Mapping PedsQL™ Generic Core Scales to EQ-5D-3L utility scores in transfusion-dependent thalassemia patients

Identification of Key Genes Related to Skin Burns Based on Bioinformatics Analysis

To further understand the regulatory network and molecular mechanisms of gene expression after skin burns, we performed bioinformatics analysis of gene expression profiles of skin burn samples and identified key genes associated with skin burns. The GSE8056 and GSE139028 datasets were downloaded from the Gene Expression Omnibus database for analysis and validation. The limma package was used to screen for differentially expressed genes (DEGs). Gene ontology and pathway enrichment analyses (KEGG) were then performed. Subsequently, LASSO regression analysis was performed on DEGs and a regulatory network map of skin burn-related genes was constructed. Finally, the infiltration of immune cells was calculated and coexpression network maps of immune-related key genes and skin regeneration genes were constructed. Analysis of the GSE8056 dataset showed that 432 genes were upregulated and 351 genes were downregulated. The DEGs were mainly focused on immune response and skin regeneration. Meanwhile, these two groups of pivotal genes were significantly associated with abnormal infiltration of nine immune cells. GSE139028 validation revealed that three hub genes associated with skin burn immunity were differentially expressed, except for S100A8, while only the DPT gene was differentially expressed among the seven hub genes associated with skin regeneration. In short, the effect of skin burn on patients is to regulate the expression of immune-related genes UPP1, MMP1, MMP3, and skin regeneration-related gene DPT, which may be the key target for the treatment of skin burn.

Scholarly knowledge fundamentals and dynamic research hotspots in the field of burns and immunology: A bibliometric analysis

BACKGROUND

An estimated 180,000 burn deaths occur each year, and the immune system plays a vital role in wound healing and burn complications, including inflammatory reactions and oxidative stress. This paper aims to explore the basic knowledge and dynamic hotspots in burns and immunology research with bibliometric methods.

METHODS

Through systematic retrieval, we ensured all the documents complied with our retrieval strategy and were included in the Science Citation Index-Expanded of the Web of Science Core Collection. Using bibliometric methodologies, the general information was delineated; and foundational knowledge, as well as dynamic research hotspots, were ascertained through VOSviewer, CiteSpace, and R-bibliometrix.

RESULTS

8758 publications were identified from January 1st, 2000, to June 17th, 2024. The most productive and collaborative country was the USA; Harvard University was the most productive affiliation; and the most productive author was David N. Herndon. According to source analysis, the highest-impact journal is Burns. Historically, "expression" was the most frequently occurring word. "Delivery" was the most frequently occurring word in recent years.

CONCLUSION

The domain of burns and immunology has reached a zenith, with a modest decline in publication output over the past two years, yet it continues to evolve robustly. The focal points of inquiry have evolved from the initial appraisal of immunotherapeutic interventions for critical burn injuries to the elucidation of immune cell mechanisms in burn patients. Future research trajectory is poised to innovate therapeutic modalities, encompassing anti-inflammatory, antioxidant, and targeted drug delivery systems, to enhance precision in immune modulation.

Measuring health-related quality of life in children with suspected genetic conditions: validation of the PedsQL proxy-report versions

PURPOSE

Measuring health-related quality of life (HRQoL) of children with suspected genetic conditions is important for understanding the effect of interventions such as genomic sequencing (GS). The Pediatric Quality of Life Inventory (PedsQL) is a widely used generic measure of HRQoL in pediatric patients, but its psychometric properties have not yet been evaluated in children undergoing diagnostic GS.

METHODS

In this cross-sectional study, we surveyed caregivers at the time of their child's enrollment into GS research studies as part of the Clinical Sequencing Evidence Generating Research (CSER) consortium. To evaluate structural validity of the PedsQL 4.0 Generic Core Scales and PedsQL Infant Scales parent proxy-report versions, we performed a confirmatory factor analysis of the hypothesized factor structure. To evaluate convergent validity, we examined correlations between caregivers' reports of their child's health, assessed using the EQ VAS, and PedsQL scores by child age. We conducted linear regression analyses to examine whether age moderated the association between caregiver-reported child health and PedsQL scores. We assessed reliability using Cronbach's alpha.

RESULTS

We analyzed data for 766 patients across all PedsQL age group versions (1-12 months through 13-18 years). Model fit failed to meet criteria for good fit, even after modification. Neither age group (categorical) nor age (continuous) significantly moderated associations between PedsQL scores and caregiver-reported child health. Cronbach's alphas indicated satisfactory internal consistency for most PedsQL scales.

CONCLUSION

The PedsQL Generic Core Scales and Infant Scales may be appropriate to measure HRQoL in pediatric patients with suspected genetic conditions across a wide age range. While we found evidence of acceptable internal consistency and preliminary convergent validity in this sample, there were some potential problems with structural validity and reliability that require further attention.

