Validity and accuracy of a risk calculator in predicting periodontal disease

Contesting the conventional wisdom of periodontal risk assessment

Over the years, several reviews of periodontal risk assessment tools have been published. However, major misunderstandings still prevail in repeated attempts to use these tools for prognostic risk prediction. Here we review the principles of risk prediction and discuss the value and the challenges of using prediction models in periodontology. Most periodontal risk prediction models have not been properly developed according to guidance given for the risk prediction model development. This shortcoming has led to several problems, including the creation of arbitrary risk scores. These scores are often labelled as 'high risk' without explicit boundaries or thresholds for the underlying continuous risk estimates of patient-important outcomes. Moreover, it is apparent that prediction models are often misinterpreted as causal models by clinicians and researchers although they cannot be used as such. Additional challenges like the critical assessment of transportability and applicability of these prediction models, as well as their impact on clinical practice and patient outcomes, are not considered in the literature. Nevertheless, these instruments are promoted with claims regarding their ability to deliver more individualized and precise periodontitis treatment and prevention, purportedly resulting in improved patient outcomes. However, people with or without periodontitis deserve proper information about their risk of developing patient-important outcomes such as tooth loss or pain. The primary objective of disseminating such information should not be to emphasize assumed treatment efficacy, hype individualization of care, or promote business interests. Instead, the focus should be on providing individuals with locally validated and regularly updated predictions of specific risks based on readily accessible and valid key predictors (e.g. age and smoking).

Efficacy of communicating periodontal risk on psychological outcomes and supragingival plaque control in patients undergoing first periodontal consultation: A parallel-arm, randomized trial

AIM

To evaluate the efficacy of the PerioRisk prognostic tool in implementing the effect of motivational interviewing (MI) on psychological outcomes and supragingival plaque control.

MATERIALS AND METHODS

Participants underwent MI immediately after their first periodontal visit. According to a parallel-arm, randomized study design, MI was performed with (RISK group) or without (CTR group) information on PerioRisk level and treatment goals based on PerioRisk output. Psychological outcomes were assessed using the Positive Affect Negative Affect Scale (PANAS) and Protection Motivation Theory (PMT). Plaque index (PI) was re-evaluated after 8-12 weeks.

RESULTS

Significant improvements in PMT overall score and PI were observed in CTR and RISK groups, without inter-group difference in PANAS and PMT overall scores and PI. A sub-analysis showed that the overall PMT scores recorded immediately after MI in both CTR and RISK groups for subjects with no tooth loss due to periodontitis were higher than those recorded before MI in subjects with tooth loss due to periodontitis.

CONCLUSIONS

At first periodontal visit, MI (implemented with without PerioRisk) has tangible effects on psychological outcomes and supragingival plaque control and seems to anticipate the awareness that is commonly generated by periodontitis-related tooth loss (ClinicalTrials.gov protocol registration ID: NCT05078411).

Artificial intelligence and personalized diagnostics in periodontology: A narrative review

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Tooth loss during supportive periodontal care: A prospective study

AIM

To assess periodontal stability and the association between tooth- and patient-related factors and tooth loss during supportive periodontal care (SPC).

MATERIALS AND METHODS

A prospective observational study was carried out on previously treated periodontitis patients followed up for 5 years in SPC. The risk profile (low, moderate, high) of each patient based on periodontal risk assessment (PRA) scoring at baseline was evaluated, and tooth loss rates were analysed.

RESULTS

Two hundred patients were included in the study, and 143 had 5-year follow-up data available for analysis. The overall annual tooth loss per patient was 0.07 ± 0.14 teeth/patient/year. Older age, smoking, staging and grading were associated with increased tooth loss rates. Most patients whose teeth were extracted belonged to the PRA high-risk group. Both PRA and a tooth prognosis system used at baseline showed high negative predictive value but low positive predictive value for tooth loss during SPC.

CONCLUSIONS

Overall, the tooth loss rate of periodontitis patients in this prospective cohort study under SPC in private practice was low. Both tooth-based and patient-based prognostic systems can identify high-risk cases, but their positive predictive value should be improved.

The economic and societal impact of periodontal and peri-implant diseases

Periodontal and peri-implant diseases result from a chronic inflammatory response to dysbiotic microbial communities and are characterized by inflammation in the soft tissue and the ensuing progressive destruction of supporting bone, resulting in tooth or implant loss. These diseases' high prevalence, multifactorial etiology, extensive treatment costs, and significant detriment to patients' quality-of-life underscore their status as a critical public health burden. This review delineates the economic and sociocultural ramifications of periodontal and peri-implant diseases on patient welfare and healthcare economics. We delve into the implications of diagnosis, treatment, supportive care, and managing destructive tissue consequences, contrasting these aspects with healthy patients.

Common practices of dental implants maintenance among dental hygiene professionals in Israel and Germany

OBJECTIVE

The aim of this study was to assess the common practices of dental implant maintenance among dental hygiene professionals (DHP) in Israel (IL) and Germany (GE).

METHODS

An online questionnaire was developed by the Periodontology Departments of Tel Aviv (IL) and Frankfurt University (GE) to address demographics, training, prevention and treatment of peri-implant diseases. The questionnaire was distributed by DHP associations via social media.

