Modeling longitudinal oral health status and pneumonia risk: secondary data analyses of an integrated dental-medical cohort.

BACKGROUND: Mounting evidence indicates potential associations between poor oral health status (OHS) and increased pneumonia risk. Relative pneumonia risk was assessed in the context of longitudinally documented OHS. METHODS: Electronic medical/dental patient data captured from 2007 through 2019 were retrieved from the integrated health records of Marshfield Clinic Health Systems. Participant eligibility initiated with an assessment of OHS, stratified into the best, moderate, or worst OHS groups, with the additional criterion of 'no pneumonia diagnosis in the past 90 days'. Pneumonia incidence was longitudinally monitored for up to 1 year from each qualifying dental visit. Models were assessed, with and without adjustment for prior pneumonia incidence, adjusted for smoking and subjected to confounding mitigation attributable to known pneumonia risk factors by applying propensity score analysis. Time-to-event analysis and proportional hazard modeling were applied to investigate relative pneumonia risk over time among the OHS groups. RESULTS: Modeling identified associations between any incident pneumonia subtype and 'number of missing teeth' (p < 0.001) and 'clinically assessed periodontal status' (p < 0.01), which remained significant following adjustment for prior pneumonia incidence and smoking. The hazard ratio (HR) for 'any incident pneumonia' in the best OHS group for 'number of missing teeth' was 0.65, 95% confidence interval (CI) [0.54 - 0.79] (unadjusted) and 0.744, 95% CI [0.61 - 0.91] (adjusted). The HR for 'any incident pneumonia' in the best 'clinically assessed periodontal status' group was 0.72, 95% CI [0.58 - 0.90] (unadjusted) and 0.78, 95% CI [0.62 - 0.97] (adjusted). CONCLUSION/CLINICAL RELEVANCE: Poor OHS increased pneumonia risk. Proactive attention of medical providers to patient OHS and health literacy surrounding oral-systemic disease association is vital, especially in high-risk populations.

DentaSeal: A school-based dental sealant efficiency assessment tool to support statewide monitoring and reporting: A field report.

BACKGROUND: The evidence base supports effectiveness of dental sealants for prevention of childhood caries in school-aged children. OBJECTIVE: This study describes planning, development, usability testing and outcomes following implementation of DentaSeal, a web-based application designed to accurately track unique student data and generate reports for all Wisconsin school-based sealant placement (SP) programs. METHODS: Application software development was informed by a steering committee of representative stakeholders who were interviewed to inform design and provide feedback for design of DentaSeal during development and evaluation. Software development proceeded based on wireframes developed to build architectural design. Usability testing followed and informed any required adjustments to the application. The DentaSeal prototype was beta tested and fully implemented subsequently in the public health sector. RESULTS: The DentaSeal application demonstrated capacity to: 1) track unique student SP data and longitudinal encounter history, 2) generate reports and 3) support administrative tracking. In 2019, DentaSeal captured SP data of 47 school-based programs in Wisconsin that sponsored > 7,000 program visits for 184,000 children from 62 counties. Delivery of > 548,000 SP services were catalogued. CONCLUSIONS: For public health initiatives targeting reduction in caries incidence, web-based applications such as DentaSeal represent useful longitudinal tracking tools for cataloguing SP in school-based program participants.

TADA: The Topology-Accommodating Direction Assignment Algorithm for Liquid Crystals.

Despite the fact that topological defects are a hallmark of liquid crystalline materials, current computational techniques for identifying topological defects in particle-based simulations of these materials─which rest upon Q-tensor theory─do not leverage topological features of the system. In this work, we describe the topology-accommodating direction assignment (TADA) algorithm, a novel approach for identifying disclination cores in liquid crystalline materials, which is sensitive to topology: this method assigns to each mesogen a unique vector, thereby extending the concept of the liquid crystal director field down to the scale of mesogens. In systems containing disclination cores, TADA identifies line segments along which this assigned vector field is discontinuous, with cores located at the interior termination points of these line segments. The mere presence of defects can be identified by searching far away from them. We validate this approach by comparing its results to those obtained using the scalar order parameter for a variety of liquid crystalline assemblies sourced from molecular-dynamics simulations. We also discuss several benefits of the TADA algorithm over existing approaches for identifying topological defects in liquid crystalline materials.

Variations in temporal trends in non-traumatic dental condition related emergencies.

