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Caries Risk Prediction Models in a Medical Health Care Setting.

ABSTRACT: Despite development of new technologies for caries control, tooth decay in primary teeth remains a major global health problem. Caries risk assessment (CRA) models for toddlers and preschoolers are rare. Among them, almost all models use dental factors (e.g., past caries experience) to predict future caries risk, with limited clinical/community applicability owing to relatively uncommon dental visits compared to frequent medical visits during the first year of life. The objective of this study was to construct and evaluate risk prediction models using information easily accessible to medical practitioners to forecast caries at 2 and 3 y of age. Data were obtained from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) mother-offspring cohort. Caries was diagnosed using modified International Caries Detection and Assessment System criteria. Risk prediction models were constructed using multivariable logistic regression coupled with receiver operating characteristic analyses. Imputation was performed using multiple imputation by chained equations to assess effect of missing data. Caries rates at ages 2 y (n = 535) and 3 y (n = 721) were 17.8% and 42.9%, respectively. Risk prediction models predicting overall caries risk at 2 and 3 y demonstrated area under the curve (AUC) (95% confidence interval) of 0.81 (0.75-0.87) and 0.79 (0.74-0.84), respectively, while those predicting moderate to extensive lesions showed 0.91 (0.85-0.97) and 0.79 (0.73-0.85), respectively. Postimputation results showed reduced AUC of 0.75 (0.74-0.81) and 0.71 (0.67-0.75) at years 2 and 3, respectively, for overall caries risk, while AUC was 0.84 (0.76-0.92) and 0.75 (0.70-0.80), respectively, for moderate to extensive caries. Addition of anterior caries significantly increased AUC in all year 3 models with or without imputation (all P < 0.05). Significant predictors/protectors were identified, including ethnicity, prenatal tobacco smoke exposure, history of allergies before 12 mo, history of chronic maternal illness, maternal brushing frequency, childbearing age, and so on. Integrating oral-general health care using medical CRA models may be promising in screening caries-susceptible infants/toddlers, especially when medical professionals are trained to "lift the lip" to identify anterior caries lesions.

SUBMITTER: Kalhan TA

PROVIDER: S-EPMC7343522 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Caries Risk Prediction Models in a Medical Health Care Setting.

Kalhan T A TA Un Lam C C Karunakaran B B Chay P L PL Chng C K CK Nair R R Lee Y S YS Fong M C F MCF Chong Y S YS Kwek K K Saw S M SM Shek L L Yap F F Tan K H KH Godfrey K M KM Huang J J Hsu C-Y S CS

Journal of dental research 20200420 7

Despite development of new technologies for caries control, tooth decay in primary teeth remains a major global health problem. Caries risk assessment (CRA) models for toddlers and preschoolers are rare. Among them, almost all models use dental factors (e.g., past caries experience) to predict future caries risk, with limited clinical/community applicability owing to relatively uncommon dental visits compared to frequent medical visits during the first year of life. The objective of this study w ...[more]

PMID: 32311276

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Project description:Expanded partnership with the medical community is an important strategy for reducing dental caries disparities. The purpose of this study was to assess the relationship between fluoride (F) "in office" (drops/tablets and/or varnish), as prescribed or applied by a health care professional by age 1 y, and 1) caries development and 2) presence of other caries risk factors or mediators (e.g., socioeconomic status). Child-primary caregiver (PCG) pairs ( N = 1,325) were recruited in Indiana, Iowa, and North Carolina as part of a longitudinal cohort study to validate a caries risk tool for primary health care settings. PCGs completed a caries risk questionnaire, while children received caries examinations per the criteria of the International Caries Detection and Assessment System at ages 1, 2.5, and 4 y. Baseline responses regarding children's history of F in office were tested for association with other caries risk variables and caries experience at ages 2.5 and 4 y via generalized estimating equation models applied to logistic regression. The sample was 48% female, and many children (61%) were Medicaid enrolled. The prevalence of cavitated caries lesions increased from 7% at age 2.5 y to 25% by age 4 y. Children who received F in office were likely deemed at higher caries risk and indeed were significantly ( P < 0.01) more likely to develop cavitated caries lesions by ages 2.5 and 4 y, even after F application (odds ratios: 3.5 and 2.3, respectively). Factors significantly associated with receiving F included the following: child being Medicaid enrolled, not having an employed adult in the household, child and PCG often consuming sugary drinks and snacks, and PCG having recent caries experience. Increased F in office from a health care provider by age 1 y was associated with known caries risk factors. Most (69%) children had never been to the dentist, suggesting that risk factors could be alerting medical providers and/or parents, thereby affecting in-office F recommendations. Differences among states could also be related to state-specific F-varnish reimbursement policies (ClinicalTrials.gov NCT01707797).

