Health and Quality of Life Outcomes最新文献

Background: The use of patient-reported outcome measures (PROMs) in clinical research increases and use of heterogeneous instruments reflects how well diverse traits are captured by a medical specialty. In order to reflect the heterogeneity of current PROM use in ophthalmology, we reviewed the available literature.

Methods: The medical literature database Web of Science was searched for the most cited articles in clinical ophthalmology. Titles, abstracts and full text articles were reviewed for the use of PROMs and a list of the 100 most cited articles using PROMs was obtained and stratified by year of publication.

Results: A total of 1,996 articles were screened. Seventy-seven out of the 100 articles identified included one PROM, and the average number of instruments was 1.5 ± 1.1. The most widely used PROMs were the National Eye Institute Visual Function Questionnaire (33%), the Ocular Surface Disease Index (14%) and the Medical Outcomes Study Short Form (13%). A simulation analysis suggested that the distribution of PROM use in ophthalmology study did not significantly differ from a power law distribution. Twenty-two percent and fifteen percent of articles did not reference and did not specify the PROM used, respectively. This rate decreased in the more recently published articles (p = 0.041).

Conclusions: Our data suggest that the heterogeneity of PROMs applied in ophthalmology studies is low. The selection of PROMs for clinical studies should be done carefully, depending on the research goal.

Background: Health-related quality of life (HRQL) has become an important outcome parameter in cardiology. The MOS 36-ltem Short-Form Health Survey (SF-36) and the PROMIS-29 are two widely used generic measures providing composite HRQL scores. The domains of the SF-36, a well-established instrument utilized for several decades, can be aggregated to physical (PCS) and mental (MCS) component summary scores. Alternative scoring algorithms for correlated component scores (PCS_c and MCS_c) have also been suggested. The PROMIS-29 is a newer but increasingly used HRQL measure. Analogous to the SF-36, physical and mental health summary scores can be derived from PROMIS-29 domain scores, based on a correlated factor solution. So far, scores from the PROMIS-29 are not directly comparable to SF-36 results, complicating the aggregation of research findings. Thus, our aim was to provide algorithms to convert PROMIS-29 data to well-established SF-36 component summary scores.

Methods: Data from n = 662 participants of the Berlin Long-term Observation of Vascular Events (BeLOVE) study were used to estimate linear regression models with either PROMIS-29 domain scores or aggregated PROMIS-29 physical/mental health summary scores as predictors and SF-36 physical/mental component summary scores as outcomes. Data from a subsequent assessment point (n = 259) were used to evaluate the agreement between empirical and predicted SF-36 scores.

Results: PROMIS-29 domain scores as well as PROMIS-29 health summary scores showed high predictive value for PCS, PCS_c, and MCS_c (R² ≥ 70%), and moderate predictive value for MCS (R² = 57% and R² = 40%, respectively). After applying the regression coefficients to new data, empirical and predicted SF-36 component summary scores were highly correlated (r > 0.8) for most models. Mean differences between empirical and predicted scores were negligible (|SMD|<0.1).

Conclusions: This study provides easy-to-apply algorithms to convert PROMIS-29 data to well-established SF-36 physical and mental component summary scores in a cardiovascular population. Applied to new data, the agreement between empirical and predicted SF-36 scores was high. However, for SF-36 mental component summary scores, considerably better predictions were found under the correlated (MCS_c) than under the original factor model (MCS). Additionally, as a pertinent byproduct, our study confirmed construct validity of the relatively new PROMIS-29 health summary scores in cardiology patients.

Objectives: Estimates of minimally important differences (MID) can assist interpretation of data collected using patient-reported outcomes (PRO), but variability exists in the emphasis placed on MIDs in health technology assessment (HTA) guidelines. This study aimed to identify to what extent information on the MID of a commonly used PRO, the EQ-5D, is required and utilised by selected HTA agencies.

Methods: Technology appraisal (TA) documents from HTA agencies in England, France, Germany, and the US between 2019 and 2021 were reviewed to identify documents which discussed MID of EQ-5D data as a clinical outcome assessment (COA) endpoint.

Results: Of 151 TAs utilising EQ-5D as a COA endpoint, 58 (38%) discussed MID of EQ-5D data. Discussion of MID was most frequent in Germany, in 75% (n = 12/16) of Gemeinsamer Bundesausschuss (G-BA) and 44% (n = 34/78) of Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen, (IQWiG) TAs. MID was predominantly applied to the EQ-VAS (n = 50), most frequently using a threshold of > 7 or > 10 points (n = 13). G-BA and IQWiG frequently criticised MID analyses, particularly the sources of MID thresholds for the EQ-VAS, as they were perceived as being unsuitable for assessing the validity of MID.

