TICS-M scores in an oldest-old normative cohort identified by computable phenotype.

Objective: To (1) examine the distribution of Telephone Interview for Cognitive Status modified (TICS-m) scores in oldest-old individuals (age 85 and above) identified as cognitively healthy by a previously validated electronic health records-based computable phenotype (CP) and (2) to compare different cutoff scores for cognitive impairment in this population. Method: CP identified 24,024 persons, 470 were contacted and 252 consented and completed the assessment. Associations of TICS-m score with age, sex, and educational categories (<10 years, 11-15 years, and >16 years) were examined. The number of participants perceived as impaired was studied with commonly used cutoff scores (27-31). Results: TICS-m score ranged from 18 to 44 with a mean of 32.6 (SD = 4.7) in older adults aged 85-99 years old. A linear regression model including (range-restricted) age, education, and sex, showed beta estimates comparable to previous findings. Different cutoff scores (27 to 31) generated slightly lower MCI and dementia prevalence rates of participants meeting the criteria for the impairments than studies of younger elderly using traditional recruitment methods. Conclusions: The use of validated computable phenotype to identify a normative cohort generated a normative distribution for the TICS-m consistent with prior findings from more effortful approaches to cohort identification and established expected TICS-m performance in the oldest-old population.

Characteristics associated with response to subcutaneously administered anti-CGRP monoclonal antibody medications in a real-world community cohort of persons living with migraine: A retrospective clinical and genetic study.

OBJECTIVE: To evaluate response to anti-calcitonin gene-related peptide (CGRP) migraine preventives in a real-world community cohort of persons living with migraine and to identify clinical and genetic characteristics associated with efficacious response. BACKGROUND: Erenumab-aooeb, fremanezumab-vrfm, and galcanezumab-gnlm target CGRP or its receptor; however, many patients are non-responsive. METHODS: In this retrospective clinical and genetic study, we identified 1077 adult patients who satisfied the International Classification of Headache Disorders, 3rd edition, criteria for migraine without aura, migraine with aura, or chronic migraine and who were prescribed an anti-CGRP migraine preventive between May 2018 and May 2021. Screening of 558 patients identified 289 with data at baseline and first follow-up visits; data were available for 161 patients at a second follow-up visit. The primary outcome was migraine days per month (MDM). In 198 genotyped patients, we evaluated associations between responders (i.e., patients with ≥50% reduction in MDM at follow-up) and genes involved in CGRP signaling or pharmacological response, and genetic and polygenic risk scores. RESULTS: The median time to first follow-up was 4.4 (0.9-22) months after preventive start. At the second follow-up, 5.7 (0.9-13) months later, 145 patients had continued on the same preventive. Preventives had strong, persistent effects in reducing MDM in responders (follow-up 1: η2 = 0.26, follow-up 2: η2 = 0.22). At the first but not second follow-up: galcanezumab had a larger effect than erenumab, while no difference was seen at either follow-up between galcanezumab and fremanezumab or fremanezumab and erenumab. The decrease in MDM at follow-up was generally proportional to baseline MDM, larger in females, and increased with months on medication. At the first follow-up only, patients with prior hospitalization for migraine or who had not responded to more preventive regimens had a smaller decrease in MDM. Reasons for stopping or switching a preventive varied between medications and were often related to cost and insurance coverage. At both follow-ups, patient tolerance (1: 92.2% [262/284]; 2: 95.2% [141/145]) and continued use (1: 77.5% [224/289]; 2: 80.6% [116/145]) were high and similar across preventives. Response consistency (always non-responders: 31.7% [46/145]; always responders: 56.5% [82/145], and one-time only responders: 11.7% [17/145]) was also similar across preventives. Non-responder status had nominally significant associations with rs12615320-G in RAMP1 (odds ratio [95% confidence interval]: 4.7 [1.5, 14.7]), and rs4680-A in COMT (0.6[0.4, 0.9]). Non-responders had a lower mean genetic risk score than responders (1.0 vs. 1.1; t(df) = -1.75(174.84), p = 0.041), and the fraction of responders increased with genetic and polygenic risk score percentile. CONCLUSIONS: In this real-world setting, anti-CGRP preventives reduced MDM persistently and had similar and large effect sizes on MDM reduction; however, clinical and genetic factors influenced response.

