Citations

AbstractAimThis study describes the process of updating the cerebral palsy (CP) common data elements (CDEs), specifically identifying tools that capture the impact of chronic pain on children's functioning.MethodThrough a partnership between the American Academy for Cerebral Palsy and Developmental Medicine and the National Institute of Neurological Disorders and Stroke (NINDS), the CP CDEs were developed as data standards for clinical research in neuroscience. Chronic pain was underrepresented in the NINDS CP CDEs version 1.0. A multi‐step methodology was applied by an interdisciplinary professional team. Following an adapted CP chronic pain tools' rating system, and a review of psychometric properties, clinical utility, and compliance with inclusion/exclusion criteria, a set of recommended pain tools was posted online for external public comment in May 2022.ResultsFifteen chronic pain tools met inclusion criteria, representing constructs across all components of the International Classification of Functioning, Disability and Health.InterpretationThis paper describes the first condition‐specific pain CDEs for a pediatric population. The proposed set of chronic pain tools complement and enhance the applicability of the existing pediatric CP CDEs. The novel CP CDE pain tools harmonize the assessment of chronic pain, addressing not only intensity of chronic pain, but also the functional impact of experiencing it in everyday activities.

AbstractConcept inventories are multiple choice exams designed with the intention to test core concepts on specific subjects and evaluate common misconceptions. These tests serve as a useful tool in the classroom to assess value added by the instructor's educational methods and to better understand how students learn. They can provide educators with a method to evaluate their current teaching strategies and to make modifications that enhance student learning and ultimately elevate the quality of medical physics education. The use of concept inventories in introductory college physics courses revealed important gaps in conceptual understanding of physics by undergraduate students and motivated a shift of physics teaching towards more effective methods, such as active learning techniques. The goal of this review is to introduce medical physicists to concept inventories as educational evaluation tools and discuss potential applications to medical physics education by development through multi‐institutional collaboration.

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1 *04 subtypes best accounted for the association, strongest with HLA-DRB1 *04:04 and HLA-DRB1 *04:07, and intermediary with HLA-DRB1 *04:01 and HLA-DRB1 *04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1 *04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1 *04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues.

PURPOSE Financial toxicity (FT) affects 20% of cancer survivors and is associated with poor clinical outcomes. No large-scale programs have been implemented to mitigate FT. We evaluated the effect of monthly FT screening as part of a larger patient-reported outcomes (PROs) digital monitoring intervention. METHODS PRO-TECT (AFT-39) is a cluster-randomized trial of patients undergoing systemic therapy for metastatic cancer. Practices were randomly assigned 1:1 to digital symptom monitoring (PRO practices) or usual care (control practices). Digital monitoring consisted of between-visit online or automated telephone patient surveys about symptoms, functioning, and FT (single-item screening question from Functional Assessment of Chronic Illness Therapy-COmprehensive Score for financial Toxicity) for up to 1 year, with automated alerts sent to practice nurses for concerning survey scores. Clinical team actions in response to alerts were not mandated. The primary outcome of this planned secondary analysis was development or worsening of financial difficulties, assessed via the European Organisation for Research and Treatment of Cancer QLQ-C30 financial difficulties measure, at any time compared with baseline. A randomly selected subset of patients and nurses were interviewed about their experiences with the intervention. RESULTS One thousand one hundred ninety-one patients were enrolled (593 PRO; 598 control) at 52 US community oncology practices. Overall, 30.2% of patients treated at practices that received the FT screening intervention developed, or experienced worsening of, financial difficulties, compared with 39.0% treated at control practices ( P = .004). Patients and nurses interviewed stated that FT screening identified patients for financial counseling who otherwise would be reluctant to seek, or unaware of the availability of, assistance. CONCLUSION In this report of a secondary outcome from a randomized clinical trial, FT screening as part of routine digital patient monitoring with PROs reduced the development, or worsening, of financial difficulties among patients undergoing systemic cancer therapy.

Abstract Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

AbstractThe clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

ABSTRACT Aims Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. Methods and results A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85–0.92), a sensitivity of 0.85 (0.78–0.95) and a specificity of 0.76 (0.74–0.79) in the internal validation cohort and an AUC of 0.82 (0.73–0.91), a sensitivity of 0.74 (0.61–0.87) and a specificity of 0.79 (0.77–0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. Conclusion A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.