Prediction of Sjögren's disease diagnosis using matched electronic dental-health record data.

BACKGROUND: Sjögren's disease (SD) is an autoimmune disease that is difficult to diagnose early due to its wide spectrum of clinical symptoms and overlap with other autoimmune diseases. SD potentially presents through early oral manifestations prior to showing symptoms of clinically significant dry eyes or dry mouth. We examined the feasibility of utilizing a linked electronic dental record (EDR) and electronic health record (EHR) dataset to identify factors that could be used to improve early diagnosis prediction of SD in a matched case-control study population. METHODS: EHR data, including demographics, medical diagnoses, medication history, serological test history, and clinical notes, were retrieved from the Indiana Network for Patient Care database and dental procedure data were retrieved from the Indiana University School of Dentistry EDR. We examined EHR and EDR history in the three years prior to SD diagnosis for SD cases and the corresponding period in matched non-SD controls. Two conditional logistic regression (CLR) models were built using Least Absolute Shrinkage and Selection Operator regression. One used only EHR data and the other used both EHR and EDR data. The ability of these models to predict SD diagnosis was assessed using a concordance index designed for CLR. RESULTS: We identified a sample population of 129 cases and 371 controls with linked EDR-EHR data. EHR factors associated with an increased risk of SD diagnosis were the usage of lubricating throat drugs with an odds ratio (OR) of 14.97 (2.70-83.06), dry mouth (OR = 6.19, 2.14-17.89), pain in joints (OR = 2.54, 1.34-4.76), tear film insufficiency (OR = 27.04, 5.37-136.), and rheumatoid factor testing (OR = 6.97, 1.94-25.12). The addition of EDR data slightly improved model concordance compared to the EHR only model (0.834 versus 0.811). Surgical dental procedures (OR = 2.33, 1.14-4.78) were found to be associated with an increased risk of SD diagnosis while dental diagnostic procedures (OR = 0.45, 0.20-1.01) were associated with decreased risk. CONCLUSION: Utilizing EDR data alongside EHR data has the potential to improve prediction models for SD. This could improve the early diagnosis of SD, which is beneficial to slowing or preventing complications of SD.

Longevity of dental restorations in Sjogren's disease patients using electronic dental and health record data.

BACKGROUND: Decreased salivary secretion is not only a risk factor for carious lesions in Sjögren's disease (SD) but also an indicator of deterioration of teeth with every restorative replacement. This study determined the longevity of direct dental restorations placed in patients with SD using matched electronic dental record (EDR) and electronic health record (EHR) data. METHODS: We conducted a retrospective cohort study using EDR and EHR data of Indiana University School of Dentistry patients who have a SD diagnosis in their EHR. Treatment history of patients during 15 years with SD (cases) and their matched controls with at least one direct dental restoration were retrieved from the EDR. Descriptive statistics summarized the study population characteristics. Cox regression models with random effects analyzed differences between cases and controls for time to direct restoration failure. Further the model explored the effect of covariates such as age, sex, race, dental insurance, medical insurance, medical diagnosis, medication use, preventive dental visits per year, and the number of tooth surfaces on time to restoration failure. RESULTS: At least one completed direct restoration was present for 102 cases and 42 controls resulting in a cohort of 144 patients' EDR and EHR data. The cases were distributed as 21 positives, 57 negatives, and 24 uncertain cases based on clinical findings. The average age was 56, about 93% were females, 54% were White, 74% had no dental insurance, 61% had public medical insurance, < 1 preventive dental visit per year, 94% used medications and 93% had a medical diagnosis that potentially causes dry mouth within the overall study cohort. About 529 direct dental restorations were present in cases with SD and 140 restorations in corresponding controls. Hazard ratios of 2.99 (1.48-6.03; p = 0.002) and 3.30 (1.49-7.31, p-value: 0.003) showed significantly decreased time to restoration failure among cases and positive for SD cases compared to controls, respectively. Except for the number of tooth surfaces, no other covariates had a significant influence on the survival time. CONCLUSION: Considering the rapid failure of dental restorations, appropriate post-treatment assessment, management, and evaluation should be implemented while planning restorative dental procedures among cases with SD. Since survival time is decreased with an increase in the number of surfaces, guidelines for restorative procedures should be formulated specifically for patients with SD.

