Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults.

Importance: Access to routine dental care prevents advanced dental disease and improves oral and overall health. Identifying individuals at risk of foregoing preventive dental care can direct prevention efforts toward high-risk populations. Objective: To predict foregone preventive dental care among adults overall and in sociodemographic subgroups and to assess the algorithmic fairness. Design, Setting, and Participants: This prognostic study was a secondary analyses of longitudinal data from the US Medical Expenditure Panel Survey (MEPS) from 2016 to 2019, each with 2 years of follow-up. Participants included adults aged 18 years and older. Data analysis was performed from December 2022 to June 2023. Exposure: A total of 50 predictors, including demographic and socioeconomic characteristics, health conditions, behaviors, and health services use, were assessed. Main Outcomes and Measures: The outcome of interest was foregoing preventive dental care, defined as either cleaning, general examination, or an appointment with the dental hygienist, in the past year. Results: Among 32 234 participants, the mean (SD) age was 48.5 (18.2) years and 17 386 participants (53.9%) were female; 1935 participants (6.0%) were Asian, 5138 participants (15.9%) were Black, 7681 participants (23.8%) were Hispanic, 16 503 participants (51.2%) were White, and 977 participants (3.0%) identified as other (eg, American Indian and Alaska Native) or multiple racial or ethnic groups. There were 21 083 (65.4%) individuals who missed preventive dental care in the past year. The algorithms demonstrated high performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.84 (95% CI, 0.84-0.85) in the overall population. While the full sample model performed similarly when applied to White individuals and older adults (AUC, 0.88; 95% CI, 0.87-0.90), there was a loss of performance for other subgroups. Removing the subgroup-sensitive predictors (ie, race and ethnicity, age, and income) did not impact model performance. Models stratified by race and ethnicity performed similarly or worse than the full model for all groups, with the lowest performance for individuals who identified as other or multiple racial groups (AUC, 0.76; 95% CI, 0.70-0.81). Previous pattern of dental visits, health care utilization, dental benefits, and sociodemographic characteristics were the highest contributing predictors to the models' performance. Conclusions and Relevance: Findings of this prognostic study using cohort data suggest that tree-based ensemble machine learning models could accurately predict adults at risk of foregoing preventive dental care and demonstrated bias against underrepresented sociodemographic groups. These results highlight the importance of evaluating model fairness during development and testing to avoid exacerbating existing biases.

A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation.

BACKGROUND: After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms. METHODS: Unsensitized recipients who received their first KTx deceased donor between January 1, 2010, and December 31, 2019, were classified according to the conduct of renal function after transplantation. Variables related to the donor, recipient, kidney preservation, and immunology were used. The patients were randomly divided into 2 groups: 70% were assigned to the training and 30% to the test group. Popular machine learning algorithms were used: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Gradient Boosting classifier, Logistic Regression, CatBoost classifier, AdaBoost classifier, and Random Forest classifier. Comparative performance analysis on the test dataset was performed using the results of the AUC values, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. RESULTS: Of the 859 patients, 21.7% (n = 186) had IGF. The best predictive performance resulted from the eXtreme Gradient Boosting model (AUC, 0.78; 95% CI, 0.71-0.84; sensitivity, 0.64; specificity, 0.78). Five variables with the highest predictive value were identified. CONCLUSIONS: Our results indicated the possibility of creating a model for the prediction of IGF, enhancing the selection of patients who would benefit from an expensive treatment, as in the case of machine perfusion preservation.

Prediction models for carbapenem-resistant Enterobacterales carriage at liver transplantation: A multicenter retrospective study.

BACKGROUND: Carbapenem-resistant Enterobacterales (CRE) colonisation at liver transplantation (LT) increases the risk of CRE infection after LT, which impacts on recipients' survival. Colonization status usually becomes evident only near LT. Thus, predictive models can be useful to guide antibiotic prophylaxis in endemic centres. AIMS: This study aimed to identify risk factors for CRE colonisation at LT in order to build a predictive model. METHODS: Retrospective multicentre study including consecutive adult patients who underwent LT, from 2010 to 2019, at two large teaching hospitals. We excluded patients who had CRE infections within 90 days before LT. CRE screening was performed in all patients on the day of LT. Exposure variables were considered within 90 days before LT and included cirrhosis complications, underlying disease, time on the waiting list, MELD and CLIF-SOFA scores, antibiotic use, intensive care unit and hospital stay, and infections. A machine learning model was trained to detect the probability of a patient being colonized with CRE at LT. RESULTS: A total of 1544 patients were analyzed, 116 (7.5%) patients were colonized by CRE at LT. The median time from CRE isolation to LT was 5 days. Use of antibiotics, hepato-renal syndrome, worst CLIF sofa score, and use of beta-lactam/beta-lactamase inhibitor increased the probability of a patient having pre-LT CRE. The proposed algorithm had a sensitivity of 66% and a specificity of 83% with a negative predictive value of 97%. CONCLUSIONS: We created a model able to predict CRE colonization at LT based on easy-to-obtain features that could guide antibiotic prophylaxis.