

University of California Los Angeles Center for Genes and Environmental in Parkinson’s Disease

UCLA CGEP

363

5

 

Diagnostic >3 years

51

University of Miami Brain Endowment Bank

UM/BEB

150

ongoing

Annually

Consent to donate brain

52

UPDRS activity of daily living score as a marker of Parkinson’s disease progression

 

162

6

Every 2 years

 

53

Washington Heights-Inwood Columbia Aging

WHICAP

2776

3.7

Annually

Elderly (>65 years)

Post-RCT = Open label extension after a Randomized Controlled Trial

aTreatment directed data sources

Table 3

Overview of data source measurements and of the number of evaluations or assessments applied (n = 53)

Nb

Study

Motor and neurological

Cognition

Psychiatry

Activities of daily living

Sleep

Quality of life

Autonomic

Other

1

A Longitudinal Observational Follow-up of the PRECEPT Study Cohort

3

4

3

1

0

0

0

0

2

Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson’s disease

2

0

0

0

0

0

0

0

3

Amyloid is linked to cognitive decline in patients with Parkinson disease without dementia

2

14

1

0

0

0

0

0

4

Arizona Study of Aging and Neurodegenerative Disease

4

12

3

0

1

0

1

1

5

Ashkenazi Jewish LRRK2 consortium cohort

3

2

2

2

1

0

1

1

6

Baltimore Longitudinal Study of Aging

0

2

3

0

0

0

0

0

7

Boston university medical center - University of Alabama Birmingham - Washington University in Saint Louis School of medicine

9

1

1

0

0

1

0

0

8

Central Control of Mobility in Aging

2

1

1

0

0

0

0

0

9

Cerebral glucose metabolic features of Parkinson disease and incident dementia: longitudinal study

1

6

0

0

0

0

0

0

10

Charting the progression of disability in parkinson disease

9

1

1

0

0

1

0

0

11

Clinical course in Parkinson’s disease with elevated homocysteine

1

9

1

1

0

0

0

0

12

Clinical Research in Neurology (CRIN) - Emory center

0

1

0

0

0

0

0

0

13

Comparative utility of the BESTest; mini-BESTest; and brief-BESTest for predicting falls in individuals with Parkinson disease: a cohort study

5

0

0

0

0

0

0

0

14

Comparison of the Agonist Pramipexole With Levodopa on Motor Complications of Parkinson’s Disease

3

1

2

2

1

3

0

0

15

Contursi kindred

1

1

1

1

1

0

1

1

16

Deprenyl and Tocopherol Antioxidative Therapy of Parkinsonism

2

5

0

0

0

0

0

0

17

Depression in Parkinson’s disease

2

0

1

1

0

0

0

0

18

Dopamine agonist withdrawal syndrome in parkinson disease

2

1

4

1

0

1

0

0

19

Einstein Aging Study (Bronx Aging Study)

2

11

1

0

0

0

0

0

20

Emergence and evolution of social self-management of Parkinson’s disease

2

2

1

1

0

4

0

0

21

Hallucinations and sleep disorders in PD: ten-year prospective longitudinal study

2

1

1

0

1

0

0

0

22

Harvard Alumni Health Study

0

0

0

0

0

0

0

0

23

Health Professionals Follow-up Study

0

0

0

0

0

0

0

0

24

Honolulu Asia Aging Study

2

4

2

0

1

0

1

1

25

Longitudinal study of normal cognition in Parkinson disease

2

6

2

1

0

0

0

0

26

Long-term outcomes of bilateral subthalamic nucleus stimulation in patients with advanced Parkinson’s disease

2

2

2

2

0

0

0

0

27

Loss of ability to work and ability to live independently in Parkinson’s disease

2

0

1

1

0

0

0

0

28

Major life events and development of major depression in Parkinson’s disease patients

1

2

2

0

0

0

0

0

29

Mayo Clinic cohort study of Personality and Aging (including Rochester Epidemiology project)

0

0

4

0

0

0

0

0

30

Mayo clinic study of aging (Olmsted county resident) - Rochester Epidemiology project indexing system

1

10

3

0

1

0

1

1

31

Molecular Epidemiology of Parkinson’s Disease

1

3

0

0

0

0

0

0

32

Mood and motor trajectories in Parkinson’s disease: multivariate latent growth curve modeling

1

0

2

0

0

0

0

0

33

Mood and Subthalamic Nucleus Deep Brain Stimulation

2

0

7

0

0

0

0

0

34

Morris K Udall Parkinson’s Disease Research Center of Excellence cohort - Veteran affair

2

3

2

1

0

1

0

1

35

National Parkinson Foundation Quality Improvement Initiative

3

2

0

0

0

1

0

1

36

NeuroGenetics Research Consortium

1

1

1

0

0

0

0

0

37

Nurses’ Health Study

0

5

0

0

0

0

0

0

38

Oxford Parkinson’s Disease Centre

6

3

2

1

2

1

0

2

39

Parkinson’s Associated Risk Study

0

0

2

0

0

0

0

1

40

Parkinson’s Disease Biomarkers Program

4

3

6

1

6

5

1

3

41

Parkinson’s Disease Research Education and Clinical Center - Parkinson’s Genetic Research Study

3

1

0

1

0

0

0

0

42

Parkinson’s disease: increased motor network activity in the absence of movement

2

1

0

0

0

0

0

0

43

Parkinson’s progression biomarkers initiative

1

5

4

2

2

0

1

2

44

Prospective cohort study of impulse control disorders in Parkinson’s disease

2

1

2

1

0

0

0

0

45

Rate of 6-18Ffluorodopa uptake decline in striatal subregions in Parkinson’s disease

