Dataset Information

Development of a new health-related quality of life measure for people with diabetes who experience hypoglycaemia: the Hypo-RESOLVE QoL.

ABSTRACT:

Aims/hypothesis

Valid and reliable patient-reported outcome measures are vital for assessing disease impact, responsiveness to healthcare and the cost-effectiveness of interventions. A recent review has questioned the ability of existing measures to assess hypoglycaemia-related impacts on health-related quality of life for people with diabetes. This mixed-methods project was designed to produce a novel health-related quality of life patient-reported outcome measure in hypoglycaemia: the Hypo-RESOLVE QoL.

Methods

Three studies were conducted with people with diabetes who experience hypoglycaemia. In Stage 1, a comprehensive health-related quality of life framework for hypoglycaemia was elicited from semi-structured interviews (N=31). In Stage 2, the content validity and acceptability of draft measure content were tested via three waves of cognitive debriefing interviews (N=70 people with diabetes; N=14 clinicians). In Stage 3, revised measure content was administered alongside existing generic and diabetes-related measures in a large cross-sectional observational survey to assess psychometric performance (N=1246). The final measure was developed using multiple evidence sources, incorporating stakeholder engagement.

Results

A novel conceptual model of hypoglycaemia-related health-related quality of life was generated, featuring 19 themes, organised by physical, social and psychological aspects. From a draft version of 76 items, a final 14-item measure was produced with satisfactory structural (χ²=472.27, df=74, p<0.001; comparative fit index =0.943; root mean square error of approximation =0.069) and convergent validity with related constructs (r=0.46-0.59), internal consistency (α=0.91) and test-retest reliability (intraclass correlation coefficient =0.87).

Conclusions/interpretation

The Hypo-RESOLVE QoL is a rigorously developed patient-reported outcome measure assessing the health-related quality of life impacts of hypoglycaemia. The Hypo-RESOLVE QoL has demonstrable validity and reliability and has value for use in clinical decision-making and as a clinical trial endpoint.

Data availability

All data generated or analysed during this study are included in the published article and its online supplementary files ( https://doi.org/10.15131/shef.

Data

23295284.v2 ).

SUBMITTER: Carlton J

PROVIDER: S-EPMC11343818 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of a new health-related quality of life measure for people with diabetes who experience hypoglycaemia: the Hypo-RESOLVE QoL.

Carlton Jill J Powell Philip A PA Broadley Melanie M de Galan Bastiaan E BE Heller Simon S Comins Jonathan J Rosilio Myriam M Pouwer Frans F Gall Mari-Anne MA Child Christopher J CJ McCrimmon Rory J RJ Rowen Donna D

Diabetologia 20240522 8

<h4>Aims/hypothesis</h4>Valid and reliable patient-reported outcome measures are vital for assessing disease impact, responsiveness to healthcare and the cost-effectiveness of interventions. A recent review has questioned the ability of existing measures to assess hypoglycaemia-related impacts on health-related quality of life for people with diabetes. This mixed-methods project was designed to produce a novel health-related quality of life patient-reported outcome measure in hypoglycaemia: the ...[more]

PMID: 38777868

Similar Datasets

Project description:BackgroundPrimary sclerosing cholangitis (PSC) is a rare incurable disease of the bile ducts and liver which can significantly impair quality of life (QoL). No existing QoL tools are entirely suitable for people living with PSC (PwPSC). We aimed to develop a measure of QoL for PwPSC in the UK, beginning by identifying relevant QoL issues. This paper describes our approach to this first stage, and discusses related benefits and limitations.MethodsScientific consensus on how to reliably stage PSC is lacking, due to its rarity and heterogeneity. We initially hypothesised four categories for PSC severity. After beginning the study, these were revised to six. For such a rare disease, the study could not recruit sufficient participants in each of these categories, particularly the more severe, in the time available. We therefore modified the design, adapting standard methodology for identifying potentially relevant issues. We started by conducting a thematic analysis of data from a previous survey of PwPSC, and extracting QoL issues from a literature review of QoL questionnaires of relevance to PwPSC. We then conducted group and individual interviews with PwPSC and clinicians, investigating the relevance, importance, phrasing, and breadth of coverage of issues identified. We also explored the validity of our hypothesised categories for disease severity.ResultsWe identified 1,052 potentially relevant QoL issues from the survey and literature review and took 396 of these forwards for discussion with 28 PwPSC. We found 168/396 issues were considered relevant by ≥ 60% of these participants. We then discussed this subset of 168 issues with 11 clinicians. PSC and clinician participants identified some problematic phrasing with 19 issues, due to potential upset (n = 12) or problems with understanding (n = 7). We included one new issue from those suggested.ConclusionWe identified a range of QoL issues relevant to PwPSC, with a good breadth of coverage, although lacking an in-depth understanding of the PSC experience. Our strategy effectively identified relevant QoL issues for people living with this rare condition, for which there is no consensus on stratifying for its severity. This strategy should however be considered specific to such circumstances, not a general recommendation for an alternative approach.

