Determining the appropriateness of requests for outpatient magnetic resonance imaging of the hip

Summary: In Ontario, Canada, wait times for magnetic resonance imaging (MRI) scans continue to exceed provincial targets. We sought to determine the incidence of inappropriate hip MRI scan referrals, based on accepted indications for hip MRI. We developed an algorithm to appraise each MRI referral based on a prescan patient questionnaire and the interpretation of the MRI by a musculoskeletal radiologist. After reviewing 84 patient questionnaires, we considered 32.1% of MRI referrals to be inappropriate; 25.9% of the inappropriate MRI referrals were ordered as a preoperative test for potential hip arthroscopy despite the patients showing severe osteoarthritis. Having no prior radiographic examination was the most common reason for inappropriate referrals, regardless of pathology (48.1%). With limited MRI scanner time available in Ontario, it is essential that guidelines and training be improved on the indications for hip MRI to reduce the wait times for these specialized tests.

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Multidimensional endotypes of asthma: topological data analysis of cross-sectional clinical, pathological, and immunological data.

BACKGROUND: Incomplete understanding of mechanisms and clinicopathobiological heterogeneity in asthma hinders research progress. Pathogenic roles for T-helper-type 17 (Th17) cells and invariant T cells implied by murine data have yet to be assessed in man. We aimed to investigate the role of Th17 and mucosal associated invariant T (MAIT) cells in airway inflammation; to characterise associations between diverse clinical and immunological features of asthma; and to identify novel multidimensional asthma endotypes. METHODS: In this single-centre, cross-sectional observational study in the UK, we assessed volunteers with mild-to-severe asthma and healthy non-atopic controls using clinical and physiological assessment and immunological sampling of blood, induced sputum, endobronchial biopsy, and bronchoalveolar lavage for flow cytometry and multiplex-electrochemiluminescence assays. Primary outcomes were changes in frequencies of Th17 and MAIT cells between health and asthma using Mann-Whitney U tests and the Jonckheere-Terpstra test (linear trend across ranked groups). The study had 80% power to detect 60% differences in T-cell frequencies at p<0·05. Bayesian Network Analysis (BNA) was used to explore associations between parameters. Topological Data Analysis (TDA) was used to identify multidimensional endotypes. The study had local research ethics approval. All participants provided informed consent. FINDINGS: Participants were 84 male and female volunteers (60 with mild-to-severe asthma and 24 healthy, non-atopic controls) aged 18-70 years recruited from clinics and research cohorts. Th17 cells and γδ17 cells were not associated with asthma, even in severe neutrophilic forms. MAIT-cell frequencies were strikingly reduced in asthma compared with health (median frequency in blood 0·9% of CD3+ cells [IQR 0·3-1·8] in asthma vs 1·6 [1·2-2·6] in health, p=0·005; in sputum 1·1 [0·7-2·0] vs 1·8 [1·6-2·3], p=0·002; and in biopsy samples 1·3 [0·7-2·3] vs 3·9% [1·3-5·3%], p=0·02), especially in severe asthma where BAL regulatory T cells were also reduced compared with those in health (4·4, 3·1-6·1, vs 8·1, 5·6-10; p=0·02). BNA and TDA identified six novel clinicopathobiological clusters of underlying disease mechanisms, with elevated mast cell mediators tryptase (p<0·0001), chymase (p=0·02), and carboxypeptidase A3 (p=0·02) in severe asthma. INTERPRETATION: This study suggests that Th17 cells do not have a major pathogenic role in human asthma. We describe a novel deficiency of MAIT cells in severe asthma. We also provide proof of concept for application of TDA to identification of multidimensional clinicopathobiological endotypes. Endotypes will require validation in further cohorts. FUNDING: Wellcome Trust.

Innate and adaptive T cells in asthmatic patients: Relationship to severity and disease mechanisms.

BACKGROUND: Asthma is a chronic inflammatory disease involving diverse cells and mediators whose interconnectivity and relationships to asthma severity are unclear. OBJECTIVE: We performed a comprehensive assessment of TH17 cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, other T-cell subsets, and granulocyte mediators in asthmatic patients. METHODS: Sixty patients with mild-to-severe asthma and 24 control subjects underwent detailed clinical assessment and provided induced sputum, endobronchial biopsy, bronchoalveolar lavage, and blood samples. Adaptive and invariant T-cell subsets, cytokines, mast cells, and basophil mediators were analyzed. RESULTS: Significant heterogeneity of T-cell phenotypes was observed, with levels of IL-13-secreting T cells and type 2 cytokines increased at some, but not all, asthma severities. TH17 cells and γδ-17 cells, proposed drivers of neutrophilic inflammation, were not strongly associated with asthma, even in severe neutrophilic forms. MAIT cell frequencies were strikingly reduced in both blood and lung tissue in relation to corticosteroid therapy and vitamin D levels, especially in patients with severe asthma in whom bronchoalveolar lavage regulatory T-cell numbers were also reduced. Bayesian network analysis identified complex relationships between pathobiologic and clinical parameters. Topological data analysis identified 6 novel clusters that are associated with diverse underlying disease mechanisms, with increased mast cell mediator levels in patients with severe asthma both in its atopic (type 2 cytokine-high) and nonatopic forms. CONCLUSION: The evidence for a role for TH17 cells in patients with severe asthma is limited. Severe asthma is associated with a striking deficiency of MAIT cells and high mast cell mediator levels. This study provides proof of concept for disease mechanistic networks in asthmatic patients with clusters that could inform the development of new therapies.