OBJECTIVE: The objective of the study was to investigate temporal trends in non-traumatic dental condition (NTDC) related emergency visits at Emergency Department (ED), urgent care (UC), and at a Federally Qualified Health Center (FQHC) that providing dental services to a mid-sized rural community. METHODS: Temporal trends over a 9-year period (2008-2016) in NTDC rates at ED, UC, FQHC and in a region around the FQHC were determined. Statistically significant changes (α = 0.05) in the proportion of NTDC visits between FQHC and UC across each of the time points were investigated. RESULTS: Proportion of NTDC ED visits was relatively stable over the study period; whereas those at FQHC exceeded those at UC site beginning 2012 and were significantly (α = 0.05) higher than that of UC subsequently (2015-2016). CONCLUSIONS: NTDCs are preventable dental conditions and the care provided in treating NTDCs in emergency settings is palliative and does not address the underlying conditions resulting in poor outcomes. The results presented elucidate the critical role of FQHCs in significantly reducing NTDC visits. These might be precursors to a potential shift in NTDC care seeking behavior and expected to favorably impact oral health outcomes.

Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach.

Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall-precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care.

A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing.

INTRODUCTION: Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format. OBJECTIVE: The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format. METHODS: A pneumonia-specific natural language processing (NLP) pipeline was strategically developed applying Clinical Text Analysis and Knowledge Extraction System (cTAKES) to validate pneumonia diagnoses following development of a pneumonia feature-specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: "positive," "negative," or "not classified: requires manual review" based on tagged concepts that support or refute diagnostic codes. RESULTS: A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest X-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as "Pneumonia-positive," 19% as (15401/81,707) as "Pneumonia-negative," and 48% (39,209/81,707) as "episode classification pending further manual review." NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%). CONCLUSION: The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date.

Identifying Pneumonia Subtypes from Electronic Health Records Using Rule-Based Algorithms.

BACKGROUND: The International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations where pneumonia is standardly subtyped by settings, exposures, and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHRs), frequently in nonstructured formats including radiological interpretation or clinical notes that complicate electronic classification. OBJECTIVE: The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR. METHODS: Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for "rule of two" pneumonia-related codes or one ICD code and radiologically confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support subclassification based on features including symptomatic patient point of entry into the health care system timing of pneumonia emergence and identification of clinical, laboratory, or medication orders that informed definition of the pneumonia subclassification algorithm. RESULTS: Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following Natural Language Processing classification of pneumonia status as "negative" or "unknown." Subtyping of 83,387 episodes identified: community-acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), health care-acquired (5%), and ventilator-associated (0.4%) cases, and 9.4% cases were not classifiable by the algorithm. CONCLUSION: Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored.

How Medical-Dental EHR Integration Can Improve Diabetes Care.

Since the mid-1990s, poor oral health has been neglected as a public health threat, despite its recognition as epidemic in scale by the US Department of Health and Human Services Office of the Surgeon General. Americans' poor oral health influences their overall health and, from a population standpoint, incurs dire economic and human costs. This article describes how health information transfer within the Marshfield Clinic Health System's integrated medical and dental practice can improve diabetes care. This article also considers ethics and equity implications of improving MDP electronic health record interoperability in this large, rural Wisconsin organization.

Systematic review of studies examining contribution of oral health variables to risk prediction models for undiagnosed Type 2 diabetes and prediabetes.

OBJECTIVE: To conduct systematic review applying "preferred reporting items for systematic reviews and meta-analyses statement" and "prediction model risk of assessment bias tool" to studies examining the performance of predictive models incorporating oral health-related variables as candidate predictors for projecting undiagnosed diabetes mellitus (Type 2)/prediabetes risk. MATERIALS AND METHODS: Literature searches undertaken in PubMed, Web of Science, and Gray literature identified eligible studies published between January 1, 1980 and July 31, 2018. Systematically reviewed studies met inclusion criteria if studies applied multivariable regression modeling or informatics approaches to risk prediction for undiagnosed diabetes/prediabetes, and included dental/oral health-related variables modeled either independently, or in combination with other risk variables. RESULTS: Eligibility for systematic review was determined for seven of the 71 studies screened. Nineteen dental/oral health-related variables were examined across studies. "Periodontal pocket depth" and/or "missing teeth" were oral health variables consistently retained as predictive variables in models across all systematically reviewed studies. Strong performance metrics were reported for derived models by all systematically reviewed studies. The predictive power of independently modeled oral health variables was marginally amplified when modeled with point-of-care biological glycemic measures in dental settings. Meta-analysis was precluded due to high inter-study variability in study design and population diversity. CONCLUSIONS: Predictive modeling consistently supported "periodontal measures" and "missing teeth" as candidate variables for predicting undiagnosed diabetes/prediabetes. Validation of predictive risk modeling for undiagnosed diabetes/prediabetes across diverse populations will test the feasibility of translating such models into clinical practice settings as noninvasive screening tools for identifying at-risk individuals following demonstration of model validity within the defined population.