Assessment of ChatGPT's performance on neurology written board examination questions.

Background and objectives: ChatGPT has shown promise in healthcare. To assess the utility of this novel tool in healthcare education, we evaluated ChatGPT's performance in answering neurology board exam questions. Methods: Neurology board-style examination questions were accessed from BoardVitals, a commercial neurology question bank. ChatGPT was provided a full question prompt and multiple answer choices. First attempts and additional attempts up to three tries were given to ChatGPT to select the correct answer. A total of 560 questions (14 blocks of 40 questions) were used, although any image-based questions were disregarded due to ChatGPT's inability to process visual input. The artificial intelligence (AI) answers were then compared with human user data provided by the question bank to gauge its performance. Results: Out of 509 eligible questions over 14 question blocks, ChatGPT correctly answered 335 questions (65.8%) on the first attempt/iteration and 383 (75.3%) over three attempts/iterations, scoring at approximately the 26th and 50th percentiles, respectively. The highest performing subjects were pain (100%), epilepsy & seizures (85%) and genetic (82%) while the lowest performing subjects were imaging/diagnostic studies (27%), critical care (41%) and cranial nerves (48%). Discussion: This study found that ChatGPT performed similarly to its human counterparts. The accuracy of the AI increased with multiple attempts and performance fell within the expected range of neurology resident learners. This study demonstrates ChatGPT's potential in processing specialised medical information. Future studies would better define the scope to which AI would be able to integrate into medical decision making.

Multifactorial assessment of Parkinson's disease course and outcomes using trajectory modeling in a multiethnic, multisite cohort - extension of the LONG-PD study.

Background: The severity, progression, and outcomes of motor and non-motor symptoms in Parkinson's disease (PD) are quite variable. Following PD cohorts holds promise for identifying predictors of disease severity and progression. Methods: PD patients (N = 871) were enrolled at five sites. Enrollment occurred within 5 years of initial motor symptom onset. Disease progression was assessed annually for 2-to-10 years after onset. Group-based trajectory modeling was used to identify groups differing in disease progression. Models were developed for UPDRS-III scores, UPDRS-III tremor and bradykinesia-rigidity subscores, Hoehn & Yahr (H&Y) stage, Mini-Mental Status Exam (MMSE) scores, and UPDRS-III, H&Y and MMSE scores considered together. Predictors of trajectory-group membership were modeled simultaneously with the trajectories. Kaplan-Meier survival analysis evaluated survival free of PD outcomes. Results: The best fitting models identified three groups. One showed a relatively benign, slowly progressing trajectory (Group 1), a second showed a moderate, intermediately progressing trajectory (Group 2), and a third showed a more severe, rapidly progressing trajectory (Group 3). Stable trajectory-group membership occurred relatively early in the disease course, 5 years after initial motor symptom. Predictors of intermediate and more severe trajectory-group membership varied across the single variable models and the multivariable model jointly considering UPDRS-III, H&Y and MMSE scores. In the multivariable model, membership in Group 2 (28.4% of patients), relative to Group 1 (50.5%), was associated with male sex, younger age-at-onset, fewer education-years, pesticide exposure, absence of reported head injury, and akinetic/rigid subtype at initial presentation. Membership in Group 3 (21.3%), relative to Group 1, was associated with older age-at-onset, fewer education-years, pesticide exposure, and the absence of a tremor-predominant subtype at initial presentation. Persistent freezing, persistent falls, and cognitive impairment occurred earliest and more frequently in Group 3, later and less frequently in Group 2, and latest and least frequently in Group 1. Furthermore, autonomic complications, dysphagia, and psychosis occurred more frequently in Groups 2 and 3 than in Group 1. Conclusion: Modeling disease course using multiple objective assessments over an extended follow-up duration identified groups that more accurately reflect differences in PD course, prognosis, and outcomes than assessing single parameters over shorter intervals.

