Correction for misclassification of caries experience in the absence of internal validation data

Validation of a Salivary miRNA Signature of Endometriosis - Interim Data

BACKGROUND: The discovery of a saliva-based micro–ribonucleic acid (miRNA) signature for endometriosis in 2022 opened up new perspectives for early and noninvasive diagnosis of the disease. The 109-miRNA saliva signature is the product of miRNA biomarkers and artificial intelligence (AI) modeling. We designed a multicenter study to provide external validation of its diagnostic accuracy. We present here an interim analysis. METHODS: The first 200 patients included in the multicenter prospective ENDOmiRNA Saliva Test study (NCT05244668) were included for interim analysis. The study population comprised women from 18 to 43 years of age with a formal diagnosis of endometriosis or with suspected endometriosis. Epidemiologic, clinical, and saliva sequencing data were collected between November 2021 and March 2022. Genomewide miRNA expression profiling by small RNA sequencing using next-generation sequencing (NGS) was performed, and a random forest algorithm was used to assess the diagnostic accuracy. RESULTS: In this interim analysis of the external validation cohort, with a population prevalence of 79.5%, the 109-miRNA saliva diagnostic signature for endometriosis had a sensitivity of 96.2% (95% confidence interval [CI], 93.7 to 97.3%), specificity of 95.1% (95% CI, 85.2 to 99.1%), positive predictive value of 95.1% (95% CI, 85.2 to 99.1%), negative predictive value of 86.7% (95% CI, 77.6 to 90.3%), positive likelihood ratio of 19.7 (95% CI, 6.3 to 108.8), negative likelihood ratio of 0.04 (95% CI, 0.03 to 0.07), and area under the receiver operating characteristic curve of 0.96 (95% CI, 0.92 to 0.98). CONCLUSIONS: The use of NGS and AI in the sequencing and analysis of miRNA provided a saliva-based miRNA signature for endometriosis. Our interim analysis of a prospective multicenter external validation study provides support for its ongoing investigation as a diagnostic tool. (Funded by Ziwig and the Conseil Régional d’Ile de France [Grant EX024087]; ClinicalTrials.gov number, NCT05244668.)

Comparison of ICDAS, CAST, Nyvad's Criteria, and WHO-DMFT for Caries Detection in a Sample of Italian Schoolchildren

Caries measurement methods vary considerably in terms of the stages of lesion considered making the comparison problematic among different surveys. In this cross-sectional study, four caries measurement methods, the WHO-DMFT, the International Caries Detection and Assessment System (ICDAS), the Caries Assessment Spectrum and Treatment (CAST), and the Nyvad Criteria were tested in a sample of children. Five-hundred 12-year old children (236 males and 264 females) were examined four times by four calibrated examiners. The calibration process showed that Cohen's Kappa exceeded the criterion of K = 0.75 and K = 0.80 for inter/intra-examiner agreement, respectively. In the survey, the total number of misclassification errors for the four methods amounted to 312 observations (67.94% regarding enamel lesions). The greatest difference among methods was shown by number of sound teeth (p < 0.01): WHO-DMFT n = 9505, 74.14%; ICDAS n = 2628, 20.49%; CAST n = 5053, 39.41%; and Nyvad Criteria n = 4117, 32.11%. At the level of dentinal Distinct/Active Cavity lesions, no statistically significant difference was observed (p = 0.40) between ICDAS (n = 1373, 10.71%), CAST (n = 1371, 0.69%), and Nyvad Criteria (n = 1720, 13.41%). In the severe caries levels, all methods were partially in agreement, while no accordance was found for the initial (enamel) lesions. A common language in caries detection is critical when different studies are compared.

Oral health research: A source for innovative new statistical developments

This article reviews the challenges and opportunities that oral health research may offer to a statistician. To illustrate this, we focus on the Signal Tandmobiel® study, a longitudinal oral health survey that triggered many statistical explorations and developments over the last two decades. For example, non-standard distributions are more the rule than the exception in oral health research. In addition, often measurement error problems need to be addressed. The hierarchical structure of the oral health data also poses non-standard challenges. For instance, caries experience in the mouth is spatially correlated with, however, a specific metric defining the distance between two occurrences. In addition, since caries experience in the context of an epidemiological study is only measured at intervals, analysis of survival involves interval-censoring. Finally, when analyzing a realistic data set in oral health, all the above issues may have to be dealt with simultaneously.