Ecological study on needs and cost of treatment for dental caries in schoolchildren aged 6, 12, and 15 years: Data from a national survey in Mexico

To determine the treatment needs and the care index for dental caries in the primary dentition and permanent dentition of schoolchildren and to quantify the cost of care that would represent the treatment of dental caries in Mexico. A secondary analysis of data from the First National Caries Survey was conducted, which was a cross-sectional study conducted in the 32 states of Mexico. Based on dmft (average number of decayed, extracted, and filled teeth in the primary dentition) and DMFT (average number of decayed, extracted, and filled teeth in permanent dentition) information, a treatment needs index (TNI) and a caries care index (CI) were calculated. At age 6, the TNI for the primary dentition ranged from 81.7% to 99.5% and the CI ranged from 0.5% to 17.6%. In the permanent dentition, the TNI ranged from 58.8% to 100%, and the CI ranged from 0.0% to 41.2%. At age 12, the TNI ranged from 55.4% to 93.4%, and the CI ranged from 6.5% to 43.4%. At age 15, the TNI ranged from 50.4% to 98.4%, and the CI ranged from 1.4% to 48.3%. The total cost of treatment at 6 years of age was estimated to range from a purchasing power parity (PPP) of USD $49.1 to 287.7 million in the primary dentition, and from a PPP of USD$3.7 to 24 million in the permanent dentition. For the treatment of the permanent dentition of 12-year-olds, the PPP ranged from USD $13.3 to 85.4 million. The estimated cost of treatment of the permanent dentition of the 15-year-olds ranged from a PPP of USD$10.9 to 70.3 million. The total estimated cost of caries treatment ranged from a PPP of USD $77.1 to 499.6 million, depending on the type of treatment and provider (public or private). High percentages of TNI for dental caries and low CI values were observed. The estimated costs associated with the treatment for caries have an impact because they represent a considerable percentage of the total health expenditure in Mexico

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

International audienceTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding: US National Institutes of Health and European Society of Intensive Care Medicine