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Background and Objective: Most evidence about difficult‐to‐treat and severe asthma (DTTA) comes from clinical trials and registries. We aimed to identify people with DTTA from a large nationally representative asthma population and describe their characteristics and healthcare utilization compared with people whose asthma was not 'difficult‐to‐treat'. Methods: We conducted a cross‐sectional survey of Australians aged ≥18 years with current asthma from large web‐based survey panels. Enrolment was stratified by gender, age‐group and state/territory based on national population data for people with asthma. Difficult‐to‐treat or severe asthma was defined by poor symptom control, exacerbations and/or oral corticosteroid/biologic use despite medium/high‐dose inhaled therapy. Outcomes included exacerbations, healthcare utilization, multimorbidity, quality of life and coronavirus disease of 2019 (COVID‐19)‐related behaviour. Weighted data were analysed using SAS version 9.4. Results: The survey was conducted in February–March 2021. The weighted sample comprised 6048 adults with current asthma (average age 47.3 ± SD 18.1 years, 59.9% female), with 1313 (21.7%) satisfying ≥1 DTTA criteria. Of these, 50.4% had very poorly controlled symptoms (Asthma Control Test ≤15), 36.2% were current smokers, and 85.4% had ≥1 additional chronic condition, most commonly anxiety/depression. More than twice as many participants with DTTA versus non‐DTTA had ≥1 urgent general practitioner (GP) visit (61.4% vs. 27.5%, OR 4.8 [4.2–5.5, p < 0.0001]), or ≥1 emergency room visit (41.9% vs. 17.9%, OR 3.8 [3.3–4.4, p < 0.0001]) in the previous 12 months. Conclusion: Our findings emphasize the burden of uncontrolled symptoms, current smoking, multimorbidity and healthcare utilization in people with DTTA in the community, who may be under‐represented in registries or clinical trials. In this cross‐sectional study of a nationally‐representative population of 6048 adults with current asthma, 21.7% had difficult‐to‐treat asthma. Burden for patients and health system was seen in poor general health status, poor asthma symptom control, frequent urgent GP and emergency department (ED)/hospital visits and high prevalence of multimorbidity, particularly anxiety and/or depression. See relatededitorial [ABSTRACT FROM AUTHOR]

Simple Summary: Congenital malformations of the equine caudal cervical vertebrae (C6 and C7) were first reported in the 1800s. However, by the 21st century these findings were no longer incidental, especially with studies reporting breed percentages as high as 38%. Therefore, this study examined the association between C6 osseous specimens (n = 85) demonstrating the congenital malformation of either a partial unilateral or bilateral absent ventral process and the transposition on the ventral surface of sequential C7s (44/85). The aim was to determine if the absent morphology on C6 corresponded to the transposition on C7 and in some cases replication of a transverse foramen (33/44). Findings revealed the larger morphological absence on C6 generated a stronger dependency for transposition on C7 that included the replication of a transverse foramen. The transposition on C7 was characterized by either a complete or incomplete likeness of the C6 ventral process. Complete transpositions on C7 appeared as a normal C6 ventral process, while incomplete transpositions appeared in most as a cranial ventral tubercle (CrVT). When complete bilateral transpositions were demonstrated, the morphology of C7 resembled C6. These findings are relevant to the reading of radiographic images and potential clinical findings. In recent years, equine complex vertebral malformation (ECVM) has been of concern in the equine community, with studies identifying numerous associative morphological variations. Here, we examine the morphological association between C6 and C7 for dependency in ECVM cases, where the partially absent ventral process of C6 transposes on the ventral surface of C7. A C6 ventral process presents two tubercles, one cranial (CrVT) and one caudal (CVT). In this study, the C6 osseous specimens (n = 85) demonstrated a partial or completely absent CVT (aCVT) graded 1–4 that often extended cranially creating a partially absent cranial ventral tubercle (aCrVT) graded 1–3. In the 85 C6 osseous specimens examined, the corresponding C7s demonstrated either a complete or incomplete transposition of the ventral process from C6 in 44/85, with 30/44 replicating a transverse foramen. A strong statistical dependency existed between C6 grade 4 aCVTs and grades 1–3 aCrVTs and C7 transpositions with replicated transverse foramen. Sidedness was also demonstrated, where a left sided absent C6 associated with transposition on the left ventral surface of C7. This likewise applied to right sidedness and most bilateral cases. These findings might benefit practitioners when radiographing the extent of the ECVM configuration in patients presenting caudal cervical pain. [ABSTRACT FROM AUTHOR]