Identifying oral disease variables associated with pneumonia emergence by application of machine learning to integrated medical and dental big data to inform eHealth approaches.

Background: The objective of this study was to build models that define variables contributing to pneumonia risk by applying supervised Machine Learning-(ML) to medical and oral disease data to define key risk variables contributing to pneumonia emergence for any pneumonia/pneumonia subtypes. Methods: Retrospective medical and dental data were retrieved from Marshfield Clinic Health System's data warehouse and integrated electronic medical-dental health records (iEHR). Retrieved data were pre-processed prior to conducting analyses and included matching of cases to controls by (a) race/ethnicity and (b) 1:1 Case: Control ratio. Variables with >30% missing data were excluded from analysis. Datasets were divided into four subsets: (1) All Pneumonia (all cases and controls); (2) community (CAP)/healthcare associated (HCAP) pneumonias; (3) ventilator-associated (VAP)/hospital-acquired (HAP) pneumonias and (4) aspiration pneumonia (AP). Performance of five algorithms were compared across the four subsets: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Random Forests. Feature (input variables) selection and ten-fold cross validation was performed on all the datasets. An evaluation set (10%) was extracted from the subsets for further validation. Model performance was evaluated in terms of total accuracy, sensitivity, specificity, F-measure, Mathews-correlation-coefficient and area under receiver operating characteristic curve (AUC). Results: In total, 6,034 records (cases and controls) met eligibility for inclusion in the main dataset. After feature selection, the variables retained in the subsets were: All Pneumonia (n = 29 variables), CAP-HCAP (n = 26 variables); VAP-HAP (n = 40 variables) and AP (n = 37 variables), respectively. Variables retained (n = 22) were common across all four pneumonia subsets. Of these, the number of missing teeth, periodontal status, periodontal pocket depth more than 5 mm and number of restored teeth contributed to all the subsets and were retained in the model. MLP outperformed other predictive models for All Pneumonia, CAP-HCAP and AP subsets, while SVM outperformed other models in VAP-HAP subset. Conclusion: This study validates previously described associations between poor oral health and pneumonia. Benefits of an integrated medical-dental record and care delivery environment for modeling pneumonia risk are highlighted. Based on findings, risk score development could inform referrals and follow-up in integrated healthcare delivery environment and coordinated patient management.

ESTABLISHING A QUALITY IMPROVEMENT CULTURE WITHIN A LARGE INTEGRATED MEDICAL-DENTAL HEALTH SYSTEM WITH A POPULATION BASED FOCUS.

OBJECTIVES: Quality improvement strategies have been an integral part of healthcare to attain improved care delivery and effective health outcomes. The dental quality initiative improvement (DQII) presented in this manuscript represents a case study of successful implementation of a quality improvement culture within a large integrated-medical-dental health system serving a largely rural population. METHODS: The key elements of DQII included steering committee establishment, definition or dental quality measures and development/implementation of a dental quality analytics dashboard (DQAD) that provides relevant data on dental quality measures. Qualitative metrics were applied to look at the improvement in performance for the various measures relative to quality benchmarks. RESULTS: DQII facilitated improved oversight of care continuity and provider performance surrounding quality measures at granular and/or institutional level. Improvement associated with care delivery performance relative to benchmarks was observed. CONCLUSIONS: DQII further advanced the quality improvement culture prevalent in our learning healthcare environment with its focus on value-based care delivery. DQII initiative and establishment of DQAD provided ability to track performance in operational care delivery for dental providers in a clinical setting in real time.

Proteomics, Lipidomics, Metabolomics, and 16S DNA Sequencing of Dental Plaque From Patients With Diabetes and Periodontal Disease.

