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

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

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

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

Safety Outcomes and Near-Adult Height Gain of Growth Hormone-Treated Children with SHOX Deficiency: Data from an Observational Study and a Clinical Trial

Background/Aims: To assess auxological and safety data for growth hormone (GH)-Treated children with SHOX deficiency. Methods: Data were examined for GH-Treated SHOX-deficient children (n = 521) from the observational Genetics and Neuroendocrinology of Short Stature International Study (GeNeSIS). For patients with near-Adult height information, GeNeSIS results (n = 90) were compared with a clinical trial (n = 28) of SHOX-deficient patients. Near-Adult height was expressed as standard deviation score (SDS) for chronological age, potentially increasing the observed effect of treatment. Results: Most SHOX-deficient patients in GeNeSIS had diagnoses of Leri-Weill syndrome (n = 292) or non-syndromic short stature (n = 228). For GeNeSIS patients with near-Adult height data, mean age at GH treatment start was 11.0 years, treatment duration 4.4 years, and height SDS gain 0.83 (95% confidence interval 0.49-1.17). Respective ages, GH treatment durations and height SDS gains for GeNeSIS patients prepubertal at baseline (n = 42) were 9.2 years, 6.0 years and 1.19 (0.76-1.62), and for the clinical trial cohort they were 9.2 years, 6.0 years and 1.25 (0.92-1.58). No new GH-related safety concerns were identified. Conclusion: Patients with SHOX deficiency who had started GH treatment before puberty in routine clinical practice had a similar height gain to that of patients in the clinical trial on which approval for the indication was based, with no new safety concerns

Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort:a secondary analysis of pooled data from insulin clinical trials

AIMS/HYPOTHESIS: The 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.METHODS: Data 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.RESULTS: For 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/INTERPRETATION: Although 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.</p

Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort:a secondary analysis of pooled data from insulin clinical trials

AIMS/HYPOTHESIS: The 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.METHODS: Data 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.RESULTS: For 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/INTERPRETATION: Although 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.</p

Epidemiology of diabetic retinopathy: expected vs reported prevalence of cases in the French population.: Diabetic retinopathy in France

International audienceAIM AND METHODS: Impaired eyesight and vision loss due to retinopathy are among the most feared complications in diabetic patients. As the number of diabetic patients is predicted to increase, a corresponding increase in the number of patients with diabetic retinopathy (DR) is also to be expected. This review is an update of the published literature pertaining to the epidemiology of DR. RESULTS: Over the past 20 years, eight population-based studies have been conducted in Western countries using photographic evidence of DR. Their results have consistently suggested that the prevalence of DR is close to 28.7%, whereas proliferative DR and macular oedema account for 9% and 17%, respectively, of all diagnosed cases. Various longitudinal studies indicate an annual incidence of DR of 2-6%. However, in France, the epidemiology of DR has mostly been investigated by observational studies. The recorded prevalence of DR, based on physicians' reports, is estimated to be 10%, suggesting that DR is underdiagnosed in the French diabetic population. The discrepancy between the expected and reported prevalences of DR could be explained by the number of patients whose retinal status is unknown. DR screening with non-mydriatic fundus photography is effective for identifying early and advanced DR. Screening programmes carried out over the past 5 years in different regions of France indicate that 10-20% of diabetic patients with previously unknown retinal status have retinopathy. CONCLUSION: Further implementation of screening programmes is the key to improving DR diagnosis and preventing vision loss in the French diabetic population

How can the Quality of Medical Data in Pharmacovigilance, Pharmacoepidemiology and Clinical Studies be Guaranteed?

The development of medicinal products is subject to quality standards aimed at guaranteeing that database contents accurately reflect the source documents. Paradoxically, these standards hardly address the quality of the source data itself. The objective of this work was to propose recommendations to improve data quality in three fields (pharmacovigilance, pharmacoepidemiology and clinical studies). The analysis was focused on the data and on the critical stages presenting critical quality problems, for which the current guidelines are insufficiently detailed, unsuitable and/or poorly applied. Finally, recommendations have been proposed, mainly focused on the origin of the data and its transcription

Comment garantir des données médicales de qualité dans les études cliniques, pharmaco-épidémiologiques et en pharmacovigilance ?

Le développement des médicaments fait l’objet de normes de qualité qui visent à garantir que le contenu d’une base de données soit un reflet fidèle du dossier source. Paradoxalement, ces normes abordent peu la qualité de la donnée source elle-même. L’objectif de ce travail était de proposer des recommandations pour améliorer la qualité des données dans trois champs (études cliniques, pharmaco-épidémiologiques et de pharmacovigilance). L’analyse s’est centrée sur les données et les étapes critiques, qui posent un problème important de qualité et pour lesquelles les recommandations actuelles ne sont pas suffisamment détaillées, peu adaptées et/ou appliquées. Au final, des recommandations ont été formulées qui portent principalement sur la genèse de la donnée et sa transcription

Growth hormone in combination with leuprorelin in pubertal children with idiopathic short stature

Objective: There is a scarcity of data from randomised controlled trials on the association of growth hormone (GH) with gonadotrophin-releasing hormone agonists in idiopathic short stature (ISS), although this off-label use is common. We aimed to test whether delaying pubertal progression could increase near-adult height (NAH) in GH-treated patients with ISS. Methods: Patients with ISS at puberty onset were randomised to GH with leuprorelin (combination, n = 46) or GH alone (n = 45). NAH standard deviation score (SDS) was the primary outcome measure. The French regulatory authority requested premature discontinuation of study treatments after approximately 2.4 years; patients from France were followed for safety. Results: Mean (s.d.) baseline height SDS was −2.5 (0.5) in both groups, increasing at 2 years to −2.3 (0.6) with combination and −1.8 (0.7) with GH alone. NAH SDS was −1.8 (0.5) with combination (n = 19) and −1.9 (0.8) with GH alone (n = 16). Treatment-emergent adverse events and bone fractures occurred more frequently with combination than GH alone. Conclusion: Due to premature discontinuation of treatments, statistical comparison of NAH SDS between the two cohorts was not possible. During the first 2–3 years of treatment, patients treated with the combination grew more slowly than those receiving GH alone. However, mean NAH SDS was similar in the two groups. No new GH-related safety concerns were revealed. A potentially deleterious effect of combined treatment on bone fracture incidence was identified