Feasibility of Utilizing Electronic Dental Record Data and Periodontitis Case Definition to Automate Diagnosis.

Periodontitis is an irreversible disease leading to tooth loss, and 42% U.S. population suffers from periodontitis. Hence, diagnosing, monitoring, and determining its prevalence is critical to develop preventive strategies. However, a nationwide epidemiological study estimating the prevalence reported a concern about the discontinuation of such studies due to cost and ethical reasons. Therefore, this study determined the feasibility of utilizing electronic dental record (EDR) data and periodontitis case definition to automate periodontitis diagnosis. We utilized EDR data from the Indiana University School of Dentistry of 28,908 unique patients. We developed and tested a computer algorithm to diagnose periodontitis using the case definition. We found 44%, 22%, and 1% of patients with moderate, severe, and mild periodontitis, respectively. The algorithm worked with 100% sensitivity, specificity, and accuracy because of the excellent quality of the EDR data. We concluded the feasibility of providing automated periodontitis diagnosis from EDR data to conduct epidemiological studies across the US.

Rule-Based Text Classification of Dental Diagnosis.

Unstructured medical records boast an abundance of information that could greatly facilitate medical decision-making and improve patient care. With the development of Natural Language Processing (NLP) methodology, the free-text medical data starts to attract more and more research attention. Most existing studies try to leverage the power of such unstructured data using Machine Learning algorithms, which would usually require a relatively large training set, and high computational capacity. However, when faced with a smaller-scale project, opting for an alternative approach may be more effective and practical. This project proposes an efficient and light-weight rule-based approach to categorize dental diagnosis data. It not only fills the void of dental records in the medical free-text processing area, but also demonstrates that with expertly designed research structure and proper implementation, simple method could achieve our study goal very competently.

Survival analysis of posterior composite restorations in National Dental PBRN general dentistry practices.

OBJECTIVE: Quantify the survival of posterior composite restorations (PCR) placed during the study period in permanent teeth in United States (US) general dental community practices and factors predictive of that survival. METHODS: A retrospective cohort study was conducted utilizing de-identified electronic dental record (EDR) data of patients who received a PCR in 99 general dentistry practices in the National Dental Practice-Based Research Network (Network). The final analyzed data set included 700,885 PCRs from 200,988 patients. Descriptive statistics and Kaplan Meier (product limit) estimator were performed to estimate the survival rate (defined as the PCR not receiving any subsequent treatment) after the first PCR was observed in the EDR during the study time. The Cox proportional hazards model was done to account for patient- and tooth-specific covariates. RESULTS: The overall median survival time was 13.3 years. The annual failure rates were 4.5-5.8 % for years 1-5; 5.3-5.7 %, 4.9-5.5 %, and 3.3-5.2 % for years 6-10, 11-15, and 16-20, respectively. The failure descriptions recorded for < 7 % failures were mostly caries (54 %) and broken or fractured tooth/restorations (23 %). The following variables significantly predicted PCR survival: number of surfaces that comprised the PCR; having at least one interproximal surface; tooth type; type of prior treatment received on the tooth; Network region; patient age and sex. Based on the magnitude of the multivariable estimates, no single factor predominated. CONCLUSIONS: This study of Network practices geographically distributed across the US observed PCR survival rates and predictive factors comparable to studies done in academic settings and outside the US. CLINICAL SIGNIFICANCE: Specific baseline factors significantly predict the survival of PCRs done in US community dental practices.

Dentists' Information Needs and Opinions on Accessing Patient Information via Health Information Exchange: Survey Study.