A blood biomarker test for brain amyloid impacts the clinical evaluation of cognitive impairment.

OBJECTIVE: The objective of this study was to examine clinicians' patient selection and result interpretation of a clinically validated mass spectrometry test measuring amyloid beta and ApoE blood biomarkers combined with patient age (PrecivityAD® blood test) in symptomatic patients evaluated for Alzheimer's disease (AD) or other causes of cognitive decline. METHODS: The Quality Improvement and Clinical Utility PrecivityAD Clinician Survey (QUIP I, ClinicalTrials.gov Identifier: NCT05477056) was a prospective, single-arm cohort study among 366 patients evaluated by neurologists and other cognitive specialists. Participants underwent blood biomarker testing and received an amyloid probability score (APS), indicating the likelihood of a positive result on an amyloid positron emission tomography (PET) scan. The primary study outcomes were appropriateness of patient selection as well as result interpretation associated with PrecivityAD blood testing. RESULTS: A 95% (347/366) concordance rate was noted between clinicians' patient selection and the test's intended use criteria. In the final analysis including these 347 patients (median age 75 years, 56% women), prespecified test result categories incorporated 133 (38%) low APS, 162 (47%) high APS, and 52 (15%) intermediate APS patients. Clinicians' pretest and posttest AD diagnosis probability changed from 58% to 23% in low APS patients and 71% to 89% in high APS patients (p < 0.0001). Anti-AD drug therapy decreased by 46% in low APS patients (p < 0.0001) and increased by 57% in high APS patients (p < 0.0001). INTERPRETATION: These findings demonstrate the clinical utility of the PrecivityAD blood test in clinical care and may have added relevance as new AD therapies are introduced.

Evaluation of a Computable Phenotype for Successful Cognitive Aging.

Objective: To establish, apply, and evaluate a computable phenotype for the recruitment of individuals with successful cognitive aging. Participants and Methods: Interviews with 10 aging experts identified electronic health record (EHR)-available variables representing successful aging among individuals aged 85 years and older. On the basis of the identified variables, we developed a rule-based computable phenotype algorithm composed of 17 eligibility criteria. Starting September 1, 2019, we applied the computable phenotype algorithm to all living persons aged 85 years and older at the University of Florida Health, which identified 24,024 individuals. This sample was comprised of 13,841 (58%) women, 13,906 (58%) Whites, and 16,557 (69%) non-Hispanics. A priori permission to be contacted for research had been obtained for 11,898 individuals, of whom 470 responded to study announcements and 333 consented to evaluation. Then, we contacted those who consented to evaluate whether their cognitive and functional status clinically met out successful cognitive aging criteria of a modified Telephone Interview for Cognitive Status score of more than 27 and Geriatric Depression Scale of less than 6. The study was completed on December 31, 2022. Results: Of the 45% of living persons aged 85 years and older included in the University of Florida Health EHR database identified by the computable phenotype as successfully aged, approximately 4% of these responded to study announcements and 333 consented, of which 218 (65%) met successful cognitive aging criteria through direct evaluation. Conclusion: The study evaluated a computable phenotype algorithm for the recruitment of individuals for a successful aging study using large-scale EHRs. Our study provides proof of concept of using big data and informatics as aids for the recruitment of individuals for prospective cohort studies.

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records.

INTRODUCTION: This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs). METHODS: A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested. RESULTS: The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified. DISCUSSION: We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.

Early-life educational attainment, APOE ε4 alleles, and incident dementia risk in late life.