2

1

0

0

0

0

0

0

46

Religious Order Study

6

11

4

1

0

0

0

0

47

Rush Memory and Aging Project

5

1

3

1

1

0

0

2

48

Study of Osteoporotic Fractures (SOF) Research Group

2

1

1

0

0

0

0

2

49

The effect of age of onset of PD on risk of dementia

1

6

1

0

0

0

0

0

50

University of California Los Angeles Center for Genes and Environmental in Parkinson’s Disease

2

1

1

0

0

0

0

0

51

University of Miami Brain Endowment Bank

1

0

0

1

0

0

0

2

52

UPDRS activity of daily living score as a marker of Parkinson’s disease progression

1

0

1

1

0

0

0

0

53

Washington Heights-Inwood Columbia Aging

1

6

0

1

0

0

0

0

Table 4

Measurements classification and use in data sources (n = 108)

Dimension

Measurement acronym

Measurement full name

Data sources (number and numbering)

Motor and neurological (n = 46)

Global

H&Y

Hoehn and Yahr

(n = 30) °1,2,3,4,5,7,9,10,13,14,16,17,18,20,21,25,26,27,31,33,34,35,38,40,41,42,44,45,50,51

UPDRS-III

Unified Parkinson’s Disease Rating Scale - motor examination

(n = 41) °1,2,3,4,5,7,8,10,11,13,14,16,17,18,19,20,21,24,25,26,27,28,30,32,33,34,35,36,38,40,41,42,43,44,45,46,47,49,50,52,53

UPDRS-IV

Unified Parkinson’s Disease Rating Scale - motor complications

(n = 2) n°1,14

Gait and balance

 

Berg balance test

(n = 2) n°7,10

 

Flamingo test

(n = 1) n°38

FGA

Functional Gait Assessment

(n = 2) n°7,10

FOGQ

Freezing of gait questionnaire

(n = 2) n°7,10

 

Gait speed

(n = 4) n°7,8,10,46

PIGD

Postural Instability / Gait Difficulty scale

(n = 2) n°5,40

 

Tandem gait

(n = 1) n°48

TUG

Time Up and Go test

(n = 6) n°7,10,35,38,40,47

 

Walk test

(n = 5) n°7,10,46,47,48

Fine movement

 

Finger tapping

(n = 3) n°4,46,47

 

Purdue pegboard test

(n = 6) n°4,7,10,38,46,47

 

Reaction time

(n = 1) n°24

 

Unknown

(n = 1) n°15

Cognition (n = 41)

Global

ACE

Addenbrooke’s Cognitive Examination

(n = 1) n°40

AD-8

Ascertian Dementia 8-item Informant

(n = 1) n°31

BDRS

Blessed Dementia Rating Scale

(n = 2) n°19,53

CAMCOG

Cambridge Cognitive Assessment

(n = 1) n°49

CASI

Cognitive Abilities Screening Instrument

(n = 1) n°24

CDR

Clinical Dementia Rating scale

(n = 5) n°3,4,6,19,30,53

 

Clock drawing test

(n = 1) n°4

DRS2

Dementia Rating Scale 2

(n = 6) n°4,19,25,26,34,53

HDS

Hasegawa Dementia Rating Scale

(n = 1) n°24

MDRS

Mattis Dementia Rating Scale

(n = 2) n°4,26

MMSE

Mini Mental State Examination

(n = 30) °1,3,4,5,7,9,10,11,12,14,15,16,18,20,21,24,26,28,31,34,36,37,38,42,44,45,46,47,48,50

MoCA

Montreal Cognitive Assessment

(n = 9) n°1,4,5,20,34,38,40,41,43

IQCODE

Informant Questionnaire on Cognitive Decline in Elderly

(n = 1) n°24

SPMSQ

Short Portable Mental Status Questionnaire

(n = 1) n°40

TICS-M

Telephone Interview Cognitive Status Modified

(n = 2) n°31,37

Attention/ Working memory

 

Digit span

(n = 6) n°3,4,11,30,37,46

 

STROOP test

(n = 2) n°4,11

Executive function

 

Comprehension

(n = 2) n°28,49

RBANS

Repeatable Battery for Assessment of Neuropsychological Status

(n = 1) n°8

 

Symbol digit

(n = 3) n°16,43,46

 

Trail Making Test

(n = 4) n°3,4,19,30

 

Verbal fluency

(n = 12) n°3,9,11,19,25,30,35,37,38,43,46,49

Language

BNT

Boston Naming Test

(n = 5) n°3,25,30,37,46

COWA

Controlled Oral Word Association

(n = 4) n°1,3,4,11

FAS

Letter-Number Sequencing and Phonemic verbal fluency

(n = 2) n°11,25

 

Naming

(n = 1) n°49

NART

American National Adult Reading test

(n = 2) n°3,46

WAIS

Wechlser Adult Intelligence Scale

(n = 6) n°3,4,9,11,19,30

Memory

BIMC

Blessed Information Memory Concentration

(n = 2) n°6,19

FCSRT

Free and Cue Selective Reminding Test

(n = 2) n°3,19

FOME

Fuld Object Memory Evaluation

(n = 1) n°19

HVLT

Hopkins Verbal Learning test

(n = 3) n°11,25,43

 

Memory

(n = 5) n°3,16,35,46,53

RAVLT

Rey auditory verbal learning test

(n = 3) n°1,4,30

 

