Inventory of real world data sources in Parkinson's disease

[Treatment practice of patients with Parkinson's disease in Saxony : A secondary data-based analysis of utilization in the observation period 2011-2019]

BACKGROUND

The consequences of demographic change are already noticeable in Saxony, the federal state with the highest average age in Germany and predominantly rural areas. In order to improve medical care for patients with Parkinson's disease (PwP), a status quo analysis of current care practice is required.

OBJECTIVE

To what extent does the utilization of medical services by PwP differ a) between urban and rural areas in Saxony and b) between PwP with and without neurologist contact in the observation period from 2011 to 2019?

MATERIAL AND METHODS

The cohort study was based on extensive routine data for Saxony from the health insurance company AOK PLUS from 2010 to 2019. A cohort of 15,744 PwP (n = 67,448 patient-years) was compared to a matched cohort (n = 674,480 patient-years; criteria: year of birth, gender, year of insurance, place of residence: urban/rural) without an ICD-10 coding of a movement disorder.

RESULTS

Overall, there was a steady increase in the number of PwP in the dynamic cohort from 2011 (n = 6829) to 2019 (n = 8254). Urban-rural differences included a smaller proportion of patients being seen by a neurologist in rural areas. The PwP had a 3.5 to 4‑fold higher risk of dying compared to those in the comparison cohort. Changes in drug therapy for Parkinson's disease (i.e., increases in COMT and MAO inhibitors) and in remedy delivery (i.e., increases in occupational therapy and speech therapy) over the observation period were primarily seen in PwP who were seen by a neurologist.

DISCUSSION

The study identified increased morbidity and mortality in PwP who are suitable targets for innovative care concepts. The increasing number of patients and the described differences document the need for this. At the same time, changes in prescription practice show that innovative forms of treatment are being used by neurologists in outpatient care.

A real-world study of wearable sensors in Parkinson's disease

Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings.

Identification and Mapping Real-World Data Sources for Heart Failure, Acute Coronary Syndrome, and Atrial Fibrillation

BACKGROUND

Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular (CV) diseases. We aimed to identify global real-world data (RWD) sources for heart failure (HF), acute coronary syndrome (ACS), and atrial fibrillation (AF).

METHODS

We conducted a systematic review of publications with RWD pertaining to HF, ACS, and AF (2010-2018), generating a list of unique data sources. Metadata were extracted based on the source type (e.g., electronic health records, genomics, and clinical data), study design, population size, clinical characteristics, follow-up duration, outcomes, and assessment of data availability for future studies and linkage.

RESULTS

Overall, 11,889 publications were retrieved for HF, 10,729 for ACS, and 6,262 for AF. From these, 322 (HF), 287 (ACS), and 220 (AF) data sources were selected for detailed review. The majority of data sources had near complete data on demographic variables (HF: 94%, ACS: 99%, and AF: 100%) and considerable data on comorbidities (HF: 77%, ACS: 93%, and AF: 97%). The least reported data categories were drug codes (HF, ACS, and AF: 10%) and caregiver involvement (HF: 6%, ACS: 1%, and AF: 1%). Only a minority of data sources provided information on access to data for other researchers (11%) or whether data could be linked to other data sources to maximize clinical impact (20%). The list and metadata for the RWD sources are publicly available at www.escardio.org/bigdata.

CONCLUSIONS

This review has created a comprehensive resource of CV data sources, providing new avenues to improve future real-world research and to achieve better patient outcomes.

RWD-Cockpit: Application for Quality Assessment of Real-World Data (Preprint)

Background

Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence.

Objective

To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD.

Methods

The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set.

Results

To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets—molecular, phenotypical, and social—and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies—de novo–generated sleep data and publicly available data sets—the RWD-Cockpit could identify and provide researchers with variables that might increase quality.

Conclusions

The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores—quality identifiers—provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings.

Clinical study of the effects of deep brain stimulation on urinary dysfunctions in patients with Parkinson's disease

Purpose: To evaluate the effect of deep brain stimulation (DBS) on urinary dysfunctions in Parkinson’s patients.

Patients and methods: A total of 416 patients, diagnosed with Parkinson’s disease (PD) based on the UK Parkinson’s Disease Society Brain Bank Diagnostic Criteria, were enrolled in the study, including 307 males and 109 females. The effects of DBS treatment on urinary functions during urination and bladder storage of these patients were evaluated using testing and assessment scales, such as the American Urological Association Symptom Index (AUA-SI), Overactive Bladder Symptom Scores (OAB-SS), Quality Of Life Scale (QOL), and urodynamic tests. The data were statistically analyzed with the chi-square test and  both independent-samples t-test and paired-samples t-test were used in this study.

Results: Symptoms of urinary dysfunctions, such as urinary frequency, urgency, and incontinence, in the patients with PD were notably relieved by DBS treatment (P<0.05), and the OAB-SS and bladder storage problems were greatly improved as well (P<0.05). Compared with those in male patients, DBS surgery significantly improved the AUA-SI, urinary symptom scores, and QOL in female PD patients (P<0.05), as well as other functional indicators related to the urinary tract, including the maximum urinary flow rate, detrusor pressure at peak flow, and residual urine volume in female PD patients (P<0.05).

Conclusion: DBS surgery is effective in improving urinary functions in PD patients, as primarily reflected by the alleviation of urinary symptoms such as urinary frequency, urgency, and incontinence. Female PD patients displayed better urinary function outcomes from DBS treatment than did male patients.

Evidence of Repurposing Drugs and Identifying Contraindications from Real World Study in Parkinson's Disease

There is great unmet need in discovering novel treatment for Parkinson's disease (PD) and identifying the new agents potentially causing drug-induced parkinsonism. New indications and contraindications of drugs are typically approved following rigorous randomized controlled trial (RCT) evaluation. However, RCTs have their inherent limitations, since they are usually conducted in ideal conditions, with high cost and limited follow-up periods. In the past decade, large cohort studies with long follow-up outcome data was derived from a PD database in a real-world setting. Studies based on real world data (RWD) can help to augment and extrapolate data obtained in RCTs and provide information about the safety and effectiveness of a medication in heterogeneous, large populations. In the present review, we focus on the published real world studies designed to develop new treatment strategies for repurposing drugs and identifying contraindications for PD. We also outline the challenges and limitations in these studies. Subsequently we introduce PaWei app platform, which hopefully can facilitate PD management and address real-world problems associated with PD. Better understanding of RWD collection and analysis is needed if RWD is to achieve its full potential.