Standardized Clinical Data Research Articles

Accuracy of MSBase criteria to diagnose secondary progressive multiple sclerosis in large German real-world patient cohort

Background and ObjectivesAccurate diagnosis of secondary progression in multiple sclerosis (MS) remains a challenge since standardized criteria are missing. In 2016, the MSBase registry presented an algorithm that enabled the diagnosis of secondary progressive multiple sclerosis (SPMS) more than three years earlier compared to diagnosis by neurologists. This work aimed to test whether this approach is equally effective in a real-world cohort of MS patients. MethodsThis longitudinal retrospective study analyzed clinical data of outpatients with MS recorded until October 2020 in the NeuroTransData registry, a Germany-wide network of 153 certified neurologists. Patient data had been captured in time during clinical visits employing a defined standardized clinical data set in the webbased NeuroTransData patient management platform DESTINY®. The time between the diagnosis of relapsing-remitting multiple sclerosis (RRMS) to SPMS onset was compared with one determined using MSBase criteria (MSBC). Group 1 consisted of patients diagnosed with SPMS during the observation period, whereas group 2 included RRMS patients who did not convert to SPMS during the observation period. ResultsOf 21,281 patients with MS included in our registry, 194 and 9506 patients were allocated to groups 1 and 2, respectively. 10.3% of patients with RRMS were diagnosed with SPMS simultaneously, whereas 60.8% were diagnosed with SPMS at least 3 months earlier by treating neurologists compared to the MSBC. In group 1, the MSBC showed a low sensitivity of 32.0% and an accuracy of 61.4% but a high specificity of 89.6%. In group 2, the MSBC identified 7.8% of patients with SPMS at some point during the observation time. Moreover, test-retest variability remains a challenge since 29.4% of patients diagnosed with SPMS by treating physicians did not fulfil the MSBC at a later point in time. DiscussionThese results are inconsistent with earlier SPMS diagnosis using the MSBC compared to clinical diagnosis by treating physicians. Therefore, there remains a need for an operational, structured, and validated approach to SPMS diagnosis.

Characterizing phenotypes and clinical and health utilization associations of young people with chronic pain: latent class analysis using the electronic Persistent Pain Outcomes Collaboration database.

Using the Australiasian electronic Persistent Pain Outcomes Collaboration, a binational pain registry collecting standardized clinical data from paediatric ePPOC (PaedsePPOC) and adult pain services (AdultePPOC), we explored and characterized nationally representative chronic pain phenotypes and associations with clinical and sociodemographic factors, health care utilization, and medicine use of young people. Young people ≥15.0 and <25.0 years captured in PaedePPOC and AdultePPOC Australian data registry were included. Data from 68 adult and 12 paediatric pain services for a 5-year period January 2018 to December 2022 (first episode, including treatment information) were analysed. Unsupervised latent class analysis was applied to explore the existence of distinct pain phenotypes, with separate models for both services. A 3-phenotype model was selected from both paediatric and adult ePPOC data, with 693 and 3518 young people included, respectively (at least one valid indicator variable). Indicator variables for paediatric models were as follows: pain severity, functional disability (quasisurrogate "pain interference"), pain count, pain duration, pain-related worry (quasisurrogate "catastrophizing"), and emotional functioning; and, for adult models: pain severity, pain interference, pain catastrophizing, emotional functioning, and pain self-efficacy. From both services, 3 similar phenotypes emerged ("low," "moderate," "high"), characterized by an increasing symptom-severity gradient in multidimensional pain-related variables, showing meaningful differences across clinical and sociodemographic factors, health service utilization, and medicines use. Derived phenotypes point to the need for novel care models that differentially respond to the needs of distinct groups of young people, providing timely, targeted, age-appropriate care. To effectively scale such care, digital technologies can be leveraged to augment phenotype-informed clinical care.

The Pancreatic Cancer Early Detection (PRECEDE) Study is a Global Effort to Drive Early Detection: Baseline Imaging Findings in High-Risk Individuals.