BACKGROUND: The integration of medical and dental records is gaining significance over the past 2 decades. However, few studies have evaluated the opinions of practicing dentists on patient medical histories. Questions remain on dentists' information needs; their perception of the reliability of patient-reported medical history; satisfaction with the available information and the methods to gather this information; and their attitudes to other options, such as a health information exchange (HIE) network, to collect patient medical history. OBJECTIVE: This study aims to determine Indiana dentists' information needs regarding patients' medical information and their opinions about accessing it via an HIE. METHODS: We administered a web-based survey to Indiana Dental Association members to assess their current medical information-retrieval approaches, the information critical for dental care, and their willingness to access or share information via an HIE. We used descriptive statistics to summarize survey results and multivariable regression to examine the associations between survey respondents' characteristics and responses. RESULTS: Of the 161 respondents (161/2148, 7.5% response rate), 99.5% (n=160) respondents considered patients' medical histories essential to confirm no contraindications, including allergies or the need for antibiotic prophylaxis during dental care and other adverse drug events. The critical information required were medical conditions or diagnosis, current medications, and allergies, which were gathered from patient reports. Furthermore, 88.2% (n=142) of respondents considered patient-reported histories reliable; however, they experienced challenges obtaining information from patients and physicians. Additionally, 70.2% (n=113) of respondents, especially those who currently access an HIE or electronic health record, were willing to use an HIE to access or share their patient's information, and 91.3% (n=147) shared varying interests in such a service. However, usability, data accuracy, data safety, and cost are the driving factors in adopting an HIE. CONCLUSIONS: Patients' medical histories are essential for dentists to optimize dental care, especially for those with chronic conditions. In addition, most dentists are interested in using an HIE to access patient medical histories. The findings from this study can provide an alternative option for improving communications between dental and medical professionals and help the health information technology system or tool developers identify critical requirements for more user-friendly designs.

Opioid Prescriptions Prior to Emergency Department Dental Visits: A Comparison of Dental Health Professional Shortage Areas (DPSAs) and Non-DPSAs Using Electronic Health Records.

Although prior research has linked dental conditions and opioid prescribing in the U.S., it is not yet known whether the receipt of opioid prescriptions prior to seeking emergency care for dental conditions differs in geographical areas that are underserved by health care professionals (Dental Professional Shortage Areas, DPSAs) compared to other areas. Using Indiana's state-wide electronic health records from January 1, 2016 to October 31, 2020, we examine if patients from DPSAs presenting at the emergency department (ED) for dental conditions are more likely to have received opioid prescriptions in the 30 days prior to their visit, compared to patients from other areas. A higher rate of opioid receipt among DPSA individuals may indicate an association between lower availability of dental professionals and the use of opioids as a coping strategy. We note that our study design has several limitations (such as a lack of data on prescription use after the ED visit) and does not prove causation. We find that individuals experiencing dental condition ED visits in DPSAs are 16% more likely (than those in non-DPSAs) to have filled an opioid prescription in the 30 days prior to the ED visit, after controlling for age, gender, and race/ethnicity. This result is statistically significant at the 1% level. The baseline rate of opioid filling in the 30 days prior to the ED visit is 12% in DPSAs. These correlational results suggest that unmet dental needs might be substantially connected to opioid prescriptions, although further research is needed to establish whether this relationship is causal.

Comparing gingivitis diagnoses by bleeding on probing (BOP) exclusively versus BOP combined with visual signs using large electronic dental records.

The major significance of the 2018 gingivitis classification criteria is utilizing a simple, objective, and reliable clinical sign, bleeding on probing score (BOP%), to diagnose gingivitis. However, studies report variations in gingivitis diagnoses with the potential to under- or over-estimating disease occurrence. This study determined the agreement between gingivitis diagnoses generated using the 2018 criteria (BOP%) versus diagnoses using BOP% and other gingival visual assessments. We conducted a retrospective study of 28,908 patients' electronic dental records (EDR) from January-2009 to December-2014, at the Indiana University School of Dentistry. Computational and natural language processing (NLP) approaches were developed to diagnose gingivitis cases from BOP% and retrieve diagnoses from clinical notes. Subsequently, we determined the agreement between BOP%-generated diagnoses and clinician-recorded diagnoses. A thirty-four percent agreement was present between BOP%-generated diagnoses and clinician-recorded diagnoses for disease status (no gingivitis/gingivitis) and a 9% agreement for the disease extent (localized/generalized gingivitis). The computational program and NLP performed excellently with 99.5% and 98% f-1 measures, respectively. Sixty-six percent of patients diagnosed with gingivitis were reclassified as having healthy gingiva based on the 2018 diagnostic classification. The results indicate potential challenges with clinicians adopting the new diagnostic criterion as they transition to using the BOP% alone and not considering the visual signs of inflammation. Periodic training and calibration could facilitate clinicians' and researchers' adoption of the 2018 diagnostic system. The informatics approaches developed could be utilized to automate diagnostic findings from EDR charting and clinical notes.

Using transfer learning-based causality extraction to mine latent factors for Sjögren's syndrome from biomedical literature.