Recall

(n = 2) n°46,49

WMS

Wechsler Memory Scale

(n = 2) n°9,30

Visual-spatial

BVRT

Benton Visual Retention Test

(n = 1) n°9

CPM

Raven’s coloured progressive matrices

(n = 2) n°19,46

JLO

Benton Judgement Line Orientation

(n = 4) n°4,25,43,46

 

Orientation

(n = 1) n°53

PARR

Picture Arrangement subtest

(n = 1) n°9

ROCF

Rey-Osterrieth Complex Figure test recall

(n = 1) n°11

 

Visual attention

(n = 1) n°19

 

Unknown

(n = 1) n°15

Psychiatric symptoms (n = 38)

 

Depression / Anxiety

AS

Apathy Evaluation Scale

(n = 3) n°4,32,33

BAI

Beck Anxiety Inventory

(n = 4) n°18,30,33,44

BDI

Beck Depression Inventory

(n = 9) n°5,11,18,26,30,32,33,36,44

CESD-10

Center for Epidemiological Studies Depression Scale

(n = 3) n°24,39,47

GDS

Geriatric Depression Screening scale

(n = 17) n°1,3,4,5,7,8,10,14,20,25,26,28,34,40,43,48,50

HAM-A

Hamilton Anxiety Rating Scale

(n = 2) n°33,40

HDRS

Hamilton Depression Rating Scale

(n = 3) n°4,15,33

Leeds

Leeds anxiety and depression scale

(n = 1) n°38

SCID

Structured Clinical Interview - Depression

(n = 2) n°28,40

STAI

State Trait Anxiety Inventory

(n = 4) n°18,24,39,43

UPDRS-I

Unified Parkinson’s Disease Rating Scale - mentation behavior and mood

(n = 7) n°1,14,17,25,27,43,52

ZUNG

Zung depression scale

(n = 1) n°19

TOC

OCI-R

Obsessive-Compulsive Inventory – Revised

(n = 1) n°18

QUIP

Questionnaire for impulsive-compulsive disorders in parkinson’s disease-rating scale

(n = 2) n°40,43

YBOCS

Yale-Brown obsessive-compulsive scale

(n = 1) n°33

Other

CoNeg

composite negative score

(n = 1) n°29

MMPI

Multiphasic Personality Inventory

(n = 1) n°29

NPI

NeuroPsychiatric Inventory questionnaire

(n = 3) n°1,34,47

QABB

Questionnaire About Buying Behaviour

(n = 1) n°40

Rush

Rush Hallucination Inventory

(n = 1) n°21

SCS

Sexual Compulsivity Scale

(n = 1) n°40

YMRS

Young Mania Rating Scale

(n = 1) n°33

 

Unknown

(n = 4) n°6,15,46,49

Activities of daily living (n = 22)

 

ACS

Activity Card Sort

(n = 1) n°20

ADCS-ADL

Alzheimer’s Disease Cooperative Study ADL Inventory

(n = 1) n°25

IADL

Katz Instrumental Activity of Daily Living

(n = 2) n°46,47

S&E

Schwab & England activities of daily living scale

(n = 10) n°5,14,18,26,34,38,41,43,44,53

UPDRS-II

Unified Parkinson’s Disease Rating Scale - self-evaluation of the activities of daily living

(n = 9) n°1,5,11,14,26,27,40,43,52

 

Unknown

(n = 3) n°15,17,51

Sleep quality (n = 11)

  

Actigraphy

(n = 1) n°47

ESS

Epworth Sleepiness Scale

(n = 4) n°5,14,38,43

FSS

Fatigue Severity Scale

(n = 1) n°40

ISI

Insomnia Severity Index

(n = 1) n°40

MSQ

Mayo clinic Sleep Questionnaire

(n = 2) n°4,30

PDSS

Parkinson’s disease sleep scale

(n = 1) n°40

PSQI

Pittsburg Sleep Quality Index

(n = 2) n°21,40

RBDSQ

REM Sleep Behaviour Disorder Screening Questionnaire

(n = 2) n°38,43

SA-SDQ

Sleep Apnea Scale of Sleep Disorders Questionnaire

(n = 1) n°40

SSS

Stanford Sleepiness Scale

(n = 1) n°40

 

Unknown

(n = 2) n°15,24

Quality of life (n = 9)

 

EQ-5D

Euro Quality of Life 5 Dimension questionnaire

(n = 2) n°14,38

Neuro-QOL

Quality of Life in Neurological Disorders

(n = 1) n°34

NHP

Nottingham Health Profile

(n = 1) n°20

PDQUALIF

Parkinson’s Disease Quality of Life Scale

(n = 3) n°14,18,40

PDQ-39

39-item Parkinson’s disease quality of life

(n = 5) n°7,10,20,35,40

PIMS

Parkinson’s Impact Scale

(n = 1) n°40

SF-12

The 12 item Short Form health survey

(n = 2) n°14,20

SF-36

The 36 item Short Form health survey

(n = 1) n°40

SWAL-QOL

Swallow-specific quality of life

(n = 1) n°40

Autonomic symptoms (n = 7)

  

Bowel movement

(n = 1) n°24

COMPASS

Composite autonomic symptom Scale

(n = 1) n°40

SCOPA-AUT

Scales for outcomes of Parkinson’s Disease – autonomic symptoms

(n = 3) n°4,5,43

 

Unknown

(n = 2) n°15,30

Other (n = 20)

Olfaction

Brief-SIT

Brief Smell Identification Test

(n = 2) n°24,47

 