Conclusion: MID of the EQ-5D was not frequently discussed outside of Germany, and this did not appear to negatively impact decision-making of these HTA agencies. While MID thresholds were often applied to EQ-VAS data in German TAs, analyses were frequently rejected in benefit assessments due to concerns with their validity. Companies should pre-specify analyses of continuous data in statistical analysis plans to be considered for treatment benefit assessment in Germany.

Background: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, chronic blood disorder. Symptoms such as fatigue can have a substantial impact on patients' physical activity levels, sleep, quality of life, and work productivity. Ravulizumab treatment can reduce thrombosis risk, improve survival and quality of life, and reduce fatigue in PNH, but information is limited on how it impacts sleep and physical activity. Here, data on resting heart rate, daily physical activity, and sleep in ravulizumab-treated patients with PNH were passively collected via a digital wearable activity-tracking device and patient-reported outcome (PRO) data were collected via weekly surveys in the same cohort.

Methods: REVEAL was a 32-week prospective observational cohort study in individuals with PNH receiving ravulizumab in the USA. A wrist-worn Fitbit™ collected data on resting heart rate, daily step count, and sleep duration from eligible patients. Patients also completed the following electronic weekly surveys: Functional Assessment of Chronic Illness Therapy (FACIT) - Fatigue, Patient-Reported Outcomes Measurement Information System (PROMIS) Global Physical Health, PROMIS Global Mental Health, PROMIS Sleep-Related Impairment and Sleep Disturbance, and Work Productivity and Activity Impairment Questionnaire - Specific Health Problem (WPAI-SHP). Data collected from the activity trackers and surveys were compared against US general population values reported in the literature.

Results: Twenty-eight ravulizumab-treated patients were included (median age: 34 years; 54% female). PRO scores were within US general population normative values, including FACIT-Fatigue (40.0), PROMIS Global Physical Health (51.0), Global Mental Health (51.0), Sleep-Related Impairment (50.0), and Sleep Disturbance (49.0). Similarly, mean resting heart rate (67 bpm), daily step count (7476), and sleep duration (7.7 h) were within the range of US general population values. Daily step count was positively correlated with PROMIS Global Physical and Mental Health scores.

Conclusions: This was the first study to use digital monitoring technology to collect data on physical activity and sleep in patients with PNH. The findings indicate that ravulizumab treatment enables patients with PNH to achieve activity levels (heart rate, sleep duration, step count) and quality of life that are comparable to those of the US general population. A weak positive correlation was identified between patient-reported physical and mental health and daily physical activity levels.

Background: Understanding the determinants of global quality of life in cancer patients is crucial for improving their overall well-being. While correlations between various factors and quality of life have been established, the causal relationships remain largely unexplored. This study aimed to identify the causal factors influencing global quality of life in cancer patients and compare them with known correlative factors.

Methods: We conducted a retrospective analysis of European Organization for Research and Treatment of Cancer Quality of Life Questionnaire data, alongside demographic and disease-related features, collected from new cancer patients during their initial visit to an oncology outpatient clinic. Correlations with global quality of life were identified using univariate and multivariate regression analyses. Causal inference analysis was performed using two approaches. First, we employed the Dowhy Python library for causal analysis, incorporating prior information and manual characterization of an acyclic graph. Second, we utilized the Linear Non-Gaussian Acyclic Model (LiNGAM) machine learning algorithm from the Lingam Python library, which automatically generated an acyclic graph without prior information. The significance level was set at p < 0.05.

Results: Multivariate analysis of 469 new admissions revealed that disease stage, role functioning, emotional functioning, social functioning, fatigue, pain and diarrhea were linked with global quality of life. The most influential direct causal factors were emotional functioning, social functioning, and physical functioning, while the most influential indirect factors were physical functioning, emotional functioning, and fatigue. Additionally, the most prominent total causal factors were identified as type of cancer (diagnosis), cancer stage, and sex, with total causal effect ratios of -9.47, -4.67, and - 1.48, respectively. The LiNGAM algorithm identified type of cancer (diagnosis), nausea and vomiting and social functioning as significant, with total causal effect ratios of -9.47, -0.42, and 0.42, respectively.

