Objectives:A case-control study examined factors contributing to emergency dental clinic access for nontraumatic dental conditions (ED-NTDC) among individuals residing within 20 miles of 10 networked dental clinics comprising a dental safety net. Materials and Methods:Phase I: using dental diagnostic codes for ED-NTDC visits and dental prophylaxis visits that occurred from January 1, 2019 through May 19, 2019, interrogation of an integrated medical-dental electronic health record (EHR) identified cases (n = 1784) and controls (n = 18,612), respectively, matched by dental center. Unadjusted odds ratios (ORs) with 95% confidence intervals (CIs) and chi-squared p-values were determined using unadjusted bivariate logistic regression analysis for each demographic variable by comparing subgroup-specific proportions of cases and controls to a designated reference subgroup. Phase II: survey tools were designed and targeted to Phase I cases (n = 84) and controls (n = 113). Applying descriptive statistics, variability identified between case and control survey question responses defined additional potential candidate variables contributing to risk for ED-NTDC visits. Results:Statistically significant risk variables ORs were graphically depicted in a forest plot and included: age 18-35 (OR 1.4), 36-50 (OR 1.2), sex (male: OR 1.2), race (non-White: (OR 1.7)), payer status (self-pay: (OR 2.4); Medicaid: (OR 1.6), sliding fee: (OR 1.8)), and E-NTDC visits in medical (OR: 2.8) or dental (OR 2.0) settings in the prior 2 years. Twice-annual dental visit rates were higher among controls vs. cases. Cases vs. controls more frequently indicated: (1) a preference for pulling painful teeth; (2) dental visits were only necessary for dental emergencies; (3) delay in seeking dental care for emergent dental pain; (4) higher rates of dental fear. Economic factors, ("could not afford dental care" and "lack of insurance") especially in younger age tiers, strongly contributed to ED-NTDC visits. Conclusions:Barriers to oral health access for vulnerable populations persist. Public health initiatives targeting dental health maintenance and literacy require expansion.

INTRODUCTION:Rural-based health care systems face unique concerns, including the struggle to recruit and retain quality clinicians. We evaluated health care providers' perceptions of their service line directors (SLDs) in the Marshfield Clinic Health System to understand how these perceptions affect job satisfaction in a rural health care setting. METHODS:Utilizing quantitative and qualitative methods, we reached out to providers within the health system, excluding SLDs to prevent bias. The survey, with a 43% response rate, encompassed 14 questions focusing on 8 domains of engagement. Data analyses included chi-squared tests, t tests, analysis of variance, and correlation matrices. To delve deeper into perceptions, a qualitative approach was employed, analyzing open-ended feedback. RESULTS:Of the 457 respondents, 70% reported satisfaction with their SLDs. High meeting frequencies with SLDs were positively correlated with satisfaction. The majority acknowledged the positive attributes of SLDs in domains like availability, recognition, and feedback. However, significant variations in perceptions arose between physicians and advanced practice clinicians and between surgeon and non-surgeon SLDs. Qualitative feedback elucidated themes including engagement, communication, and advocacy. Positive attributes, such as competence and proactivity, were mentioned frequently, while negatives highlighted disconnectedness and being uninformed. CONCLUSIONS:The quality of interactions with SLDs significantly influences clinician satisfaction. Regular, meaningful interactions - especially recognizing and providing feedback - enhance satisfaction. However, certain groups like advanced practice clinicians under surgeon SLDs felt less engaged. Our findings underscore the importance of tailored leadership training for SLDs and suggest organizational strategies to boost satisfaction, potentially affecting recruitment and retention in rural health care settings.

Early detection of atrial fibrillation (AFib) is crucial for altering its natural progression and complication profile. Traditional demographic and lifestyle factors often fail as predictors of AFib. This study investigated pre-operative, circulating microRNAs (miRNAs) as potential biomarkers for post-operative AFib (POAF) in patients undergoing coronary artery bypass grafting (CABG). We used an array polymerase chain reaction method to detect pre-operative, circulating miRNAs in seven patients who subsequently developed POAF after CABG (cases) and eight patients who did not develop POAF after CABG (controls). The top 10 miRNAs from 84 candidates were selected and assessed for their performance in predicting POAF using machine learning models, including Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Support Vector Machine (SVM). The Random Forest and XGBoost models showed superior predictive performance, with test area under the curve (AUC) values of 0.76 and 0.83, respectively. Differential expression analysis revealed four upregulated miRNAs-hsa-miR-96-5p, hsa-miR-184, hsa-miR-17-3p, and hsa-miR-200-3p-that overlapped with the POAF-miRNA signature. The POAF-miRNA signature was significantly associated with various cardiovascular diseases, including acute myocardial infarction, hypertrophic cardiomyopathy, and heart failure. Biological pathway analysis indicated these miRNAs target key signaling pathways involved in cardiovascular pathology, such as the MAPK, PI3K-Akt, and TGF-beta signaling pathways. The identified miRNAs demonstrate significant potential as predictive biomarkers for AFib post-CABG, implicating critical cardiovascular pathways and highlighting their role in POAF development and progression. These findings suggest that miRNA signatures could enhance predictive accuracy for POAF, offering a novel, noninvasive approach to early detection and personalized management of this condition.

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.

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.