16-item sniffin’ Sticks Odour Identification test

(n = 1) n°38

UPSIT

University of Pennsylvania Smell Identification Test

(n = 6) n°1,4,5,34,39,43

Restless legs syndrome

CH-RLSQ

Cambridge-Hopkins Restless Legs Syndrome Diagnostic Questionnaire

(n = 1) n°40

IRLSSG

Instrument for the Assessment of Restless Legs Syndrome Severity

(n = 1) n°4

Caregiver

CSI

caregiver strain index

(n = 1) n°35

 

deJong-Gierveld Loneliness Scale

(n = 1) n°47

MCSI

Multidimensional Caregiver Strain Index

(n = 1) n°35

 

Caregiver interview

(n = 1) n°21

Other

 

Agonal state questionnaire

(n = 1) n°51

CGI

Clinical Global Impression scale

(n = 1) n°38

CIRS

Chronic Illness Resource Survey

(n = 1) n°20

GHS

Global Health Score

(n = 1) n°8

GIS

Global Impression Scale

(n = 1) n°51

Howard-Dohlman device

(n = 1) n°48

MNA

Mini Nutritional Assessment

(n = 1) n°40

MOS

Medical outcome study

(n = 1) n°20

MSSSS

Medical Outcomes Study Social Support Scale

(n = 1) n°28

Pain

(n = 1) n°40

PASE

Physical Activity Scale for the Elderly

(n = 3) n°7,10,43

SRRS

Social Readjustment Rating scale

(n = 1) n°28

SSCI

Stigma Scale for Chronic Illness

(n = 1) n°20

Tremor rating

(n = 1) n°4

Visual acuity

(n = 1) n°48

Unknown

(n = 1) n°15

Most sources assessed motor and neurological functions (87%), cognition (77%) and psychiatric symptoms (72%). Activity level (42%), sleep quality (21%), quality of life (17%) and autonomic symptoms (13%) were reported to a lesser extent. The most commonly measurements used to assess motor and neurological symptoms were the Unified Parkinson’s Disease Rating Scale part III (UPDRS-III, 77% of included data sources) and the Hoehn and Yahr scale (H&Y, 57% of included data sources)(Table 4). To evaluate the cognitive impairment, the Mini Mental State Examination (MMSE, 57%) was the most frequent. Those most frequently used to assess psychiatric symptoms were the Geriatric Depression Scale (GDS, 32%) and Beck Depression Inventory (BDI, 15%). For the other dimensions, the most commonly used measurements were: the Epworth Sleepiness Scale (ESS, 8%, for sleep), the Schwab and England (S&E, 19%, for activities of daily living), the 39-item Parkinson’s disease Quality of life (PDQ-39, 9%, for the quality of life) and the autonomic part of the Scales for outcomes of Parkinson’s disease (SCOPA-AUT, 6%, for autonomic symptoms). In absolute frequency, the use of ESS, PDQ-39 and SCOPA-AUT is very low, even if they were the most frequently used measurements in their dimension.

The analysis reveals some interesting differences between sources on the number of measurements applied by dimension. Some sources evaluate only one dimension (source n°13) when others evaluate seven dimensions (source n°43). Completed sources have more frequent measurements of motor and neurological symptoms (92% vs 75%), psychiatric symptoms (76% vs 63%) and activities of daily living (43% vs 38%) than ongoing sources. US sources evaluate more frequently the cognitive impairment then international sources (86% vs 45%) but less frequently all the other dimensions. “Generic” sources evaluate three dimensions more frequently than specific sources including only Parkinsonian patients: cognition (86% vs 68%), sleep (32% vs 8%) and autonomic symptoms (25% vs 0%).

Lastly, the frequencies of these assessments are dependent on the primary objective of the sources but with an important overlap: 100% of the sources investigating motor symptoms used measurements of motor symptoms and mainly the UPDRS-III, but they also frequently assessed cognition (88%), sleep (25%) and quality of life (25%). The sources investigating non-motor symptoms frequently assessed cognition (82%), psychiatric symptoms (88%) most of the time with, respectively, the GDS (41%) and the MMSE (65%). The two genetic sources have several patient reported outcomes and they both measured motor and psychiatric symptoms.

Some measurements were used more often for some above-mentioned objectives. While the GDS and the UPDRS-III were used specifically in sources investigating, respectively, the non-motor symptoms and the motor symptoms as a primary objective, the BDI and the H&Y were used in sources investigating the other objectives.

Discussion

A large number of longitudinal real world data sources for PD have been identified. There is no consistency of the dimensions assessed, nor of the measurements used across sources, reflecting the absence of harmonization on the optimal choice of measurements.

There are a number of issues with collecting real world data such as limited size of the databases [1], inability to accurately determine specific outcomes [62], and more chance of bias and confounding factors [5]. Nevertheless, they have an important role to play in the evaluation of epidemiology, burden of disease and treatments patterns [6]; and in assisting health-care decision-makers, especially related to coverage and payment decisions [63]. In this context, a harmonization seems necessary. These results are quite consistent with those observed in Europe where a “consensus on domains incorporated in different studies [was observed] with a substantial variability in the choice of the evaluation method” [4]. There are a number of possible explanations for this absence of harmonization and some of them are discussed here.