We examined the interactions between educational attainment and genetic susceptibility on dementia risk among adults over 60 years old. A total of 174,161 participants were free of dementia at baseline. The APOE ε4-related genetic risk was evaluated by the number of APOE ε4 alleles. The overall genetic susceptibility of dementia was evaluated by polygenetic risk score (PRS). Cox proportional hazards models were used to estimate the association between educational attainment and incident dementia. During a median of 8.9 years of follow-up, a total of 1482 incident cases of dementia were documented. After adjustment for covariates, we found that low education attainment was significantly associated with higher dementia risk in the APOE ε4 carriers, and such relation appeared to be stronger with the increasing number of ε4 alleles. In contrast, educational attainment was not associated with dementia risk in non-APOE ε4 carriers (P for multiplicative interaction = 0.006). In addition, we observed that the dementia risk associated with a combination of low educational attainment and high APOE ε4-related genetic risk was more than the addition of the risk associated with each of these factors (P for additive interaction < 0.001). We found similar significant interactions between educational attainment and PRS on both the multiplicative and additive scales on the dementia risk, mainly driven by the APOE genotype. These data indicate that higher educational attainment in early life may attenuate the risk for dementia, particularly among people with high genetic predisposition.

Mediterranean diet adherence, gut microbiota, and Alzheimer's or Parkinson's disease risk: A systematic review.

Alzheimer's disease (AD) and Parkinson's disease (PD) are the two most prevalent neurodegenerative diseases, both without prevention or cure. The Mediterranean diet (MeDi) may be neuroprotective by modulating gut microbiota. We aimed to assess the effects of adherence to MeDi on the gut microbiota in relation to AD or PD risk. A search from inception to November 2020 was conducted in PubMed, CINAHL, EMBASE, Web of Science, Global Health, Biological Abstracts, and Grey Literature Report databases. Two searches were conducted: 1) (MeDi or Microbiota) and (PD or AD) and 2) MeDi and microbiota. Inclusion criteria for papers were specified prior to review. Of 4672 studies identified, 64 were eligible for inclusion. These studies were divided into five groups: MeDi and AD risk (n = 4), MeDi and PD risk (n = 2), MeDi and microbial composition or metabolomics (n = 21), AD and microbial composition or metabolomics (n = 7), and PD and microbial composition or metabolomics (n = 30). Adherence to the MeDi was associated with a lower risk of AD and PD development. Eight genera and two species of bacteria had an inverse relationship with MeDi and AD, and one family, eight genera and three species of bacteria had an inverse relationship with MeDi and PD. More studies are needed to investigate if MeDi, gut microbiota, and neurodegeneration are causally related.

Standardizing Care of Neuro-oncology Patients Using a Customized Electronic Medical Record Toolkit.

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.

Variable Effects of PD-Risk Associated SNPs and Variants in Parkinsonism-Associated Genes on Disease Phenotype in a Community-Based Cohort.

Genetic risk factors for Parkinson's disease (PD) risk and progression have been identified from genome-wide association studies (GWAS), as well as studies of familial forms of PD, implicating common variants at more than 90 loci and pathogenic or likely pathogenic variants at 16 loci. With the goal of understanding whether genetic variants at these PD-risk loci/genes differentially contribute to individual clinical phenotypic characteristics of PD, we used structured clinical documentation tools within the electronic medical record in an effort to provide a standardized and detailed clinical phenotypic characterization at the point of care in a cohort of 856 PD patients. We analyzed common SNPs identified in previous GWAS studies, as well as low-frequency and rare variants at parkinsonism-associated genes in the MDSgene database for their association with individual clinical characteristics and test scores at baseline assessment in our community-based PD patient cohort: age at onset, disease duration, Unified Parkinson's Disease Rating Scale I-VI, cognitive status, initial and baseline motor and non-motor symptoms, complications of levodopa therapy, comorbidities and family history of neurological disease with one or more than one affected family members. We find that in most cases an individual common PD-risk SNP identified in GWAS is associated with only a single clinical feature or test score, while gene-level tests assessing low-frequency and rare variants reveal genes associated in either a unique or partially overlapping manner with the different clinical features and test scores. Protein-protein interaction network analysis of the identified genes reveals that while some of these genes are members of already identified protein networks others are not. These findings indicate that genetic risk factors for PD differentially affect the phenotypic presentation and that genes associated with PD risk are also differentially associated with individual disease phenotypic characteristics at baseline. These findings raise the intriguing possibility that different SNPs/gene effects impact discrete phenotypic characteristics. Furthermore, they support the hypothesis that different gene and protein-protein interaction networks that underlie PD risk, the PD phenotype, and the neurodegenerative process leading to the disease phenotype, and point to the significance of the genetic background on disease phenotype.

