COVID-19 and saliva: A primer for dental health care professionals

This article is made available for unrestricted research re-use and secondary analysis in any form or be any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.To contain the COVID‐19 pandemic, it is essential to find methods that can be used by a wide range of health care professionals to identify the virus. The less potential contagious nature of the collection process, the ease of collection and the convenience of frequent collection for real‐time monitoring makes saliva an excellent specimen for home‐based collection for epidemiological investigations. With respect to COVID‐19, the use of saliva offers the added advantages of greater sensitivity and potential for detection at an early stage of infection. However, the advantages from a diagnostic perspective also reflect the potential risk to dental professionals from saliva from infected patients. Although not validated in COVID‐19 patients, but by extension from studies of SARS‐CoV‐1 studies, it is suggested that using antimicrobial mouthrinses such as chlorhexidine, hydrogen peroxide or sodium hypochlorite solutions could reduce the viral load in saliva droplets and reduce the risk of direct transmission. Because large saliva droplets could deposit on inanimate surfaces, changing the personal protective equipment including the clinical gown, gloves, masks, protective eye wear and face shield between patients, as well as decontamination of the work surfaces in the clinic, could reduce the risk of indirect contact transmission

Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease

Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care

Advancing cognitive engineering methods to support user interface design for electronic health records

Background Despite many decades of research on the effective development of clinical systems in medicine, the adoption of health information technology to improve patient care continues to be slow, especially in ambulatory settings. This applies to dentistry as well, a primary care discipline with approximately 137,000 practitioners in the United States. A critical reason for slow adoption is the poor usability of clinical systems, which makes it difficult for providers to navigate through the information and obtain an integrated view of patient data. Objective In this study, we documented the cognitive processes and information management strategies used by dentists during a typical patient examination. The results will inform the design of a novel electronic dental record interface. Methods We conducted a cognitive task analysis (CTA) study to observe ten general dentists (five general dentists and five general dental faculty members, each with more than two years of clinical experience) examining three simulated patient cases using a think-aloud protocol. Results Dentists first reviewed the patient’s demographics, chief complaint, medical history and dental history to determine the general status of the patient. Subsequently, they proceeded to examine the patient’s intraoral status using radiographs, intraoral images, hard tissue and periodontal tissue information. The results also identified dentists’ patterns of navigation through patient’s information and additional information needs during a typical clinician-patient encounter. Conclusion This study reinforced the significance of applying cognitive engineering methods to inform the design of a clinical system. Second, applying CTA to a scenario closely simulating an actual patient encounter helped with capturing participants’ knowledge states and decision-making when diagnosing and treating a patient. The resultant knowledge of dentists’ patterns of information retrieval and review will significantly contribute to designing flexible and task-appropriate information presentation in electronic dental records

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.Dr. Thyvalikakath's start-up funds through the IU School of Dentistr

Targeted Gene Panel Sequencing for Early-onset Inflammatory Bowel Disease and Chronic Diarrhea

Background: In contrast to adult-onset inflammatory bowel disease (IBD), where many genetic loci have been shown to be involved in complex disease etiology, early-onset IBD (eoIBD) and associated syndromes can sometimes present as monogenic conditions. As a result, the clinical phenotype and ideal disease management in these patients often differ from those in adult-onset IBD. However, due to high costs and the complexity of data analysis, high-throughput screening for genetic causes has not yet become a standard part of the diagnostic work-up of eoIBD patients. Methods: We selected 28 genes of interest associated with monogenic IBD and performed targeted panel sequencing in 71 patients diagnosed with eoIBD or early-onset chronic diarrhea to detect causative variants. We compared these results to whole-exome sequencing (WES) data available for 25 of these patients. Results: Target coverage was significantly higher in the targeted gene panel approach compared with WES, whereas the cost of the panel was considerably lower (approximately 25% of WES). Disease-causing variants affecting protein function were identified in 5 patients (7%), located in genes of the IL10 signaling pathway (3), WAS (1), and DKC1 (1). The functional effects of 8 candidate variants in 5 additional patients (7%) are under further investigation. WES did not identify additional causative mutations in 25 patients. Conclusions: Targeted gene panel sequencing is a fast and effective screening method for monogenic causes of eoIBD that should be routinely established in national referral centers.info:eu-repo/semantics/publishedVersio

HLA-DQA1*05 carriage associated with development of anti-drug antibodies to infliximab and adalimumab in patients with Crohn's Disease

Anti-tumor necrosis factor (anti-TNF) therapies are the most widely used biologic drugs for treating immune-mediated diseases, but repeated administration can induce the formation of anti-drug antibodies. The ability to identify patients at increased risk for development of anti-drug antibodies would facilitate selection of therapy and use of preventative strategies.This article is freely available via Open Access. Click on Publisher URL to access the full-text

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

Abstract 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

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

Additional file 1 of Prediction of Sjögren’s disease diagnosis using matched electronic dental-health record data

Additional file 1: ICD codes by diagnosis category. A list of ICD codes we identified to be relevant to SD patients, summarized into groups to create analysis variables