First of all, some dimensions are broad. In consequence many measurements are available according to each source objective, design and population. This heterogeneity probably reflects both the absence of harmonization and the complexity of the evaluation of a dimension like cognition [64]. A single measurement cannot assess all necessary information. For example, the combination of patient reported outcomes and medical reported outcomes can be very informative and complement one another. In a consistent manner, the combination of Parkinson specific and generic measurements can be a necessity especially for “generic” data sources including not only Parkinsonian patients. In another example, while the objectives of the UPDRS-III and the H&Y (or of the GDS and the BDI) are close, the difference of their use according to the study primary objective of the source seems more linked to the investigator choice than to the suitability of the measurement.

Secondly, PD is characterized by several initial system disorders and treatment complications [65]. To date, motor subtyping has dominated the landscape of PD research but non-motor dimensions evaluations are increasing [9, 66], and thus the number of dimensions to evaluate. For non-motor dimensions, some have validated measurements such as psychiatry [67], activity disability [7], sleep [68] or quality of life [69]; but others have no clear review of validated and used scales [4]. Among the psychiatric scales, the two most frequently used were the GDS and the BDI. This finding highlights the well-known relationship between PD and depression, and the fact that when validated scales [70] are available, a harmonization of practice is observed. The lack of evaluation and validation of the measurements in PD is probably partly a source of such an heterogeneity.

Thirdly, clinical research purposes and outcomes are in permanent evolution over time [71, 72], as highlighted by the many differences between completed and ongoing sources. New trends are not well covered right now, either due to lack of measurements or due to lack of capture (i.e. utilization of available measurements in databases). Among the most important of those are the genetic testing, the caregiver burden and the costs. The important development of genetic testing has come in the last few years, with an increase of the mutations and treatment discoveries such as LRRK2 and its kinase inhibitors. But research is necessary to understand the role of genetic mutations in PD [73]. Sources based on caregiver burden and relevant validated measurements are very limited [7]. But the interest for these data is growing with the recognition of their physical, emotional and economic burden [74]. The only data source identified as measuring healthcare costs associated with PD was ongoing. It probably reflects both the recent growing interest of health economic evaluation and the fact that this type of study is more often conducted in automated healthcare databases [75].

Fourthly, there is a possible improvement of the access to the data source details. Given information is fragmented between different sources of information and study protocols or outcomes lists are not always available. In consequence identifying and gathering this information to produce an integrated view can be really difficult.

Finally, the variability of our results is greater than in the European study. This may be because the classification is based on dimensions assessing mostly symptoms, 5 out of 8 dimensions. This classification probably more appropriate for data sources with a primary objective of treatment evaluation (e.g. open-label extension), which are a minority of the included sources. The classification may not be as applicable to assess other data sources focused on the evaluation of burden. Real world evidence collection is done for various purposes and such a restricted classification can lead to ambiguous conclusions. It can lead to a perception of consensus while actually missing important aspects such as burden, function or complications of treatments.

Our study has several limitations. First of all, only one reader has conducted the record selection and the data extraction unlike systematic reviews. Nevertheless, the search methods identified a large number of PD data sources for extraction and comparison. No contact was established with investigators of the included studies to confirm data extraction results. To address this issue, a second step has been performed after the data extraction from the publications, to update and complete the published information with all other available sources. At risk/prodromal cohorts have not been separated from clinical PD cohorts, but the distinction between these two subgroups has recently been described as artificial [4].

Our study has several strengths. It is the first review of existing real world longitudinal data sources on PD in USA to our knowledge. Moreover, it was performed with broad research criteria and without any limitation on language, type of publication or type of measurements. This review creates an integrated view and should assist investigators and clinicians to identify and optimize the measurements that best match with their objectives and the already existing data sources.

Conclusion

In conclusion, many longitudinal real world data sources on PD exist. Different types of measurements have been used over time. To allow comparison and pooling of these multiple data sources, it will be essential to harmonize practices in terms of types of measurements.

Abbreviations

BDI: 

Beck Depression Inventory

ESS: 

Epworth Sleepiness Scale

FDA: 

Food and Drug Administration

GDS: 

Geriatric Depression Scale

H&Y: 

Hoehn and Yahr scale

MMSE: 

Mini Mental State Examination

PD: 

Parkinson’s disease

PDQ-39: 

39-item Parkinson’s disease Quality of life

S&E: 

Schwab and England

SCOPA-AUT: 

autonomic part of the Scales for outcomes of Parkinson’s disease

UPDRS-III: 

Unified Parkinson’s Disease Rating Scale part III

USA: 

United States of America

Declarations

Acknowledgements

Highly appreciated is also the support of Sandrine Thoreau for assisting with the search strategy.

Funding

The study was funded by Lundbeck SAS. The funding source, beyond the employees involved as authors, did not participate in the design of the study; collection, analysis nor interpretation of the data; nor the writing of the manuscript.

Availability of data and materials

Not applicable.

Authors’ contributions

AT: Research project execution, statistical analysis execution, manuscript writing, review and critique. LJ: Research project conception and organization, statistical analysis review and critique, manuscript review and critique. LI: Research project conception and organization, statistical analysis review and critique, manuscript review and critique. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

LJ is a current employee and AT was a resident in Lundbeck SAS and LI was an employee of Lundbeck SAS at the time the research was carried out.