Oral microbiome influences human health, specifically prediabetes and type 2 diabetes (Pre-DM/DM) and periodontal diseases (PDs), through complex microbial interactions. To explore these relations, we performed 16S rDNA sequencing, metabolomics, lipidomics, and proteomics analyses on supragingival dental plaque collected from individuals with Pre-DM/DM (n = 39), Pre-DM/DM and PD (n = 37), PD alone (n = 11), or neither (n = 10). We identified on average 2790 operational taxonomic units and 2025 microbial and host proteins per sample and quantified 110 metabolites and 415 lipids. Plaque samples from Pre-DM/DM patients contained higher abundance of Fusobacterium and Tannerella than plaques from metabolically healthy patients. Phosphatidylcholines, plasmenyl phosphatidylcholines, ceramides containing non-OH fatty acids, and host proteins related to actin filament rearrangement were elevated in plaques from PD versus non-PD samples. Cross-omic correlation analysis enabled the detection of a strong association between Lautropia and monomethyl phosphatidylethanolamine (PE-NMe), which is striking because synthesis of PE-NMe is uncommon in oral bacteria. Lipidomics analysis of in vitro cultures of Lautropia mirabilis confirmed the synthesis of PE-NMe by the bacteria. This comprehensive analysis revealed a novel microbial metabolic pathway and significant associations of host-derived proteins with PD.

Knowledge, Attitudes, Behaviors of Women Related to Pregnancy, and Early Childhood Caries Prevention: A Cross-Sectional Pilot Study.

OBJECTIVE: Health education interventions during pregnancy can influence maternal oral health (OH), maternal OH-behaviors and children's OH. Interventions that can be delivered at anytime and anywhere, for example mobile-health (mHealth) provides an opportunity to address challenges of health education and support activation of women in underserved and rural communities to modify their health behavior. This pilot study was undertaken as a part of a mHealth initiative to determine knowledge, attitudes, and behaviors related to pregnancy and ECC prevention among women attending obstetrics/gynecology (OB/GYN) practices at a large rurally-based clinic. METHODS: A cross-sectional survey study was voluntarily engaged by women (n = 191) aged 18 to 59 years attending OB/GYN visits, over a 3-week period from 12/2019 to 1/2020. Survey results were analyzed applying descriptive statistics, X2 and Fisher's Exact tests. The significance level was set at P < .0001 for all analyses. RESULTS: Approximately half of respondents were between 18 and 29 years (53%), had a college degree (55%), and 100% reported cell phone use. Whereas 53% and 31%, respectively, indicated that they were "somewhat" or "very" sure of how to prevent ECC in their children, only 9% recognized evidence of early decay and 30% did not know the purpose of fluoride. Overall, only 27% of participants correctly answered the knowledge-based questions. Further, only 57% reported their provider explained things in a way that was easy to understand. Only 24% reported seeing a dentist during their current pregnancy. CONCLUSIONS: Study results suggested potential gaps in knowledge and behaviors related to ECC prevention and provided baseline data to inform future interventions to improve ECC prevention practices. Notably, majority of participants used their cell phones for making medical/dental appointments and reported using their phones to look up health-related information. This demographic represents a potentially receptive target for mHealth approaches to improve understanding of oral health maintenance during pregnancy and ECC prevention.

Longitudinal Observation of Outcomes and Patient Access to Integrated Care Following Point-of-Care Glycemic Screening in Community Health Center Dental Safety Net Clinics.

Introduction: Rates of diabetes/prediabetes continue to increase, with disparity populations disproportionately affected. Previous field trials promoted point-of-care (POC) glycemic screening in dental settings as an additional primary care setting to identify potentially at-risk individuals requiring integrated care intervention. The present study observed outcomes of POC hemoglobin A1c (HbA1c) screening at community health center (CHC) dental clinics (DC) and compliance with longitudinal integrated care management among at-risk patients attending dental appointments. Materials and Methods: POC HbA1c screening utilizing Food and Drug Administration (FDA)-approved instrumentation in DC settings and periodontal evaluation of at-risk dental patients with no prior diagnosis of diabetes/prediabetes and no glycemic testing in the preceding 6 months were undertaken. Screening of patients attending dental appointments from October 24, 2017, through September 24, 2018, was implemented at four Wisconsin CHC-DCs serving populations with a high representation of disparity. Subjects meeting at-risk profiles underwent POC HbA1c screening. Individuals with measures in the diabetic/prediabetic ranges were advised to seek further medical evaluation and were re-contacted after 3 months to document compliance. Longitudinal capture of glycemic measures in electronic health records for up to 2 years was undertaken for a subset (n = 44) of subjects with available clinical, medical, and dental data. Longitudinal glycemic status and frequency of medical and dental access for follow-up care were monitored. Results: Risk assessment identified 224/915 (24.5%) patients who met inclusion criteria following two levels of risk screening, with 127/224 (57%) qualifying for POC HbA1c screening. Among those tested, 62/127 (49%) exhibited hyperglycemic measures: 55 in the prediabetic range and seven in the diabetic range. Moderate-to-severe periodontitis was more prevalent in patients with prediabetes/diabetes than in individuals with measures in the normal range. Participant follow-up compliance at 3 months was 90%. Longitudinal follow-up documented high rates of consistent access (100 and 89%, respectively), to the integrated medical/DC environment over 24 months for individuals with hyperglycemic screening measures. Conclusion: POC glycemic screening revealed elevated HbA1c measures in nearly half of at-risk CHC-DC patients. Strong compliance with integrated medical/dental management over a 24-month interval was observed, documenting good patient receptivity to POC screening in the dental setting and compliance with integrated care follow-up by at-risk patients.

