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Background: There is a need for risk prediction tools in caries research.

Risk indicators and risk predictors of dental caries in schoolchildren. The purpose of this study was to identify risk indicators of high caries level at baseline HCLB based on cross-sectional data and predictors of high caries increment HCI based on a 7-year-follow-up examination in year-old schoolchildren. Two hundred and six schoolchildren were examined in and in by the same two calibrated dentists, in Piracicaba, Brazil.

2013, Number 2

Background: There is a need for risk prediction tools in caries research. This investigation aimed to estimate and evaluate a risk score for prediction of dental caries. Materials and Methods: This case-cohort study included a random sample of cases with dental caries and controls randomly sampled from the study population at baseline , followed for 3 years. The risk ratio RR for each potential predictor was estimated using a logistic regression model. Conclusions: The present study estimated a simple risk score for prediction of dental caries retrieved from a risk algorithm with good discrimination.

Dental caries is the most prevalent disease worldwide. The negative influence of dental caries impacts far beyond overall health, affecting other important aspects of life such as social and employment opportunities [ 3 ].

Dental caries are dependent on lifestyle and dietary factors [ 4 , 5 ], with diet and smoking suggested as potential risk factors in a recent consensus [ 5 ]. The fact that lifestyle factors have important effects not only on dental caries but also on diabetes management rendered the suggestion of appropriate risk factor management procedures to be adopted in the dental setting such as the reduction of sugar consumption in order to increase the probability of a successful management of patients at risk of one or both diseases [ 7 ].

The multifactorial origin of most chronical conditions represents a challenge for clinicians to correctly diagnose and manage the risk of a specific patient for developing that condition. Risk algorithms for disease modeling assumes a central role in modern medicine as it allows the clinicians to access a tool to aid the diagnostic and decision process [ 8 ]. The study was approved by an independent ethical committee Ethical Committee for Health, authorization No.

The study was conducted in full accordance with ethical principles, including the World Medical Association Declaration of Helsinki version Written informed consent was obtained from each participant according to the aforementioned principles. This was a prospective case-cohort study based on the data collected from a prospective epidemiological surveillance study on oral diseases [ 9 ].

The data was collected from July to December An initial cohort of 22, participants was established. From these, there were 19, patients with natural teeth. All patients were part of a recall control maintenance program with diagnosis, prophylaxis and motivation indications including plaque control, dietary habits and fluoride daily use.

Patients with presence of dental caries in the first observation were excluded from this study. A total of patients with teeth and without dental caries in the first observation were eligible for inclusion, from which, registered dental caries attack rate of Examinations were performed by 22 clinicians trained and calibrated to diagnose and differentiate between sound surfaces and caries lesions both clinically and radiographically.

Training and reliability assessment of dental examinations was conducted within the same day with 30 patients in each annual workshop session, resulting in 90 observations per clinician during the three workshop sessions. The overall inter-examiner reliability was estimated using a weighted average of the pairwise inter-examiner reliability estimates. The reliability of outcome assessors collecting clinical information assessed through the weighted kappa scores during the three years of follow-up were 0.

One trained outcome assessor was responsible for collecting information from the records. The sample size calculation was performed using a software program [ 10 ]. The authors planned a study with independent cases and controls with 1 control s per case. Given the nature of the study design case-cohort study the number of controls had to be necessarily adjusted. A total of patients were selected. The cases with dental caries were selected randomly from the sample of patients with dental caries.

Given the nature of the study design case-cohort design , the controls were selected randomly from the global sample of patients controls could have the presence or absence of dental caries at the end of the study follow-up given the random sample. The random sampling was performed using a random numbers generator www. Baseline radiographic and clinical evaluations were performed to attest the absence of dental caries [ 9 ]. Descriptive statistics were applied for all variables measures of central tendency and variance for continuous variables, ratios and frequencies for dichotomous variables.

To retrieve the risk model, the statistics were performed according to previously described methods [ 20 ]. The authors developed the risk score, based on previously described statistical methods [ 21 , 22 , 23 ].

The independent predictor with lowest beta coefficient systemic conditions: 0. A sum of weight points for each predictor was calculated to define the final score. In order to compare the incidence of dental caries, the patients were divided into three risk groups.

The cutoff points for the three risk groups were defined based on the multiples of the pre-analysis risk: less than half the pre-analysis risk low risk , more than half the pre-analysis risk and less than the pre-analysis risk moderate risk , and more than the pre-analysis risk high risk. The sample of patients included in the study had an average age standard deviation of There were patients with dental caries The average time of follow-up free from dental caries standard deviation was Univariable and multivariable risk ratio estimates, multivariable beta coefficients, and risk score points for the prediction of dental caries.

