Leveraging Electronic Dental Record Data for Clinical Research in the National Dental PBRN Practices

Learning from data in dentistry: Summary of the third annual OpenWide conference

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) 2022 annual conference.

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.

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.

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.

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.

Natural language processing for clinical notes in dentistry: A systematic review

OBJECTIVE

To identify and synthesise research on applications of natural language processing (NLP) for information extraction and retrieval from clinical notes in dentistry.

MATERIALS AND METHODS

A predefined search strategy was applied in EMBASE, CINAHL and Medline. Studies eligible for inclusion were those that that described, evaluated, or applied NLP to clinical notes containing either human or simulated patient information. Quality of the study design and reporting was independently assessed based on a set of questions derived from relevant tools including CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). A narrative synthesis was conducted to present the results.

RESULTS

Of the 17 included studies, 10 developed and evaluated NLP methods and 7 described applications of NLP-based information retrieval methods in dental records. Studies were published between 2015 and 2021, most were missing key details needed for reproducibility, and there was no consistency in design or reporting. The 10 studies developing or evaluating NLP methods used document classification or entity extraction, and 4 compared NLP methods to non-NLP methods. The quality of reporting on NLP studies in dentistry has modestly improved over time.

CONCLUSIONS

Study design heterogeneity and incomplete reporting of studies currently limits our ability to synthesise NLP applications in dental records. Standardisation of reporting and improved connections between NLP methods and applied NLP in dentistry may improve how we can make use of clinical notes from dentistry in population health or decision support systems.

PROTOCOL REGISTRATION

PROSPERO CRD42021227823.

Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records

OBJECTIVE

 Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms.

METHODS

 We conducted a retrospective study using EDR data of patients (n = 27,138) who received care at Temple University Maurice H. Kornberg School of Dentistry from January 1, 2017 to August 31, 2021. We determined the completeness of patient demographics, periodontal charting, and PD diagnoses information in the EDR. Next, we developed two automated computer algorithms to automatically diagnose patients' PD statuses from clinical notes and periodontal charting data. Last, we phenotyped PD diagnoses using automated computer algorithms and reported the improved completeness of diagnosis.

RESULTS

 The completeness of PD diagnosis from the EDR was as follows: periodontal diagnosis codes 36% (n = 9,834), diagnoses in clinical notes 18% (n = 4,867), and charting information 80% (n = 21,710). After phenotyping, the completeness of PD diagnoses improved to 100%. Eleven percent of patients had healthy periodontium, 43% were with gingivitis, 3% with stage I, 36% with stage II, and 7% with stage III/IV periodontitis.

CONCLUSIONS

 We successfully developed, tested, and deployed two automated algorithms on big EDR datasets to improve the completeness of PD diagnoses. After phenotyping, EDR provided 100% completeness of PD diagnoses of 27,138 unique patients for research purposes. This approach is recommended for use in other large databases for the evaluation of their EDR data quality and for phenotyping PD diagnoses and other relevant variables.

Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data

Despite advances in periodontal disease (PD) research and periodontal treatments, 42% of the US population suffer from periodontitis. PD can be prevented if high-risk patients are identified early to provide preventive care. Prediction models can help assess risk for PD before initiation and progression; nevertheless, utilization of existing PD prediction models is seldom because of their suboptimal performance. This study aims to develop and test the PD prediction model using machine learning (ML) and electronic dental record (EDR) data that could provide large sample sizes and up-to-date information. A cohort of 27,138 dental patients and grouped PD diagnoses into: healthy control, mild PD, and severe PD was generated. The ML model (XGBoost) was trained (80% training data) and tested (20% testing data) with a total of 74 features extracted from the EDR. We used a five-fold cross-validation strategy to identify the optimal hyperparameters of the model for this one-vs.-all multi-class classification task. Our prediction model differentiated healthy patients vs. mild PD cases and mild PD vs. severe PD cases with an average area under the curve of 0.72. New associations and features compared to existing models were identified that include patient-level factors such as patient anxiety, chewing problems, speaking trouble, teeth grinding, alcohol consumption, injury to teeth, presence of removable partial dentures, self-image, recreational drugs (Heroin and Marijuana), medications affecting periodontium, and medical conditions such as osteoporosis, cancer, neurological conditions, infectious diseases, endocrine conditions, cardiovascular diseases, and gastroenterology conditions. This pilot study demonstrated promising results in predicting the risk of PD using ML and EDR data. The model may provide new information to the clinicians about the PD risks and the factors responsible for the disease progression to take preventive approaches. Further studies are warned to evaluate the prediction model's performance on the external dataset and determine its usability in clinical settings.

