Development and Validation of a Hypoglycemia Risk Model for Intensive Insulin Therapy in Patients with Type 2 Diabetes

Hu, Xiling; Xu, W.; Lin, Shuo; Zhang, Cang; Ling, Cong; Chen, Miaoxia

doi:10.1155/2020/7292108

Cited by 8 publications

(7 citation statements)

References 39 publications

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“…Furthermore, black race, sulfonylurea use, low HbA1c, low serum creatinine levels, and poor cognitive function were also identified as risk factors for SH ( 44 , 91 ). Hu et al ( 84 ) performed ROC analysis on fasting insulin, fasting blood glucose, and total insulin treatment time of 257 T2D patients receiving intensive therapy and reported an AUC of 0.666 in hypoglycemia prediction. Karter et al ( 57 ) classified 165,148 patients into high, medium, and low-risk groups for SH hospitalization by assessing six factors: number of previous hypoglycemia hospitalizations, insulin use, sulfonylurea use, previous emergency history, CKD stage, and age.…”

Section: Resultsmentioning

confidence: 99%

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

Zhang

Yang

Zhou

2023

Front. Public Health

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Background and objectiveHypoglycemia 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 methodsPubMed, 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).ResultsFrom 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.ConclusionIn 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.

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Section: Resultsmentioning

confidence: 99%

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

Zhang

Yang

Zhou

2023

Front. Public Health

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“…After an initial screening, the remaining 56 published studies were screened by full text; 41 were excluded after the screening of the full text (reasons outlined in Figure 1). The remaining 15 studies met our eligible criteria (Chandran et al, 2019; Chow et al, 2018; Claydon-Platt et al, 2014; Elbaz et al, 2021; Ena et al, 2018; K. Han et al, 2018; Heller et al, 2020; Hu et al, 2020; Karter et al, 2017; Lagani et al, 2015; Misra-Hebert et al, 2020; Murata et al, 2004; Ruan et al, 2020; Schroeder et al, 2017; Shah et al, 2019). In addition, we found one eligible study by hand-search (Mathioudakis et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

Prediction Models for Prognosis of Hypoglycemia in Patients with Diabetes: A Systematic Review and Meta-Analysis

Zhang

et al. 2022

Biological Research For Nursing

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Objective To systematically summarize the reported prediction models for hypoglycemia in patients with diabetes, compare their performance, and evaluate their applicability in clinical practice. Methods We selected studies according to the PRISMA, appraised studies according to the Prediction model Risk of Bias Assessment Tool (PROBAST), and extracted and synthesized the data according to the CHARMS. The databases of PubMed, Web of Science, Embase, and Cochrane Library were searched from inception to 31 October 2021 using a systematic review approach to capture all eligible studies developing and/or validating a prognostic prediction model for hypoglycemia in patients with diabetes. The risk bias and clinical applicability were assessed using the PROBAST. The meta-analysis of the performance of the prediction models were also conducted. The protocol of this study was recorded in PROSPERO (CRD42022309852). Results Sixteen studies with 22 models met the eligible criteria. The predictors with the high frequency of occurrence among all models were age, HbA1c, history of hypoglycemia, and insulin use. A meta-analysis of C-statistic was performed for 21 prediction models, and the summary C-statistic and its 95% confidence interval and prediction interval were 0.7699 (0.7299–0.8098), 0.7699 (0.5862–0.9536), respectively. Heterogeneity exists between different hypoglycemia prediction models (τ2 was 0.00734≠0). Conclusions The existing predictive models are not recommended for widespread clinical use. A high-quality hypoglycemia screening tool should be developed in future studies.

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“…A strength of this study is that we included participants who had undergone short‐term intensive insulin treatment and therefore the population of this study was similar to that seen in current clinical treatment in China. Previous studies comparing the efficacy and safety of a basal insulin regimen versus a premixed insulin regimen typically enrolled participants with high FPG values, and, as such, the results of those studies are not applicable to people who have undergone short‐term intensive insulin treatment, who have an FPG closer to normal values and may be at greater risk of hypoglycaemia when receiving insulin treatment 21,25,26 . There are some limitations to this study, namely, the open‐label nature of drug administration, which can be associated with reporting bias, and the homogeneous study population.…”

Section: Discussionmentioning

confidence: 99%

Efficacy and safety of a basal insulin + 2‐3 oral antihyperglycaemic drugs regimen versus a twice‐daily premixed insulin + metformin regimen after short‐term intensive insulin therapy in individuals with type 2 diabetes: The multicentre, open‐label, randomized controlled BEYOND‐V trial

Pan

Wan

et al. 2022

Diabetes Obesity Metabolism

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Aim: To compare the efficacy and safety of basal insulin glargine 100 units/ml (Gla) + 2-3 oral antihyperglycaemic drugs (OADs) with twice-daily premixed insulin aspart 70/30 (Asp30) + metformin (MET) after short-term intensive insulin therapy in adults with type 2 diabetes in China. Materials and Methods:This open-label trial enrolled insulin-naïve adults with type 2 diabetes and an HbA1c of 7.5%-11.0% (58-97 mmol/mol) despite treatment with 2-3 OADs. All participants stopped previous OADs except MET, then received shortterm intensive insulin therapy during the run-in period, when those with a fasting plasma glucose of less than 7.0 mmol/L and 2-hour postprandial glucose of less than 10.0 mmol/L were randomized to Gla + MET + a dipeptidyl peptidase-4 inhibitor or twice-daily Asp30 + MET. If HbA1c was more than 7.0% (>53 mmol/mol) at week 12, participants in the Gla group were added repaglinide or acarbose, at the physician's discretion, and participants in the Asp30 group continued to titrate insulin dose. The change in HbA1c from baseline to week 24 was assessed in the per protocol (PP) population (primary endpoint).Results: There were 384 enrollees (192 each to Gla and Asp30); 367 were included in the PP analysis. The threshold for non-inferiority of Gla + OADs versus Asp30 + MET was met, with a least squares mean change from baseline in HbA1c of -1.72% and -1.70% (-42.2 and -42.1 mmol/mol), respectively (estimated difference -0.01%; 95% CI -0.20%, 0.17% [-0.1 mmol/mol; 95% CI -2.2, 1.9]). Achievement of

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Development and Validation of a Hypoglycemia Risk Model for Intensive Insulin Therapy in Patients with Type 2 Diabetes

Cited by 8 publications

References 39 publications

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

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

Prediction Models for Prognosis of Hypoglycemia in Patients with Diabetes: A Systematic Review and Meta-Analysis

Efficacy and safety of a basal insulin + 2‐3 oral antihyperglycaemic drugs regimen versus a twice‐daily premixed insulin + metformin regimen after short‐term intensive insulin therapy in individuals with type 2 diabetes: The multicentre, open‐label, randomized controlled BEYOND‐V trial

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