Positioning operations in the dental safety net to enhance value-based care delivery in an integrated health-care setting.

OBJECTIVES: Impact of implementing data-driven performance metric-tracking across a 10-dental center infrastructure established by Family Heath Center of Marshfield (FHC-M) was examined for relative impact on achieving value-based care delivery in serving a patient population characterized by 88% Medicaid representation. METHODS: To track progress toward national benchmarks for preventive care delivery, dental quality analytics dashboard tracking was implemented in real time with sharing of performance metrics across centers. Compliance rate with Uniform Data Systems reporting requirements for sealant placement on permanent first molars in children aged 6-9 years of age at moderate-to-high risk of caries was targeted at FHC-M dental centers for comparison with those of other community health centers statewide and nationally. Hygienist-to-dentist ratio to support robust sealant placement capacity was further examined. RESULTS: Uniform Data Systems data for rate of sealant placement between 2016-2018 revealed that FHC-M consistently exceeded rates reported statewide and nationally. For this quality indicator, performance across all dental practices in 27 states reported by Centers for Medicare and Medicaid Services in 2018 achieved 23% in 2017 compared to 73% and 52% placement rates reported by FHC-M and community health centers, respectively. A 1:1 hygienist-to-dentist was documented across FHC-M dental centers compared to 0.5:1 reported nationally. CONCLUSIONS: Implementation of quality metric dashboard and a 1:1 dentist-to-hygienist ratio supported realization of value-based dental care delivery relative to caries prevention in a moderate-to-high risk pediatric Medicaid population through achievement of robust sealant placement. Importance of adequate hygienist staffing, "same day" sealant placement and performance feedback supported by technology are highlighted.

Structuring, reuse and analysis of electronic dental data using the Oral Health and Disease Ontology.

BACKGROUND: A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information collected during dental care exposes many of the challenges that confront a wider scale approach. For example, to improve the quality of dental care, we must be able to collect and analyze data about dental procedures from multiple practices. However, a number of challenges make doing so difficult. First, dental electronic health record (EHR) information is often stored in complex relational databases that are poorly documented. Second, there is not a commonly accepted and implemented database schema for dental EHR systems. Third, integrative work that attempts to bridge dentistry and other settings in healthcare is made difficult by the disconnect between representations of medical information within dental and other disciplines' EHR systems. As dentistry increasingly concerns itself with the general health of a patient, for example in increased efforts to monitor heart health and systemic disease, the impact of this disconnect becomes more and more severe. To demonstrate how to address these problems, we have developed the open-source Oral Health and Disease Ontology (OHD) and our instance-based representation as a framework for dental and medical health care information. We envision a time when medical record systems use a common data back end that would make interoperating trivial and obviate the need for a dedicated messaging framework to move data between systems. The OHD is not yet complete. It includes enough to be useful and to demonstrate how it is constructed. We demonstrate its utility in an analysis of longevity of dental restorations. Our first narrow use case provides a prototype, and is intended demonstrate a prospective design for a principled data backend that can be used consistently and encompass both dental and medical information in a single framework. RESULTS: The OHD contains over 1900 classes and 59 relationships. Most of the classes and relationships were imported from existing OBO Foundry ontologies. Using the LSW2 (LISP Semantic Web) software library, we translated data from a dental practice's EHR system into a corresponding Web Ontology Language (OWL) representation based on the OHD framework. The OWL representation was then loaded into a triple store, and as a proof of concept, we addressed a question of clinical relevance - a survival analysis of the longevity of resin filling restorations. We provide queries using SPARQL and statistical analysis code in R to demonstrate how to perform clinical research using a framework such as the OHD, and we compare our results with previous studies. CONCLUSIONS: This proof-of-concept project translated data from a single practice. By using dental practice data, we demonstrate that the OHD and the instance-based approach are sufficient to represent data generated in real-world, routine clinical settings. While the OHD is applicable to integration of data from multiple practices with different dental EHR systems, we intend our work to be understood as a prospective design for EHR data storage that would simplify medical informatics. The system has well-understood semantics because of our use of BFO-based realist ontology and its representation in OWL. The data model is a well-defined web standard.

