Progression of Celiac Disease in Children With Antibodies Against Tissue Transglutaminase and Normal Duodenal Architecture

BACKGROUND & AIMS

Potential celiac disease is characterized by positive results from serologic tests for tissue transglutaminase antibodies (anti-TG2) but normal duodenal architecture (Marsh stages 0-1). There is controversy over the best way to manage these patients. We investigated risk factors associated with the development of villous atrophy in children with potential celiac disease.

METHODS

We performed a prospective study of 280 children (ages 2-18 years) in Italy with suspected celiac disease, followed for up to 12 years (range, 18-150 months; median 60 months). The subjects had 2 consecutive positive results from tests for anti-TG2, tested positive for the endomysial antibody (anti-EMA), had total serum levels of immunoglobulin A in the normal range, normal duodenal architecture (Marsh stages 0-1) in 5 biopsies, and HLA DQ2- or DQ8-positive haplotypes. The children underwent serologic tests and clinical analyses every 6 months and a small bowel biopsy was taken every 2 years. A total of 210 patients of the original cohort were assessed at the 9-year follow-up evaluation. We performed multivariate analyses of clinical, genetic, and histologic data to identify factors associated with progression to villous atrophy.

RESULTS

During the follow-up period, 42 (15%) of 280 children developed villous atrophy, whereas 89 (32%) children no longer tested positive for anti-TG2 or anti-EMA. The cumulative incidence of progression to villous atrophy was 43% at 12 years. In multivariate analysis, the baseline factors most strongly associated with development of villous atrophy were numbers of γδ intraepithelial lymphocyte cells followed by age and homozygosity for the HLA DQB1*02. In discriminant analysis, these baseline factors identified 80% of the children who developed baseline atrophy.

CONCLUSIONS

In a long-term study of 280 children with suspected celiac disease (based on anti-TG2 and anti-EMA) on gluten-containing diets, the cumulative incidence of progression to villous atrophy was 43% over a 12-year period. We identified factors that can be used to identify children at highest risk for villous atrophy. This approach might be used to determine whether children with suspected celiac disease should immediately start a gluten-free diet or be monitored on their regular diet.

Factors Associated With Efficacy of Cognitive Behavior Therapy vs Education for Patients With Irritable Bowel Syndrome

BACKGROUND & AIMS

Among patients with irritable bowel syndrome (IBS), it would be helpful to identify those most likely to respond to specific treatments, yet few factors have been identified that reliably predict positive outcome. We sought to identify pretreatment baseline characteristics that associate with gastrointestinal symptom improvement in patients who received empirically validated regimens of cognitive behavior therapy (CBT) or IBS education.

METHODS

We analyzed data from the IBS Outcome Study, in which 436 patients with IBS (average age, 41 years; 80%, female) were randomly assigned to groups that received 4 or 10 sessions of cognitive behavior therapy or education over 10 weeks. Baseline data were collected from all participants on sociodemographic and clinical features and comorbidities. Interaction analyses used a modified linear probability model with Huber-White robust estimators to identify baseline factors that moderated as a function of treatment condition GI symptom improvement based on the IBS-version of the Clinical Global Impressions-Improvement Scale.

RESULTS

Whether the primary outcome of IBS symptom improvement was rated by patients or physician assessors blind to treatment 2 weeks after it ended, higher percentages of patients had symptom improvement after CBT compared with EDU among those with low levels of trait anxiety (71.3% vs 34.9%; P < .05) or anxiety sensitivity (71.7% vs 38.6%; P < .05) and for those with baseline typical levels of trait anxiety (66.0% vs 47.1%; P < .05) or anxiety sensitivity (66.3% vs 47.1%; P < .05). For patients with high trait anxiety or anxiety sensitivity, the difference in percentage of responders to CBT vs EDU was non-significant for trait anxiety (60.6% vs 59.2%) and anxiety sensitivity (60.9% vs 55.9%). If patients scored at or below 22 on the Trait Anxiety Inventory, CBT had a statistically significant advantage over EDU. If patients scored at or below 29 on the Anxiety Sensitivity Inventory, there was a statistically significant advantage for CBT vs EDU.

CONCLUSIONS

In analyses of outcomes of patients with treatment-refractory IBS, baseline levels of trait anxiety and anxiety sensitivity (fear of arousal symptoms) were associated with improved gastrointestinal symptoms following CBT compared to IBS education. These findings and approaches might be used to optimize selection of treatment for patients with IBS.

Tree-based Ensemble Methods For Individualized Treatment Rules

There is a growing interest in development of statistical methods for personalized medicine or precision medicine, especially for deriving optimal individualized treatment rules (ITRs). An ITR recommends a patient to a treatment based on the patient's characteristics. The common parametric methods for deriving an optimal ITR, which model the clinical endpoint as a function of the patient's characteristics, can have suboptimal performance when the conditional mean model is misspecified. Recent methodology development has cast the problem of deriving optimal ITR under a weighted classification framework. Under this weighted classification framework, we develop a weighted random forests (W-RF) algorithm that derives an optimal ITR nonparametrically. In addition, with the W-RF algorithm, we propose the variable importance measures for quantifying relative relevance of the patient's characteristics to treatment selection, and the out-of-bag estimator for the population average outcome under the estimated optimal ITR. Our proposed methods are evaluated through intensive simulation studies. We illustrate the application of our methods using data from Clinical Antipsychotic Trials of Intervention Effectiveness Alzheimers Disease Study (CATIE-AD).

Interactive model building for Q-learning

Evidence-based rules for optimal treatment allocation are key components in the quest for efficient, effective health care delivery. Q-learning, an approximate dynamic programming algorithm, is a popular method for estimating optimal sequential decision rules from data. Q-learning requires the modeling of nonsmooth, nonmonotone transformations of the data, complicating the search for adequately expressive, yet parsimonious, statistical models. The default Q-learning working model is multiple linear regression, which is not only provably misspecified under most data-generating models, but also results in nonregular regression estimators, complicating inference. We propose an alternative strategy for estimating optimal sequential decision rules for which the requisite statistical modeling does not depend on nonsmooth, nonmonotone transformed data, does not result in nonregular regression estimators, is consistent under a broader array of data-generation models than Q-learning, results in estimated sequential decision rules that have better sampling properties, and is amenable to established statistical approaches for exploratory data analysis, model building, and validation. We derive the new method, IQ-learning, via an interchange in the order of certain steps in Q-learning. In simulated experiments IQ-learning improves on Q-learning in terms of integrated mean squared error and power. The method is illustrated using data from a study of major depressive disorder.