Introduction The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule." Framework We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice. Recommendations Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models. [ABSTRACT FROM AUTHOR]

Purpose: Severe asthma poses a significant health burden in those with the disease, therefore a timely diagnosis can ensure patients receive specialist care and appropriate medication management. This study qualitatively explored the patient experience of adult Australians with severe asthma regarding specialist referral, to identify potential opportunities to streamline the process of severe asthma diagnosis and treatment and optimise referral pathways., Patients and Methods: Adults currently being treated with medication for severe asthma were invited to participate in this study. Participants were interviewed and asked to describe initial diagnosis of their asthma or severe asthma, and how they came to be referred to secondary care. Interviews were transcribed verbatim, coded by two members of the research team and thematically analysed., Results: Thirty-two people completed the study; 72% were female. Mean interview length was 33 minutes. The major themes generated were patient-related factors contributing to seeking a severe asthma diagnosis; perceptions of health care provision; diagnosis of severe asthma and the referral journey. Key findings were that both patient and healthcare provider attitudes contributed to participants' willingness to seek or receive a referral, and referral to respiratory specialists was often delayed. Contributing factors included a mismatch between patient expectations and general practice, lack of continuity of primary care, and a lack of patient understanding of the role of the respiratory specialist., Conclusion: Timely severe asthma diagnosis in Australia appears to be hampered by an absence of a clear referral process, lack of general practitioner (GP) knowledge of additional treatment options, underutilisation of pharmacists, and multiple specialists treating patient comorbidities. Directions for future research might include interviewing healthcare providers regarding how well the referral process works for severe asthma patients, and researching the time between referral and when a patient sees the respiratory specialist., Competing Interests: Biljana Cvetkovski reports personal fees from Sanofi, GSK and Viatris, outside the submitted work. Joy W Lee received speaker and travel fees from Sanofi, GSK and Astra Zeneca unrelated to the preparation of this manuscript. Janet Rimmer has honoraria with Sanofi Aventis, Novartis, Mundipharma, BioCSL, Stallergenes. Sinthia Bosnic-Anticevich has received research funding and/or fees for expert lectures and advisory board participation from AstraZeneca, GSK, TEVA Pharmaceuticals, Menarini, Chiesi, Mundipharma, Boehringer Ingelheim, Sanofi, Mylan, J&J, Menarini. Sinthia Bosnic-Anticevich reports grants from AstraZeneca, GSK, Teva, Mylan/Viatris; personal fees from Teva, AstraZeneca, Boehringer Ingelheim, GSK, Sanofi, Mylan/Viatris, Menarini and Chiesi, outside the submitted work. The other authors report no conflicts of interest in this work., (© 2024 Davis et al.)

Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants' knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app's design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users., (©Rebecca Monachelli, Sharon Watkins Davis, Allison Barnard, Michelle Longmire, John P Docherty, Ingrid Oakley-Girvan. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 26.03.2024.)

Several studies have found associations between air pollution and respiratory disease outcomes. However, there is minimal prognostic research exploring whether integrating air quality into clinical prediction models can improve accuracy and utility. In this study, we built models using both logistic regression and random forests to determine the benefits of including air quality data with meteorological and clinical data in prediction of COPD exacerbations requiring medical care. Logistic models were not improved by inclusion of air quality. However, the net benefit curves of random forest models showed greater clinical utility with the addition of air quality data. These models demonstrate a practical and relatively low-cost way to include environmental information into clinical prediction tools to improve the clinical utility of COPD prediction. Findings could be used to provide population level health warnings as well as individual-patient risk assessments., (©2023 AMIA - All rights reserved.)