Longitudinal Monitoring of Parkinson's Disease in Different Ethnic Cohorts: The DodoNA and LONG-PD Study.

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. 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.

Quality improvement and practice-based research in sleep medicine using structured clinical documentation in the electronic medical record.

BACKGROUND: We developed and implemented a structured clinical documentation support (SCDS) toolkit within the electronic medical record, to optimize patient care, facilitate documentation, and capture data at office visits in a sleep medicine/neurology clinic for patient care and research collaboration internally and with other centers. METHODS: To build our SCDS toolkit, physicians met frequently to develop content, define the cohort, select outcome measures, and delineate factors known to modify disease progression. We assigned tasks to the care team and mapped data elements to the progress note. Programmer analysts built and tested the SCDS toolkit, which included several score tests. Auto scored and interpreted tests included the Generalized Anxiety Disorder 7-item, Center for Epidemiological Studies Depression Scale, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, Insomnia Severity Index, and the International Restless Legs Syndrome Study Group Rating Scale. The SCDS toolkits also provided clinical decision support (untreated anxiety or depression) and prompted enrollment of patients in a DNA biobank. RESULTS: The structured clinical documentation toolkit captures hundreds of fields of discrete data at each office visit. This data can be displayed in tables or graphical form. Best practice advisories within the toolkit alert physicians when a quality improvement opportunity exists. As of May 1, 2019, we have used the toolkit to evaluate 18,105 sleep patients at initial visit. We are also collecting longitudinal data on patients who return for annual visits using the standardized toolkits. We provide a description of our development process and screenshots of our toolkits. CONCLUSIONS: The electronic medical record can be structured to standardize Sleep Medicine office visits, capture data, and support multicenter quality improvement and practice-based research initiatives for sleep patients at the point of care.

Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer's Disease and Related Dementia (ADRD).

With vast amounts ofpatients' medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimer's disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.

Optimizing the electronic medical record to improve patient care and conduct quality improvement initiatives in a concussion specialty clinic.

Objective: To use the electronic medical record (EMR) to optimize patient care, facilitate documentation, and support quality improvement and practice-based research in a concussion (mild traumatic brain injury; mTBI) clinic.Methods: We built a customized structured clinical documentation support (SCDS) toolkit for patients in a concussion specialty clinic. The toolkit collected hundreds of fields of discrete, standardized data. Autoscored and interpreted score tests include the Generalized Anxiety Disorder 7-item scale, Center for Epidemiology Studies Depression scale, Insomnia Severity Index, and Glasgow Coma Scale. Additionally, quantitative score measures are related to immediate memory, concentration, and delayed recall. All of this data collection occurred in a standard appointment length.Results: To date, we evaluated 619 patients at an initial office visit after an mTBI. We provided a description of our toolkit development process, and a summary of the data electronically captured using the toolkit.Conclusions: The electronic medical record can be used to effectively structure and standardize care in a concussion clinic. The toolkit supports the delivery of care consistent with Best Practices, provides opportunities for point of care decision support, and writes comprehensive progress notes that can be communicated to other providers.

Using EHRs to advance epilepsy care.

The improved use of Electronic Health Record (EHR) Systems provides an opportunity to improve the overall efficiency and quality of care of patients with epilepsy. Tools and strategies that may be incorporated into the use of EHRs include utilizing patient generated data, clinical decision support systems and natural language processing systems. Standardization of data from EHR systems may lead to improvement in clinical research through the creation of data collections and multi-center collaborations. Challenges to collaborative use of EHR Systems across centers include costs and the diversity of EHR systems.