Dental Decision Simulation (DDSim): Development of a virtual training environment.

PURPOSE: Case-based simulations are powerful training tools that can enhance learning and drive behavior change. This is an overview of the design/development of Dental Decision Simulation (DDSim), a web-based simulation of an electronic dental record (EDR). The purpose was to use DDSim to train dentists to make evidence-based treatment planning decisions consistent with current evidence. This simulated EDR provides case-based information in support of a set of defined evidence-based learning objectives. METHODS: The development of this complex simulation model required coordinated efforts to create several components: identify behavior changes, case authoring mechanism, create virtual patient visits, require users to make treatment plan decisions related to learning objectives, and a feedback mechanism to help users recognize departures from those learning objectives. This simulation was evaluated in a 2-arm, clinic-randomized, controlled pilot study examining the extent to which DDSim changed dentists' planned treatment to conform to evidence-based treatment guidelines relative to change in dentists not exposed to DDSim. Outcomes were measured by comparing preintervention and postintervention patient EDR treatment data. RESULTS: Changes in behavior over time did not favor intervention or control clinics. CONCLUSION: DDSim provides a standardized learning platform that cannot be achieved through the use of live patients. Both live patients and case-based simulations can be used to transfer knowledge and skill development. DDSim offers the advantage of providing a platform for developing treatment planning skills in a low-risk environment. However, further research examining behavior change is needed.

A Secondary Analysis of Panoramic Radiographs Reveals Hotspots in the Maxillofacial Region Associated with Diabetes.

Diabetes mellitus is the putative cause of a number of pathologies occurring in the bony and soft tissues of the maxillo-facial region and is known to exacerbate other oral diseases such as periodontitis.We present the first use of clinical panoramic radiographs for a secondary analysis of disease, with a focus on identifying hotspots in the maxillofacial region that are associated with diabetes. We developed a curated data set using Consensus Landmark Points (CLPs) and used that data to develop an analysis pipeline. This pipeline entailed automatic data cleansing, registration, and intensity normalization. The pipeline was used to process 7280 uncurated images that were subsequently analyzed using pixel-wise methods for a case/control study of patients with a history of diabetes. We detected statistically significant clusters of pixels that demarcated anatomical hotspots specific to the diabetic patients.

Correlation Between Oral Health and Systemic Inflammation (COHESION): A Randomized Pilot Follow-Up Trial of a Plaque-Identifying Toothpaste.

BACKGROUND: Inflammation is intimately involved in the pathogenesis of atherosclerosis and is accurately measured by high-sensitivity C-reactive protein (hs-CRP), a sensitive marker for future risk of cardiovascular disease. The Correlation between Oral Health and Systemic Inflammation (COHESION) trial was designed to test the hypothesis that PlaqueHD, a plaque-identifying toothpaste, reduces hs-CRP. METHODS: The trial was designed initially to include 132 subjects with hs-CRP between 2.0 and 10.0 mg/L but instead randomized 112 between 0.5 and 10.0, of which 103 had baseline and follow-up data and comprised the intention-to-treat sample. Of these, a prespecified subgroup analysis included 40 with baseline hs-CRP >2.0 and all hs-CRP <10. Because the distribution of hs-CRP was skewed toward higher values, to achieve normality assumptions, the significance of changes in hs-CRP between groups over time was tested on log-transformed data using a mixed effects analysis of variance. RESULTS: The intention-to-treat analysis showed no significant differences between the PlaqueHD and placebo group (P = .615). The prespecified subgroup analysis showed a significant difference between the PlaqueHD and placebo group (P = .047). Results of the analysis showed a reduction in hs-CRP at follow-up of 0.58 in the PlaqueHD and an increase of 0.55 in the placebo group. CONCLUSIONS: These findings are compatible with those of a prior pilot trial that also suggested benefits only in subjects with baseline elevations. Future trials targeting reductions of hs-CRP levels should randomize subjects with baseline hs-CRP between 2.0 and 10.0 mg/L.