Conclusions: This study identified that causal factors for global quality of life in new cancer patients are distinct from correlative factors. Understanding these causal relationships could provide valuable insights into the complex dynamics of quality of life in cancer patients and guide targeted interventions to improve their well-being.

Background: Preference-based measures of health-related quality of life (HRQoL), such as the EQ-5D or the SF-6D, are essential for health economic evaluation. However, they are rarely included in clinical trials of ankylosing spondylitis (AS). This study aims to develop mapping algorithms to predict EQ-5D-3L and EQ-5D-5L health utility scores from the Bath Ankylosing Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI).

Methods: Patients with AS were recruited from the largest tertiary hospital in Shandong province, China, between December 2019 and October 2020. Patients were selected by convenience sampling method according to the following criteria: (1) diagnosed with AS according to the New York criteria; (2) aged 18 years and above; and (3) without mental disorders; (4) able to understand the questionnaires; (5) without serious complications. There were 243 patients who completed the face-to-face questionnaire survey, and 5 cases with missing values in key variables were excluded. Ordinary least squares, censored least absolute deviations, Tobit, adjusted limited dependent variable mixture model and beta-mixture model (BM) in the direct approach and ordered logit and multinomial logit (Mlogit) model in the response approach were used to develop mapping algorithms. Mean absolute error, root mean square error, Spearman's correlation coefficient and concordance correlation coefficient were used to access predictive performance.

Results: The 238 patients with AS had a mean age of 35.19 (SD = 9.59) years, and the majority (74.47%) were male. The observed EQ-5D-3L and EQ-5D-5L health utility values were 0.88 (SD = 0.12) and 0.74 (SD = 0.27), respectively. The EQ-5D-5L had higher conceptual overlap with the BASDAI and BASFI than the EQ-5D-3L did. The Mlogit was the best-performing model for the EQ-5D-3L, and the BM showed better performance in predicting EQ-5D-5L than other direct and indirect mapping models did.

Conclusion: This study demonstrates that the EQ-5D-5L, rather than EQ-5D-3L, should be selected as the target outcome measure of HRQoL in patients with AS in China, and the BM mapping algorithm could be used to predict EQ-5D-5L values from BASDAI and BASFI for health economic evaluation.

Background: Haemophilia A (HA; Factor VIII deficiency) is a congenital X-linked bleeding disorder characterized by trauma-related or spontaneous bleeding events, most notably arising within the intraarticular space and resulting in chronic inflammation and degeneration of affected joints. Endogenous clotting factor activity relative to normal levels determines the severity of HA symptoms, as mild (> 5-40%), moderate (1-5%), or severe (< 1%). Within the current environment of rapid evolution in HA management, we seek to understand the interplay of condition severity and health-related quality of life (HRQoL) to characterise and differentiate unmet needs among people with HA (PwHA).

Methods: A generalised linear regression model (GLM) was developed to explore the relationship between HA severity and EQ-5D-5 L index score from adult HA patients sampled in the "Cost of Haemophilia across Europe - a Socioeconomic Survey II" (CHESS II) cross-sectional, retrospective burden of illness study among adults with hereditary haemophilia A or B from eight European countries. HA patients of any severity with no active inhibitors during the 12 months prior to data capture and a completeEQ-5D-5 L response were included. A base GLM model was specified with covariates for demographic and clinical characteristics (age, body mass index, country, employment, HA severity, annual bleeding rate, problem joints, and chronic pain).

Results: Of 381 evaluable patients, 221 (58.0%) had severe HA, 96 (25.2%) had moderate HA, and 64 (16.8%) had mild HA. Among the covariates included in the GLM model and after controlling for haemophilia-related outcomes, a significant association was observed between mild HA and higher EQ-5D-5 L index score (average marginal effects, 0.084; p = 0.016) relative to severe HA. Patient country of residence and magnitude of HA-related chronic pain were also associated with significant differences in index scores, with the latter showing a negative relationship with HRQoL outcomes.

Conclusions: Condition severity and chronic pain are significant predictors of HRQoL in PwHA. Durable bleeding protection and effective management of chronic pain have the potential to address unmet treatment needs in this population.

Objective: To evaluate and compare the measurement properties and consistency between the Chinese versions of EQ-5D-3L and EQ-5D-Y-3L among Chinese adolescent populations aged 15-17 years.

Methods: Chinese adolescents aged 15-17 studying in high school were recruited through online survey. Social-demographic characteristics and self-reported EQ-5D-3L and EQ-5D-Y-3L responses were collected in the survey. The consistency of responses between the two measures was assessed using redistribution property, and the consistency of utility values was assessed by intraclass correlation coefficients (ICC). Convergent validity and known-group validity were examined using Spearman's rank correlation, F-test and effect sizes, respectively. Sensitivity was compared using relative efficiency (RE).

