Long-term efficacy and safety of questionnaire-based initiation of urgency urinary incontinence treatment

OBJECTIVES: To determine the longer-term efficacy and safety of initiating treatment for urgency-predominant urinary incontinence (UUI) in women diagnosed using a simple questionnaire rather than an extensive evaluation. STUDY DESIGN: Women completing a 12-week randomized controlled trial of fesoterodine therapy for UUI diagnosed by questionnaire were invited to participate in a 9-month open label continuation study. UUI and voiding episodes were collected using voiding diaries. Participant satisfaction was measured by questionnaire. Safety was assessed by measurement of post void residual volume and adverse event monitoring; if necessary, women underwent specialist evaluation. Longitudinal changes in UUI and voiding episodes were evaluated using linear mixed models adjusting for baseline. RESULTS: Of the 567 women completing the randomized trial, 498 (87.8%) took at least one dose of medication during this open label study. Compared to the enrollment visit in the randomized trial, fesoterodine was associated with a reduction in total incontinence episodes/day and urgency incontinence episodes/day at the end of the open label study [adjusted mean (standard error (SE)) 4.6 (0.12) to 1.2 (0.13) and 3.9 (0.11) to 0.9 (0.11) respectively, p-value<.0001 for both]. Most women were satisfied with treatment (89%, 92%, and 93% at 3, 6, and 9 months). Twenty-six women experienced 28 serious adverse events, one of which was considered possibly treatment-related. Twenty-two women had specialist evaluation: 5 women’s incontinence was misclassified by the 3IQ; none experienced harm due to misclassification. CONCLUSIONS: Using a simple validated questionnaire to diagnose and initiate treatment for UUI in community dwelling women is safe and effective, allowing timely treatment by primary care practitioners

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers