Indirect comparison of bronchial thermoplasty versus omalizumab for uncontrolled severe asthma

Objective: Bronchial thermoplasty (BT) as an add-on therapy for uncontrolled severe asthma is an alternative to biologic therapies like omalizumab (OM). We conducted an indirect treatment comparison (ITC) to appraise comparative effectiveness of BT and OM. Methods: A systematic literature review identified relevant randomized controlled trials. The ITC followed accepted methodology. Results: The ITC comprised a sham-controlled trial of BT (AIR2) and two placebo-controlled trials of OM (INNOVATE; EXTRA). Comparing the BT post-treatment period to ongoing treatment with OM, showed no significant differences in the rate ratios (RRs) for severe exacerbations (RR of BT versus OM = 0.91 [95% CI: 0.64, 1.30]; p = 0.62) or hospitalizations (RR = 0.57 [95% CI: 0.17, 1.86]; p = 0.53); emergency department visits were significantly reduced by 75% with BT (RR = 0.25 [95% CI: 0.07, 0.91]; p = 0.04); the proportions of patients with clinically meaningful response on the asthma quality-of-life questionnaire were comparable (RR = 1.06 [95% CI: 0.86, 1.34]; p = 0.59). The RR for exacerbations statistically favours OM over the total study period in AIR2 (RR = 1.50 [95% CI: 1.11, 2.02]; p = 0.009) likely reflecting a transient increase in events during the BT peri-treatment period. Conclusions: The ITC should be interpreted cautiously considering the differences between patient populations in the included trials. However, based on the analysis, BT compares well with a potentially more costly pharmacotherapy for asthma. Clinicians evaluating the relative merits of using these treatments should consider the totality of evidence and patient preferences to make an informed decision.     

A simple Bayesian decision‐theoretic design for dose‐finding trials

A flexible and simple Bayesian decision‐theoretic design for dose‐finding trials is proposed in this paper. In order to reduce the computational burden, we adopt a working model with conjugate priors, which is flexible to fit all monotonic dose‐toxicity curves and produces analytic posterior distributions. We also discuss how to use a proper utility function to reflect the interest of the trial. Patients are allocated based on not only the utility function but also the chosen dose selection rule. The most popular dose selection rule is the one‐step‐look‐ahead (OSLA), which selects the best‐so‐far dose. A more complicated rule, such as the two‐step‐look‐ahead, is theoretically more efficient than the OSLA only when the required distributional assumptions are met, which is, however, often not the case in practice. We carried out extensive simulation studies to evaluate these two dose selection rules and found that OSLA was often more efficient than two‐step‐look‐ahead under the proposed Bayesian structure. Moreover, our simulation results show that the proposed Bayesian method's performance is superior to several popular Bayesian methods and that the negative impact of prior misspecification can be managed in the design stage. Copyright © 2012 John Wiley & Sons, Ltd.     

Women Like Me

Abstract

Hormone replacement therapy (HRT) traditionally has been withheld from women with breast cancer to minimize their risk of recurrence. However, the benefits of HRT include relief from menopausal symptoms and protection against osteoporosis and possibly heart disease. Focus groups were conducted among women diagnosed with breast cancer to explore how the women approached health decisions, especially regarding HRT, and to identify factors that influenced their decision-making process. Factors identified included relief of symptoms, fear of cancer, side effects of treatment, attitudes toward risk, perception of risk and mastery, and physicians' recommendations. The article discusses these factors and provides suggestions, such as the following, to care providers who counsel women concerning menopause and HRT: Help patients understand areas in which the evidence is solid, address their possibly unrealistic expectations, and help them articulate their feelings and fears.     

Extracting MRS discriminant functional features of brain tumors

The current challenge in automatic brain tumor classification based on MRS is the improvement of the robustness of the classification models that explicitly account for the probable breach of the independent and identically distributed conditions in the MRS data points. To contribute to this purpose, a new algorithm for the extraction of discriminant MRS features of brain tumors based on a functional approach is presented. Functional data analysis based on region segmentation (RSFDA) is based on the functional data analysis formalism using nonuniformly distributed B splines according to spectral regions that are highly correlated. An exhaustive characterization of the method is presented in this work using controlled and real scenarios. The performance of RSFDA was compared with other widely used feature extraction methods. In all simulated conditions, RSFDA was proven to be stable with respect to the number of variables selected and with respect to the classification performance against noise and baseline artifacts. Furthermore, with real multicenter datasets classification, RSFDA and peak integration (PI) obtained better performance than the other feature extraction methods used for comparison. Other advantages of the method proposed are its usefulness in selecting the optimal number of features for classification and its simplified functional representation of the spectra, which contributes to highlight the discriminative regions of the MR spectrum for each classification task. Copyright © 2012 John Wiley & Sons, Ltd.     

Differential diagnosis of jaw pain using informatics technology

This study aimed to deduce evidence‐based clinical clues that differentiate temporomandibular disorders (TMD)‐mimicking conditions from genuine TMD by text mining using natural language processing (NLP) and recursive partitioning. We compared the medical records of 29 patients diagnosed with TMD‐mimicking conditions and 290 patients diagnosed with genuine TMD. Chief complaints and medical histories were preprocessed via NLP to compare the frequency of word usage. In addition, recursive partitioning was used to deduce the optimal size of mouth opening, which could differentiate TMD‐mimicking from genuine TMD groups. The prevalence of TMD‐mimicking conditions was more evenly distributed across all age groups and showed a nearly equal gender ratio, which was significantly different from genuine TMD. TMD‐mimicking conditions were caused by inflammation, infection, hereditary disease and neoplasm. Patients with TMD‐mimicking conditions frequently used “mouth opening limitation” (P < .001), but less commonly used words such as “noise” (P < .001) and “temporomandibular joint” (P < .001) than patients with genuine TMD. A diagnostic classification tree on the basis of recursive partitioning suggested that 12.0 mm of comfortable mouth opening and 26.5 mm of maximum mouth opening were deduced as the most optimal mouth‐opening cutoff sizes. When the combined analyses were performed based on both the text mining and clinical examination data, the predictive performance of the model was 96.6% with 69.0% sensitivity and 99.3% specificity in predicting TMD‐mimicking conditions. In conclusion, this study showed that AI technology‐based methods could be applied in the field of differential diagnosis of orofacial pain disorders.