Pancreatic adenocarcinoma (PC) is a highly lethal malignancy with a survival rate of only 12%. Surveillance is recommended for high-risk individuals (HRIs), but it is not widely adopted. To address this unmet clinical need and drive early diagnosis research, we established the Pancreatic Cancer Early Detection (PRECEDE) Consortium. PRECEDE is a multi-institutional international collaboration that has undertaken an observational prospective cohort study. Individuals (aged 18-90 years) are enrolled into 1 of 7 cohorts based on family history and pathogenic germline variant (PGV) status. From April 1, 2020, to November 21, 2022, a total of 3,402 participants were enrolled in 1 of 7 study cohorts, with 1,759 (51.7%) meeting criteria for the highest-risk cohort (Cohort 1). Cohort 1 HRIs underwent germline testing and pancreas imaging by MRI/MR-cholangiopancreatography or endoscopic ultrasound. A total of 1,400 participants in Cohort 1 (79.6%) had completed baseline imaging and were subclassified into 3 groups based on familial PC (FPC; n=670), a PGV and FPC (PGV+/FPC+; n=115), and a PGV with a pedigree that does not meet FPC criteria (PGV+/FPC-; n=615). One HRI was diagnosed with stage IIB PC on study entry, and 35.1% of HRIs harbored pancreatic cysts. Increasing age (odds ratio, 1.05; P<.001) and FPC group assignment (odds ratio, 1.57; P<.001; relative to PGV+/FPC-) were independent predictors of harboring a pancreatic cyst. PRECEDE provides infrastructure support to increase access to clinical surveillance for HRIs worldwide, while aiming to drive early PC detection advancements through longitudinal standardized clinical data, imaging, and biospecimen captures. Increased cyst prevalence in HRIs with FPC suggests that FPC may infer distinct biological processes. To enable the development of PC surveillance approaches better tailored to risk category, we recommend adoption of subclassification of HRIs into FPC, PGV+/FPC+, and PGV+/FPC- risk groups by surveillance protocols.

Assessment of quality of life, psychosocial, and epidemiological aspects in patients diagnosed with tuberculous uveitis.

To assess the quality of life in patients diagnosed as having tuberculous uveitis and its association with sociodemographic, clinical, and psychosocial aspects. By conducting standardized interviews, clinical and demographic data were collected using a measure developed in this study. This measure was applied in addition to other measures, namely SF-12, Hospital Anxiety and Depression Scale, and NEI-VFQ-39, which were used to assess health-related quality of life, anxiety and depression symptoms, and visual functioning. The study included 34 patients [mean age: 46.5 ± 15.1 years, female patients: 21 (61.8%)]. The mean of the VFQ-39 score was 74.5 ± 16.6 and that of SF-12 physical and mental component scores were 45.8 ± 10.1 and 51.6 ± 7.5, respectively, for the health-related quality of life. Anxiety symptoms were the most prevalent compared with depression symptoms and were found in 35.3% of the participants. Tuberculous uveitis affects several scales of quality of life, thereby affecting a population economically active with a social, psychological, and economic burden.

Exploration on Standardization of Clinical Research Data in a Clinical Trial Institute of Traditional Chinese Medicine

Traditional Chinese Medicine(TCM) products and practices are widely used worldwide with increasing popularity. The number of TCM clinical trials has increased significantly in the past two decades. There is an urgent need to standardize TCM data and terminology for use in clinical studies. It will ensure data quality, improve the efficiency and expedite scientific communications in TCM. This paper illustrates our exploration processes in the application and development of the data standards in TCM clinical trials. We hope these explorations will help TCM data exchange, sharing, and aggregation which will ultimately accelerate the launch of TCM products and bring benefits to people.

Mission and vision of clinical research in bariatric and metabolic surgery

Clinical research progress in bariatric and metabolism research had promoted the development in clinical practice. In U. S and Europe, pivotal scientific questions had been explored by clinical researchers, which brought the updating of guidelines, accumulation in key evidences, promotion of the consensus of metabolic benefits, following further development in scientific area. Compared to U. S and Europe, clinical practice in bariatric and metabolic surgery development in China started later. Clinical research in China just developed into initial phase with few achievements with high quality and breakthrough contribution. However, with wider practice, larger patient population and higher quality of surgery, clinical research in bariatric and metabolic surgery in China will go to a new stage through clinical research with standardized guidance, broad cooperation and clinical data digitalization and standardization.

Surgeon-specific factors have a larger impact on decision-making for the management of proximal humerus fractures than patient-specific factors: a prospective cohort study.