Mapping of Family Reported Outcome Measure (FROM-16) scores to EQ-5D: algorithm to calculate utility values

OBJECTIVE

Although decision scientists and health economists encourage inclusion of family member/informal carer utility in health economic evaluation, there is a lack of suitable utility measures comparable to patient utility measures such those based on the EQ-5D. This study aims to predict EQ-5D-3L utility values from Family Reported Outcome Measure (FROM-16) scores, to allow the use of FROM-16 data in health economic evaluation when EQ-5D data is not available.

METHODS

Data from 4228 family members/partners of patients recruited to an online cross-sectional study through 58 UK-based patient support groups, three research support platforms and Welsh social services departments were randomly divided five times into two groups, to derive and test a mapping model. Split-half cross-validation was employed, resulting in a total of ten multinomial logistic regression models. The Monte Carlo simulation procedure was used to generate predicted EQ-5D-3L responses, and utility scores were calculated and compared against observed values. Mean error and mean absolute error were calculated for all ten validation models. The final model algorithm was derived using the entire sample.

RESULTS

The model was highly predictive, and its repeated fitting using multinomial logistic regression demonstrated a stable model. The mean differences between predicted and observed health utility estimates ranged from 0.005 to 0.029 across the ten modelling exercises, with an average overall difference of 0.015 (a 2.2% overestimate, not of clinical importance).

CONCLUSIONS

The algorithm developed will enable researchers and decision scientists to calculate EQ-5D health utility estimates from FROM-16 scores, thus allowing the inclusion of the family impact of disease in health economic evaluation of medical interventions when EQ-5D data is not available.

Mapping IWQOL-Lite onto EQ-5D-5L and SF-6Dv2 among overweight and obese population in China

PURPOSE

To develop the mapping functions from the Impact of Weight on Quality of Life-Lite (IWQOL-Lite) scores onto the EQ-5D-5L and SF-6Dv2 utility values among the overweight and obese population in China.

METHODS

A representative sample of the overweight and obese population in China stratified by age, sex, body mass index (BMI), and area of residence was collected by online survey and the sample was randomly divided into development (80%) and validation (20%) datasets. The conceptual overlap between the IWQOL-Lite and the EQ-5D-5L or SF-6Dv2 was evaluated by Spearman's correlation coefficients. Five models, including OLS, Tobit, CLAD, GLM, and PTM were explored to derive mapping functions using the development dataset. The model performance was assessed using MAE, RMSE, and the percentage of AE > 0.05 and AE > 0.1 in the validation dataset.

RESULTS

A total of 1000 respondents (48% female; mean [SD] age: 51.7 [15.3]; mean [SD] BMI: 27.4 [2.8]) were included in this study. The mean IWQOL-Lite scores and the utility values of EQ-5D-5L and SF-6Dv2 were 78.5, 0.851, and 0.734, respectively. The best-performing models predicting EQ-5D-5L and SF-6Dv2 utilities both used IWQOL-Lite total score as a predictor in the CLAD model (MAE: 0.083 and 0.076 for the EQ-5D-5L and SF-6Dv2; RMSE: 0.125 and 0.103 for the EQ-5D-5L and SF-6Dv2; AE > 0.05: 20.5% and 27.5% for the EQ-5D-5L and SF-6Dv2; AE > 0.10: 9.5% and 15.0% for the EQ-5D-5L and SF-6Dv2).

CONCLUSION

CLAD models with the IWQOL-Lite total score can be used to predict both the EQ-5D-5L and SF-6Dv2 utility values among overweight and obese population in China.

Mapping the Seattle Angina Questionnaire to EQ-5D-5L in patients with coronary heart disease

BACKGROUND

Health economic evaluation is critical in supporting novel cardiovascular disease therapies. However, most clinical studies do not include preference-based questionnaires to calculate utilities for health economic evaluations. Thus, this study aimed to develop mapping algorithms that convert the Seattle Angina Questionnaire (SAQ) to EQ-5D-5L health utility scores for patients with coronary health disease (CHD) in China.

METHODS

Data were obtained from a longitudinal study of patients with CHD conducted at the Tianjin Medical University General Hospital in China. Convenience sampling was used to recruit patients with CHD. The inclusion criteria were having been diagnosed with CHD through a medical examination and being aged 18 years or older. The exclusion criteria were a lack of comprehension ability, serious comorbidities, mental illness, and hearing or vision impairment. All eligible patients were invited to participate, and 305 and 75 patients participated at baseline and in the follow-up, respectively. Seven regression models were developed using a direct approach. Furthermore, we predicted the five EQ-5D items using ordered logit model and derived the utility score from predicted responses using an indirect approach. Model performances were evaluated using mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (ρ), and Lin's concordance correlation coefficient (CCC). A five-fold cross-validation method was used to evaluate internal validation.