Publisher’s Note

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Authors’ Affiliations

(1)
Lundbeck SAS, Issy-les-Moulineaux, France

References

  1. Kowal SL, Dall TM, Chakrabarti R, Storm MV, Jain A. The current and projected economic burden of Parkinson’s disease in the United States. Mov Disord. 2013;28:311–8.View ArticlePubMedGoogle Scholar
  2. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices: FDA Draft Guidance for Industry and Staff. Food Drug Administration. http://www.fda.gov/ucm/groups/fdagov-public/@fdagov-meddev-gen/documents/document/ucm513027.pdf. Accessed 29 Nov 2017.
  3. Bot BM, Suver C, Neto EC, et al. The mPower study, Parkinson disease mobile data collected using ResearchKit. Scientific Data. 2016;3:160011. doi:10.1038/sdata.2016.11.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Lerche S, Liepelt-Scarfone I, Alves G, et al. Methods in Neuroepidemiology characterization of European longitudinal cohort studies in Parkinson’s disease – report of the JPND working group BioLoc-PD. Neuroepidemiology. 2015;45:282–97.View ArticlePubMedGoogle Scholar
  5. Mahajan R. Real world data: additional source for making clinical decisions. Int J Appl Basic Med Res. 2015; doi:10.4103/2229-516X.157148.
  6. Annemans L, Aristides M, Kubin M. Real life data: a growing need. ISPOR Connections. http://www.ispor.org/news/articles/oct07/rld.asp. Accessed 29 Nov 2017.
  7. Shulman LM, Armstrong M, Ellis T, et al. Disability rating scales in Parkinson’s disease: critique and recommendations. Mov Disord. 2016;31:1455–65.View ArticlePubMedGoogle Scholar
  8. Doty RL. Olfaction in Parkinson’s disease and related disorders. Neurobiol Dis. 2012;46:527–52.View ArticlePubMedGoogle Scholar
  9. Ravina B, Tanner C, Dieuliis D, et al. A longitudinal program for biomarker development in Parkinson's disease: a feasibility study. Mov Disord. 2009;24(14):2081–90.View ArticlePubMedGoogle Scholar
  10. Tang CC, Poston KL, Dhawan V, et al. Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson's disease. J Neurosci. 2010;30(3):1049–56.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Gomperts SN, Locascio JJ, Rentz D, et al. Amyloid is linked to cognitive decline in patients with Parkinson disease without dementia. Neurology. 2013;80(1):85–91.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Caviness JN, Hentz JG, Belden CM, et al. Longitudinal EEG changes correlate with cognitive measure deterioration in Parkinson's disease. J Parkinsons Dis. 2015;5(1):117–24.PubMedGoogle Scholar
  13. Alcalay RN, Mirelman A, Saunders-Pullman R, et al. Parkinson disease phenotype in Ashkenazi Jews with and without LRRK2 G2019S mutations. Mov Disord. 2013;28(14):1966–71.View ArticlePubMedGoogle Scholar
  14. O'Brien RJ, Resnick SM, Zonderman AB, et al. Neuropathologic studies of the Baltimore longitudinal study of aging (BLSA). J Alzheimers Dis. 2009;18(3):665–75.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Duncan RP, Leddy AL, Cavanaugh JT, et al. Detecting and predicting balance decline in Parkinson disease: a prospective cohort study. J Parkinsons Dis. 2015;5(1):131–9.PubMedPubMed CentralGoogle Scholar
  16. Mahoney JR, Verghese J, Holtzer R, et al. The evolution of mild parkinsonian signs in aging. J Neurol. 2014;261(10):1922–8.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Bohnen NI, Koeppe RA, Minoshima S, et al. Cerebral glucose metabolic features of Parkinson disease and incident dementia: longitudinal study. J Nucl Med. 2011;52(6):848–55.View ArticlePubMedGoogle Scholar
  18. Dibble LE, Cavanaugh JT, Earhart GM, et al. Charting the progression of disability in Parkinson disease: study protocol for a prospective longitudinal cohort study. BMC Neurol. 2010;10:110.View ArticlePubMedPubMed CentralGoogle Scholar
  19. O'Suilleabhain PE, Oberle R, Bartis C, et al. Clinical course in Parkinson's disease with elevated homocysteine. Parkinsonism Relat Disord. 2006;12(2):103–7.View ArticlePubMedGoogle Scholar
  20. Evatt ML, Delong MR, Khazai N, et al. Prevalence of vitamin d insufficiency in patients with Parkinson disease and Alzheimer disease. Arch Neurol. 2008;65(10):1348–52.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Duncan RP, Leddy AL, Cavanaugh JT, et al. Comparative utility of the BESTest, mini-BESTest, and brief-BESTest for predicting falls in individuals with Parkinson disease: a cohort study. Phys Ther. 2013;93(4):542–50.View ArticlePubMedGoogle Scholar
  22. Holloway R, Marek K, Biglan K, et al. Long-term effect of initiating pramipexole vs levodopa in early Parkinson disease. Arch Neurol. 2009;66(5):563–70.View ArticleGoogle Scholar
  23. Golbe LI, Di Iorio G, Sanges G, et al. Clinical genetic analysis of Parkinson's disease in the Contursi kindred. Ann Neurol. 1996;40(5):767–75.View ArticlePubMedGoogle Scholar
  24. Liu C, Cholerton B, Shi M, et al. CSF tau and tau/Aβ42 predict cognitive decline in Parkinson's disease. Parkinsonism Relat Disord. 2015;21(3):271–6.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Jasinska-Myga B, Putzke JD, Wider C, et al. Depression in Parkinson's disease. Can J Neurol Sci. 2010;37(1):61–6.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Rabinak CA, Nirenberg MJ. Dopamine agonist withdrawal syndrome in Parkinson disease. Arch Neurol. 2010;67(1):58–63.View ArticlePubMedGoogle Scholar