There is significant variability both in how proximal humerus fractures (PHFs) are treated and the ensuing patient outcomes. The purpose of this study was to investigate which surgeon- and patient-specific factors contribute to decision-making in the treatment of adult PHFs. We hypothesized that orthopedic sub-specialty training creates inherent bias and plays an important role in management algorithms for PHFs. We performed a prospective cohort investigation in 2 groups of surgeons-traumatologists (N=25) and shoulder & elbow/sports surgeons (SES) (N=26)-and asked them to provide treatment recommendations for 30 distinct clinical cases with standardized radiographic and clinical data. This is a population-based sample of surgeons who take trauma call and treat PHFs with different sub-specializations and practice settings including academic, hospital-employed, and private. Surgeons characterized based on subspecialty (trauma vs. SES), experience level (>10 vs. ≤10-years), and employment type (hospital- vs. non-hospital-employed). Chi-square analyses, logistic mixed-effects modeling, and relative importance analysis were used to evaluate the data. Of the patient-specific factors, we found that the management of PHFs is largely dependent on initial radiographs obtained. Traumatologists were more likely to offer open reduction internal fixation (ORIF) and less likely to offer arthroplasty: 69% ORIF (traumatologists) vs. 51% ORIF (SES, P<.001), 8% arthroplasty (traumatologists) vs. 17% (SES, P<.001). Traumatologists were less likely to change from operative (either ORIF or arthroplasty) to non-operative management compared to SES surgeons when presented with additional patient demographic data. Surgeon-specific factors contributed to more than one-half of the variability in decision-making of PHF management while patient-specific factors contributed to about one-third of the variability in decision-making. As physicians strive to advance the treatment for PHFs and optimize patient outcomes, our findings highlight the complex overlap between surgeon-, fracture-, and patient-specific factors in the final decision-making process.

Development of a Multivariable Seizure Likelihood Assessment Based on Clinical Information and Short Autonomic Activity Recordings for Children With Epilepsy

BackgroundPredicting seizure likelihood for the following day would enable clinicians to extend or potentially schedule video-electroencephalography (EEG) monitoring when seizure risk is high. Combining standardized clinical data with short-term recordings of wearables to predict seizure likelihood could have high practical relevance as wearable data is easy and fast to collect. As a first step toward seizure forecasting, we classified patients based on whether they had seizures or not during the following recording. MethodsPediatric patients admitted to the epilepsy monitoring unit wore a wearable that recorded the heart rate (HR), heart rate variability (HRV), electrodermal activity (EDA), and peripheral body temperature. We utilized short recordings from 9:00 to 9:15 pm and compared mean values between patients with and without impending seizures. In addition, we collected clinical data: age, sex, age at first seizure, generalized slowing, focal slowing, and spikes on EEG, magnetic resonance imaging findings, and antiseizure medication reduction. We used conventional machine learning techniques with cross-validation to classify patients with and without impending seizures. ResultsWe included 139 patients: 78 had no seizures and 61 had at least one seizure after 9 pm during the concurrent video-EEG and E4 recordings. HR (P < 0.01) and EDA (P < 0.01) were lower and HRV (P = 0.02) was higher for patients with than for patients without impending seizures. The average accuracy of group classification was 66%, and the mean area under the receiver operating characteristics was 0.72. ConclusionsShort-term wearable recordings in combination with clinical data have great potential as an easy-to-use seizure likelihood assessment tool.

The Veterans Affairs Healthcare System (VA) National Neurology Community Care Dashboard: Development and Evaluation (P4-7.003)