Project description:BackgroundPreterm birth complications are the leading causes of death among children under five years. However, the inability to accurately identify pregnancies at high risk of preterm delivery is a key practical challenge, especially in resource-constrained settings with limited availability of biomarkers assessment.MethodsWe evaluated whether risk of preterm delivery can be predicted using available data from a pregnancy and birth cohort in Amhara region, Ethiopia. All participants were enrolled in the cohort between December 2018 and March 2020. The study outcome was preterm delivery, defined as any delivery occurring before week 37 of gestation regardless of vital status of the foetus or neonate. A range of sociodemographic, clinical, environmental, and pregnancy-related factors were considered as potential inputs. We used Cox and accelerated failure time models, alongside decision tree ensembles to predict risk of preterm delivery. We estimated model discrimination using the area-under-the-curve (AUC) and simulated the conditional distributions of cervical length (CL) and foetal fibronectin (FFN) to ascertain whether they could improve model performance.ResultsWe included 2493 pregnancies; among them, 138 women were censored due to loss-to-follow-up before delivery. Overall, predictive performance of models was poor. The AUC was highest for the tree ensemble classifier (0.60, 95% confidence interval = 0.57-0.63). When models were calibrated so that 90% of women who experienced a preterm delivery were classified as high risk, at least 75% of those classified as high risk did not experience the outcome. The simulation of CL and FFN distributions did not significantly improve models' performance.ConclusionsPrediction of preterm delivery remains a major challenge. In resource-limited settings, predicting high-risk deliveries would not only save lives, but also inform resource allocation. It may not be possible to accurately predict risk of preterm delivery without investing in novel technologies to identify genetic factors, immunological biomarkers, or the expression of specific proteins.

Project description:OBJECTIVE:To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings. DESIGN:Systematic review. DATA SOURCES:MEDLINE and Embase databases combined from inception and up to the end of January 2019. ELIGIBILITY:Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). DATA EXTRACTION AND SYNTHESIS:Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance. RESULTS:An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21. CONCLUSION:Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

Project description:BackgroundAssessing caries risk is an essential element in the planning of preventive and therapeutic strategies. Different caries risk assessment (CRA) models have been proposed for the identification of individuals running a risk of future caries. This systematic review was designed to evaluate whether standardized caries risk assessment (CRA) models are able to evaluate the risk according to the actual caries status and/or the future caries increment.MethodsRandomized clinical trials, cross-sectional studies, cohort studies, comparative studies, validation studies and evaluation studies, reporting caries risk assessment using standardized models (Cariogram, CAMBRA, PreViser, NUS-CRA and CAT) in patients of any age related to caries data recorded by DMFT/S or ICDAS indices, were included. PubMed, Scopus and Embase were searched from 2000 to 2016. A search string was developed. All the papers meeting the inclusion criteria were subjected to a quality assessment.ResultsOne thousand three-undred ninety-two papers were identified and 32 were included. In all but one, the Cariogram was used both as sole model or in conjunction with other models. All the papers on children (n = 16) and adults (n = 12) found a statistically significant association between the risk levels and the actual caries status and/or the future caries increment. Nineteen papers, all using the Cariogram except one, were classified as being of good quality. Three of four papers comprising children and adults found a positive association. For seven of the included papers, Cariogram sensibility and specificity were calculated; sensibility ranged from low (41.0) to fairly low (75.0), while specificity was higher, ranging from 65.8 to 88.0. Wide 95% confidence intervals for both parameters were found, indicating that the reliability of the model differed in different caries risk levels.ConclusionsThe scientific evidence relating to standardized CRA models is still limited; even if Cariogram was tested in children and adults in few studies of good quality, no sufficient evidence is available to affirm the method is effective in caries assessment and prediction. New options of diagnosis, prognosis and therapy are now available to dentists but the validity of standardized CRA models still remains limited.

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Caries Risk Prediction Models in a Medical Health Care Setting.

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Caries Risk Prediction Models in a Medical Health Care Setting.

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