The algorithm used to determine the predicted probability of dental caries according to the risk points and risk groups is illustrated in Table 3. The observed incidence of dental caries in the low-, moderate-, and high-risk groups was 6. A real-life clinical situation illustrates the practical use of the risk score Figure 2 , Figure 3 and Figure 4. A total of 12 points were registered in the risk score, placing the patient in the high-risk category for dental caries. Radiographic view on November of the first sextant with tooth of interest second premolar with absence of dental caries.

The patient missed the control appointment scheduled for 4 months. Radiographic view on November one year after of the first sextant with tooth of interest second premolar exhibiting dental caries on the distal aspect. The main finding of the present study was the estimation and validation of a simple risk score for prediction of dental caries that enabled risk stratification: a score of less than 3 points to distinguish low risk, a score between 4—7 points for moderate risk and a score of 8 or more points for high risk.

The final application of this research is to be used in a clinical setting to help stratify patients and provide tailored preventive actions, recall regimens and diet counseling. Risk scores are a useful tool to motivate patients into changing risk behaviors; while for clinicians, it enables simplification of complex statistical calculations used in multi-factorial analysis and their inclusion in clinical practice [ 23 ]. Certainly, while clinical evaluations based on the diagnosis of lesions only dichotomize patients into diseased or healthy, the risk score provides two thresholds.

First, patients with a score lower or equal than 3 points be positively considered as low-risk patients. Patients with a score between 4 and 7 points can be perceived as moderate risk patients. Undeniably, higher risk patients will have the presence of more risk factors and will be prone to carious lesions [ 24 ].

The case-cohort design [ 26 ] was chosen to conduct our study as an alternative to full cohort design when data collection and follow-up is time-consuming and expensive. The case-cohort design has the particularity of randomly selecting from the source population, regardless of their disease status. Among the advantages of the case-cohort design compared to case-control studies, the fact that risk ratios can easily be obtained directly from the cross-product of exposed and unexposed cases and controls, the control group representing a random sample of the source population, and that the control group can easily be used as a reference group to investigate multiple outcomes.

However, the necessity of increasing the number of controls to compensate the reduced statistical power and the necessity of an increased statistical expertise compared to traditional case-control study designs are considered limitations of the approach [ 12 ]. A recent systematic review [ 28 ] appraised the evidence for the prediction of caries using four caries risk-assessment systems Cariogram, CAMBRA, American Dental Association and American Academy of Pediatric Dentistry , focusing on prospective cohort studies or randomized controlled trials.

The authors concluded that the evidence on the validity for existing systems was limited and that there was a necessity to develop valid and reliable methods for caries risk assessment. Furthermore, caries risk assessment systems such as Cariogram including 9 factors and CAMBRA including 25 factors performed at a level that did not assure that including a large number of factors was more beneficial than including only a few [ 28 ].

Based on the reports of previous systematic reviews, the analysis of risk indicators for dental caries was clustered in a reduced number of variables. Ritter et al. Tellez et al. In our analysis, all three variables referenced by Ritter et al. Bacterial plaque represents a pathological factor with a significant association with dental caries odds ratio range: 2.

Considering the presence of systemic conditions as a risk factor for dental caries, its significance in the model may be explained by the influence that these conditions may affect oral health in general, disturbing the host response to the plaque biofilm by upsetting the host-microbial balance [ 32 ].

Furthermore, a previous study investigating the relation between systemic disease and caries experience registered associations between systemic conditions hepatitis, cardiovascular disease and diabetes and asthma with higher caries experience [ 33 ].

Restorations with more than 5 years and the number of teeth restored both represent disease indicators that potentially provide high predictive values when assessing the risk of dental caries, acting as more quantitative proxy variables for caries experience.

This was previously reported in systematic reviews of indicators of risk in caries management, where a previous caries experience was considered an important predictor in community-based studies [ 34 , 35 ].

The limitations of this study are related to the study design, lack of control for other potential variables of interest, the short-term follow-up and the study setting private practice. Concerning the study design, as the most prevalent disease globally [ 1 ], dental caries imply a great difficulty to study risk factors using only incident cases in a cohort study design.

Attempting to study incident cases would require an extensive time-consuming procedure, which together with the potential susceptibility to sample erosion would render the investigation virtually unfeasible. In an attempt to shorten that limitation, the case-cohort design was chosen and its statistical limitations suppressed, rendering valid estimations [ 12 ].

This implies from a causal component point of view that different combinations of risk factors may produce different causal mechanisms in different populations [ 39 ]. The lack of control for other potential risk indicators such as dietary habits [ 4 ], exposure to fluoride, salivary flow and testing for the level of cariogenic bacteria [ 40 ] constitute potential limitations of this study.

Furthermore, the short-term follow-up of this study may imply an underestimation of the dental caries prevalence. The study setting, being conducted in a private practice may imply caution in extrapolating the results to the community, as illustrated by the distribution of the variable socioeconomic status, with a larger distribution of patients in the first category higher status compared to the third category lower status. The present study estimated and validated a simple risk score for predicting dental caries and performing appropriate risk stratification.