<h3>Objective:</h3> Evaluate a national neurology community care dashboard and describe its build and functionality. Demonstrate its public health application and neurological care coordination. <h3>Background:</h3> Neurological care coordination at a population level requires an accurate and reliable database that is user-friendly and produces timely and relevant reports. Furthermore, the ability to forecast public health needs requires standardized clinical and financial data collection over time. A neurology dashboard was set up to monitor and predict cost estimates of the medical services provided through community care referrals. The system was also responsible for assessing the number of patients remaining in the network. <h3>Design/Methods:</h3> A quality improvement (QI) study is currently in progress. A mixed methods design is being used, drawing from the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A 15-item survey of the value and usability of the dashboard and in-depth key informant interviews of neurology dashboard users are being conducted. <h3>Results:</h3> After several cycles of building and editing the dashboard, a beta version of the dashboard has been deployed. The current iteration displays utilization of community care among veterans with various neurology related conditions. Volume and cost of care by facility, patient demographic, ICD co-morbidities, and procedure codes can be extracted and visualized. Preliminary results of stakeholder interviews indicate that the dashboard was perceived as a useful tool among its users and was beneficial in improving cost analysis and monitoring patient flow in the network. <h3>Conclusions:</h3> The dashboard has the potential to be a highly valuable tool to monitor the coordination of neurological care. Although the tool was developed for a specific healthcare network, modules, and components of the dashboard may help inform the development of population-level neurology dashboards for other healthcare systems. <b>Disclosure:</b> Dr. Baig has nothing to disclose. Dr. Gopaul has nothing to disclose. Dr. Martini has nothing to disclose. The institution of Dr. Altalib has received research support from UCB. The institution of Dr. Altalib has received research support from Eisai. The institution of Dr. Altalib has received research support from Sunovian. The institution of Dr. Altalib has received research support from DEPARTMENT OF DEFENSE - CONGRESSIONALLY DIRECTED MEDICAL RESEARCH PROGRAMS.

Targeted bisulfite sequencing analysis of candidate genes associated with Alzheimer’s disease.

AbstractBackgroundRecent epigenome‐wide association studies (EWAS) have identified a number of loci in specific genes that show robust and reproducible alterations in DNA methylation in Alzheimer’s disease brain samples. The standard method to assess methylation in EWAS is via microarrays, however, these only target a limited number of methylation sites in each gene. Therefore, further analysis of methylation changes across the entire gene are required to determine the exact extent and pattern of methylation changes in disease. In this study we have performed targeted bisulfite sequencing for candidate genes in the Brains for Dementia Research (BDR) tissue sample resource, which is a highly characterized cohort containing tissue with a high degree of standardized pathological, clinical and administrative data available to allow comparative studies.MethodPrefrontal cortex brain samples from 60 individuals were selected from the BDR cohort and grouped by Braak stage (Control 0‐II; mild cognitive impairment III‐IV; AD V‐VI). The DNA was extracted, before 30 genomic regions of interest, identified from previous AD EWAS, were captured using Agilent Sure Select target baits. The DNA was next‐generation bisulfite sequenced, before the sequence reads were aligned and the methylation status of cytosine residues were called using the Bismark Bisulfite Mapper program. Differentially methylated positions (DMPs) were then analyzed across the three groups.ResultThe degree and extent of methylation within the targeted genomic regions were identified and quantified for each group before being analyzed using linear regression models.ConclusionThis study builds on previous work that identified differential methylation in several genomic regions that were associated with Braak stage. By identifying the exact positions that are subjected to differential methylation this work provides further evidence that dysregulation of methylation is associated with pathological changes in AD prefrontal cortex.

Sex Differences in Mild Cognitive Impairment Risk by APOE Genotype

AbstractBackgroundMale sex is suggested as a risk factor for mild cognitive impairment (MCI), but this association may be influenced by the female verbal memory advantage, and the use of verbal memory tests has been shown to delay MCI diagnosis in women. Moreover, this association may vary by apolipoprotein E (APOE) genotype. The National Alzheimer’s Coordinating Center (NACC) uniform data set (UDS) contains demographic data and annual standardized neuropsychological (with memory being assessed primarily with verbal tests) and clinical diagnostic data obtained by 41 NIA‐funded Alzheimer’s Disease Centers across the U.S. This large database is a rich resource for evaluating sex effects on MCI/Dementia diagnosis by APOE genotype.MethodWe restricted to NACC enrollees above 60 years old, with available APOE genotype, cognitively normal at their initial UDS visit, had at least one follow‐up visit, and without a documented dominantly inherited Alzheimer mutation, leaving 10,461 participants (35.3% men, 77.0% non‐Hispanic White). Cox proportional hazards models of time to incident MCI/dementia controlling for sex, baseline age, years of education, baseline Mini‐Mental Status Exam (MMSE), and interaction terms as indicated were fit separately to APOE E3/E3 homozygotes and E3/E4 heterozygotes. Small sample size precluded analyses of other APOE genotypes. Secondary analyses restricting to non‐Hispanic White participants were also examined.ResultAmong APOE E3/E3 homozygotes, male sex is a risk factor for clinically diagnosed MCI/dementia (Hazard ratio (HR) = 1.27, p &lt; 0.001) independent of age at enrollment. Among APOE E3/E4 heterozygotes, male sex is a risk factor at the lower range of age (age 60–75), while female sex is a risk factor at the higher range (interaction p = 0.006). Similar findings were obtained when restricting to the non‐Hispanic White participants.ConclusionThe higher risk of MCI/dementia in men in the NACC cohort may reflect previously‐reported sex differences in verbal memory performance at early stage AD pathogenesis and the use of verbal memory diagnostic tests more sensitive in men early in the disease.