Root Canal Treatment Survival Analysis in National Dental PBRN Practices

Few studies have examined the longevity of endodontically treated teeth in nonacademic clinical settings where most of the population receives its care. This study aimed to quantify the longevity of teeth treated endodontically in general dentistry practices and test the hypothesis that longevity significantly differed by the patient's age, gender, dental insurance, geographic region, and placement of a crown and/or other restoration soon after root canal treatment (RCT). This retrospective study used deidentified data of patients who underwent RCT of permanent teeth through October 2015 in 99 general dentistry practices in the National Dental Practice-Based Research Network (Network). The data set included 46,702 patients and 71,283 RCT permanent teeth. The Kaplan-Meier (product limit) estimator was performed to estimate survival rate after the first RCT performed on a specific tooth. The Cox proportional hazards model was done to account for patient- and tooth-specific covariates. The overall median survival time was 11.1 y; 26% of RCT teeth survived beyond 20 y. Tooth type, presence of dental insurance any time during dental care, placement of crown and/or receiving a filling soon after RCT, and Network region were significant predictors of survival time (P < 0.0001). Gender and age were not statistically significant predictors in univariable analysis, but in multivariable analyses, gender was significant after accounting for other variables. This study of Network practices geographically distributed across the United States observed shorter longevity of endodontically treated permanent teeth than in previous community-based studies. Also, having a crown placed following an RCT was associated with 5.3 y longer median survival time. Teeth that received a filling soon after the RCT before the crown was placed had a median survival time of 20.1 y compared to RCT teeth with only a crown (11.4 y), only a filling (11.2 y), or no filling and no crown (6.5 y).

Providing Effective Dental Care for an Ageing Population

Background

Effective treatment produces improved outcomes from the patient and clinician perspectives. The focus of this article is effective dental care for ageing patients. This concept must be embraced through research, education and, finally, clinical care.

Research

Older adults often carry a higher burden of health and socioeconomic issues that limit their participation in clinical trials. This leaves providers to extrapolate care decisions from research in other age groups. However, electronic health records allow researchers to converge extensive medical, pharmacologic, and dental data, thereby including older patients in research questions.

Education

Dental and medical educators are tasked with teaching skills specific to ageing patients. This requires teaching and active use of concepts such as whole health and patient-centred outcomes.

Provision of care

For ageing patients, effective care is precision care (the right care to the right patient at the right time). Clinicians must be trained and then actively participate in the interdisciplinary approach to assure good oral health for all older patients.

Current state of dental informatics in the field of health information systems: a scoping review

Background

Over the past 50 years, dental informatics has developed significantly in the field of health information systems. Accordingly, several studies have been conducted on standardized clinical coding systems, data capture, and clinical data reuse in dentistry.

Methods

Based on the definition of health information systems, the literature search was divided into three specific sub-searches: “standardized clinical coding systems,” “data capture,” and “reuse of routine patient care data.” PubMed and Web of Science were searched for peer-reviewed articles. The review was conducted following the PRISMA-ScR protocol.

Results

A total of 44 articles were identified for inclusion in the review. Of these, 15 were related to “standardized clinical coding systems,” 15 to “data capture,” and 14 to “reuse of routine patient care data.” Articles related to standardized clinical coding systems focused on the design and/or development of proposed systems, on their evaluation and validation, on their adoption in academic settings, and on user perception. Articles related to data capture addressed the issue of data completeness, evaluated user interfaces and workflow integration, and proposed technical solutions. Finally, articles related to reuse of routine patient care data focused on clinical decision support systems centered on patient care, institutional or population-based health monitoring support systems, and clinical research.