Understanding causality is a longstanding goal across many different domains. Different articles, such as those published in medical journals, disseminate newly discovered knowledge that is often causal. In this paper, we use this intuition to build a model that leverages causal relations to unearth factors related to Sjögren's syndrome from biomedical literature. Sjögren's syndrome is an autoimmune disease affecting up to 3.1 million Americans. Due to the uncommon nature of the illness, symptoms across different specialties coupled with common symptoms of other autoimmune conditions such as rheumatoid arthritis, it is difficult for clinicians to diagnose the disease timely. Due to the lack of a dedicated dataset for causal relationships built from biomedical literature, we propose a transfer learning-based approach, where the relationship extraction model is trained on a wide variety of datasets. We conduct an empirical analysis of numerous neural network architectures and data transfer strategies for causal relation extraction. By conducting experiments with various contextual embedding layers and architectural components, we show that an ELECTRA-based sentence-level relation extraction model generalizes better than other architectures across varying web-based sources and annotation strategies. We use this empirical observation to create a pipeline for identifying causal sentences from literature text, extracting the causal relationships from causal sentences, and building a causal network consisting of latent factors related to Sjögren's syndrome. We show that our approach can retrieve such factors with high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN. We apply this model to a corpus of research articles related to Sjögren's syndrome collected from PubMed to create a causal network for Sjögren's syndrome. The proposed causal network for Sjögren's syndrome will potentially help clinicians with a holistic knowledge base for faster diagnosis.

Characterizing clinical findings of Sjögren's Disease patients in community practices using matched electronic dental-health record data.

Established classifications exist to confirm Sjögren's Disease (SD) (previously referred as Sjögren's Syndrome) and recruit patients for research. However, no established classification exists for diagnosis in clinical settings causing delayed diagnosis. SD patients experience a huge dental disease burden impairing their quality of life. This study established criteria to characterize Indiana University School of Dentistry (IUSD) patients' SD based on symptoms and signs in the electronic health record (EHR) data available through the state-wide Indiana health information exchange (IHIE). Association between SD diagnosis, and comorbidities including other autoimmune conditions, and documentation of SD diagnosis in electronic dental record (EDR) were also determined. The IUSD patients' EDR were linked with their EHR data in the IHIE and queried for SD diagnostic ICD9/10 codes. The resulting cohorts' EHR clinical findings were characterized and classified using diagnostic criteria based on clinical experts' recommendations. Descriptive statistics were performed, and Chi-square tests determined the association between the different SD presentations and comorbidities including other autoimmune conditions. Eighty-three percent of IUSD patients had an EHR of which 377 patients had a SD diagnosis. They were characterized as positive (24%), uncertain (20%) and negative (56%) based on EHR clinical findings. Dry eyes and mouth were reported for 51% and positive Anti-Ro/SSA antibodies and anti-nuclear antibody (ANA) for 17% of this study cohort. One comorbidity was present in 98% and other autoimmune condition/s were present in 53% respectively. Significant differences were observed between the three SD clinical characteristics/classifications and certain medical and autoimmune conditions (p<0.05). Sixty-nine percent of patients' EDR did not mention SD, highlighting the huge gap in reporting SD during dental care. This study of SD patients diagnosed in community practices characterized three different SD clinical presentations, which can be used to generate SD study cohorts for longitudinal studies using EHR data. The results emphasize the heterogenous SD clinical presentations and the need for further research to diagnose SD early in community practice settings where most people seek care.

Characteristics of Chemosensory Perception in Long COVID and COVID Reinfection.

Emerging data suggest an increasing prevalence of persistent symptoms in individuals affected by coronavirus disease-19 (COVID-19). The objective of this study was to determine the relative frequency of altered taste and smell in COVID reinfection (multiple COVID positive tests) and long COVID (one COVID positive test). We sent an electronic survey to patients in the Indiana University Health COVID registry with positive COVID test results, querying if they were experiencing symptoms consistent with long COVID including altered chemosensory perceptions. Among the 225 respondents, a greater long COVID burden and COVID reinfection was observed in women. Joint pain was reported as the most common symptom experienced by 18% of individuals in the long COVID cohort. In the COVID reinfection cohort >20% of individuals reported headache, joint pain, and cough. Taste perception worse than pre-COVID was reported by 29% and 42% of individuals in the long COVID and COVID reinfection cohorts, respectively. Smell perception worse than pre-COVID was reported by 37% and 46% of individuals in long COVID and COVID reinfection cohorts, respectively. Further, Chi-square test suggested significant association between pre-COVID severity of taste/smell perception and headache in both cohorts. Our findings highlight the prevalence of persistent chemosensory dysfunction for two years and longer in long COVID and COVID reinfection.

Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records.

OBJECTIVE: To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). METHODS: We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years. RESULTS: Thirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients' disease status progressed, and 589 (11%) patients' disease improved. CONCLUSIONS: This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.

FINDINGS AND FUTURE DIRECTIONS FROM A SMOKING CESSATION TRIAL UTILIZING A CLINICAL DECISION SUPPORT TOOL.

BACKGROUND: Tobacco smoking is the leading cause of disease, death, and disability in the United States. Dental practitioners are advised to provide evidence-based smoking cessation interventions to their patients, yet dental practitioners frequently fail to deliver brief smoking cessation advice. OBJECTIVES: To test whether giving dental practitioners a clinical decisions support (CDS) system embedded in their electronic dental record would increase the rate at which patients who smoke (1) report receiving a brief intervention or referral to treatment during a recent dental visit, (2) taking action related to smoking cessation within 7 days of visit, and (3) stop smoking for 1 day or more or reduce the amount smoked by 50% within 6 months. METHODS: Two-group, parallel arm, cluster-randomized trial. From March through December 2019, 15 nonacademic primary care dental clinics were randomized via covariate adaptive randomization to either a usual care arm or the CDS arm. Adult smokers completed an initial telephone survey within 7 days of their visit and another survey after 6 months. RESULTS: Forty-three patients from 5 CDS and 13 patients from 2 usual care clinics completed the 7-day survey. While the proportion of patients who reported receipt of a brief intervention or referral to treatment was significantly greater in the CDS arm than the usual care arm (84.3% vs 58.6%; P = .005), the differences in percentage of patients who took any action related to smoking cessation within 7 days (44.4% vs 22.3%; P = .077), or stopped smoking for one day or more and/or reduced amount smoked by 50% within 6 months (63.1% vs 46.2%; P = .405) were large but not statistically significant. CONCLUSIONS: Despite interruption by COVID-19, these results demonstrate a promising approach to assist dental practitioners in providing their patients with smoking cessation screening, brief intervention and referral to treatment.

Retrospective Study of the Reasons and Time Involved for Dental Providers' Medical Consults.

Background: Patient-reported medical histories and medical consults are primary approaches to obtaining patients' medical histories in dental settings. While patient-reported medical histories are reported to have inconsistencies, sparse information exists regarding the completeness of medical providers' responses to dental providers' medical consults. This study examined records from a predoctoral dental student clinic to determine the reasons for medical consults; the medical information requested, the completeness of returned responses, and the time taken to receive answers for medical consult requests. Methods: A random sample of 240 medical consult requests for 179 distinct patients were selected from patient encounters between 1 January 2015 and 31 December 2017. Descriptive statistics and summaries were calculated to determine the reasons for the consult, the type of information requested and returned, and the time interval for each consult. Results: The top two reasons for medical consults were to obtain more information (46.1%) and seek medical approval to proceed with treatment (30.3%). Laboratory and diagnostic reports (56.3%), recommendations/medical clearances (39.6%), medication information (38.3%), and current medical conditions (19.2%) were the frequent requests. However, medical providers responded fewer times to dental providers' laboratory and diagnostic report requests (41.3%), recommendations/medical clearances (19.2%), and current medical conditions (13.3%). While 86% of consults were returned in 30 days and 14% were completed after 30 days. Conclusions: The primary reasons for dental providers' medical consults are to obtain patient information and seek recommendations for dental care. Laboratory/diagnostic reports, current medical conditions, medication history, or modifications constituted the frequently requested information. Precautions for dental procedures, antibiotic prophylaxis, and contraindications included reasons to seek medical providers' recommendations. The results also highlight the challenges they experience, such as requiring multiple attempts to contact medical providers, the incompleteness of information shared, and the delays experienced in completing at least 25% of the consults. Practical Implications: The study results call attention to the importance of interdisciplinary care to provide optimum dental care and the necessity to establish systems such as integrated electronic dental record-electronic health record systems and health information exchanges to improve information sharing and communication between dental and medical providers.