OBJECTIVE The frequency of Preventable Infectious Dental Disease (PIDD) visits in medical centers was examined pre and post establishment of expanded dental access and adoption of an integrated medical-dental care delivery model. METHODS A retrospective observational study of patient attributes and frequency of unscheduled PIDD visits between January 1, 1990 and February 29, 2020. Chi-squared tests compared (a) the number of PIDD visits (pre/post dental center establishment), (b) age at first diagnosis, (c) gender, (d) race, (e) primary insurance at the time of PIDD visits and (f) healthcare setting where visit occurred. RESULTS System-wide, 21,957 unique patients were documented with a total of 34,892 PIDD visits as the primary diagnosis. Patients between 18-30 years and patients with Medicaid had the highest frequency of PIDD visits in medical settings. Following the establishment of dental centers, reduced relative risk of PIDD visits was observed for patients with no health insurance or self-pay/other coverage. PIDD visits in primary care settings was 0.87 times as likely as PIDD visits at ED/UCs after dental centers opened. CONCLUSIONS The number of PIDD visits to medical centers increased before the dental infrastructure was established, followed by a decline afterwards, inclusive of disparity populations. Some residual persistence of PIDD visits to primary care settings was identified. This study reinforced importance of dental healthcare access for achieving appropriate PIDD management while reducing PIDD visits to medical settings.

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.

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.

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.

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.

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.

Aberrant DNA methylation has been firmly established as a factor contributing to the pathogenesis of colorectal cancer (CRC) via its capacity to silence tumour suppressor genes. However, the methylation status of multiple tumour suppressor genes and their roles in promoting CRC metastasis are not well characterised. We explored the methylation and expression profiles of CPEB1 (the gene encoding cytoplasmic polyadenylation element-binding protein 1), a candidate CRC tumour suppressor gene, using The Cancer Genome Atlas (TCGA) database and validated these results in both CRC cell lines and cells from Han Chinese CRC patients (n = 104). The functional role of CPEB1 in CRC was examined in experiments performed in vitro and in vivo. A candidate transcription factor capable of regulating CPEB1 expression was predicted in silico and validated by luciferase reporter, DNA pull-down, and electrophoretic mobility shift assays. Hypermethylation and decreased expression of CPEB1 in CRC tumour tissues were revealed by TCGA database. We also identified a significant inverse correlation (Pearson’s R = − 0.43, P < 0.001) between promoter methylation and CPEB1 expression. We validated these results in CRC samples and two CRC cell lines. We also demonstrated that up-regulation of CPEB1 resulted in significantly decreased tumour growth, migration, invasion, and tumorigenicity and promoted tumour cell apoptosis both in vitro and in vivo. We identified the transcription factors CCAAT enhancer-binding protein beta (CEBPB) and transcription factor CP2 (TFCP2) as critical regulators of CPEB1 expression. Hypermethylation of the CPEB1 promoter resulted in a simultaneous increase in the capacity for TFCP2 binding and a decreased likelihood of CEBPB binding, both of which led to diminished expression of CPEB1. Our results identified a novel tumour-suppressive role of CPEB1 in CRC and found that hypermethylation of the CPEB1 promoter may lead to diminished expression due to decreased chromatin accessibility and transcription factor binding. Collectively, these results suggest a potential role for CPEB1 in the diagnosis and treatment of CRC.

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.

Introduction: To conduct a statewide survey among Wisconsin-based dental providers evaluating current knowledgeability, attitudes and practice behaviors surrounding management of patients with diabetes/prediabetes in the dental setting. The study explored perceptions on feasibility, value, barriers, and current status of integrated care model (ICM) adoption by dental practices Materials and Methods: A 32-question paper-based survey was mailed to all licensed dentists and dental hygienists practicing in Wisconsin. The study was conducted over a 4 week period in 2019. The survey instrument was adapted from a previous validated survey and was expanded to include questions on ICM adoption. Content and validity analyses and beta testing were conducted prior to dissemination of the survey. Descriptive statistics and chi-square tests were applied for data analyses. Thematic analyses was performed on open-ended questions. Results: Survey response rate was 12% ( N = 854/7,356) representing 41% dentists and 59% dental hygienists. While 68% reported educating patients on oral health-diabetes association, only 18% reported medical consultations to inform dental treatment, and “frequent” (22%) or “occasional” (40%), medical triage. Knowledge-based questions were correctly answered by >70% of participants. While 50% valued chair-side glycemic screening and 85% supported non-invasive chair-side screening to identify at-risk patients,>88% relied on patient-reported diabetic status. Barriers to ICM adoption included time investment (70%), patient activation/cooperation (62%), cost (50%), insurance coverage (50%), infrequent interdisciplinary communication (46%), lack of equipment (33%) and provider (31%). Conclusion: Low rates of ICM adoption, chair-side testing, medical consultation and triage, and need for educational curricula reform were identified.

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, X 2 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.

Objective To systematically review retrospective studies examining prognostic potentials of candidate biomarkers to stratify malignant progression of oral leukoplakia (OL) and proliferative verrucous leukoplakia (PVL). Materials and Methods A systematic literature search of PubMed, EMBASE, Evidence-Based Medicine and Web of Science databases targeted literature published through 29 March 2018. Inter-rater agreement was ascertained during title, abstract and full-text reviews. Eligibility evaluation and data abstraction from eligible studies were guided by predefined PICO questions and bias assessment by the Quality in Prognosis Studies tool. Reporting followed Preferred Reporting Items for Systematic Review and Meta-Analysis criteria. Biomarkers were stratified based on cancer hallmarks. Results Eligible studies (n = 54/3,415) evaluated 109 unique biomarkers in tissue specimens from 2,762 cases (2,713 OL, 49 PVL). No biomarker achieved benchmarks for clinical application to detect malignant transformation. Inter-rater reliability was high, but 65% of included studies had high "Study Confounding" bias risk. Conclusion There was no evidence to support translation of candidate biomarkers predictive of malignant transformation of OL and PVL. Systematically designed, large, optimally controlled, collaborative, prospective and longitudinal studies with a priori-specified methods to identify, recruit, prospectively follow and test for malignant transformation are needed to enhance feasibility of prognostic biomarkers predicting malignant OL or PVL transformation.

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.