  27. San Luciano M, Lipton RB, Wang C, et al. Clinical expression of LRRK2 G2019S mutations in the elderly. Mov Disord. 2010;25(15):2571–6.View ArticlePubMedGoogle Scholar
  28. Tickle-Degnen L, Saint-Hilaire M, Thomas CA, et al. Emergence and evolution of social self-management of Parkinson's disease: study protocol for a 3-year prospective cohort study. BMC Neurol. 2014;14:95.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Goetz CG, Ouyang B, Negron A, et al. Hallucinations and sleep disorders in PD: ten-year prospective longitudinal study. Neurology. 2010;75(20):1773–9.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Logroscino G, Sesso HD, Paffenbarger RS Jr, et al. Physical activity and risk of Parkinson's disease: a prospective cohort study. J Neurol Neurosurg Psychiatry. 2006;77(12):1318–22.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Chen H, Zhang SM, Schwarzschild MA, et al. Survival of Parkinson's disease patients in a large prospective cohort of male health professionals. Mov Disord. 2006;21(7):1002–7.View ArticlePubMedGoogle Scholar
  32. Wong KT, Grove JS, Grandinetti A, et al. Association of fibrinogen with Parkinson disease in elderly Japanese-American men: a prospective study. Neuroepidemiology. 2010;34(1):50–4.View ArticlePubMedGoogle Scholar
  33. Pigott K, Rick J, Xie SX, et al. Longitudinal study of normal cognition in Parkinson disease. Neurology. 2015;85(15):1276–82.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Liang GS, Chou KL, Baltuch GH, et al. Long-term outcomes of bilateral subthalamic nucleus stimulation in patients with advanced Parkinson's disease. Stereotact Funct Neurosurg. 2006;84(5-6):221–7.View ArticlePubMedGoogle Scholar
  35. Jasinska-Myga B, Heckman MG, Wider C, et al. Loss of ability to work and ability to live independently in Parkinson's disease. Parkinsonism Relat Disord. 2012;18(2):130–5.View ArticlePubMedGoogle Scholar
  36. Rod NH, Bordelon Y, Thompson A, et al. Major life events and development of major depression in Parkinson's disease patients. Eur J Neurol. 2013;20(4):663–70.View ArticlePubMedGoogle Scholar
  37. Bower JH, Grossardt BR, Maraganore DM, et al. Anxious personality predicts an increased risk of Parkinson's disease. Mov Disord. 2010;25(13):2105–13.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Roberts RO, Geda YE, Knopman DS, et al. The Mayo Clinic study of aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology. 2008;30(1):58–69.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Markopoulou K, Biernacka JM, Armasu SM, et al. Does α-synuclein have a dual and opposing effect in preclinical vs. clinical Parkinson's disease? Parkinsonism Relat Disord. 2014;20(6):584–9.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Zahodne LB, Marsiske M, Okun MS, et al. Mood and motor trajectories in Parkinson's disease: multivariate latent growth curve modeling. Neuropsychology. 2012;26(1):71–80.View ArticlePubMedGoogle Scholar
  41. Okun MS, Wu SS, Fayad S, et al. Acute and chronic mood and apathy outcomes from a randomized study of unilateral STN and GPi DBS. PLoS One. 2014;9(12):e114140.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Cholerton BA, Zabetian CP, Quinn JF, et al. Pacific Northwest Udall center of excellence clinical consortium: study design and baseline cohort characteristics. J Parkinsons Dis. 2013;3(2):205–14.PubMedPubMed CentralGoogle Scholar
  43. Okun MS, Siderowf A, Nutt JG, et al. Piloting the NPF data-driven quality improvement initiative. Parkinsonism Relat Disord. 2010;16(8):517–21.View ArticlePubMedGoogle Scholar
  44. Kay DM, Zabetian CP, Factor SA, et al. Parkinson's disease and LRRK2: frequency of a common mutation in U.S. movement disorder clinics. Mov Disord. 2006;21(4):519–23.View ArticlePubMedGoogle Scholar
  45. Chen H, Schernhammer E, Schwarzschild MA, et al. A prospective study of night shift work, sleep duration, and risk of Parkinson's disease. Am J Epidemiol. 2006;163(8):726–30.View ArticlePubMedGoogle Scholar
  46. Szewczyk-Krolikowski K, Menke RA, Rolinski M, et al. Functional connectivity in the basal ganglia network differentiates PD patients from controls. Neurology. 2014;83(3):208–14.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Qiang JK, Wong YC, Siderowf A, et al. Plasma apolipoprotein A1 as a biomarker for Parkinson disease. Ann Neurol. 2013;74(1):119–27.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Ofori E, Pasternak O, Planetta PJ, et al. Increased free water in the substantia nigra of Parkinson's disease: a single-site and multi-site study. Neurobiol Aging. 2015;36(2):1097–104.View ArticlePubMedGoogle Scholar
  49. Swanson CR, Li K, Unger TL, et al. Lower plasma apolipoprotein A1 levels are found in Parkinson's disease and associate with apolipoprotein A1 genotype. Mov Disord. 2015;30(6):805–12.View ArticlePubMedGoogle Scholar