How Do Dental Clinicians Obtain Up-To-Date Patient Medical Histories? Modeling Strengths, Drawbacks, and Proposals for Improvements.

Background: Access to up-to-date patient medical history is essential for dental clinicians (DCs) to avoid potential harm to patients and to improve dental treatment outcomes. The predominant approach for dental clinicians (DCs) to gather patients' medical history is through patient-reported medical histories and medical consults. However, studies reported varied concordance and reliability of patient-reported medical conditions and medication histories compared to the patient medical records and this process also places a significant burden on patients. Information technology tools/platforms such as an integrated electronic health record containing an electronic dental record module may address these issues. However, these integrated systems are expensive and technically complex and may not be easily adopted by DCs in solo and small group practice who provide the most dental care. The recent expansion of regional healthcare information exchange (HIE) provides another approach, but to date, studies on connecting DCs with HIE are very limited. Our study objectives were to model different aspects of the current approaches to identify the strengths and weaknesses, and then model the HIE approach that addresses the weaknesses and retain the strengths of current approaches. The models of current approaches identified the people, resources, organizational aspects, workflow, and areas for improvement; while models of the HIE approach identified system requirements, functions, and processes that may be shared with software developers and other stakeholders for future development. Methods: There are three phases in this study. In Phase 1, we retrieved peer-reviewed PubMed indexed manuscripts published between January 2013 and November 2020 and extracted modeling related data from selected manuscripts. In Phase 2, we built models for the current approaches by using the Integrated DEFinition Method 0 function modeling method (IDEF0), the Unified Modeling Language (UML) Use Case Diagram, and Business Process Model and Notation (BPMN) methods. In Phase 3, we created three conceptual models for the HIE approach. Results: From the 47 manuscripts identified, three themes emerged: 1) medical consult process following patient-reported medical history, 2) integrated electronic dental record-electronic health record (EDR-EHR), and 3) HIE. Three models were built for each of the three themes. The use case diagrams described the actions of the dental patients, DCs, medical providers and the use of information systems (EDR-EHR/HIE). The IDEF0 models presented the major functions involved. The BPMN models depicted the detailed steps of the process and showed how the patient's medical history information flowed through different steps. The strengths and weaknesses revealed by the models of the three approaches were also compared. Conclusions: We successfully modeled the DCs' current approaches of accessing patient medical history and designed an HIE approach that addressed the current approaches' weaknesses as well as leveraged their strengths. Organizational management and end-users can use this information to decide the optimum approach to integrate dental and medical care. The illustrated models are comprehensive and can also be adopted by EHR and EDR vendors to develop a connection between dental systems and HIEs.

Nutritional Assessment of Denture Wearers Using Matched Electronic Dental-Health Record Data.

PURPOSE: To assess the nutritional profile of denture wearers through a retrospective cohort study using nutritional biomarkers from matched electronic dental and health record (EDR-EHR) data. MATERIALS AND METHODS: The case group (denture wearers) included matched EDR-EHR data of patients who received removable partial, complete, and implant-supported prosthodontic treatments between January 1, 2010 and December 31, 2018, study time. The control (nondenture wearers) group did not have recorded denture treatments and included patient records within 1 year of the denture index date (first date of case patients' receiving complete or partial denture) of the matching cases. The qualified patients' EDR were matched with their EHR based on the availability of laboratory reports within 2 years of receiving the dentures (index date). Nutritional biomarkers were selected from laboratory reports for complete blood count, comprehensive and basic metabolic profile, lipid, and thyroid panels. Summary statistics were performed, and general linear mixed effect models were used to evaluate the rate of change over time (slope) of nutritional biomarkers before and after the index date. Likelihood ratio tests were performed to determine the differences between dentures and controls. RESULTS: The final cohort included 10,481 matched EDR-EHR data with 3,519 denture wearers and 6,962 controls that contained laboratory results within the study time. The denture wearers' mean age was 57 ±10 years and the control group was 56 ±10 years with 55% females in both groups. Pre-post analysis among denture wearers revealed decreased serum albumin (p = 0.002), calcium (p = 0.039), creatinine (p < 0.001) during the post-index time. Hemoglobin (Hb) was higher pre-index, and was decreasing during the time period but did not change post-index (p < 0.001). Among denture wearers, completely edentulous patients had a significant decrease in serum albumin, creatinine, blood urea nitrogen (BUN), but increased estimated glomerular filtration rate (eGFR). In partially edentulous patients, total cholesterol decreased (p = 0.018) and TSH (p = 0.004), BUN (p < 0.001) increased post-index. Patients edentulous in either upper or lower arch had decreased BUN and eGFR during post-index. Compared to controls, denture wearers showed decreased serum albumin and protein (p = 0.008), serum calcium (p = 0.001), and controls showed increased Hb (p = 0.035) during post-index. CONCLUSIONS: The study results indicate nutritional biomarker variations among denture wearers suggesting a risk for undernutrition and the potential of using selected nutritional biomarkers to monitor nutritional profile.

