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The Parkinson’s Disease Comprehensive Response (PDCORE): a composite approach integrating three standard outcome measures

ABSTRACT: Abstract There is an increasing need for improved endpoints to assess clinical trial effects in Parkinson’s disease. We propose the Parkinson’s Disease Comprehensive Response as a novel weighted composite endpoint integrating changes measured in three established Parkinson’s outcomes, including: OFF state Movement Disorder Society Unified Parkinson’s Disease Rating Scale Motor Examination scores; Motor Experiences of Daily Living scores; and total good-quality ON time per day. The data source for the initial development of the composite described herein was a recent Phase II trial of glial cell line-derived neurotrophic factor. A wide range of clinically derived relative weights was assessed to normalize for differentially scoring base rates with each endpoint component. The Parkinson’s disease comprehensive response, in contrast to examining practically defined OFF state Unified Parkinson’s Disease Rating Scale Motor Examination scores alone, showed stability over 40?weeks in placebo patients, and all 432 analyses in this permutation exercise yielded significant differences in favour of glial cell line-derived neurotrophic factor. The findings were consistent with results obtained employing three different global statistical test methodologies and with patterns of intra-patient change. Based on our detailed analyses, we conclude it worth prospectively evaluating the clinical utility, validity and regulatory feasibility of using clinically supported final Parkinson’s disease comprehensive response formulas (for both the Unified Parkinson’s Disease Rating Scale-based and Movement Disorders Society-Unified Parkinson’s Disease Rating Scale-based versions) in future disease-modifying Parkinson’s trials. Whilst the data source employed in the initial development of this weighted composite score is from a recent Phase II trial of glial cell line-derived neurotrophic factor, we wish to stress that the results are not described to provide post hoc evidence of the efficacy of glial cell line-derived neurotrophic factor but rather are presented to further the debate of how current regulatory approved rating scales may be combined to address some of the recognized limitations of using individual scales in isolation. We need improved endpoints to assess neurorestorative therapies in Parkinson’s. We propose the Parkinson’s disease comprehensive response as a novel weighted composite endpoint. The data are presented to further the debate of how regulatory approved rating-scales may be combined to address some of the limitations of individual scales in isolation. Graphical Abstract Graphical Abstract

SUBMITTER: Luz M

PROVIDER: S-EPMC7759656 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description:Implementation outcome measures are essential for monitoring and evaluating the success of implementation efforts. Yet, currently available measures lack conceptual clarity and have largely unknown reliability and validity. This study developed and psychometrically assessed three new measures: the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM).Thirty-six implementation scientists and 27 mental health professionals assigned 31 items to the constructs and rated their confidence in their assignments. The Wilcoxon one-sample signed rank test was used to assess substantive and discriminant content validity. Exploratory and confirmatory factor analysis (EFA and CFA) and Cronbach alphas were used to assess the validity of the conceptual model. Three hundred twenty-six mental health counselors read one of six randomly assigned vignettes depicting a therapist contemplating adopting an evidence-based practice (EBP). Participants used 15 items to rate the therapist's perceptions of the acceptability, appropriateness, and feasibility of adopting the EBP. CFA and Cronbach alphas were used to refine the scales, assess structural validity, and assess reliability. Analysis of variance (ANOVA) was used to assess known-groups validity. Finally, half of the counselors were randomly assigned to receive the same vignette and the other half the opposite vignette; and all were asked to re-rate acceptability, appropriateness, and feasibility. Pearson correlation coefficients were used to assess test-retest reliability and linear regression to assess sensitivity to change.All but five items exhibited substantive and discriminant content validity. A trimmed CFA with five items per construct exhibited acceptable model fit (CFI = 0.98, RMSEA = 0.08) and high factor loadings (0.79 to 0.94). The alphas for 5-item scales were between 0.87 and 0.89. Scale refinement based on measure-specific CFAs and Cronbach alphas using vignette data produced 4-item scales (?'s from 0.85 to 0.91). A three-factor CFA exhibited acceptable fit (CFI = 0.96, RMSEA = 0.08) and high factor loadings (0.75 to 0.89), indicating structural validity. ANOVA showed significant main effects, indicating known-groups validity. Test-retest reliability coefficients ranged from 0.73 to 0.88. Regression analysis indicated each measure was sensitive to change in both directions.The AIM, IAM, and FIM demonstrate promising psychometric properties. Predictive validity assessment is planned.

