Development of a Simple, Serum Biomarker-based Model Predictive of the Need for Early Biologic Therapy in Crohn's Disease

Predictors of Complicated Disease Course in Adults and Children With Crohn's Disease: A Nationwide Study from the epi-IIRN

BACKGROUND

Since data on predictors of complicated Crohn's disease (CD) from unselected populations are scarce, we aimed to utilize a large nationwide cohort, the epi-IIRN, to explore predictors of disease course in children and adults with CD.

METHODS

Data of patients with CD were retrieved from Israel's 4 health maintenance organizations, whose records cover 98% of the population (2005-2020). Time-to-event modeled a complicated disease course, defined as CD-related surgery, steroid-dependency, or the need for >1 class of biologics. Hierarchical clustering categorized disease severity at diagnosis based on available laboratory results.

RESULTS

A total of 16 659 patients (2999 [18%] pediatric-onset) with 121 695 person-years of follow-up were included; 3761 (23%) had a complicated course (750 [4.5%] switched to a second biologic class, 1547 [9.3%] steroid-dependency, 1463 [8.8%] CD-related surgery). Complicated disease was more common in pediatric- than adult-onset disease (26% vs 22%, odds ratio, 1.3; 95% confidence interval [CI], 1.2-1.4). In a Cox multivariate model, complicated disease was predicted by induction therapy with biologics (hazard ratio [HR], 2.1; 95% CI, 1.2-3.6) and severity of laboratory tests at diagnosis (HR, 1.7; 95% CI, 1.2-2.2), while high socioeconomic status was protective (HR, 0.94; 95% CI, 0.91-0.96). In children, laboratory tests predicted disease course (HR, 1.8; 95% CI, 1.2-2.5), as well as malnutrition (median BMI Z score -0.41; 95% CI, -1.42 to 0.43 in complicated disease vs -0.24; 95% CI, -1.23 to 0.63] in favorable disease; P < .001).

CONCLUSIONS

In this nationwide cohort, CD course was complicated in one-fourth of patients, predicted by laboratory tests, type of induction therapy, socioeconomic status, in addition to malnutrition in children.

Combined Use of Fecal Biomarkers in Inflammatory Bowel Diseases: Oncostatin M and Calprotectin

Background

Fecal biomarkers have emerged as one of the most useful tools for clinical management of inflammatory bowel disease (IBD). Oncostatin M (OSM), like fecal calprotectin (FC), is highly expressed in the inflamed intestinal mucosa which may have potential usefulness. We aimed to evaluate the additional utility of these two fecal biomarkers for IBD diagnosis, activity, and prediction of infliximab response over FC alone.

Methods

In group 1, 236 IBD patients (145 Crohn’s disease, 91 ulcerative colitis), 50 disease controls, and 32 healthy controls were recruited for IBD diagnosis and activity. In group 2, baseline stool samples were collected from 62 patients to predict infliximab response at week 28 and 52. The performance of fecal biomarkers for IBD management was assessed by the area under the receiver operating characteristic curve (AUC).

Results

Fecal OSM and FC levels were increased in IBD patients and were positively correlated with clinical and endoscopic activity. Their combination showed a better ability for disease diagnosis (AUC = 0.93) and slightly improved the capability to identify mucosal healing (AUC = 0.923). Baseline OSM and FC levels were elevated in non-responders at week 28 and 52. The AUCs of OSM, FC, and their combination to predict therapeutic response were 0.763, 0.834, and 0.859 at week 28, 0.638, 0.661, and 0.704 at week 52, respectively. Combined use of fecal and blood biomarkers improved predictive accuracy with an AUC of 0.919 at week 28 and 0.887 at week 52.

Conclusion

In addition to FC, OSM is a novel fecal biomarker, and their combination is more beneficial for disease diagnosis and prediction of infliximab response but not for disease activity in IBD patients. Further larger-scale studies are required to confirm our findings.

Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study

BACKGROUND

Traditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional approaches are unable to take advantage of more complex data structures such as repeated measurements. Deep learning methods have the potential ability to automatically find and learn complex, hidden relationships between predictive markers and outcomes, but their application to clinical prediction in CD and IBD has not been explored previously.

AIM

To determine and compare the utility of deep learning with conventional algorithms in predicting response to anti-tumor necrosis factor (anti-TNF) therapy in CD.

METHODS

This was a retrospective single-center cohort study of all CD patients who commenced anti-TNF therapy (either adalimumab or infliximab) from January 1, 2010 to December 31, 2015. Remission was defined as a C-reactive protein (CRP) < 5 mg/L at 12 mo after anti-TNF commencement. Three supervised learning algorithms were compared: (1) A conventional statistical learning algorithm using multivariable logistic regression on baseline data only; (2) A deep learning algorithm using a feed-forward artificial neural network on baseline data only; and (3) A deep learning algorithm using a recurrent neural network on repeated data. Predictive performance was assessed using area under the receiver operator characteristic curve (AUC) after 10× repeated 5-fold cross-validation.