RESULTS

The average age was 63.04 years, and 53.72% of the included patients were male. Most (70.05%) patients had unstable angina pectoris, and the mean illness duration was 2.50 years. The EQ-5D scores were highly correlated with five subscales of the SAQ, with Spearman's rank correlation coefficients ranging from 0.6184 to 0.7093. The mixture beta model outperformed the other regression models in the direct approach, with the lowest MAE and RMSE and highest ρ and CCC. The ordered logit model in the indirect approach performed the same as the mixture beta regression with equal MAE, lower RMSE, and higher ρ and CCC.

CONCLUSION

Mapping algorithms developed using mixture beta and ordered logit models accurately converted SAQ scores to EQ-5D-5L health utility values, which could support health economic evaluations related to coronary heart disease.

Mapping the Peds QLTM 4.0 onto CHU-9D: a cross-sectional study in functional dyspepsia population from China

Objective

The study aims to develop a mapping algorithm from the Pediatric Quality of Life Inventory™ 4. 0 (Peds QL 4.0) onto Child Health Utility 9D (CHU-9D) based on the cross-sectional data of functional dyspepsia (FD) children and adolescents in China.

Methods

A sample of 2,152 patients with FD completed both the CHU-9D and Peds QL 4.0 instruments. A total of six regression models were used to develop the mapping algorithm, including ordinary least squares regression (OLS), the generalized linear regression model (GLM), MM-estimator model (MM), Tobit regression (Tobit) and Beta regression (Beta) for direct mapping, and multinomial logistic regression (MLOGIT) for response mapping. Peds QL 4.0 total score, Peds QL 4.0 dimension scores, Peds QL 4.0 item scores, gender, and age were used as independent variables according to the Spearman correlation coefficient. The ranking of indicators, including the mean absolute error (MAE), root mean squared error (RMSE), adjusted R², and consistent correlation coefficient (CCC), was used to assess the predictive ability of the models.

Results

The Tobit model with selected Peds QL 4.0 item scores, gender and age as the independent variable predicted the most accurate. The best-performing models for other possible combinations of variables were also shown.

Conclusion

The mapping algorithm helps to transform Peds QL 4.0 data into health utility value. It is valuable for conducting health technology evaluations within clinical studies that have only collected Peds QL 4.0 data.

How do common conditions impact health-related quality of life for children? Providing guidance for validating pediatric preference-based measures

BACKGROUND

There is increasing interest in the validation of pediatric preference-based health-related quality of life measurement instruments. It is critical that children with various degrees of health-related quality of life (HRQoL) impact are included in validation studies. To inform patient sample selection for validation studies from a pragmatic perspective, this study explored HRQoL impairments between known-groups and HRQoL changes over time across 27 common chronic child health conditions and identified conditions with the largest impact on HRQoL.

METHODS

The health dimensions of two common preference-based HRQoL measures, the EQ-5D-Y and CHU9D, were constructed using Pediatric Quality of Life Inventory items that overlap conceptually. Data was from the Longitudinal Study of Australian Children, a nationally representative sample with over 10,000 children at baseline. Seven waves of data were included for the analysis, with child age ranging from 2 to18 years. Impacts to specific health dimensions and overall HRQoL between those having a specific condition versus not were compared using linear mixed effects models. HRQoL changes over time were obtained by calculating the HRQoL differences between two consecutive time points, grouped by "Improved" and "Worsened" health status. Comparison among various health conditions and different age groups (2-4 years, 5-12 years and 13-18 years) were made.

RESULTS

Conditions with the largest statistically significant total HRQoL impairments of having a specific condition compared with not having the condition were recurrent chest pain, autism, epilepsy, anxiety/depression, irritable bowel, recurrent back pain, recurrent abdominal pain, and attention deficit hyperactivity disorder (ADHD) for the total sample (2-18 years). Conditions with largest HRQoL improvement over time were anxiety/depression, ADHD, autism, bone/joint/muscle problem, recurrent abdominal pain, recurrent pain in other part, frequent headache, diarrhea and day-wetting. The dimensions included in EQ-5D-Y and CHU9D can generally reflect HRQoL differences and changes. The HRQoL impacts to specific health dimensions differed by condition in the expected direction. The conditions with largest HRQoL impacts differed by age group.

CONCLUSIONS

The conditions with largest HRQoL impact were identified. This information is likely to be valuable for recruiting patient samples when validating pediatric preference-based HRQoL instruments pragmatically.