RESULTS

The responses of 376 DHPs (IL: 169; GE: 207) were analysed. Most participants were female (IL: 168/99%; GE: 203/98%), had received education (IL: 179/97%; GE: 207/97%) and were working in their home countries (IL: 182/99%; GE: 211/99%). Peri-implant probing was not performed by 22% of DHPs in IL and 5% in GE. Of the DHPs who used probes, 49% used metal probes in IL, while 40% used plastic probes in GE (p < 0.001). A majority of DHPs performed peri-implant instrumentation (IL: 168/99%; GE: 190/92%). Most DHPs from IL did not use devices other than hand and/or sonic/ultrasonic instruments for peri-implant cleaning (IL: 130/77%; GE 5/2%); in GE, the use of airflow (IL: 31/18%; GE: 199/96%) is popular (p < 0.001).

CONCLUSIONS

Most DHPs in IL and GE perform peri-implant probing and debridement. However, there are some distinct differences between the two countries regarding the choice of instruments and treatment regimens.

Development of a probability calculator for psychosis risk in children, adolescents, and young adults

BACKGROUND

Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of 'risk calculators' for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples.

METHODS

We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting 'psychosis risk' enriched community samples. We assessed 632 participants age 8-21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines.

RESULTS

Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70.

CONCLUSIONS

Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.

Periodontal risk assessment in a teaching hospital population in Saudi Arabia's Eastern Province

OBJECTIVE

With this cross-sectional study, we aimed to evaluate factors associated with moderate and high risk of periodontal disease (PD) progression in the Saudi population.

METHODS

We reviewed 281 patients' clinical charts from predoctoral periodontal clinics at the dental teaching hospital in the College of Dentistry (COD) at Imam Abdulrahman Bin Faisal University (IAU) in Dammam, Saudi Arabia. After obtaining ethical approval, we determined the Periodontal Risk Assessment (PRA) of the included patients based on the modified criteria developed by Lang and Tonetti (2003). We used logistic regression on stratified data and divided the results into two categories (low-moderate and high risk) to assess the effect modifier for potential risk factors. We used SPSS version 22 for data analysis, and considered a P-value ≤ 0.05 to be statistically significant.

RESULTS

Out of the 281 patients, 104 (37.0%) were male and 177 (63.0%) were female, with a mean age of 39.9 ± 14.0 years; 78.1% were Saudi nationals, 77% were married, and 44.6% were in the age group of 30 to 49. The PRA revealed 86 (30.5%) to represent high risk, 108 (38.3%) denoted moderate risk, and 88 (31.2%) signaled low risk for periodontitis. Logistic regression analysis showed that males were three times more likely to have high PRA (OR = 3.24) and to be married (OR = 2.77), as well as to be active smokers (OR = 8.87). The highest predictive factors of high PRA were 8 or more pockets ≥ 5 mm (OR = 29.0), those with active diabetes mellitus (DM; OR = 10.2), and those with 8 or more missing teeth (OR = 9.15).

CONCLUSION

Saudi males who are married and have residual periodontal pockets, are actively diabetic, and with missing teeth are at high risk of PD. Further research is needed with a larger sample size comparing the general population with and without PD.

Ortho-Perio Risk Assessment and timing flowchart for lingual orthodontics in an interdisciplinary adult ortho-perio patient: A case report of ``Perio-Guided'' Orthodontic treatment

INTRODUCTION

The treatment of ortho-perio patients is a challenge for the interdisciplinary team. Not only are adult patients with overt perio pathology involved, but any ortho patient, even young ones, can be a perio patient and vice versa. Diagnosis and risk assessment of every ortho-perio patient is essential to establish a correct treatment plan, schedule and prognosis. Orthodontics becomes a ``Perio-Guided" Orthodontic Treatment and Periodontics a "Ortho-Guided Periodontal Treatment".

MATERIAL AND METHODS

This case report presents a man with a very compromised dentition asking for a complete interdisciplinary rehabilitation treated with a combined ortho-perio treatment in lingual mechanics. The periodontal evaluation confirmed the possibility of performing orthodontic treatment after active periodontal treatment. Treatment objectives were the resolution of the crowding, the correction of the levels of the gingival margin, the bone levelling, the preparation for restorative spaces; the objectives of the latter prior to implant placement were: redistribution of space, optimization of the position of adjacent teeth and their parallelism, exploitation of edentulous sites to correct dental class II and placement of the least number of implants possible. After integrating the conventional perio risk assessment with a new Ortho-Perio Risk Assessment (OPRA), a lingual fixed appliance was applied with the help of miniscrews to correct class II division 2 by substituting the upper right first premolar into a canine and retracting the entire upper arch, while correcting the deep bite and optimising the occlusion.

RESULTS

At the end of the treatment, the patient had molar relationships of class II on the right and class I on the left with a class I canine and the 14 in the position of 13. Incisal relationships were corrected, the position of the incisors was optimized, the spaces in the upper arch were fully resolved by orthodontics. During the treatment, orthodontics corrected the uneven gingival margin of the anterior teeth and levelled the bone.

CONCLUSIONS

Correct ortho-perio risk assessment (OPRA) is necessary to plan the risk of expression of the periodontal phenotype in ortho-patient. OPRA and the lingual mechanics allowed an orthodontic resolution of the malocclusion and an enhancement of the perio-implant-restorative contributions. OPRA followed by periodontal therapy and lingual mechanics resolved the malocclusion by improving the restorative peri-implant conditions. Orthodontists and periodontists should be aware of the characteristics of the individual expression of the periodontal phenotype at the beginning of treatment and involve patients in the outcome, sequencing of combined treatments, ortho-perio retention and stability.

