Response to “prognostic biomarkers in oral leukoplakia”

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152757/1/odi13185.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152757/2/odi13185_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152757/3/odi13185-sup-0001-AppendixS1.pd

Clustering of venous thrombosis events at the start of tamoxifen therapy in breast cancer: A population-based experience

Introduction: The epidemiology of tamoxifen and venous thromboembolism (VTE) is not well understood, and most data on tamoxifen toxicity are from adjuvant clinical trials. This study examined the relationship between the duration of tamoxifen use in female patients with breast cancer and the risk of VTE in a large population-based setting. Materials and Methods: Retrospective electronic data extraction on tamoxifen utilization was undertaken among a cohort of 3572 women with breast cancer seen at Marshfield Clinic between January 1, 1994 and June 31, 2009. Observational follow-up extended until February, 2010. Results: On initial exposure to tamoxifen, women had a clustering of VTE events. Cox proportional hazards regression, adjusting for multiple clinically-important covariates including age, body mass index, cancer stage, and concurrent diabetes, demonstrated that as use of tamoxifen continued in those without earlier VTE events, risk of subsequent VTE gradually increased, albeit at a lower rate (hazard ratio per year of tamoxifen duration = 1.225, P < 0.0001). Conclusions: In our study population, initiating tamoxifen coincided with an initial clustering of VTE events, with risks due specifically to tamoxifen, increasing during continued exposure. Evidence suggested that the VTE clustering occurred in high risk individuals at initiation of tamoxifen therapy. Careful selection of patients for whom tamoxifen therapy is appropriate based on susceptibility to VTE is thus required prior to initiation of therapy

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

Progress in oral personalized medicine: contribution of ‘omics’

Background: Precision medicine (PM), representing clinically applicable personalized medicine, proactively integrates and interprets multidimensional personal health data, including clinical, ‘omics’, and environmental profiles, into clinical practice. Realization of PM remains in progress. Objective: The focus of this review is to provide a descriptive narrative overview of: 1) the current status of oral personalized medicine; and 2) recent advances in genomics and related ‘omic’ and emerging research domains contributing to advancing oral-systemic PM, with special emphasis on current understanding of oral microbiomes. Design: A scan of peer-reviewed literature describing oral PM or ‘omic’-based research conducted on humans/data published in English within the last 5 years in journals indexed in the PubMed database was conducted using mesh search terms. An evidence-based approach was used to report on recent advances with potential to advance PM in the context of historical critical and systematic reviews to delineate current state-of-the-art technologies. Special focus was placed on oral microbiome research associated with health and disease states, emerging research domains, and technological advances, which are positioning realization of PM. Results: This review summarizes: 1) evolving conceptualization of personalized medicine; 2) emerging insight into roles of oral infectious and inflammatory processes as contributors to both oral and systemic diseases; 3) community shifts in microbiota that may contribute to disease; 4) evidence pointing to new uncharacterized potential oral pathogens; 5) advances in technological approaches to ‘omics’ research that will accelerate PM; 6) emerging research domains that expand insights into host–microbe interaction including inter-kingdom communication, systems and network analysis, and salivaomics; and 7) advances in informatics and big data analysis capabilities to facilitate interpretation of host and microbiome-associated datasets. Furthermore, progress in clinically applicable screening assays and biomarker definition to inform clinical care are briefly explored. Conclusion: Advancement of oral PM currently remains in research and discovery phases. Although substantive progress has been made in advancing the understanding of the role of microbiome dynamics in health and disease and is being leveraged to advance early efforts at clinical translation, further research is required to discern interpretable constituency patterns in the complex interactions of these microbial communities in health and disease. Advances in biotechnology and bioinformatics facilitating novel approaches to rapid analysis and interpretation of large datasets are providing new insights into oral health and disease, potentiating clinical application and advancing realization of PM within the next decade

World Workshop on Oral Medicine VII: Prognostic biomarkers in oral leukoplakia and proliferative verrucous leukoplakia—A systematic review of retrospective studies

ObjectiveTo 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 MethodsA 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.ResultsEligible 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.ConclusionThere 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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167520/1/odi13363_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167520/2/odi13363.pd

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

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