Evidence map›Paper›PMID 37381607›Full record

ArticleJournal of diabetes science and technology2025

Development and Validation of an Electronic Health Record-Based Risk Assessment Tool for Hypoglycemia in Patients With Type 2 Diabetes Mellitus.

Sisi Ma, Alison Alvear, Pamela J Schreiner, Elizabeth R Seaquist, Thomas Kirsh, Lisa S Chow

Abstract readValidation Study
In one paragraph

Article in Journal of diabetes science and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sisi MaInstitute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Alison AlvearDepartment of Medicine, University of Minnesota, Minneapolis, MN, USA.
Pamela J SchreinerDivision of Epidemiology & Community Health, University of Minnesota, Minneapolis, MN, USA.
Elizabeth R SeaquistDepartment of Medicine, University of Minnesota, Minneapolis, MN, USA.
Thomas KirshInstitute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Lisa S ChowDepartment of Medicine, University of Minnesota, Minneapolis, MN, USA.ORCID 0000-0002-6210-2307

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR000114 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$35.0M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
Markers of Beta Cell Dysfunction and Hypoglycemia in ACCORDR01DK095360 · NIDDK · UNIVERSITY OF MINNESOTA · PI SEAQUIST, ELIZABETH R. · 2011 to 2013
$646k
NCATS NIH HHS UL1 TR000114NCATS NIH HHS UL1 TR002494NIDDK NIH HHS R01 DK095360
6 · The paper itself

Abstract

backgroundThe recent availability of high-quality data from clinical trials, together with machine learning (ML) techniques, presents exciting opportunities for developing prediction models for clinical outcomes.

methodsAs a proof-of-concept, we translated a hypoglycemia risk model derived from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study into the HypoHazardScore, a risk assessment tool applicable to electronic health record (EHR) data. To assess its performance, we conducted a 16-week clinical study at the University of Minnesota where participants (N = 40) with type 2 diabetes mellitus (T2DM) had hypoglycemia assessed prospectively by continuous glucose monitoring (CGM).

resultsThe HypoHazardScore combines 16 risk factors commonly found within the EHR. The HypoHazardScore successfully predicted (area under the curve [AUC] = 0.723) whether participants experienced least one CGM-assessed hypoglycemic event (glucose <54 mg/dL for ≥15 minutes from two CGMs) while significantly correlating with frequency of CGM-assessed hypoglycemic events (r = 0.38) and percent time experiencing CGM-assessed hypoglycemia (r = 0.39). Compared to participants with a low HypoHazardScore (N = 19, score <4, median score of 4), participants with a high HypoHazardScore (N = 21, score ≥4) had more frequent CGM-assessed hypoglycemic events (high: 1.6 ± 2.2 events/week; low: 0.3 ± 0.5 events/week) and experienced more CGM-assessed hypoglycemia (high: 1.4% ± 2.0%; low: 0.2% ± 0.4% time) during the 16-week follow-up.

conclusionsWe demonstrated that a hypoglycemia risk model can be successfully adapted from the ACCORD data to the EHR, with validation by a prospective study using CGM-assessed hypoglycemia. The HypoHazardScore represents a significant advancement toward implementing an EHR-based decision support system that can help reduce hypoglycemia in patients with T2DM.

Indexed as

Diabetes Mellitus, Type 2Electronic Health RecordsHypoglycemiaAgedBlood GlucoseBlood Glucose Self-MonitoringFemaleHumansHypoglycemic AgentsMachine LearningMaleMiddle AgedProspective StudiesRisk AssessmentRisk FactorsBlood GlucoseHypoglycemic Agentscontinuous glucose monitoringhypoglycemiarisk scoreT2DMvalidation

Identifiers

PMID37381607
PMCPMC11688684

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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.