Building of EMR Tools to Support Quality and Research in a Memory Disorders Clinic.

The electronic medical record (EMR) presents an opportunity to standardize patient data collection based on quality guidelines and conduct practice-based research. We describe the development of a customized EMR "toolkit" that standardizes patient data collection with hundreds of discrete fields that supports Best Practices for treating patients with memory disorders. The toolkit also supports practice-based research. We describe the design and successful implementation of a customized EMR toolkit to support Best Practices in the care of patients with memory disorders. We discuss applications, including quality improvement projects and current research initiatives, using the toolkit. This toolkit is being shared with other departments of Neurology as part of the Neurology Practice-Based Research Network. Data collection is ongoing, including longitudinal follow-up. This toolkit will generate data that will allow for descriptive and hypothesis driven research as well-quality improvement among patients seen in a memory clinic.

Sensitivity and Specificity of Diagnostic Criteria for Progressive Supranuclear Palsy.

BACKGROUND: In 2017, the International Parkinson and Movement Disorder Society put forward new clinical criteria for the diagnosis of PSP, recognizing diverse PSP phenotypes. In this study, we compared the sensitivity and specificity of the new criteria with the National Institutes of Neurological Disease and Society for Progressive Supranuclear Palsy criteria at different times. METHODS: Patients with clinical parkinsonism, clinical and/or neuropathological diagnosis of PSP, were identified from the Society for Progressive Supranuclear Palsy brain bank. All patients had neuropathologic diagnoses and detailed clinical examination performed by a neurologist at 1 of the 3 Mayo Clinic sites, in Florida, Arizona, and Minnesota. Clinical symptoms and signs were abstracted retrospectively in a blinded fashion and used to determine whether patients met either diagnostic criterion. Patients were divided into early and late disease stage groups using a 3-year cutoff. RESULTS: A total of 129 patients were included, of whom 66 had PSP pathology (51%). The remainder had other neurodegenerative diseases. The overall sensitivity of the International Parkinson and Movement Disorder Society criteria was 87.9%, compared with 45.5% for the National Institutes of Neurological Disease and Society for Progressive Supranuclear Palsy criteria, whereas the specificity of the International Parkinson and Movement Disorder Society probable PSP criteria was 85.7%, compared with 90.5% for the National Institutes of Neurological Disease and Society for Progressive Supranuclear Palsy. Individual patients were noted to have features of multiple PSP phenotypes. CONCLUSION: The International Parkinson and Movement Disorder Society criteria recognize several phenotypes of progressive supranuclear palsy and hence have higher sensitivity than the previous criteria. © 2019 International Parkinson and Movement Disorder Society.

Design and Implementation of Structured Clinical Documentation Support Tools for Treating Stroke Patients.

BACKGROUND AND PURPOSE: Standardized electronic medical record tools provide an opportunity to efficiently provide care that conforms to Best Practices and supports quality improvement and practice-based research initiatives. METHODS: We describe the development of a customized structured clinical documentation "toolkit" that standardizes patient data collection to conform to Best Practices for treating patients with stroke. The toolkit collects patients' demographic information, relevant score test measures, and captures information on disability, treatment, and outcomes. RESULTS: We describe here our creation and implementation of the toolkits and provide example screenshots. As of August 1, 2018, we have evaluated 2332 patients at an initial visit for a possible stroke. We provide basic descriptive data gathered from the use of the toolkits, demonstrating their utility in collecting patient data in a manner that supports both quality clinical care and research initiatives. CONCLUSIONS: We have developed an EMR toolkit to support Best Practices in the care of patients with stroke. We discuss quality improvement projects and current research initiatives using the toolkit. This toolkit is being shared with other Departments of Neurology as part of the Neurology Practice-Based Research Network.