Development of a prognostic tool: based on risk factors for tooth loss after active periodontal therapy

Objectives

The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT).

Material and methods

Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting.

Results

Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77.

Conclusion

This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value.

Clinical relevance

In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.

Assessing the completeness of periodontal disease documentation in the EHR: a first step in measuring the quality of care

Background

Our objective was to measure the proportion of patients for which comprehensive periodontal charting, periodontal disease risk factors (diabetes status, tobacco use, and oral home care compliance), and periodontal diagnoses were documented in the electronic health record (EHR). We developed an EHR-based quality measure to assess how well four dental institutions documented periodontal disease-related information. An automated database script was developed and implemented in the EHR at each institution. The measure was validated by comparing the findings from the measure with a manual review of charts.

Results

The overall measure scores varied significantly across the four institutions (institution 1 = 20.47%, institution 2 = 0.97%, institution 3 = 22.27% institution 4 = 99.49%, p-value < 0.0001). The largest gaps in documentation were related to periodontal diagnoses and capturing oral homecare compliance. A random sample of 1224 charts were manually reviewed and showed excellent validity when compared with the data generated from the EHR-based measure (Sensitivity, Specificity, PPV, and NPV > 80%).

Conclusion

Our results demonstrate the feasibility of developing automated data extraction scripts using structured data from EHRs, and successfully implementing these to identify and measure the periodontal documentation completeness within and across different dental institutions.

Long term comparison of the prognostic performance of PerioRisk, periodontal risk assessment, periodontal risk calculator, and staging and grading systems

BACKGROUND

Clinicians predominantly use personal judgment for risk assessment. Periodontal risk assessment tools (PRATs) provide an effective and logical system to stratify patients based on their individual treatment needs. This retrospective longitudinal study aimed to validate the association of different risk categories of four PRATs (Staging and grading; Periodontal Risk Assessment (PRA); Periodontal Risk Calculator; and PerioRisk) with periodontal related tooth loss (TLP), and to compare their prognostic performance.

METHODS

Data on medical history, smoking status, and clinical periodontal parameters were retrieved from patients who received surgical and non-surgical periodontal treatment. A comparison of the rate of TLP and non-periodontal related tooth loss (TLO) within the risk tool classes were performed by means of Kruskal-Wallis test followed by post-hoc comparison with the Bonferroni test. Both univariate and multivariate Cox Proportional hazard regression models were built to analyze the prognostic significance for each single risk assessment tool class on TLP.

RESULTS

A total of 167 patients with 4321 teeth followed up for a mean period of 26 years were assigned to four PRATs. PerioRisk class 5 had a hazard ratio of 18.43, Stage 4 had a hazard ratio of 7.99, and PRA class 3 had a hazard ratio of 6.13 compared with class/stage I. With respect to prognostic performance, PerioRisk tool demonstrated the best discrimination and model fit followed by PRA.

CONCLUSION

All PRATs displayed very good predictive capability of TLP. PerioRisk showed the best discrimination and model fit, followed by PRA.

Current status of periodontitis and its association with tobacco use amongst adult population of Sunsari district, in Nepal

BACKGROUND

Tobacco products are considered significant, but preventable factors related to initiation and progression of periodontal diseases. We assessed the prevalence of periodontitis and evaluated its association with tobacco use and other factors amongst the adult population of Sunsari district in eastern Nepal.

METHODS

A community-based, cross-sectional study was conducted in rural municipalities in the province one of eastern Nepal. A total of 440 adults were interviewed with a set of a pre-tested semi-structured questionnaire. Data on social demographics, adverse oral habits followed by periodontal clinical examination were recorded. Prevalence of periodontitis was assessed by a case definition provided by CDC-AAP. Univariate and multivariate logistic regression analysis was done to measure the association between tobacco use and other factors with periodontitis.

RESULTS

The overall prevalence of periodontitis was found to be 71.6%. Majority (85.4%) of tobacco users had periodontitis and they were significantly associated with the disease and its severity. The study identified age groups, 45-65 years (AOR = 7.58, 95% CI 3.93-14.61), plaque accumulation (AOR = 1.01, 95% CI 1.00-1.02), smoking (AOR = 3.14, 95% CI 1.36-7.27), khaini users (smokeless tobacco, AOR = 2.27, 95% CI 1.12-4.61) and teeth loss (AOR = 2.02, 95% CI 1.21-3.38) as the significant factors associated with periodontitis.

CONCLUSION

The prevalence of periodontitis is high in the surveyed rural adult population. Cigarette smoking along with the use of smokeless tobacco in the form of khaini were identified as significant factors associated with periodontitis.

Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting

BACKGROUND

Periodontitis (PD), a form of gum disease, is a major public health concern as it is globally prevalent and harms both individual quality of life and economic productivity. Global cost in lost productivity is estimated at US$54 billion annually. Moreover, current PD assessment applies only after the damage has already occurred.

OBJECTIVE

This study proposes and tests a new PD risk assessment model applicable at point-of-care, using supervised machine learning methods.

METHODS

We compare the performance of five algorithms using retrospective clinical data: Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree (DT).

