ArticleJournal of diabetes science and technology2026
Efficacy of an AI-Enabled Low Glucose Prediction: A Pooled Performance Analysis With Capillary Blood Glucose as Ground Truth.
Article in Journal of diabetes science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Models for Glycemic Prediction in Type 1 and Type 2 Diabetes: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Smart wearable biosensors: a transformative synergy between diagnosis and treatment of disease.RSC advances · 2026Review
- Empowering digital health management with on-device large language models for glucose prediction: a model development and validation study.EBioMedicine · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHypoglycemia is a critical challenge for insulin-dependent people with diabetes using multiple daily injections (MDI), who rely on reactive responses to continuous glucose monitoring (CGM) alerts. To meet the need for a proactive safety tool, we evaluated the performance of the Low Glucose Predict (LGP) feature in the Accu-Chek SmartGuide Predict App.
methodsThis retrospective analysis pooled data from three prospective trials, including 85 subjects over 2709 recording days. The LGP feature uses a XGBoost model to predict low glucose events up to 30 minutes in advance. Performance was assessed rigorously against both capillary blood glucose (BG) and CGM values, including an analysis with "close-call" predictions (+10 mg/dL above the threshold). Metrics included sensitivity, specificity, and ROC-AUC.
resultsAgainst the stringent capillary BG reference, LGP showed high performance: sensitivity of 87.13% and specificity of 97.43% (ROC-AUC 0.9787). Including close-call events improved sensitivity to 91.89% and specificity to 98.09%. Referenced against CGM, sensitivity was 94.40% and specificity was 98.25%. The system provided an actionable mean lead time of 14.71 ± 8.30 minutes (CGM reference), with a low average daily true notification rate of 1.31 (2.60 including close-calls).
conclusionThe LGP feature is an accurate, highly sensitive, and specific tool for timely, proactive low glucose prediction, validated against both capillary BG and CGM. This predictive intelligence is a crucial mechanism for people with diabetes to safely mitigate hypoglycemia risk, addressing a significant clinical gap and potentially reducing fear of hypoglycemia and diabetes distress.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.