Can Salivary Innate Immune Molecules Provide Clue on Taste Dysfunction in COVID-19?

Emerging concerns following the severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) pandemic are the long-term effects of coronavirus disease (COVID)-19. Dysgeusia in COVID-19 is supported by the abundant expression of the entry receptor, angiotensin-converting enzyme-2 (ACE2), in the oral mucosa. The invading virus perturbs the commensal biofilm and regulates the host responses that permit or suppress viral infection. We correlated the microbial recognition receptors and soluble ACE2 (sACE2) with the SARS-CoV2 measures in the saliva of COVID-19 patients. Data indicate that the toll-like receptor-4, peptidoglycan recognition protein, and sACE2 are elevated in COVID-19 saliva and correlate moderately with the viral load.

Differences in medication usage of dental patients by age, gender, race/ethnicity and insurance status.

BACKGROUND: Limited studies have investigated the medication profile of young adult dental patients despite the high prevalence of prescription opioid abuse in this population. OBJECTIVE: This study investigated the extent and differences in medication usage of dental patients older than 18 years by age, race/ethnicity, gender, insurance status and mechanism of action in an academic dental clinic setting. METHODS: Using an automated approach, medication names in the electronic dental record were retrieved and classified according to the National Drug Code directory. Descriptive statistics, multivariable ANOVA and Post hoc tests were performed to detect differences in the number of medications by patient demographics. RESULTS: Of the 11,220 adult patients, 53 percent reported taking at least one medication with significant differences in medication usage by demographics. Hydroxymethylglutaryl-coenzyme A reductase inhibitors (21-36%), and angiotensin-converting enzyme inhibitors (19-23%) ranked the top two medication classes among patients 55 years and older. Opioid agonists (7-14%), and Selective Serotonin Reuptake Inhibitors (SSRIs) (5-12%) ranked the top two medication classes among patients aged 18-54 years. CONCLUSIONS: The results underscore the importance of dental providers to review medical and medication histories of patients regardless of their age to avoid adverse events and to determine patient's risk for opioid abuse.

Association between intracranial carotid artery calcifications and periodontitis: A cone-beam computed tomography study.

BACKGROUND: Intracranial carotid artery calcifications (ICACs) are one type of calcification that may be detected as incidental findings in cone-beam computed tomography (CBCT). This retrospective study aimed to examine the prevalence of ICACs on CBCT images and their associations among age, gender, chronic periodontitis, and patient-reported cardiovascular diseases (CVDs). METHODS: A total of 303 CBCT scans were reviewed and a total of 208 patients met the inclusion criteria. The presence or absence of ICACs was evaluated in the ophthalmic and cavernous segments of each scan. Patient demographic data, including age, gender, and medical history, specifically focused on CVDs were recorded. The presence or absence of periodontitis was recorded from each subject with full mouth radiographs and clinical measurements. Odds ratios (ORs) were calculated as part of the logistic regression analysis. RESULTS: Overall, ICACs were found in 93 subjects (45%). The bilateral ICACs were found in 43 subjects (21% of the total subjects, 46% of the subjects with ICACs). There were statistically significant associations between presence of ICACs and periodontitis (OR = 4.55), hypertension (OR = 3.02), hyperlipidemia (OR = 2.87), increasing age (OR = 2.24), and the male gender (OR = 1.85). Smoking status was not significantly correlated with ICACs. CONCLUSION: This study revealed that nearly half (45%) of the subjects displayed ICACs on the CBCT images. ICACs are significantly related to the status of chronic periodontitis, age, gender, and CVDs. A more careful review of CBCT scans is highly recommended to detect these calcifications and refer patients for further medical evaluation.