Use of real-world data to assess the effectiveness of ustekinumab in treating IBD patients: a retrospective linked database study in northwest London

BACKGROUND

Data on the optimum positioning of biologics in the treatment of inflammatory bowel disease (IBD) are limited.

RESEARCH DESIGN AND METHODS

This was a longitudinal retrospective study of linked health-care data from northwest London, UK, for adults who started ustekinumab for IBD from 1 April 20161 April 2016 to 1 April 20211 April 2021. We compared outcomes by line of therapy (1 vs. 2 or 3+) and age group (18‒59 years or ≥ 60 years). In an analysis of CD patients, we calculated risks of IBD-related hospitalization, IBD-related abdominal surgery, ustekinumab persistence, and switching by line of therapy.

RESULTS

Of 163 patients screened, 149 were eligible. Age had no effect on outcomes. Elective all-cause hospital admissions were significantly higher when ustekinumab was used as second-line or third-line therapy compared with first-line treatment (p = 0.0048 and p = 0.001, respectively). In CD patients the numbers of hospital admissions were also higher with second-line or third-line therapy (p = 0.040 and p = 0.018, respectively). Use of ustekinumab as third-line therapy significantly increased the risk of IBD-related hospitalization (hazard ratio 2.5, 95% CI 1.1‒5.6, p = 0.029), IBD-related abdominal surgery (9.45, 1.2‒75.7, p = 0.03), and switching (14.6, 1.6‒131.0, p = 0.02). Drug persistence risks did not differ.

CONCLUSIONS

These findings support the use of ustekinumab as first-line therapy.

A tandem segmentation-classification approach for the localization of morphological predictors of C. elegans lifespan and motility

C. elegans is an established model organism for studying genetic and drug effects on aging, many of which are conserved in humans. It is also an important model for basic research, and C. elegans pathologies is a new emerging field. Here we develop a proof-of-principal convolutional neural network-based platform to segment C. elegans and extract features that might be useful for lifespan prediction. We use a dataset of 734 worms tracked throughout their lifespan and classify worms into long-lived and short-lived. We designed WormNet - a convolutional neural network (CNN) to predict the worm lifespan class based on young adult images (day 1 - day 3 old adults) and showed that WormNet, as well as, InceptionV3 CNN can successfully classify lifespan. Based on U-Net architecture we develop HydraNet CNNs which allow segmenting worms accurately into anterior, mid-body and posterior parts. We combine HydraNet segmentation, WormNet prediction and the class activation map approach to determine the segments most important for lifespan classification. Such a tandem segmentation-classification approach shows the posterior part of the worm might be more important for classifying long-lived worms. Our approach can be useful for the acceleration of anti-aging drug discovery and for studying C. elegans pathologies.