Quality of Life Research最新文献

Purpose: This study aimed to identify feasible, evidence-based strategies to improve the use of Patient-reported outcome measures (PROMs) implemented in clinical oncology practice.

Methods: A mixed-method study involving observations of consultations and semi-structured interviews with patients and healthcare professionals (HCPs) was conducted to identify facilitators and barriers for using PROMs; barriers and facilitators were structured following the Theoretical Domains Framework. For each barrier, evidence-based improvement strategies were selected using the Behaviour Change Techniques Taxonomy v1. Subsequently, improvement strategies were ranked on priority and feasibility by an expert panel of HCPs, information technology professionals, and PROMs implementation specialists, creating an implementation improvement strategy.

Results: Ten consultations were observed and 14 interviews conducted. Barriers for implementation included that the electronic health record and PROMs did not align to the individual needs of end users, the HCPs' hesitance to advice patients about health-related quality-of-life issues, and a lack of consensus on which HCPs were responsible for discussing PROMs with patients. Forty-one improvement strategies were identified, of which 25 remained after ranking. These included: redesigning the PROMs dashboard by including patient management advice, enhancing patient support to complete PROMs, and clarifying HCPs' responsibilities for discussing PROMs. Strategies currently considered less feasible were: improving user-friendliness of the patient portal due to technical constraints, aligning PROMs assessment frequency with clinical courses, and using baseline PROMs for early identification of vulnerabilities and supportive care needs. These will be studied in future research.

Conclusion: Evidence-based improvement strategies to ensure lasting adoption of PROMs in clinical practice were identified.

Purpose: The relationship between quality of life (QoL) and frailty has previously been investigated cross-sectionally and longitudinally as unidirectional where QoL depends upon frailty and where frailty depends on QoL. Here a bidirectional relationship is examined.

Methods: This work uses a latent curve model with structured residuals to address the bidirectional association between QoL and frailty in older English people considering within-person and group levels. The study measures frailty using a functional frailty measure and quality of life using CASP-12. The sample size is 17,529.

Results: There is a strong relationship between QoL (Quality of Life) and frailty, which is almost linear and inversely proportional over time. Although the cross-lagged coefficients from QoL to frailty and vice versa showed statistical significance, the impact was found to be minimal. The time between assessments (which are two years apart) and/or the few observations available per individual may have impacted the effect of this relationship. When accounting for gender, age, net wealth, and multimorbidity, some variations in the results were observed at the group level but not at the within-person level.

Conclusion: The study provides empirical evidence that supports a bidirectional association between QoL and frailty in older individuals who reside at home. These results offer valuable insights for healthcare providers, as participants did not exhibit an advanced need for health services. Additionally, involving participants in evaluating and assessing these services enhances their effectiveness and overall benefit.

ISOQOL's Australia and New Zealand Special Interest Group (ISOQOL ANZ SIG) connects local researchers and clinicians in a geographically remote region of the world. Members of our ANZ SIG have been conducting a review following COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) guidance, which outlines a comprehensive process to critically evaluate the properties of outcome measurement instruments, including patient-reported outcome measures (PROMs). Our experience has highlighted challenges for undertaking this type of review, notably a lack of consistency in how COSMIN guidance is applied and reported. In this Letter to the Editor, members of the ANZ SIG leadership discuss the PRISMA-COSMIN for Outcome Measurement Instruments; a new guideline developed to support and improve reporting of systematic reviews of outcome measurement instruments. We explore the potential of this guidance to improve how systematic reviews of PROMs are conducted and reported, from the perspective of a regional SIG.

Purpose: This study evaluates the interpretability of Patient-Reported Outcomes Measurement Information System^® (PROMIS^®)-16 profile domain scores (physical function, ability to participate in social roles and activities, anxiety, depression, sleep disturbance, pain interference, cognitive function - abilities, and fatigue) compared to the PROMIS-29 scores and a 5-item PROMIS cognitive function score. The study aims to provide insights into using these measures in clinical and research settings.

Methods: Analyses were conducted using data from 4130 adults from a nationally representative, probability-based internet panel between September and October 2022. A subset of 1256 individuals with back pain was followed up at six months. We compared the PROMIS-16 profile with the corresponding domain scores from the PROMIS-29 and a custom five-item cognitive function measure. We evaluated (1) reliability through inter-item correlations within each domain and (2) criterion validity by comparing PROMIS-16 profile with the corresponding longer PROMIS measures: (a) standardized mean differences in domain scores, (b) correlations, and (c) concordance of change (i.e., got worse, stayed the same, got better) among those with back pain from baseline to six months later using the reliable change index. We report the Kappa coefficient of agreement and the frequency and percentage of participants with concordant classifications.

Results: Inter-item correlations for the PROMIS-16 domains ranged from 0.65 in cognitive function to 0.92 in pain interference. Standardized mean differences between PROMIS-16 and the scores for the corresponding longer PROMIS domains were minimal (< 0.2). Correlations among the corresponding domain scores ranged from 0.82 for sleep disturbance to 0.98 for pain interference. The percentage of concordance in change groups ranged from 63% for sleep disturbance to 88% for pain interference. Except for sleep disturbance, the change groups derived from the PROMIS-16 showed moderate to substantial agreement with scores estimated from the longer PROMIS measures (Kappa coefficients ≥ 0.41).

Conclusion: The PROMIS-16 domain scores perform similarly to the longer PROMIS measures and can be interpreted in the same way. This similarity indicates that PROMIS-16 can be useful for research as a brief health-related quality-of-life profile measure.

Purpose: Determining if group-level differences in health outcomes are meaningful has recently been neglected in favour of determining if individuals have experienced a meaningful change. We explore interpretation of a meaningful between-group difference (MBGD) in clinical outcome assessment scores, primarily in the context of randomized clinical trials.

Methods: We constructed a series of possible 'viewpoints' on how to conceptualize MBGD thresholds. Each viewpoint is discussed critically in terms of potential advantages and disadvantages, with simulated data to facilitate their consideration.

Results: Five viewpoints are presented and discussed. The first considers whether thresholds for meaningful within-individual change over time can be equally applied at the group-level, which is shown to be untenable. Viewpoints 2-4 consider what would have to be observed in treatment groups to conclude a meaningful between-group difference has occurred, framed in terms of the proportion of patients perceiving that they had meaningfully improved. The final viewpoint considers an alternative framework where stakeholders are directly questioned on the meaningfulness of varying magnitudes of between-group differences. The choice of a single threshold versus general interpretative guidelines is discussed.

Conclusion: There does not appear to be a single method with clear face validity for determining MBGD thresholds. Additionally, the notion that such thresholds can be purely data-driven is challenged, where a degree of subjective stakeholder judgement is likely required. Areas for future research are proposed, to move towards robust method development.

Purpose: Arthrogryposis multiplex congenita (AMC) describes a heterogeneous group of rare congenital conditions. Health-related quality of life (HRQL) may be reduced in AMC due to broadly heterogeneous physical impairments and participation limitations. This study described HRQL in children and youth with AMC, compared HRQL between child self- and parent-proxy reports, and identified factors associated with better/worse HRQL.

Methods: Data on 205 children with AMC (age 8-21 years) from a North American AMC registry across eight hospital sites was used. HRQL was assessed cross-sectionally using the Patient Reported Outcome Measurement Information System (PROMIS) and European Quality of Life-5 Dimensions-Youth-3 Levels (EQ-5D-Y-3 L) by self-report, parent proxy-report or both.

Results: Mean child-reported PROMIS T-scores were significantly lower than the normal mean for the Upper Extremity (mean = 33.0) and Mobility (mean = 37.2) but in the normal range for Pain Interference (mean = 46.6) and Peer Relationships (mean = 51.7). A lot of problems in EQ-5D-Y-3 L was reported by 37% in Feeling Worried/ Sad/ Unhappy, 46% in Having Pain/Discomfort, 50% in Doing Usual Activities, 56% in Mobility, and 57% in Looking After Myself. Compared to child-report, parents reported significantly worse PROMIS T-scores and higher problems in EQ-5D domains. Wheelchair use, being small for gestational age, prolonged hospitalization after birth, increased number of orthopedic surgeries, and caregiver's stress were associated with lower HRQL scores.

Conclusion: Findings indicate the importance of considering both the child's and parents' reports of HRQL, and to provide multimodal interventions that focus on the effect of childhood and parental characteristics to promote HRQL among children with AMC.

Purpose: Fatigue is the most prevalent symptom across cancer types. To support clinicians in providing fatigue-related supportive care, this study aims to develop and compare models predicting clinically relevant fatigue (CRF) occurring between two and three years after diagnosis, and to assess the validity of the best-performing model across diverse cancer populations.

Methods: Patients with non-metastatic bladder, colorectal, endometrial, ovarian, or prostate cancer who completed a questionnaire within three months after diagnosis and a subsequent questionnaire between two and three years thereafter, were included. Predictor variables included clinical, socio-demographic, and patient-reported variables. The outcome was CRF (EORTC QLQC30 fatigue ≥ 39). Logistic regression using LASSO selection was compared to more advanced Machine Learning (ML) based models, including Extreme gradient boosting (XGBoost), support vector machines (SVM), and artificial neural networks (ANN). Internal-external cross-validation was conducted on the best-performing model.

Results: 3160 patients were included. The logistic regression model had the highest C-statistic (0.77) and balanced accuracy (0.65), both indicating good discrimination between patients with and without CRF. However, sensitivity was low across all models (0.22-0.37). Following internal-external validation, performance across cancer types was consistent (C-statistics 0.73-0.82).

Conclusion: Although the models' discrimination was good, the low balanced accuracy and poor calibration in the presence of CRF indicates a relatively high likelihood of underdiagnosis of future CRF. Yet, the clinical applicability of the model remains uncertain. The logistic regression performed better than the ML-based models and was robust across cohorts, suggesting an advantage of simpler models to predict CRF.