GENERATION OF BIG DATA FOR FERTILITY TREATMENT AT THE NATIONAL LEVEL IN SOUTH KOREA

AI research projects to automatically grade embryos and predict pregnancy has been a popular topic. However, it is challenging to collect standardized embryo images and clinical data from multiple medical institutions using different electronic health records (EHR). Also, clinics use slightly different criteria to evaluate embryos. In South Korea, the cost of infertility treatment is covered by the National Health Insurance and fertility data has been accumulating. We would like to introduce the government funded project to collect and label fertility data at the national level.

Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis.

The definitive diagnosis and early treatment of many immune-mediated inflammatory diseases (IMIDs) is hindered by variable and overlapping clinical manifestations. Psoriatic arthritis (PsA), which develops in ~30% of people with psoriasis, is a key example. This mixed-pattern IMID is apparent in entheseal and synovial musculoskeletal structures, but a definitive diagnosis often can only be made by clinical experts or when an extensive progressive disease state is apparent. As with other IMIDs, the detection of multimodal molecular biomarkers offers some hope for the early diagnosis of PsA and the initiation of effective management and treatment strategies. However, specific biomarkers are not yet available for PsA. The assessment of new markers by genomic and epigenomic profiling, or the analysis of blood and synovial fluid/tissue samples using proteomics, metabolomics and lipidomics, provides hope that complex molecular biomarker profiles could be developed to diagnose PsA. Importantly, the integration of these markers with high-throughput histology, imaging and standardized clinical assessment data provides an important opportunity to develop molecular profiles that could improve the diagnosis of PsA, predict its occurrence in cohorts of individuals with psoriasis, differentiate PsA from other IMIDs, and improve therapeutic responses. In this review, we consider the technologies that are currently deployed in the EU IMI2 project HIPPOCRATES to define biomarker profiles specific for PsA and discuss the advantages of combining multi-omics data to improve the outcome of PsA patients.

Correlates of Opioid Use Among Ontario Long-Term Care Residents and Variation by Pain Frequency and Intensity: A Cross-sectional Analysis.

BackgroundChronic non-cancer pain is common among older residents of long-term care (LTC) homes and often poorly recognized and treated. With heightened concerns regarding opioid prescribing in recent years, it is important to examine the current prevalence of opioid use and its association with resident characteristics to help identify those potentially at risk of medication harms as well as suboptimal pain management.ObjectivesThe aims were to estimate the prevalence and correlates of opioid use among non-palliative LTC residents and explore variation in opioid prevalence and correlates across strata defined by pain frequency and intensity.MethodsWe conducted a population-based cross-sectional study of all older (aged > 65 years) LTC residents (excluding those with cancer or receiving palliative care) in Ontario, Canada during 2018–2019. Health administrative databases were linked with standardized clinical assessment data to ascertain residents’ health and pain characteristics and their opioid and other medication use. Modified Poisson regression models estimated unadjusted and adjusted associations between residents’ characteristics and opioid use, overall and across strata capturing pain frequency and intensity.ResultsAmong 75,020 eligible residents (mean age 85.1 years; 70% female), the prevalence of opioid use was 18.5% and pain was 29.4%. Opioid use ranged from 12.2% for residents with no current pain to 55.7% for those with severe pain. In adjusted models, residents newly admitted to LTC (adjusted risk ratio [aRR] = 0.60, 95% confidence interval [CI] 0.57–0.62) and with moderate to severe cognitive impairment (aRR = 0.69, 95% CI 0.66–0.72) or dementia (aRR = 0.76, 95% CI 0.74–0.79) were significantly less likely to receive an opioid, whereas residents with select conditions (e.g., arthritis, aRR = 1.37, 95% CI 1.32–1.41) and concurrently using gabapentinoids (aRR = 1.80, 95% CI 1.74–1.86), benzodiazepines (aRR = 1.33, 95% CI 1.28–1.38), or antidepressants (aRR = 1.31, 95% CI 1.27–1.35) were significantly more likely to receive an opioid. The associations observed for residents newly admitted, with dementia, and concurrently using gabapentinoids, benzodiazepines, or antidepressants were largely consistent across all pain strata.ConclusionsOur findings describe resident sub-groups at potentially higher risk of adverse health outcomes in relation to both opioid use and non-use. LTC clinical and policy changes informed by research are required to ensure the appropriate recognition and management of non-cancer pain in this setting.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40266-022-00972-9.