Conclusions

While the development of health information systems, and especially standardized clinical coding systems, has led to significant progress in research and quality measures, most reviewed articles were published in the US. Clinical decision support systems that reuse EDR data have been little studied. Likewise, few studies have examined the working environment of dental practitioners or the pedagogical value of using health information systems in dentistry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02163-9.

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.

Dental Recordkeeping: Practice in Federation of Bosnia and Herzegovina

Background

Dental documentation which includes main information about a patient and dental treatment provided is a very important asset of each dental office.

Objective

This research aims to analyze the way of fulfilling and keeping mandatory dental records and periodic reporting forms by doctors of dental medicine in the Federation of Bosnia and Herzegovina (FB&H).

Methods

The study was observational with a cross-sectional design using a questionnaire as a study tool. The questionnaire was distributed electronically to the participants working in public health care facilities and private practice.

Results

A total of 426 Doctors of Dental Medicine (DDM) participated in the study, of whom 58.7% of respondents were employed in dental offices in the public health sector and 41.3% in dental offices in the private health sector. Dental records are filled out only manually by 53.5% of respondents, while 9.4% fill out the records only electronically, while 37.1% of respondents fill out records both manually and electronically. The manner of keeping dental documentation between respondents employed in dental offices in the public health sector and dental offices in the private health sector differs significantly (p<0.05). Almost all respondents understand the purpose and significance of keeping dental records.

Conclusion

This paper points out that good dental records are of great importance as they allow monitoring the quality of services provided to patients for a longer period.

Efficient Clinical Data Sharing Framework Based on Blockchain Technology

BACKGROUND

While electronic health records have been collected for many years in Taiwan, their interoperability across different health care providers has not been entirely achieved yet. The exchange of clinical data is still inefficient and time consuming.

OBJECTIVES

This study proposes an efficient patient-centric framework based on the blockchain technology that makes clinical data accessible to patients and enable transparent, traceable, secure, and effective data sharing between physicians and other health care providers.

METHODS

Health care experts were interviewed for the study, and medical data were collected in collaboration with Ministry of Health and Welfare (MOHW) Chang-Hua hospital. The proposed framework was designed based on the detailed analysis of this information. The framework includes smart contracts in an Ethereum-based permissioned blockchain to secure and facilitate clinical data exchange among different parties such as hospitals, clinics, patients, and other stakeholders. In addition, the framework employs the Logical Observation Identifiers Names and Codes (LOINC) standard to ensure the interoperability and reuse of clinical data.

RESULTS

The prototype of the proposed framework was deployed in Chang-Hua hospital to demonstrate the sharing of health examination reports with many other clinics in suburban areas. The framework was found to reduce the average access time to patient health reports from the existing next-day service to a few seconds.

CONCLUSION

The proposed framework can be adopted to achieve health record sharing among health care providers with higher efficiency and protected privacy compared to the system currently used in Taiwan based on the client-server architecture.

A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record System

BACKGROUND

Although federal regulations mandate documentation of structured race data according to Office of Management and Budget (OMB) categories in electronic health record (EHR) systems, many institutions have reported gaps in EHR race data that hinder secondary use for population-level research focused on underserved populations. When evaluating race data available for research purposes, we found our institution's enterprise EHR contained structured race data for only 51% (1.6 million) of patients.

OBJECTIVES

We seek to improve the availability and quality of structured race data available to researchers by integrating values from multiple local sources.

METHODS

To address the deficiency in race data availability, we implemented a method to supplement OMB race values from four local sources-inpatient EHR, inpatient billing, natural language processing, and coded clinical observations. We evaluated this method by measuring race data availability and data quality with respect to completeness, concordance, and plausibility.