Results: 762 respondents (48.8% male; age 15-17 years;) were recruited. The EQ-5D-3L showed a more severe ceiling effect than EQ-5D-Y-3L (78.2% vs. 66.0%). Respondents reported higher proportions of having problems in four dimensions using the EQ-5D-Y-3L than using the EQ-5D-3L. The consistency of corresponding dimensions between the two measures was relatively good, while non-negligible proportions of inconsistency were observed in "pain/discomfort" (11.4%) and "anxiety/depression" (15.7%) dimensions. The ICC of the utility values between the EQ-5D-3L and EQ-5D-Y-3L was 0.852 (p < 0.001). The Spearman's rank correlation (range: 0.385-0.620) indicated an acceptable convergent validity between the correlative dimensions of the EQ-5D-3L and EQ-5D-Y-3L. The EQ-5D-Y-3L had a higher efficiency than the EQ-5D-3L at detecting differences across EQ VAS subgroups (ES = 1.793 for EQ-5D-3L, ES = 1.920 for EQ-5D-Y-3L). Mixed results were observed in sensitivity.

Conclusions: Both the EQ-5D-3L and EQ-5D-Y-3L are demonstrated to be valid and generally consistent for measuring HRQoL among adolescents aged 15-17 years in China. Respondents reported higher proportions of having problems using the EQ-5D-Y-3L than using the EQ-5D-3L. More research is warranted to compare the discriminant validity and test-retest reliability between the two measures.

Background: This study aims to identify disability classes among people with schizophrenia spectrum disorder, depression, anxiety or diabetes via the WHODAS 2.0; investigate the invariance of disability patterns among the four diagnostic groups; and examine associations between disability classes and sociodemographic variables.

Methods: Patients seeking treatment for schizophrenia spectrum disorder, depression, anxiety or diabetes (n=1076) were recruited. Latent class analysis was used to identify disability classes based on WHODAS 2.0 responses. Measurement invariance was tested using multi-group latent class analysis. Associations between classes and sociodemographic variables were tested via multinomial logistic regression.

Results: A five-class solution was identified; examination of model invariance showed that the partially constrained five-class model was most appropriate, suggesting that class structure was consistent while class membership differed across diagnostic groups. Finally, significant associations were found between class membership and ethnicity, education level, and employment status.

Conclusions: The results show the feasibility of using the WHODAS 2.0 to identify and compare different disability classes among people with mental or physical conditions and their sociodemographic correlates. Establishing a typology of different disability profiles will help guide research and treatment plans that tackle not just clinical but also functional aspects of living with either a chronic psychiatric or physical condition.

Background: Emotional problems can be evaluated using categorical approaches to guide treatment choices focused on targeting specific disorders, or dimensional approaches to reduce symptom severity. Moreover, recent evidence points out the need to intervene in patients' quality of life (QoL), which often remains low even after the remission of emotional problems. Thus, assessment instruments are needed to provide information on diagnosis, symptom severity, and QoL. The present study aimed to provide diagnostic and QoL cutoffs for the Inventory of Depression and Anxiety Symptoms-II (IDAS-II).

Methods: 273 patients recruited from mental health services in Huelva (Spain) completed the IDAS-II, Mini International Neuropsychiatric Interview, and Short Form-36 Health Survey. Receiver operating characteristic curve analyses were used to establish cutoff values. Diagnostic, balanced, and screening cutoffs were provided for each IDAS-II scale to detect corresponding diagnoses and poor QoL.

Results: The specific IDAS-II scales Suicidality, Panic, Social Anxiety, Claustrophobia, and Traumatic Intrusions showed adequate discrimination values for their corresponding diagnoses (suicidal behavior disorder, panic disorder, social anxiety disorder, agoraphobia, and post-traumatic stress disorder, respectively). Both the General Depression and Dysphoria scales showed adequate ability to detect major depressive disorder. The IDAS-II scales showed a higher discrimination ability for Mental Health-related QoL, than for General Health-related QoL.

Conclusions: The diagnostic and QoL cutoffs expand the clinical utility of the IDAS-II in clinical practice and research, making it a comprehensive, detailed, and versatile self-report tool. The IDAS-II allows for the assessment of emotional problems consistent with the dimensional, categorical, transdiagnostic, and QoL approaches.