The simple risk score may enable proper clinical interventions and elucidate the patient regarding self-perceived oral health status, with the final objective of providing health gains. Conceptualization, M. National Center for Biotechnology Information , U. Journal List J Clin Med v. J Clin Med. Published online Feb 7. Author information Article notes Copyright and License information Disclaimer.

Received Jan 23; Accepted Feb 4. Abstract Background: There is a need for risk prediction tools in caries research. Keywords: tooth, caries, risk, epidemiology. Introduction Dental caries is the most prevalent disease worldwide. Materials and Methods The study was approved by an independent ethical committee Ethical Committee for Health, authorization No. Sample Size Calculation and Sampling The sample size calculation was performed using a software program [ 10 ].

Statistical Analysis 2. Assessment of Risk Factors Descriptive statistics were applied for all variables measures of central tendency and variance for continuous variables, ratios and frequencies for dichotomous variables.

Conventional diagnostic aids in dental caries

View My Stats. Dental caries is a major cause of tooth loss in children and young adults. Dental caries have been linked to the situation of underprivileged families, nutritional imbalance, and poor oral hygiene techniques, including lack of tooth brushing or flossing the teeth, and also have a genetic etiology. Dietary habits and dental hygiene practice can result in high caries in school children. This research aimed to reveal the correlation between dietary habits and dental hygiene practice with dental caries among school children in urban area of Semarang.


Diagnosis and Risk Prediction of Dental Caries, Volume 2. Kenneth S. Kurtz DDS Previous FigureNext Figure. Caption. Download PDF. back.


Predicting Dental Caries Outcomes in Children

Realization that dental caries is a reversible, dynamic biochemical event at a micron level has changed the way the profession recognizes the caries disease and the caries lesion. The diagnosis of dental caries poses challenges due to the complex interaction of multiple endogenous causal factors. The most appropriate diagnostic aid for this purpose is the risk model of caries risk assessment. The analyses of the biological determinants provide clues to the dominant causal factor.

Diagnosis and risk prediction of dental caries

Conventional diagnostic aids in dental caries

In recent years, unprecedented gains in the understanding of the biology and mechanisms underlying human health and disease have been made. In the domain of oral health, although much remains to be learned, the complex interactions between different systems in play have begun to unravel: host genome, oral microbiome with its transcriptome, proteome and metabolome, and more distal influences, including relevant behaviors and environmental exposures. A reasonable expectation is that this emerging body of knowledge can help improve the oral health and optimize care for individuals and populations. Key processes in these efforts are the discovery of causal factors or mechanistic pathways and the identification of individuals or population segments that are most likely to develop any or severe forms of oral disease.

Key words:. Barnes CM. Dental Clinics of North America. Nonsurgical Treatment of Incipient and Hidden Caries. Prevention and inter professional relation. Caries diagnosis and risk assessment.


Diagnosis and. Risk Prediction of Dental Caries,. Volume 2. Per Axellson, Quintessence Publishing Co, Inc, Carol. Stream, IL, ISBN


Caries process is dynamic with demineralization and remineralization occurring over time such that the net balance of these events determines the caries activity and severity. The dilemma in clinical detection arises not with the advanced lesion, but primarily with the early, non-cavitated lesion of dentin, recurrent caries, and sublingual root caries. According to Pitts, [ 2 ] the ideal tool for diagnosis of the carious lesion would be noninvasive, reliable, valid, sensitive, specific and provide a robust measurement of lesion size and activity and would be based on the biological process directly related to the carious process.

Dental caries is a public health problem due to its widespread characteristic, cost of treatment and effects on the quality of life This investigation aimed to estimate and evaluate a risk score for prediction of dental caries. Materials and Methods: This case-cohort study included a random sample of cases with dental caries and controls randomly sampled from the study population at baseline , followed for 3 years.

Dental caries assessment needs to be targeted at specific age groups, as many risk factors are related to patient age. Pre-teen and teenage patients, who are still at risk of occurrence of new carious lesions, need more individualized caries management strategies. Therefore, this study aimed to identify caries-related risk factors and develop a simplified risk prediction model for dental caries. Risk factors for caries were assessed in participants aged years, based on a questionnaire survey, previous history of caries, oral hygiene, microorganism colonization, saliva secretion, saliva buffer capacity examinations, and the acidogenicity of dental biofilms. These risk factors were entered into a computer-based risk assessment program the Cariogram , and correlations between these factors and Cariogram scores were investigated.

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    Request PDF | On Dec 1, , Kenneth S. Kurtz published Diagnosis and Risk Prediction of Dental Caries, Volume 2 | Find, read and cite all.

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