Higher treatment focus diffusion in Multisystemic Therapy is associated with less functional improvement over the course of treatment.

Treatment focus diffusion (TFD), dividing focus across multiple concerns during treatment, is common in public mental health care and differs from the more narrowly focused empirically supported treatments for youth reported in the literature. The present study examined whether and to what extent TFD is associated with youth functional improvement over the course of therapy. This study utilized multi-level modeling techniques to analyze 12 consecutive years of standardized routine clinical service data from youth receiving treatment in one of two intensive in-home service settings: (a) Multisystemic Therapy (MST; n = 776 youths, 99 therapists), an implemented evidence-based treatment based on ecological theories of behavior in which therapists work with the multiple systems a youth interacts with (school, community, family), and (b) a standards-based service (n = 1854 youth, 413 therapists). Both service settings operate in the context of a publicly funded mental health care system which serves youth and families who are typically from underserved and low-income backgrounds. Majority of youth in this sample identified as multi-ethnic and male, and they, on average, were approximately 13 years old with three co-occurring diagnoses. A significant TFD by service format interaction on youth functional improvement (alone and in the overall model) and follow up simple effects indicated that higher TFD was associated with significantly poorer outcomes in MST. The parameter estimate for TFD on functional improvement in the standards-based service format was in the same direction, but not statistically significant. Our findings suggest that maintaining a narrower treatment focus might be beneficial to clients, particularly in implemented evidence-based treatments. Likewise, TFD could be a helpful case monitoring tool for clinicians, supervisors, and systems leaders when reviewing intensive-in-home cases.

Epilepsy Course and Developmental Trajectories in STXBP1-DEE.

Background and ObjectivesClinical manifestations in STXBP1 developmental and epileptic encephalopathy (DEE) vary in severity and outcome, and the genotypic spectrum is diverse. We aim to trace the neurodevelopmental trajectories in individuals with STXBP1-DEE and dissect the relationship between neurodevelopment and epilepsy.MethodsRetrospective standardized clinical data were collected through international collaboration. A composite neurodevelopmental score system compared the developmental trajectories in STXBP1-DEE.ResultsForty-eight patients with de novo STXBP1 variants and a history of epilepsy were included (age range at the time of the study: 10 months to 35 years, mean 8.5 years). At the time of inclusion, 65% of individuals (31/48) had active epilepsy, whereas 35% (17/48) were seizure free, and 76% of those (13/17) achieved remission within the first year of life. Twenty-two individuals (46%) showed signs of developmental impairment and/or neurologic abnormalities before epilepsy onset. Age at seizure onset correlated with severity of developmental outcome and the developmental milestones achieved, with a later seizure onset associated with better developmental outcome. In contrast, age at seizure remission and epilepsy duration did not affect neurodevelopmental outcomes. Overall, we did not observe a clear genotype-phenotype correlation, but monozygotic twins with de novo STXBP1 variant showed similar phenotype and parallel disease course.DiscussionThe disease course in STXBP1-DEE presents with 2 main trajectories, with either early seizure remission or drug-resistant epilepsy, and a range of neurodevelopmental outcomes from mild to profound intellectual disability. Age at seizure onset is the only epilepsy-related feature associated with neurodevelopment outcome. These findings can inform future dedicated natural history studies and trial design.

Implementation of a Data Warehouse in Primary Care: First Analyses with Elderly Patients.

