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

Abstract

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.