RESULTS

A total of 146 patients were included (median age 36 years, 48% male). Concomitant therapy at anti-TNF commencement included thiopurines (68%), methotrexate (18%), corticosteroids (44%) and aminosalicylates (33%). After 12 mo, 64% had CRP < 5 mg/L. The conventional learning algorithm selected the following baseline variables for the predictive model: Complex disease behavior, albumin, monocytes, lymphocytes, mean corpuscular hemoglobin concentration and gamma-glutamyl transferase, and had a cross-validated AUC of 0.659, 95% confidence interval (CI): 0.562-0.756. A feed-forward artificial neural network using only baseline data demonstrated an AUC of 0.710 (95%CI: 0.622-0.799; P = 0.25 vs conventional). A recurrent neural network using repeated biomarker measurements demonstrated significantly higher AUC compared to the conventional algorithm (0.754, 95%CI: 0.674-0.834; P = 0.036).

CONCLUSION

Deep learning methods are feasible and have the potential for stronger predictive performance compared to conventional model building methods when applied to predicting remission after anti-TNF therapy in CD.

Intestinal stricture in Crohn's disease: A 2020 update

Crohn's disease (CD) is a chronic and relapsing-remitting inflammatory disorder of the gastrointestinal tract. Approximately 70% of patients inevitably develop fibrosis-associated intestinal stricture after 10 years of CD diagnosis, which seriously affects their quality of life. Current therapies play limited role in preventing or reversing the process of fibrosis and no specific anti-fibrotic therapy is yet available. Nearly half of patients thus have no alternative but to receive surgery. The potential mechanisms of intestinal fibrosis remain poorly understood; extracellular matrix remodeling, aberrant immune response, intestinal microbiome imbalance and creeping fat might exert fundamental influences on the multiple physiological and pathophysiological processes. Recently, the emerging new diagnostic techniques have markedly promoted an accurate assessment of intestinal stricture by distinguishing fibrosis from inflammation, which is crucial for guiding treatment and predicting prognosis. In this review, we concisely summarized the key studies published in the year 2020 covering pathogenesis, diagnostic modalities, and therapeutic strategy of intestinal stricture. A comprehensive and timely review of the updated researches in intestinal stricture could provide insight to further elucidate its pathogenesis and identify novel drug targets with anti-fibrotic potentiality.

Biomarker dynamics during infliximab salvage for acute severe ulcerative colitis: C-reactive protein (CRP)-lymphocyte ratio and CRP-albumin ratio are useful in predicting colectomy

Background/Aims

The residual risk of colectomy after infliximab salvage in steroid-refractory acute severe ulcerative colitis (ASUC) is required to inform the need for subsequent maintenance biologic therapy. The aim of this study was to determine the dynamic response of common serum biomarkers to infliximab salvage and assess their utility in predicting subsequent colectomy.

Methods

A retrospective single-center cohort study was conducted on all patients who received infliximab salvage for steroid-refractory ASUC between January 1, 2010, and July 31, 2019. Biomarkers were assessed on admission and days 1 and 3 post infliximab, and included C-reactive protein (CRP)-albumin-ratio (CAR), CRP-lymphocyte-ratio (CLR), platelet-lymphocyte-ratio (PLR) and neutrophil-lymphocyte-ratio (NLR).

Results

Of 94 patients (median age, 35 years; 67% of male), 20% required colectomy at 12 months. Biomarkers on day 3 post-infliximab best differentiated nonresponders, who had higher CRP, lower albumin and lower lymphocyte count (each P < 0.05). Day 3 predictive performance (area under the curve) for 12-month colectomy was best for CAR (0.871) and CLR (0.874), which were similar to Lindgren (0.829; P > 0.05) but superior to Mayo (0.726), partial Mayo (0.719), PLR (0.719), Ho index (0.714), NLR (0.675), Travis score (0.657) and endoscopic Mayo (0.609) (each P < 0.05). A day 3 CAR cutoff of 0.47 mg/g had 79% sensitivity, 80% specificity, 94% negative predictive value (NPV) to predict colectomy; while a day 3 CLR cutoff of 6.0 mg/10⁹ had 84% sensitivity, 84% specificity, 96% NPV.

Conclusions

CAR and CLR measured on day 3 post infliximab salvage for steroid-refractory ASUC represent simple and routinely performed biomarkers that appear to be strong predictors of colectomy. Prospective studies are required to confirm the utility of these predictive scores.