Project description:Identifying and classifying tumors are critical in-patient care and treatment planning within the medical domain. Nevertheless, the conventional approach of manually examining tumor images is characterized by its lengthy duration and subjective nature. In response to this challenge, a novel method is proposed that integrates the capabilities of Gray-Level Co-Occurrence Matrix (GLCM) features and Local Binary Pattern (LBP) features to conduct a quantitative analysis of tumor images (Glioma, Meningioma, Pituitary Tumor). The key contribution of this study pertains to the development of interaction features, which are obtained through the outer product of the GLCM and LBP feature vectors. The utilization of this approach greatly enhances the discriminative capability of the extracted features. Furthermore, the methodology incorporates aggregated, statistical, and non-linear features in addition to the interaction features. The GLCM feature vectors are utilized to compute these values, encompassing a range of statistical characteristics and effectively modifying the feature space. The effectiveness of this methodology has been demonstrated on image datasets that include tumors. Integrating GLCM (Gray-Level Co-occurrence Matrix) and LBP (Local Binary Patterns) features offers a comprehensive representation of texture characteristics, enhancing tumor detection and classification precision. The introduced interaction features, a distinctive element of this methodology, provide enhanced discriminative capability, resulting in improved performance. Incorporating aggregated, statistical, and non-linear features enables a more precise representation of crucial tumor image characteristics. When utilized with a linear support vector machine classifier, the approach showcases a better accuracy rate of 99.84%, highlighting its efficacy and promising prospects. The proposed improvement in feature extraction techniques for brain tumor classification has the potential to enhance the precision of medical image processing significantly. The methodology exhibits substantial potential in facilitating clinicians to provide more accurate diagnoses and treatments for brain tumors in forthcoming times.

Project description:BackgroundObtaining patients' views of their health and outcomes of interventions utilising patient-reported outcome measures (PROMs) is a well-established method, but there is still uncertainty about the impact of PROMs on services and patient care. Studies are now needed of alternative ways of implementing PROMs. This paper describes a case study of the introduction of a new PROM to assess musculoskeletal (MSK) problems, known as the Musculoskeletal Health Questionnaire (MSK-HQ).MethodsFollowing an invitation from the Arthritis Research UK (ARUK), 11 groups and organisations agreed to become 'partners' in piloting the MSK-HQ. Twenty-nine interviews and a focus group were carried out with key informants from the partners. Interviews were supplemented with some documentary evidence of partners' meetings. Data were coded and analysed with NVivo software V.10. Analysis was carried out via a framework method.ResultsParticipants reported positive evidence that the MSK-HQ is feasible and practical for use in patient care with content that helped health professionals identify and address patients' main presenting problems. Although mediated and reported through health professionals' judgments, the questionnaire was also seen as very relevant and acceptable to a wide spectrum of patients.There was also broad support for the view that whilst the MSK-HQ is relevant to individual patient care, it could also, when aggregated, reflect the experiences of patients as a group and be used as evidence for third parties concerned with the provision and commissioning of services.The main difficulties revealed by the case study were in the form of logistics and sustainability. It was recognised that electronic systems would be more effective for administration and data processing but they were not feasible to develop and implement within reasonable timelines and available budgets. A sustainable approach to using the PROM required significant long-term commitment of budget, a coherent system, and active support from diverse organisations.ConclusionsThe current study supports the view that a bottom-up approach is a promising method to generate PROM-related insights that are relevant to patients and health professionals. The partnership approach to developing and using PROMs may have wider relevance and potential as a model of implementation.

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The Parkinson’s Disease Comprehensive Response (PDCORE): a composite approach integrating three standard outcome measures

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