Investigating Novel Predictions of Hypoglycemia Occurrence in Real-world Models

Study Overview
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Brief Summary
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Hypoglycemia is the most common diabetes-related adverse event. However, it is often under-reported to healthcare providers by patients and simultaneously, not often asked about by healthcare providers. As a result, little is known about how often hypoglycemia occurs and consequently, which individuals with diabetes will experience such events. The aims of this study are to determine the real- world occurrence of hypoglycemia and develop/validate real-world risk prediction models for hypoglycemia. These risk prediction models will generate a risk score that indicates an individual's risk for hypoglycemia given their socio-demographic, clinical, and/or behaviour-related characteristics. They can be used to promote clinician awareness around patients' hypoglycemia risks, guide point- of-care and patient decision-making with regard to treatment changes, inform the development and conduct of population-based interventions, and lead to tailored, cost-effective management strategies.

Detailed Description
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The overarching purpose of the proposed investigation is to develop and validate three real-world risk prediction models for: 1) severe hypoglycemia, 2) non-severe daytime hypoglycemia, and 3) non-severe nighttime hypoglycemia, that are applicable to the general population with diabetes (Type 1 and Type 2). These prediction models, which will produce risk scores, will be generated using long-term, prospective data on the frequency and multidimensional risk factors of real-world hypoglycemia. Self-reported hypoglycemia data - a pragmatic and significant patient-important outcome in the clinical management of diabetes - will collected in a non-clinical setting as they are crucial to determining the true distributional burden of events and impactful avenues for prevention, especially given the known epidemiological challenges of existent data collection strategies (e.g., via RCT- or registry-based designs). The use of real-world data will also enhance the generalizability and thus, clinical value of hypoglycemia risk prediction models. The study will employ an ambidirectional (one-year retrospective and one-year prospective) observational cohort design such that multiple exposures (i.e., risk factors) will be collected and evaluated in relation to the occurrence of an outcome (hypoglycemia events). Participants will be enrolled into a prospective, observational cohort referred to as the 'Diabetes iNPHORM Community'. Data will be collected through online questionnaires administered at baseline (to collect retrospective data) and each month of the one-year prospective period. A pilot test will be conducted prior to the enrollment of participants into the Diabetes iNPHORM Community. The purpose of this pilot test is to test the usability of the online question platform, flow and format of the questionnaires, and the readability of the questions. Participants will be recruited into the pilot test and the observational cohort of the study from a pre-existing online panel representative of the general public that has been developed and managed by Ipsos Interactive Services (IIS), a global leader in survey conduct. All individuals in the pre-existing online panel provided profile information and consented to be approached by IIS and its subsidiary partners to complete surveys. For this study, individuals approached to participate in the pilot tests will not subsequently be invited to participate in the observational cohort.

Official Title
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Investigating Novel Predictions of Hypoglycemia Occurrence in Real-world Models

Conditions
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Hypoglycemia, Diabetes Mellitus, Type 2, Diabetes Mellitus, Type 1

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Self-reported diagnosis of T1DM or T2DM Use of insulin and/or secretagogues for at least one year at the time of enrolment Living in the United States of America for at least one year at the time of enrolment Exclusion Criteria: Unable to read and understand English Currently pregnant or pregnant within the previous year Currently participating in an interventional clinical trial or research study

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 90 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Incidence proportions and densities of severe hypoglycemia, non-severe daytime hypoglycemia, and non-severe nighttime hypoglycemia | Self-reported through questionnaires | Up to 12 months prospectively | 
| Risk scores for severe hypoglycemia, non-severe daytime hypoglycemia, non-severe nighttime hypoglycemia | Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-World Models (iNPHORM) Hypoglycemia Risk Score: Risk scores using the probabilities (0-100%) from our validated multivariable prediction models will be calculated to reflect the degree of risk due to the candidate variables (low to high risk scores will denote low to high risks of hypoglycemia occurrence, respectively). Any selected ranges of predicted probabilities used as boundaries for risk stratification will be justified. Details relevant to the calculation of subject-specific risks will be reported, including the intercepts and betas from the logistic regression models and nomograms. | Up to 12 months prospectively | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Exploratory causal estimates of different treatment regimens and hypoglycemia rates | Derived from data captured through questionnaires | Up to 12 months prospectively | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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hypoglycemia, diabetes mellitus