RESULTS

DT and ANN demonstrated higher accuracy in classifying the patients with high or low PD risk as compared to NB, LR and SVM. The resultant model with DT showed a sensitivity of 87.08% (95% CI 84.12% to 89.76%) and specificity of 93.5% (95% CI 91% to 95.49%).

CONCLUSIONS

A predictive model with high sensitivity and specificity to stratify individuals into low and high PD risk tiers was developed. Validation in other populations will inform translational value of this approach and its potential applicability as clinical decision support tool.

Age-Specific Predictive Models of the Upper Quintile of Periodontal Attachment Loss

A practical method to identify people who are most affected by periodontitis in their age group is currently unavailable. We focused on individuals with mean clinical attachment loss (CAL) above the 80th percentile within each of 10 age groups (5-y intervals between 30 and 74 y as well as ≥75 y). We developed predictive models using combined data from 2 cohorts (2009 to 2010 and 2011 to 2012) from the NHANES (National Health and Nutrition Examination Survey; development cohort [DC], n = 6,757), and we carried out external validation using data from a third NHANES cohort (2013 to 2014; validation cohort [VC], n = 3,447). We used 1) age-specific logistic regression models with stepwise selection to identify significant demographic variables, habits, medical conditions, and selected clinical periodontal parameters (proportion of teeth with probing depth ≥4 mm at incisors and molars and with visible [≥2 mm] recession) and to calculate propensity scores (PSs); 2) Youden's J statistic to select optimum PS cutoffs to maximize diagnostic performance using receiver operating characteristic curves; and 3) bootstrap resampling with 1,000 replicates to validate the age-specific models and adjust the PS and optimal PS cutoffs for overfitting. The bootstrap-adjusted PSs were used as single predictors of mean CAL over the 80th percentile in the VC. The age-specific upper quintiles of mean CAL ranged between 1.63 and 3.24 mm in the DC and between 1.87 and 3.20 mm in the VC. The area under the curve of the models exceeded 0.85 in all age groups in the DC and 0.84 in the VC, indicating well-validated diagnostic performance. In the DC, sensitivity values ranged between 0.75 and 0.97 and exceeded 0.83 in 8 of 10 age groups. Corresponding values in the VC ranged between 0.56 and 0.89 and exceeded 0.68 in 8 of 10 age groups. We conclude that modeling that incorporates readily obtainable variables through a brief patient interview and an abbreviated periodontal examination accurately identifies individuals who are most affected by periodontitis in different ages.

Impact of Modifiable Risk Factors on Bone Loss During Periodontal Maintenance

Objectives:

The aim of this study was to analyze modifiable patient risk factors from dental chart histories and radiographs for progressive mild-moderate periodontitis during periodontal maintenance (PM).

Methods and Materials:

Bitewing radiographs of 442 elderly periodontal maintenance patients were taken before and after two years of periodontal maintenance. Each progressive periodontitis (PP) patient (with at least one site of posterior interproximal bone loss of ≥2 mm, n=71) was matched to a periodontitis stable (PS) patient (no sites with bone loss, n=71) of the same gender and age (±five years) to control for these variables and was compared for measurements of general patient (medical history, smoking, hygiene and compliance habits) and tooth-related (bone loss, overhangs, interproximal dimensions) factors at baseline. Fisher exact and t-tests were used to compare groups.

Results:

While the elderly PM patients with mild-moderate periodontitis were generally stable, 71 of 442 were PP patients. No significant differences from PS patients were observed at baseline with regard to the systemic factors measured. However, the PP group had less cementoenamel junction to bone length (bone loss p&lt;0.0001) and more interproximal width (2.3±1.0 mm) than did the PS group (1.7±0.6 mm, p=0.0016). This was reflected in more open sites without adjacent tooth contact in PP (42% vs 15%, p=0.0006).

Conclusions:

In the short term, systemic and behavior factors are of limited value in identifying mild-moderate periodontitis patients on PM at increased risk of bone loss. However, interproximal width and lack of adjacent tooth contacts are related to the likelihood of losing interproximal bone during periodontal maintenance, suggesting the need for restorative therapy.

Time between recall visits and residual probing depths predict long-term stability in patients enrolled in supportive periodontal therapy

AIM

To relate the time between recall visits and residual periodontal probing depths (PPDs) to periodontal stability in patients enrolled in supportive periodontal therapy (SPT).

MATERIALS AND METHODS

Retrospective data on residual PPDs from 11,842 SPT visits were evaluated in SPT patients at the Medi School of Dental Hygiene (MSDH), Bern, Switzerland, 1985-2011. A residual PPD-based algorithm was developed to compute SPT intervals with no expected change of residual PPD.

RESULTS

A total of 883 patients aged 43.9 (±13.0) years and 55.4% (n = 489) being females were identified. Linear mixed model analysis yielded highest statistically significant impact on PPD change with time between SPT visits, presence of residual PPD ≥4 mm, and bleeding on probing (p < 0.0001). Patients returning for SPT five times consecutively earlier than computed presented mean % PPDs ≥4 mm of 5.8% (±3.9) compared with patients returning later (19.2%, ±7.6) (p < 0.0001). Additionally, patients attending >50% of their SPT visits earlier versus later demonstrated increased periodontal stability after 5 years (p = 0.0002) and a reduced frequency of tooth loss (0.60, ±0.93 versus 1.45, ±2.07) after 20 years (p < 0.0001).

CONCLUSIONS

To reach and maintain periodontal stability during SPT, individual quantitative data from comprehensive residual PPD profiles may contribute to the improved planning of SPT intervals.

Innovative Models of Dental Care Delivery and Coverage: Patient-Centric Dental Benefits Based on Digital Oral Health Risk Assessment

Innovative models of dental care delivery and coverage are emerging across oral health care systems causing changes to treatment and benefit plans. A novel addition to these models is digital risk assessment, which offers a promising new approach that incorporates the use of a cloud-based technology platform to assess an individual patient's risk for oral disease. Risk assessment changes treatment by including risk as a modifier of treatment and as a determinant of preventive services. Benefit plans are being developed to use risk assessment to predetermine preventive benefits for patients identified at elevated risk for oral disease.

Evidence-Based Update on Diagnosis and Management of Gingivitis and Periodontitis

This article is an overview to update the practicing general dental practitioner about clinically relevant evidence-based topics published in the recent past in the diagnosis, etiopathogenesis, and management of gingivitis and periodontitis.

Perceptions and attitudes toward performing risk assessment for periodontal disease: a focus group exploration

Background

Currently, many risk assessment tools are available for clinicians to assess a patient’s periodontal disease risk. Numerous studies demonstrate the potential of these tools to promote preventive management and reduce morbidity due to periodontal disease. Despite these promising results, solo and small group dental practices, where most people receive care, have not adopted risk assessment tools widely, primarily due to lack of studies in these settings. The objective of this study was to explore the knowledge, attitudes, and beliefs of dental providers in these settings toward risk-based care through focus groups.

Methods

We conducted six focus group sessions with 52 dentists and dental hygienists practicing in solo and small group practices in Pittsburgh, PA and New York City (NYC), NY. An experienced moderator and a note-taker conducted the six sessions, each including 8–10 participants and lasting approximately 90 min. All sessions were audio-recorded and transcribed verbatim. Two researchers coded the focus group transcripts. Using a thematic analysis approach, they reviewed the coding results to identify important themes and selected representative excerpts that best described each theme.

Results

Providers strongly believed identifying risk factors could predict periodontal disease and use this information to change their patients’ behavior. A successful risk assessment tool could assist them in educating and changing their patient’s behaviors to adopt a healthy lifestyle, thus enabling them to play a major role in their patients’ overall health. However, to achieve this goal, it is essential to educate all dental providers and not just dentists on performing risk assessment and translating the results into actionable recommendations for patients. According to study participants, the research community has focused more on translating research findings into a risk assessment tool, and less on how clinicians would use these tools during patient encounters and if it affects a patients’ risk or outcome.

Conclusions

Dental practitioners were open to performing risk assessment as routine care and playing a bigger role in their patients’ overall health. Recommendations to overcome major barriers included educating dental providers at all levels, conducting more research about their adoption and use in real-world settings and developing appropriate reimbursement models.

Electronic supplementary material

The online version of this article (10.1186/s12903-018-0550-2) contains supplementary material, which is available to authorized users.

Periodontal profile classes predict periodontal disease progression and tooth loss

BACKGROUND

Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss.

METHODS

The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]).

RESULTS

Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss.

CONCLUSIONS

These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived personalized propensity scores provide clinical periodontal definitions that reflect disease patterns in the population and offer a useful system for patient stratification that is predictive for disease progression and tooth loss.

Introduction of a prediction model to assigning periodontal prognosis based on survival time

AIMS

To develop a prediction model for tooth loss due to periodontal disease (TLPD) in patients following periodontal maintenance (PM), and assess its performance using a multicentre approach.

MATERIAL AND METHODS

A multilevel analysis of eleven predictors of TLPD in 500 patients following PM was carried out to calculate the probability of TLPD. This algorithm was applied to three different TLPD samples (369 teeth) gathered retrospectively by nine periodontist, associating several intervals of probability with the corresponding survival time, based on significant differences in the mean survival time. The reproducibility of these associations was assessed in each sample (One-way ANOVA and pairwise comparison with Bonferroni corrections).

RESULTS

The model presented high specificity and moderate sensitivity, with optimal calibration and discrimination measurements. Seven intervals of probability were associated with seven survival time and these associations contained close to 80% of the cases: the probability predicted the survival time at this percentage. The model performed well in the three samples, as the mean survival time of each association were significantly different within each sample, while no significant differences between the samples were found in pairwise comparisons of means.

CONCLUSIONS

This model might be useful for predicting survival time in different TLPD samples.

A comprehensive approach to assigning periodontal prognosis

AIM

The purpose of this retrospective case series study was to evaluate the usefulness and performance of a comprehensive approach to assigning periodontal prognosis by assessing the risk of tooth loss due to periodontal disease (TLPD) and estimate the survival time (ST) of periodontally compromised teeth (PCT).

MATERIAL AND METHODS

The Long-Term Outcome (LTO) index was utilized to assess the risk of TLPD, at baseline, in 100 patients following periodontal maintenance for a mean 24.7 year (±2.4). A TLPD prediction model was utilized to assign ST to PCT. The performance of the TLPD prediction based on both parameters was assessed.

