The role of upadacitinib in the treatment of ulcerative colitis.

The JAK signaling pathway plays a major role in the immunopathology of autoimmune diseases, including inflammatory bowel disease. JAK enzymes provide novel targets for rapidly effective inflammatory bowel disease therapy, particularly in ulcerative colitis. Upadacitinib is a targeted JAK1 inhibitor. In multiple phase III clinical trials, upadacitinib has demonstrated significant improvement in clinical and endoscopic outcomes and quality of life for patients with moderate-to-severe ulcerative colitis. In this drug evaluation we describe the role of the JAK signaling pathway in ulcerative colitis, the mechanism of action of upadacitinib and the current clinical evidence for its use in ulcerative colitis; we also review its safety and tolerability, including for special populations.

The drug upadacitinib has recently been approved for use in the USA and the UK for patients with moderate-to-severe ulcerative colitis. This article discusses a protein called JAK, which increases inflammation in the body. Inflammation causes symptoms such as soreness, swelling and redness. Upadacitinib blocks JAK from working, which means there is less inflammation and a reduction in these symptoms. This article also discusses the clinical trials that have looked at the safety and efficacy (how well a treatment works) of upadacitinib for ulcerative colitis. Overall, the treatment has been shown to be fast-acting, safe and effective in patients with moderate-to-severe ulcerative colitis. People with ulcerative colitis should ask their healthcare provider if upadacitinib is a suitable treatment option for their disease.

Validation of a tool predicting important findings on computed tomography among Crohn's disease patients.

BACKGROUND: Patients with Crohn's disease (CD) are frequently subjected to computed tomography (CT) in the emergency department (ED). This young population is at higher risk of malignancy from radiation exposure. OBJECTIVES: We aimed to validate a decision tool predicting complications (perforation, abscess or other serious finding) on imaging at two sites. METHODS: We conducted a retrospective review of CT outcomes among patients with CD with ED visits at two tertiary care centers. Inclusion criteria were a CT of the abdomen/pelvis with contrast and complete lab data (erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP)) within 24 hours of arrival at the University of Michigan (UM) (2012-2013) and the University of Pittsburgh (UPMC) (2009-2012). Sensitivity, negative predictive value (NPV), miss rate and CT avoidance rate were calculated. RESULTS: At UPMC (n = 210), the tool had a sensitivity of 88.9% and NPV of 98.0%, potentially saving 47.1% from CT with a miss rate of 1.0%. At UM (n = 248), the tool had a sensitivity of 90.9% and NPV of 96.0%, saving 40.3% from CT with a miss rate of 1.6%. CONCLUSION: A decision tool using CRP and ESR predicting CT outcomes among CD patients performed well in an external validation, allowing providers to forgo CT use with a low miss rate.

Imaging of intestinal fibrosis: current challenges and future methods.

Crohn's disease (CD) activity assessments are dominated by inflammatory changes without discrete measurement of the coexisting fibrotic contribution to total bowel damage. Intestinal fibrosis impacts the development of severe structural complications and the overall natural history of CD. Measuring intestinal fibrosis is challenging and existing methods of disease assessment are unable to reliably distinguish fibrosis from inflammation. Both the immediate clinical need to measure fibrosis for therapeutic decision-making and the near-future need for tools to assess pipeline anti-fibrotic medications highlight the demand for biomarkers of fibrosis in CD. Developing non-invasive technologies exploit changes in intestinal perfusion, mechanical properties, and macromolecular content to provide quantitative markers of fibrosis. In this review of existing and experimental technologies for imaging intestinal fibrosis, we discuss the expanding capabilities of quantitative MR and ultrasound imaging, encouraging developments in non-invasive elastography, and emerging novel methods including photoacoustic imaging.

Increasing ultraviolet light exposure is associated with reduced mortality from Clostridium difficile infection.

BACKGROUND: Clostridium difficile infection (CDI) is an increasingly common cause of inpatient mortality. Vitamin D deficiency is associated with more aggressive CDI. We aimed to determine if average annual ultraviolet light (UV) exposure was associated with mortality in patients with CDI. METHODS: We used the US National Inpatient Sample (NIS) from 2004-2011 to assess the mortality risk in patients with a diagnosis of CDI (as per ICD-9CM 008.45). Annual average state UV exposure was assigned to each hospitalization. Logistic regression was used to determine the effects of UV exposure on mortality, controlling for age, gender, race and other comorbidities. RESULTS: During the study period, there were 2.61 million hospitalizations with a diagnosis of CDI. The mortality rate was 9.0%. In univariate analysis, the odds ratio (OR) of inpatient mortality for the UV index was 0.97 (95% CI 0.95-0.99; p = 0.008) per unit of UV exposure. In a multivariable model adjusting for age, gender, race, Charlson-Deyo index, season and coexisting inflammatory bowel disease, the UV index remained a protective predictor, with an OR of 0.94 (95% CI 0.92-0.96; p < 0.001). In the multivariate model, a seasonal effect was also present, with the highest risk of inpatient mortality in the period from January to March (OR 1.11; 95% CI 1.08-1.14) and the lowest risk, from July to September (OR 0.95; 95% CI 0.92-0.98). CONCLUSIONS: An increase in UV exposure index is associated with a reduced risk of inpatient mortality in patients with CDI. A seasonal effect is also present, with the highest risk of death during winter months. Further studies exploring the role of UV light in CDI are necessary.

The IBD drug pipeline-ready to deliver?

The study of modified-release phosphatidylcholine in this issue of the American Journal of Gastroenterology by Karner et al. is a substantial step forward for a mucosal barrier agent in inflammatory bowel disease (IBD). It is the first agent in a novel class to demonstrate clinical efficacy in a phase 2 study. There are a number of new agents with new mechanisms of action that are rapidly advancing through the IBD drug development pipeline. This article reviews the promising candidates and mechanisms of action in late-phase trials in IBD.

Comparison of imputation methods for missing laboratory data in medicine.

OBJECTIVES: Missing laboratory data is a common issue, but the optimal method of imputation of missing values has not been determined. The aims of our study were to compare the accuracy of four imputation methods for missing completely at random laboratory data and to compare the effect of the imputed values on the accuracy of two clinical predictive models. DESIGN: Retrospective cohort analysis of two large data sets. SETTING: A tertiary level care institution in Ann Arbor, Michigan. PARTICIPANTS: The Cirrhosis cohort had 446 patients and the Inflammatory Bowel Disease cohort had 395 patients. METHODS: Non-missing laboratory data were randomly removed with varying frequencies from two large data sets, and we then compared the ability of four methods-missForest, mean imputation, nearest neighbour imputation and multivariate imputation by chained equations (MICE)-to impute the simulated missing data. We characterised the accuracy of the imputation and the effect of the imputation on predictive ability in two large data sets. RESULTS: MissForest had the least imputation error for both continuous and categorical variables at each frequency of missingness, and it had the smallest prediction difference when models used imputed laboratory values. In both data sets, MICE had the second least imputation error and prediction difference, followed by the nearest neighbour and mean imputation. CONCLUSIONS: MissForest is a highly accurate method of imputation for missing laboratory data and outperforms other common imputation techniques in terms of imputation error and maintenance of predictive ability with imputed values in two clinical predicative models.