Background: Different factors influence severity, progression, and outcomes in Parkinson's disease (PD). Lack of standardized clinical assessment limits comparison of outcomes and availability of well-characterized cohorts for collaborative studies. Methods: Structured clinical documentation support (SCDS) was developed within the DNA Predictions to Improve Neurological Health (DodoNA) project to standardize clinical assessment and identify molecular predictors of disease progression. The Longitudinal Clinical and Genetic Study of Parkinson's Disease (LONG-PD) was launched within the Genetic Epidemiology of Parkinson's disease (GEoPD) consortium using a Research Electronic Data Capture (REDCap) format mirroring the DodoNA SCDS. Demographics, education, exposures, age at onset (AAO), Unified Parkinson's Disease Rating Scale (UPDRS) parts I-VI or Movement Disorders Society (MDS)-UPDRS, Montreal Cognitive Assessment (MoCA)/Short Test of Mental Status (STMS)/Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Epworth Sleepiness Scale (ESS), dopaminergic therapy, family history, nursing home placement, death and blood samples were collected. DodoNA participants (396) with 6 years of follow-up and 346 LONG-PD participants with up to 3 years of follow-up were analyzed using group-based trajectory modeling (GBTM) focused on: AAO, education, family history, MMSE/MoCA/STMS, UPDRS II-II, UPDRS-III tremor and bradykinesia sub-scores, Hoehn and Yahr staging (H&Y) stage, disease subtype, dopaminergic therapy, and presence of autonomic symptoms. The analysis was performed with either cohort as the training/test set. Markopoulou et al. Parkinson's Disease Longitudinal Monitoring Results: Patients are classified into slowly and rapidly progressing courses by AAO, MMSE score, H &Y stage, UPDRS-III tremor and bradykinesia sub-scores relatively early in the disease course. Late AAO and male sex assigned patients to the rapidly progressing group, whereas tremor to the slower progressing group. Classification is independent of which cohort serves as the training set. Frequencies of disease-causing variants in LRRK2 and GBA were 1.89 and 2.96%, respectively. Conclusions: Standardized clinical assessment provides accurate phenotypic characterization in pragmatic clinical settings. Trajectory analysis identified two different trajectories of disease progression and determinants of classification. Accurate phenotypic characterization is essential in interpreting genomic information that is generated within consortia, such as the GEoPD, formed to understand the genetic epidemiology of PD. Furthermore, the LONGPD study protocol has served as the prototype for collecting standardized phenotypic information at GEoPD sites. With genomic analysis, this will elucidate disease etiology and lead to targeted therapies that can improve disease outcomes.

Epilepsy patients are more likely to experience depressive symptoms and cognitive impairment compared to individuals in the general population. As the reasons for this are not definitively known, we sought to determine what factors correlate most strongly with cognition and a screening test for depression in epilepsy patients. Methods: Our study population included 379 adult patients diagnosed with epilepsy or seizure in our neurology clinic. We collected detailed demographic and clinical data during patient visits using structured clinical documentation support tools that we have built within our commercial electronic medical records system (Epic), including a depression score (Neurological Disorders Depression Inventory for Epilepsy, NDDIE) and cognition score test measures (specifically in this study, Mini-Mental State Examination [MMSE]). Medication, age, gender, body mass index, duration of epilepsy, seizure frequency, current number of anti-epileptic medications, years of education were assessed in relation to baseline score as well as change in score from initial visit to first annual follow-up. Results: Of the analyzed factors, two statistically significant associations were found after correction for multiple testing. Male gender and lower anti-seizure medication count were associated with better mood, as assessed by NDDIE score, at initial visit. Specifically, male gender was associated with a 1.3 decrease in NDDIE and for each additional anti-seizure medication, there was an associated 1.2 increase in NDDIE. Conclusions: However, these factors were not associated with change in NDDIE score from initial to first annual follow-up visit. These findings, although interesting, are preliminary. Additionally, these findings were based on a homogenous (mainly Caucasian) clinic-based population and detailed information on previous medication use was lacking. Further work is needed to replicate these findings and to understand any mechanisms that may explain these associations.

ObjectiveTo examine the relationship between self-reported mood symptoms and severity of presenting concussion symptoms in an adult sports and non-sports post-concussion population.BackgroundPast studies have identified a relationship between pre-morbid and concurrent anxiety and depression and number, severity, and duration of postconcussion symptoms.Design/MethodsUsing our structured clinical documentation support toolkit for concussion patients, we analyzed previously collected discrete standardized data. Each patient with a confirmed mTBI diagnosis by the clinician, reported mood symptoms on the Generalized Anxiety Disorder 7-item (GAD-7) scale and Center for Epidemiology Studies Depression (CES-D) scale. Rivermead Post-concussion Symptoms Questionnaire (RPQ) was self-reported for non-sports concussion patients and the Sport Concussion Assessment Tool (SCAT) symptom checklist was self-reported for sports concussion patients. RPQ or SCAT scores were correlated with GAD-7 and CES-D scores at initial visit. Cohorts were stratified by gender and age decile.ResultsRPQ score was weakly correlated with GAD-7 scores and peaked at 0.71 for males in their 40s and 0.69 for females in their 50s. RPQ was weakly correlated with CES-D for males: corr = 0.65 for all age groups, and females around 0.50, peaking at 0.76 for females in their 50s. For SCAT and GAD-7, males had a stronger correlation than females (0.58–0.21) in their 20s, while females exhibited a stronger correlation for SCAT and CES-D than males (0.63–0.23) in their 20s.ConclusionsCorrelations were found between symptom scores and mood scores. Strongest correlations were found for non-sports mTBI patients between RPQ scores and GAD-7 scores in males in their 40s and females in their 50s, and between RPQ scores and CES-D scores in females in their 50s. This analysis lends support to the relationship between mood symptoms and intensity of somatic concussion symptoms following injury and may encourage clinicians to discuss mental health treatment or resources when appropriate.

Objective: To develop and implement a customized toolkit within the electronic medical record (EMR) to standardize care of patients with brain tumors. Patients and Methods: We built a customized structured clinical documentation support toolkit to capture standardized data at office visits. We detail the process by which this toolkit was conceptualized and developed. Toolkit development was a physician-led process to determine a work flow and necessary elements to support best practices as defined by the neuro-oncology clinical team. Results: We have developed in our EMR system a customized work flow for clinical encounters with neuro-oncology patients. In addition to providing a road map for clinical care by our neuro-oncology team, the toolkit is designed to maximize discrete data capture. Several hundred fields of discrete data are captured through the toolkit in the context of our routine office visits. We describe the characteristics of patients seen at our clinic, the adoption of the toolkit, current initiatives supported by the toolkit, and future applications. Conclusion:The EMR can be effectively structured to standardize office visits and improve discrete data capture. This toolkit can be leveraged to support quality improvement and practice-based research initiatives at the point of care in a neuro-oncology practice.