RESULTS

The supplementation method improved race data availability in the enterprise EHR up to 10% for some minority groups and 4% overall. We identified structured OMB race values for more than 142,000 patients, nearly a third of whom were from racial minority groups. Our data quality evaluation indicated that the supplemented race values improved completeness in the enterprise EHR, originated from sources in agreement with the enterprise EHR, and were unbiased to the enterprise EHR.

CONCLUSION

Implementation of this method can successfully increase OMB race data availability, potentially enhancing accrual of patients from underserved populations to research studies.

Use of digital periodontal data to compare periodontal treatment outcomes in a practice-based research network (PBRN): a proof of concept

Background

Scientific studies in dentistry are mainly conducted at universities. However, most patients are treated in dental practices, which differ in many ways from treatment at the university. Through the establishment of practice-based research networks, however, it is also possible to examine studies in a real-world setting in dental practices. For this reason the aim of this non-interventional, observational study was to develop and evaluate a digital procedure to access, extract and analyse recorded clinical data in practices to assess periodontal treatment outcomes.

Methods

Participating periodontists were former or active postgraduate students of a master’s course in periodontics in Freiburg who routinely used a digital periodontal diagnostic program. All available stored periodontal patient charts were extracted, anonymized and digitally sent to the study centre.

Results

In this study, data were collected from 6301 patients from 9 different practices. Information such as probing depth (PD), bleeding on probing (BOP), mobility, furcation and gingival attachment for 153,163 teeth at first visit were successfully transferred to the study centre. During the average observational period of 9.77 years, only 2.8% of all teeth were lost. The number of visits was significantly negatively correlated with BOP (p < 0.0001), and the number of BOP-positive sites was significantly correlated with deeper PDs (p < 0.001).

Conclusion

The presented procedure was able to gather a large amount of practice-based periodontal data, and thus this study may support practice-based research networks. The data indicate that systematic and supportive periodontal therapy is successful on a practice-based level.

Trial registration The study was internationally registered on 4 January 2017 in the German Clinical Trials Register (DRKS 00011448). https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011448

Does Nonsurgical Periodontal Treatment Improve Systemic Health?

Clinicians frequently stress the importance of maintaining good oral health for multiple reasons, including its link to systemic health. Because periodontal treatment reduces inflammation in oral tissues, some hypothesize it may positively affect systemic outcomes by reducing inflammation in the body. A significant number of systematic reviews (SRs) and meta-analyses (MAs) have evaluated the effect of periodontal treatment on systemic outcomes. However, inconsistent findings and questionable methodological rigor make drawing conclusions difficult. We conducted a systematic review of reviews that studied the effect of nonsurgical periodontal treatment on systemic disease outcomes. We report on outcomes evaluated, categorizing them as biomarkers, and surrogate or clinical endpoints. In addition, we used A MeaSurement Tool to Access systematic Reviews 2 (AMSTAR 2) to evaluate the methodological quality of the reviews. Of the 52 studies included in our review, 21 focused on diabetes, 15 on adverse birth outcomes, 8 on cardiovascular disease, 3 each on obesity and rheumatoid arthritis, and 2 on chronic kidney disease. Across all studies, surrogate endpoints predominated as outcomes, followed by biomarkers and, rarely, actual disease endpoints. Ninety-two percent of studies had "low" or "critically low" AMSTAR 2 confidence ratings. Criteria not met most frequently included advance registration of the protocol, justification for excluding individual studies, risk of bias from individual studies being included in the review, and appropriateness of meta-analytical methods. There is a dearth of robust evidence on whether nonsurgical periodontal treatment improves systemic disease outcomes. Future reviews should adhere more closely to methodological guidelines for conducting and reporting SRs/MAs than has been the case to date. Beyond improved reviews, additional rigorous research on whether periodontal treatment affects systemic health is needed. We highlight the potential of large-scale databases containing matched medical and dental record data to inform and complement future clinical research studying the effect of periodontal treatment on systemic outcomes.

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

BACKGROUND

Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement.

OBJECTIVES

This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR.

METHODS

This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking.

RESULTS

Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%).

CONCLUSION

For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.