RESULTS

The mean TLPD rate increased as the LTO index increased (Kruskal-Wallis p < .001). The percentage of TLPD increased as the ST decreased, with significantly differences between the LTO categories <4 and ≥4 for all ST intervals (Mann-Whitney p < .001 to p .021). Only 21% of PCT were lost. This percentage was 58% for teeth assigned the shortest ST and increased to 71% and 88% for these teeth in LTO ≥4 patients.

CONCLUSIONS

This approach might be useful to predict TLPD in patients with an initial higher risk of TLPD while it was not useful in patients with lower risk of TLPD.

Predictors of long-term outcomes in patients undergoing periodontal maintenance

AIM

This retrospective study aimed to characterize the baseline status of patients following periodontal maintenance, analysing the association between the long-term outcome of these patients, smoking, bruxism, and the main clinical and radiographic variables.

MATERIAL AND METHODS

A sample of 174 patients with moderate to severe periodontitis was refined into homogeneous subsamples according to smoking and bruxism and the rate of tooth loss due to periodontal disease (TLPD): 0, 1-2, and >2 teeth. The association and the distribution (χ² test) of the variables within the subsamples were analysed.

RESULTS

Smoking and bruxism were significantly associated with higher TLPD rates. Vertical and circumferential bone defects (p < .0001), and abfractions (p < .0001) were associated with bruxism and particularly with bruxism and TLPD >2. Furcation defects (p = .0002), fewer radio-opaque subgingival calculus (χ² p < .0001), a lower mean Gingival index (χ² p = .027), and increased mean recessions >1.5 mm (χ² p = .0026) were associated with smoking and higher TLPD rates. The mean baseline mobility, abfractions, and recessions characterized two basic types of TLPD.

CONCLUSIONS

Smoking, bruxism, and routine clinical and radiological parameters can be used to characterize the baseline status of patients with worse outcomes.

Risk communication in the dental practice

The communication of risk in dental settings is a routine task that most clinicians are familiar with in their clinical encounters. However, work from medical settings has suggested that using this process in order to support health behaviour change in people may well be undermined by difficulties in understanding risk information, in presenting the information in a way that is clearly understood by the recipient and in the effects that such information may have for supporting further health behaviours by patients. This paper synthesises literature in the area that addresses these issues and explores approaches dental care professionals might consider when communicating risks in the dental surgery.

Bleeding on probing around dental implants: a retrospective study of associated factors

AIM

To (i) identify factors associated with the probability of a peri-implant site to be positive to bleeding on probing (BoP+) and (ii) compare BoP+ probability around dental implants and contra-lateral teeth.

METHODS

In 112 patients, data related to 1725 peri-implant sites and 1020 contra-lateral dental sites were retrospectively obtained. To analyse the association between patient-, implant- and site-related factors and BoP+ probability, a logistic, three-level model was built with BoP as the binary outcome variable (+/-).

RESULTS

BoP+ probability for a peri-implant site with probing depth (PD) of 4 mm was 27%, and the odds ratio increased by 1.6 for each 1-mm increment in PD (p < 0.001). Also, BoP+ probability was higher in females compared to males (OR = 1.61; p = 0.048), and lower at posterior compared to anterior dental implants (OR = 0.55; p < 0.01). No significant difference in BoP+ probability was observed between peri-implant and contra-lateral dental sites when controlling for the difference in PD.

CONCLUSIONS

The probability of a peri-implant site to bleed upon probing is (i) associated with PD, implant position and gender, and (ii) similar to that observed at contra-lateral dental sites when controlling for the effect of PD.

Dental Data Mining: Potential Pitfalls and Practical Issues

Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports ( e.g., Kattan et al., 1998 ) show that ANN and CART models can perform better than classic regression models: CART models excel at covariate interactions, while ANN models excel at nonlinear covariates. Model prediction performance is examined with the use of validation procedures and evaluating concordance, sensitivity, specificity, and likelihood ratio. To aid interpretation, various plots of predicted probabilities are utilized, such as lift charts, receiver operating characteristic curves, and cumulative captured-response plots. A dental caries study is used as an illustrative example. This paper compares the performance of logistic regression with KDD methods of CART and ANN in analyzing data from the Rochester caries study. With careful analysis, such as validation with sufficient sample size and the use of proper competitors, problems of naïve KDD analyses ( Schwarzer et al., 2000 ) can be carefully avoided.

The Chief Clinical Informatics Officer (CCIO): AMIA Task Force Report on CCIO Knowledge, Education, and Skillset Requirements

INTRODUCTION

The emerging operational role of the "Chief Clinical Informatics Officer" (CCIO) remains heterogeneous with individuals deriving from a variety of clinical settings and backgrounds. The CCIO is defined in title, responsibility, and scope of practice by local organizations. The term encompasses the more commonly used Chief Medical Informatics Officer (CMIO) and Chief Nursing Informatics Officer (CNIO) as well as the rarely used Chief Pharmacy Informatics Officer (CPIO) and Chief Dental Informatics Officer (CDIO).

BACKGROUND

The American Medical Informatics Association (AMIA) identified a need to better delineate the knowledge, education, skillsets, and operational scope of the CCIO in an attempt to address the challenges surrounding the professional development and the hiring processes of CCIOs.