Project description:Aims/hypothesisThe objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.MethodsData from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.ResultsFor both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.Conclusions/interpretationAlthough hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.

Project description:Aims/hypothesisWhether hypoglycaemia increases the risk of other adverse outcomes in diabetes remains controversial, especially for hypoglycaemia episodes not requiring assistance from another person. An objective of the Hypoglycaemia REdefining SOLutions for better liVEs (Hypo-RESOLVE) project was to create and use a dataset of pooled clinical trials in people with type 1 or type 2 diabetes to examine the association of exposure to all hypoglycaemia episodes across the range of severity with incident event outcomes: death, CVD, neuropathy, kidney disease, retinal disorders and depression. We also examined the change in continuous outcomes that occurred following a hypoglycaemia episode: change in eGFR, HbA1c, blood glucose, blood glucose variability and weight.MethodsData from 84 trials with 39,373 participants were pooled. For event outcomes, time-updated Cox regression models adjusted for age, sex, diabetes duration and HbA1c were fitted to assess association between: (1) outcome and cumulative exposure to hypoglycaemia episodes; and (2) outcomes where an acute effect might be expected (i.e. death, acute CVD, retinal disorders) and any hypoglycaemia exposure within the last 10 days. Exposures to any hypoglycaemia episode and to episodes of given severity (levels 1, 2 and 3) were examined. Further adjustment was then made for a wider set of potential confounders. The within-person change in continuous outcomes was also summarised (median of 40.4 weeks for type 1 diabetes and 26 weeks for type 2 diabetes). Analyses were conducted separately by type of diabetes.ResultsThe maximally adjusted association analysis for type 1 diabetes found that cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of neuropathy, kidney disease, retinal disorders and depression, with risk ratios ranging from 1.55 (p=0.002) to 2.81 (p=0.002). Associations of a similar direction were found when level 1 episodes were examined separately but were significant for depression only. For type 2 diabetes cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of death, acute CVD, kidney disease, retinal disorders and depression, with risk ratios ranging from 2.35 (p<0.0001) to 3.00 (p<0.0001). These associations remained significant when level 1 episodes were examined separately. There was evidence of an association between hypoglycaemia episodes of any kind in the previous 10 days and death, acute CVD and retinal disorders in both type 1 and type 2 diabetes, with rate ratios ranging from 1.32 (p=0.017) to 2.68 (p<0.0001). These associations varied in magnitude and significance when examined separately by hypoglycaemia level. Within the range of hypoglycaemia defined by levels 1, 2 and 3, we could not find any evidence of a threshold at which risk of these consequences suddenly became pronounced.Conclusions/interpretationThese data are consistent with hypoglycaemia being associated with an increased risk of adverse events across several body systems in diabetes. These associations are not confined to severe hypoglycaemia requiring assistance.

Dataset Information

Development of a new health-related quality of life measure for people with diabetes who experience hypoglycaemia: the Hypo-RESOLVE QoL.

Aims/hypothesis

Methods

Results

Conclusions/interpretation

Data availability

Data

Publications

Development of a new health-related quality of life measure for people with diabetes who experience hypoglycaemia: the Hypo-RESOLVE QoL.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets