A validation of models for prediction of pathogenic variants in mismatch repair genes.

PURPOSE: Models used to predict the probability of an individual having a pathogenic homozygous or heterozygous variant in a mismatch repair gene, such as MMRpro, are widely used. Recently, MMRpro was updated with new colorectal cancer penetrance estimates. The purpose of this study was to evaluate the predictive performance of MMRpro and other models for individuals with a family history of colorectal cancer. METHODS: We performed a validation study of 4 models, Leiden, MMRpredict, PREMM5, and MMRpro, using 784 members of clinic-based families from the United States. Predicted probabilities were compared with germline testing results and evaluated for discrimination, calibration, and predictive accuracy. We analyzed several strategies to combine models and improve predictive performance. RESULTS: MMRpro with additional tumor information (MMRpro+) and PREMM5 outperformed the other models in discrimination and predictive accuracy. MMRpro+ was the best calibrated with an observed to expected ratio of 0.98 (95% CI = 0.89-1.08). The combination models showed improvement over PREMM5 and performed similar to MMRpro+. CONCLUSION: MMRpro+ and PREMM5 performed well in predicting the probability of having a pathogenic homozygous or heterozygous variant in a mismatch repair gene. They serve as useful clinical decision tools for identifying individuals who would benefit greatly from screening and prevention strategies.

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7-84.1%), 69.9% (95% CI 69.8-69.9%), 86.5% (95% CI 81.6-91.3%), 61.5% (95% CI 56.6-66.4%), 53.2% (95% CI 47.7-58.7%), 65.9% (95% CI 60.2-71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.