DISCUSSION

An AMIA task force developed knowledge, education, and operational skillset recommendations for CCIOs focusing on the common core aspect and describing individual differences based on Clinical Informatics focus. The task force concluded that while the role of the CCIO currently is diverse, a growing body of Clinical Informatics and increasing certification efforts are resulting in increased homogeneity. The task force advised that 1.) To achieve a predictable and desirable skillset, the CCIO must complete clearly defined and specified Clinical Informatics education and training. 2.) Future education and training must reflect the changing body of knowledge and must be guided by changing day-to-day informatics challenges.

CONCLUSION

A better defined and specified education and skillset for all CCIO positions will motivate the CCIO workforce and empower them to perform the job of a 21st century CCIO. Formally educated and trained CCIOs will provide a competitive advantage to their respective enterprise by fully utilizing the power of Informatics science.

Periodontal Management by Risk Assessment: A Pragmatic Approach

An evidence-based periodontal disease risk assessment and diagnosis system has been developed and combined with a clinical decision support and management program to improve treatment and measure patient outcomes.

BACKGROUND

There is little agreement on a universally accepted periodontal risk assessment, periodontal diagnosis, and treatment management tool and their incorporation into dental practice to improve patient care. This article highlights the development and use of a practical periodontal management and risk assessment program that can be implemented in dental settings.

METHODS

The approach taken by Willamette Dental Group to develop a periodontal disease risk assessment, periodontal diagnosis, and treatment management tool is described using evidence-based best practices. With goals of standardized treatment interventions while maintaining personalized care and improved communication, this process is described to facilitate its incorporation into other dental settings.

CONCLUSIONS

Current electronic health records can be leveraged to enhance patient-centered care through the use of risk assessments and standardized guidelines to more effectively assess, diagnose, and treat patients to improve outcomes. Dental hygienists, and other committed providers, with their emphasis on prevention of periodontal disease can be principal drivers in creation and implementation of periodontal risk assessments and personalized treatment planning. Willamette Dental Group believes that such evidence-based tools can advance dentistry to new diagnostic and treatment standards.

Drivers Advancing Oral Health in a Large Group Dental Practice Organization

Three change drivers are being implemented to high standards of patient centric and evidence-based oral health care within the context of a large multispecialty dental group practice organization based on the commitment of the dental hygienist chief operating officer and her team.

BACKGROUND AND PURPOSE

A recent environmental scan elucidated 6 change drivers that can impact the provision of oral health care. Practitioners who can embrace and maximize aspects of these change drivers will move dentistry forward and create future opportunities. This article explains how 3 of these change drivers are being applied in a privately held, accountable risk-bearing entity that provides individualized treatment programs for more than 417,000 members. To facilitate integration of the conceptual changes related to the drivers, a multi-institutional, multidisciplinary, highly functioning collaborative work group was formed.

METHODS AND APPROACH

The document Dental Hygiene at a Crossroads for Change(1) inspired the first author, a dental hygienist in a unique position as chief operating officer of a large group practice, to pursue evidence-based organizational change and to impact the quality of patient care. This was accomplished by implementing technological advances including dental diagnosis terminology in the electronic health record, clinical decision support, standardized treatment guidelines, quality metrics, and patient engagement to improve oral health outcomes at the patient and population levels. The systems and processes used to implement 3 change drivers into a large multi-practice dental setting is presented to inform and inspire others to implement change drivers with the potential for advancing oral health.

CONCLUSIONS

Technology implementing best practices and improving patient engagement are excellent drivers to advance oral health and are an effective use of oral health care dollars. Improved oral health can be leveraged through technological advances to improve clinical practice.

Appropriate Recall Interval for Periodontal Maintenance: A Systematic Review

OBJECTIVES

A systematic review of the literature was undertaken to assess the evidence to support a specific time interval between periodontal maintenance (PM) visits.

METHODS

Relevant articles were identified through searches in MEDLINE, EMBASE and PubMed using specific search terms, until April, 2014, resulting in 1095 abstracts and/or titles with possible relevance. Critical Appraisal Skills Programme (CASP) guidelines were used to evaluate the strength of studies and synthesize findings. If mean recall interval was not reported for study groups, authors were contacted to attempt to retrieve this information.

RESULTS

Eight cohort studies met the inclusion criteria. No randomized control trials were found. All included studies assessed the effect of PM recall intervals in terms of compliance with a recommended regimen (3-6 months) as a primary outcome. Shorter PM intervals (3-6 months) favored more teeth retention but also statistically insignificant differences between RC and IC/EC, or converse findings are also found. In the 2 studies reporting mean recall interval in groups, significant tooth loss differences were noted as the interval neared the 12 month limit.

CONCLUSIONS

Evidence for a specific recall interval (e.g. every 3 months) for all patients following periodontal therapy is weak. Further studies, such as RCTs or large electronic database evaluations would be appropriate. The merits of risk-based recommendations over fixed recall interval regimens should be explored.

Periodontal risk calculator versus periodontal risk assessment

Introduction:

The purpose of study was twofold: To determine the extent of inter valuator and inter group variation in risk scores assigned to study subjects by PRC and PRA. To explore the relationship between risk scores assigned by PRC and using the PRA.

Materials and Methods:

57 patients (33 male patients and 24 Female patients between 20 and 65 years age group) were assessed with PRC and PRA tools during their first visit.