The implementation of clinical data warehouses has advanced in recent years. The standardization of clinical data in these warehouses has made it possible to carry out multicenter studies and to formalize the clinical vocabulary. However, there is limited insight into a patient's overall care pathway in the clinical domain. Regarding primary care data, the implementation of this type of warehouse in a routine way is hindered in particular by the analysis of textual data provided by general practitioners during patient consultations. In our study we collected primary care data for standardization in a data warehouse. The purpose of this analysis was to assess the feasibility of analyzing primary care data, and particularly to study the consultations and prescriptions of the elderly patient contained in our primary care data warehouse.

Current state of dental informatics in the field of health information systems: a scoping review

BackgroundOver the past 50 years, dental informatics has developed significantly in the field of health information systems. Accordingly, several studies have been conducted on standardized clinical coding systems, data capture, and clinical data reuse in dentistry.MethodsBased on the definition of health information systems, the literature search was divided into three specific sub-searches: “standardized clinical coding systems,” “data capture,” and “reuse of routine patient care data.” PubMed and Web of Science were searched for peer-reviewed articles. The review was conducted following the PRISMA-ScR protocol.ResultsA total of 44 articles were identified for inclusion in the review. Of these, 15 were related to “standardized clinical coding systems,” 15 to “data capture,” and 14 to “reuse of routine patient care data.” Articles related to standardized clinical coding systems focused on the design and/or development of proposed systems, on their evaluation and validation, on their adoption in academic settings, and on user perception. Articles related to data capture addressed the issue of data completeness, evaluated user interfaces and workflow integration, and proposed technical solutions. Finally, articles related to reuse of routine patient care data focused on clinical decision support systems centered on patient care, institutional or population-based health monitoring support systems, and clinical research.ConclusionsWhile the development of health information systems, and especially standardized clinical coding systems, has led to significant progress in research and quality measures, most reviewed articles were published in the US. Clinical decision support systems that reuse EDR data have been little studied. Likewise, few studies have examined the working environment of dental practitioners or the pedagogical value of using health information systems in dentistry.

Concept and Proof of the Lifelog Bigdata Platform for Digital Healthcare and Precision Medicine on the Cloud.

PurposeWe propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system.Materials and MethodsThe Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components: data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis.ResultsThe big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB.ConclusionThe Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.

Nevada exploratory Alzheimer’s disease research center: Advancing dementia care in rural communities

AbstractBackgroundPeople with MCI and AD living in rural areas represent an understudied and underserved population in the broader AD landscape. Very little works have been done on rural cohorts by NIA‐designated Alzheimer’s Disease Research Centers (ADRCs) in the past. The Nevada Exploratory ADRC (NVeADRC) is specifically set to study AD and related dementias in rural individuals, and to systematically generate novel data on the clinical features of AD, clinical outcomes, and social determinants of health.MethodThe leading scientific objectives of the NVeADRC are to: 1) understand the occurrence of AD and optimize treatment in underserved rural settings; 2) examine risk factors associated with the development of AD in rural areas; and 3) evaluate the trajectories and progression of AD in rural versus urban communities. The NVeADRC will deploy a top‐down strategy to: 1) establish a network of stakeholders and medical partners to increase awareness of dementia in rural communities and enroll rural participants in our research cohort; 2) develop a research enterprise for longitudinal studies on dementia in rural populations; 3) progressively implement a research infrastructure rooted in technology to facilitate standardized clinical data collection from patients in rural areas.ResultThe data collected will be part of the National Alzheimer’s Coordinating Center (NACC) and shared with the larger scientific community to ultimately to improve healthcare for patients in rural areas. Target recruitment for the NVeADRC include 20 Mild Cognitive Impairment, 40 dementia due to Alzheimer’s disease and 40 cognitively normal subjects. In addition to the Uniform Data Set 3.0, we intend to collect volumetric MRI, biofluids (blood, saliva, and cerebrospinal fluid), as well as, a series of both static and dynamic social determinants of health on study subjects and their partners. All participants will be followed to voluntary withdrawal or autopsy.ConclusionThe NVeADRC represents a unique, high impact entity within the ADRC network, and will generate high‐quality biomedical data that advance AD/ADRD research on individuals living in underserved rural communities. As we grow our project, we intend to share biospecimens and data with the scientific community.