  50. Ko JH, Mure H, Tang CC, et al. Parkinson's disease: increased motor network activity in the absence of movement. J Neurosci. 2013;33(10):4540–9.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Chahine LM, Xie SX, Simuni T, et al. Longitudinal changes in cognition in early Parkinson's disease patients with REM sleep behavior disorder. Parkinsonism Relat Disord. 2016;27:102–6.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Bastiaens J, Dorfman BJ, Christos PJ, et al. Prospective cohort study of impulse control disorders in Parkinson's disease. Mov Disord. 2013;28(3):327–33.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Gallagher CL, Oakes TR, Johnson SC, et al. Rate of 6-[18F]fluorodopa uptake decline in striatal subregions in Parkinson's disease. Mov Disord. 2011;26(4):614–20.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Bennett DA, Schneider JA, Arvanitakis Z, et al. Overview and findings from the religious orders study. Curr Alzheimer Res. 2012;9(6):628–45.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Bennett DA, Schneider JA, Buchman AS, et al. Overview and findings from the rush memory and aging project. Curr Alzheimer Res. 2012;9(6):646–63.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Schneider JL, Fink HA, Ewing SK, et al. The association of Parkinson's disease with bone mineral density and fracture in older women. Osteoporos Int. 2008;19(7):1093–7.View ArticlePubMedGoogle Scholar
  57. Aarsland D, Kvaløy JT, Andersen K, et al. The effect of age of onset of PD on risk of dementia. J Neurol. 2007;254(1):38–45.View ArticlePubMedGoogle Scholar
  58. Ritz B, Rhodes SL, Bordelon Y, et al. α-Synuclein genetic variants predict faster motor symptom progression in idiopathic Parkinson disease. PLoS One. 2012;7(5):e36199.View ArticlePubMedPubMed CentralGoogle Scholar
  59. Papapetropoulos S, Mash DC. Motor fluctuations and dyskinesias in advanced/end stage Parkinson's disease: a study from a population of brain donors. J Neural Transm (Vienna). 2007;114(3):341–5.View ArticleGoogle Scholar
  60. Harrison MB, Wylie SA, Frysinger RC, et al. UPDRS activity of daily living score as a marker of Parkinson's disease progression. Mov Disord. 2009;24(2):224–30.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Louis ED, Tang MX, Schupf N. Mild parkinsonian signs are associated with increased risk of dementia in a prospective, population-based study of elders. Mov Disord. 2010;25(2):172–8.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Marras C, Chaudhuri KR. Nonmotor features of Parkinson’s disease subtypes. Mov Disord. 2016;31(8):1095–102.View ArticlePubMedGoogle Scholar
  63. Garrison LP Jr, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real-world data for coverage and payment decisions: the ISPOR real-world data task force report. Value Health. 2007;10(5):326–35.View ArticlePubMedGoogle Scholar
  64. Litvan I, Goldman JG, Tröster AI, et al. Diagnostic criteria for mild cognitive impairment in Parkinson's disease: Movement Disorder Society task force guidelines. Mov Disord. 2012;27(3):349–56.View ArticlePubMedPubMed CentralGoogle Scholar
  65. De Virgilio A, Greco A, Fabbrini G, et al. Parkinson's disease: autoimmunity and neuroinflammation. Autoimmun Rev. 2016;15(10):1005–11.View ArticlePubMedGoogle Scholar
  66. Sagna A, Gallo JJ, Pontone GM. Systematic review of factors associated with depression and anxiety disorders among older adults with Parkinson's disease. Parkinsonism Relat Disord. 2014;20(7):708–15. doi:10.1016/j.parkreldis.2014.03.020.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Martinez-Martin P, Leentjens AF, de Pedro-Cuesta J, Chaudhuri KR, Schrag AE, Weintraub D. Accuracy of screening instruments for detection of neuropsychiatric syndromes in Parkinson's disease. Mov Disord. 2016;31(3):270–9.View ArticlePubMedGoogle Scholar
  68. Zea-Sevilla MA, Martinez-Martin P. Rating scales and questionnaires for assessment of sleep disorders in Parkinson's disease: what they inform about? J Neural Transm. 2014;121:33. doi:10.1007/s00702-014-1217-z.View ArticleGoogle Scholar
  69. Martinez-Martin P, Jeukens-Visser M, Lyons KE, et al. Health-related quality-of-life scales in Parkinson's disease: critique and recommendations. Mov Disord. 2011;26(13):2371–80.View ArticlePubMedGoogle Scholar
  70. Goodarzi Z, Mrklas KJ, Roberts DJ, Jette N, Pringsheim T, Holroyd-Leduc J. Detecting depression in Parkinson disease: a systematic review and meta-analysis. Neurology. 2016;87(4):426–37.View ArticlePubMedPubMed CentralGoogle Scholar
  71. Engler K, Lessard D, Lebouché B. A review of HIV-specific patient-reported outcome measures. Patient. 2016; doi:10.1007/s40271-016-0195-7.
  72. Goldman J, Weintraub D. Advances in the treatment of cognitive impairment in Parkinson’s disease. Mov Disord. 2015;30(11):1471–89.View ArticlePubMedGoogle Scholar
  73. Wallings R, Manzoni C, Bandopadhyay R. Cellular processes associated with LRRK2 function and dysfunction. FEBS J. 2015;282(15):2806–26.View ArticlePubMedPubMed CentralGoogle Scholar
  74. Boland D, Stacy M. The economic and quality of life burden associated with Parkinson’s disease: a focus on symptoms. Am J Manag Care. 2012;18(7):S168–75.PubMedGoogle Scholar
  75. Noyes K, Liu H, Temkin-Greener H. Cost of caring for Medicare beneficiaries with Parkinson's disease: impact of the CMS-HCC risk-adjustment model. Dis Manag. 2006;9(6):339–48.View ArticlePubMedGoogle Scholar

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