Results and Conclusion:

We entered the resulting information in to the PRC and PRA to obtained a riskscore for each subject at first visit. The chi-square test significance between PRC and PRA is < 0.05 indicatesthe accuracy of the both tools.

Learned and unlearned concepts in periodontal diagnostics: a 50-year perspective

In the past 50 years, conceptual changes in the field of periodontal diagnostics have paralleled those associated with a better scientific understanding of the full spectrum of processes that affect periodontal health and disease. Fifty years ago, concepts regarding the diagnosis of periodontal diseases followed the classical pathology paradigm. It was believed that the two basic forms of destructive periodontal disease were chronic inflammatory periodontitis and 'periodontosis'- a degenerative condition. In the subsequent 25 years it was shown that periodontosis was an infection. By 1987, major new concepts regarding the diagnosis and pathogenesis of periodontitis included: (i) all cases of untreated gingivitis do not inevitably progress to periodontitis; (ii) progression of untreated periodontitis is often episodic; (iii) some sites with untreated periodontitis do not progress; (iv) a rather small population of specific bacteria ('periodontal pathogens') appear to be the main etiologic agents of chronic inflammatory periodontitis; and (v) tissue damage in periodontitis is primarily caused by inflammatory and immunologic host responses to infecting agents. The concepts that were in place by 1987 are still largely intact in 2012. However, in the decades to come, it is likely that new information on the human microbiome will change our current concepts concerning the prevention, diagnosis and treatment of periodontal diseases.

Belief in numbers: When and why women disbelieve tailored breast cancer risk statistics

OBJECTIVE

To examine when and why women disbelieve tailored information about their risk of developing breast cancer.

METHODS

690 women participated in an online program to learn about medications that can reduce the risk of breast cancer. The program presented tailored information about each woman's personal breast cancer risk. Half of women were told how their risk numbers were calculated, whereas the rest were not. Later, they were asked whether they believed that the program was personalized, and whether they believed their risk numbers. If a woman did not believe her risk numbers, she was asked to explain why.

RESULTS

Beliefs that the program was personalized were enhanced by explaining the risk calculation methods in more detail. Nonetheless, nearly 20% of women did not believe their personalized risk numbers. The most common reason for rejecting the risk estimate was a belief that it did not fully account for personal and family history.

CONCLUSIONS

The benefits of tailored risk statistics may be attenuated by a tendency for people to be skeptical that these risk estimates apply to them personally.

PRACTICE IMPLICATIONS

Decision aids may provide risk information that is not accepted by patients, but addressing the patients' personal circumstances may lead to greater acceptance.

Risk assessment for periodontal disease

The prevention and treatment of periodontal disease is based on accurate diagnosis, reduction or elimination of causative agents, risk management and correction of the harmful effects of the disease. The practice of risk assessment involves dental care providers identifying patients and populations at increased risk of developing periodontal disease. This can have a significant impact on clinical decision making. Risk assessment reduces the need for complex periodontal therapy, improve patient outcome and, ultimately, reduce oral health care cost. The awareness of risk factors also helps with the identification and treatment of co-morbidities in the general population as many periodontal disease risk factors are common to other chronic diseases such as diabetes, cardiovascular diseases and stroke.

Clinical, microbiological, and salivary biomarker profiles of dental implant patients with type 2 diabetes

OBJECTIVE

Regulators of peri-implant bone loss in patients with diabetes appear to involve multiple risk factors that have not been clearly elucidated. This study was conducted to explore putative local etiologic factors on implant bone loss in relation to type 2 diabetes mellitus, including clinical, microbial, salivary biomarker, and psychosocial factors.

MATERIALS AND METHODS

Thirty-two subjects (divided into type 2 diabetes mellitus and non-diabetic controls), having at least one functional implant and six teeth, were enrolled in a 1-year longitudinal investigation. Analyses of clinical measurements and standardized intra-oral radiographs, saliva and serum biomarkers (via protein arrays for 20 selected markers), and plaque biofilm (via qPCR for eight periodontal pathogens) were performed at baseline and 1 year. In addition, the subjects were asked to respond to questionnaires to assess behavioral and psychosocial variables.

RESULTS

There was a significant increase from baseline to 1 year in the probing depth of implants in the diabetes group (1.95 mm to 2.35 mm, P = 0.015). The average radiographic bone loss during the study period marginally increased at dental implants compared to natural teeth over the study period (0.08 mm vs. 0.05 mm; P = 0.043). The control group harbored higher levels of Treponema denticola at their teeth at baseline (P = 0.046), and the levels of the pathogen increased significantly over time around the implants of the same group (P = 0.003). Salivary osteoprotegerin (OPG) levels were higher in the diabetes group than the control group at baseline only; in addition, the salivary levels of IL-4, IL-10, and OPG associated with host defense were significantly reduced in the diabetes group (P = 0.010, P = 0.019, and P = 0.024), while controls showed an increase in the salivary OPG levels (P = 0.005). For psychosocial factors, there were not many significant changes over the observation period, except for some findings related to coping behaviors at baseline.

CONCLUSIONS

The study suggests that the clinical, microbiological, salivary biomarker, and psychosocial profiles of dental implant patients with type 2 diabetes who are under good metabolic control and regular maintenance care are very similar to those of non-diabetic individuals. Future studies are warranted to validate the findings in longer-term and larger clinical trials (ClinicalTrials.gov # NCT00933491).