5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

      systematic-review

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background and objective

          Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.

          Materials and methods

          PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related).

          Results

          From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.

          Conclusion

          In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.

          Related collections

          Most cited references124

          • Record: found
          • Abstract: found
          • Article: not found

          The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus

          Long-term microvascular and neurologic complications cause major morbidity and mortality in patients with insulin-dependent diabetes mellitus (IDDM). We examined whether intensive treatment with the goal of maintaining blood glucose concentrations close to the normal range could decrease the frequency and severity of these complications. A total of 1441 patients with IDDM--726 with no retinopathy at base line (the primary-prevention cohort) and 715 with mild retinopathy (the secondary-intervention cohort) were randomly assigned to intensive therapy administered either with an external insulin pump or by three or more daily insulin injections and guided by frequent blood glucose monitoring or to conventional therapy with one or two daily insulin injections. The patients were followed for a mean of 6.5 years, and the appearance and progression of retinopathy and other complications were assessed regularly. In the primary-prevention cohort, intensive therapy reduced the adjusted mean risk for the development of retinopathy by 76 percent (95 percent confidence interval, 62 to 85 percent), as compared with conventional therapy. In the secondary-intervention cohort, intensive therapy slowed the progression of retinopathy by 54 percent (95 percent confidence interval, 39 to 66 percent) and reduced the development of proliferative or severe nonproliferative retinopathy by 47 percent (95 percent confidence interval, 14 to 67 percent). In the two cohorts combined, intensive therapy reduced the occurrence of microalbuminuria (urinary albumin excretion of > or = 40 mg per 24 hours) by 39 percent (95 percent confidence interval, 21 to 52 percent), that of albuminuria (urinary albumin excretion of > or = 300 mg per 24 hours) by 54 percent (95 percent confidence interval 19 to 74 percent), and that of clinical neuropathy by 60 percent (95 percent confidence interval, 38 to 74 percent). The chief adverse event associated with intensive therapy was a two-to-threefold increase in severe hypoglycemia. Intensive therapy effectively delays the onset and slows the progression of diabetic retinopathy, nephropathy, and neuropathy in patients with IDDM.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            10-year follow-up of intensive glucose control in type 2 diabetes.

            During the United Kingdom Prospective Diabetes Study (UKPDS), patients with type 2 diabetes mellitus who received intensive glucose therapy had a lower risk of microvascular complications than did those receiving conventional dietary therapy. We conducted post-trial monitoring to determine whether this improved glucose control persisted and whether such therapy had a long-term effect on macrovascular outcomes. Of 5102 patients with newly diagnosed type 2 diabetes, 4209 were randomly assigned to receive either conventional therapy (dietary restriction) or intensive therapy (either sulfonylurea or insulin or, in overweight patients, metformin) for glucose control. In post-trial monitoring, 3277 patients were asked to attend annual UKPDS clinics for 5 years, but no attempts were made to maintain their previously assigned therapies. Annual questionnaires were used to follow patients who were unable to attend the clinics, and all patients in years 6 to 10 were assessed through questionnaires. We examined seven prespecified aggregate clinical outcomes from the UKPDS on an intention-to-treat basis, according to previous randomization categories. Between-group differences in glycated hemoglobin levels were lost after the first year. In the sulfonylurea-insulin group, relative reductions in risk persisted at 10 years for any diabetes-related end point (9%, P=0.04) and microvascular disease (24%, P=0.001), and risk reductions for myocardial infarction (15%, P=0.01) and death from any cause (13%, P=0.007) emerged over time, as more events occurred. In the metformin group, significant risk reductions persisted for any diabetes-related end point (21%, P=0.01), myocardial infarction (33%, P=0.005), and death from any cause (27%, P=0.002). Despite an early loss of glycemic differences, a continued reduction in microvascular risk and emergent risk reductions for myocardial infarction and death from any cause were observed during 10 years of post-trial follow-up. A continued benefit after metformin therapy was evident among overweight patients. (UKPDS 80; Current Controlled Trials number, ISRCTN75451837.) 2008 Massachusetts Medical Society
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes.

              Intensive diabetes therapy aimed at achieving near normoglycemia reduces the risk of microvascular and neurologic complications of type 1 diabetes. We studied whether the use of intensive therapy as compared with conventional therapy during the Diabetes Control and Complications Trial (DCCT) affected the long-term incidence of cardiovascular disease. The DCCT randomly assigned 1441 patients with type 1 diabetes to intensive or conventional therapy, treating them for a mean of 6.5 years between 1983 and 1993. Ninety-three percent were subsequently followed until February 1, 2005, during the observational Epidemiology of Diabetes Interventions and Complications study. Cardiovascular disease (defined as nonfatal myocardial infarction, stroke, death from cardiovascular disease, confirmed angina, or the need for coronary-artery revascularization) was assessed with standardized measures and classified by an independent committee. During the mean 17 years of follow-up, 46 cardiovascular disease events occurred in 31 patients who had received intensive treatment in the DCCT, as compared with 98 events in 52 patients who had received conventional treatment. Intensive treatment reduced the risk of any cardiovascular disease event by 42 percent (95 percent confidence interval, 9 to 63 percent; P=0.02) and the risk of nonfatal myocardial infarction, stroke, or death from cardiovascular disease by 57 percent (95 percent confidence interval, 12 to 79 percent; P=0.02). The decrease in glycosylated hemoglobin values during the DCCT was significantly associated with most of the positive effects of intensive treatment on the risk of cardiovascular disease. Microalbuminuria and albuminuria were associated with a significant increase in the risk of cardiovascular disease, but differences between treatment groups remained significant (P< or =0.05) after adjusting for these factors. Intensive diabetes therapy has long-term beneficial effects on the risk of cardiovascular disease in patients with type 1 diabetes. Copyright 2005 Massachusetts Medical Society.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                26 January 2023
                2023
                : 11
                : 1044059
                Affiliations
                National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University , Changsha, China
                Author notes

                Edited by: João Valente Cordeiro, New University of Lisbon, Portugal

                Reviewed by: Nestoras Mathioudakis, Johns Hopkins University, United States; Clara Mosquera-Lopez, Oregon Health and Science University, United States

                *Correspondence: Lin Yang ✉ yanglin_nfm@ 123456csu.edu.cn

                This article was submitted to Digital Public Health, a section of the journal Frontiers in Public Health

                Article
                10.3389/fpubh.2023.1044059
                9910805
                36778566
                c65e18da-f405-4030-8ad2-61596dc9cbdf
                Copyright © 2023 Zhang, Yang and Zhou.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 September 2022
                : 10 January 2023
                Page count
                Figures: 5, Tables: 10, Equations: 0, References: 126, Pages: 26, Words: 17485
                Funding
                Funded by: Ministry of Science and Technology of the People's Republic of China, doi 10.13039/501100002855;
                This work was supported by the National Key R&D Program of China (2018YFC2001005).
                Categories
                Public Health
                Systematic Review

                diabetes mellitus,hypoglycemia,prediction,data-based algorithms or models,machine learning

                Comments

                Comment on this article