Evidence map›Paper›PMID 41556335›Full record

ArticleDiabetes technology & therapeutics2026

The Development and Validation of Multivariable Electronic Health Record-Based Models to Predict Diabetic Ketoacidosis-Related Hospitalizations for Adults with Type 1 Diabetes.

Jacob Kohlenberg, Meng Xu, Ryan Coopergard, Erika S Helgeson, Amy C Gross, Nestoras Mathioudakis, Craig Vandervelden, Mark Clements, Lisa S Chow, Sisi Ma

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes technology & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jacob KohlenbergDivision of Diabetes, Endocrinology and Metabolism, University of Minnesota, Minneapolis, Minnesota, USA.
Meng XuInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Ryan CoopergardInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Erika S HelgesonDivision of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Amy C GrossDepartment of Pediatrics, Center for Pediatric Obesity Medicine, University of Minnesota, Minneapolis, Minnesota, USA.
Nestoras MathioudakisDivision of Endocrinology, Diabetes & Metabolism, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Craig VanderveldenEndocrinology, Children's Mercy Kansas City, Kansas City, Missouri, USA.
Mark ClementsEndocrinology, Children's Mercy Kansas City, Kansas City, Missouri, USA.
Lisa S ChowDivision of Diabetes, Endocrinology and Metabolism, University of Minnesota, Minneapolis, Minnesota, USA.
Sisi MaInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.

Funding

Diabetes-Docs: Physician-Scientist Career Development Program (DiabDocs)K12DK133995 · STANFORD UNIVERSITY · 2025 to 2025
$3.7M
NIDDK NIH HHS K12 DK133995
6 · The paper itself

Abstract

aimTo develop and validate models that use electronic health record (EHR) data to predict diabetic ketoacidosis (DKA)-related hospitalizations over 90 and 180 days among adults with type 1 diabetes (T1D).

methodsWe used EHR data from adults with T1D treated at an academic health system in the United States, between January 1, 2017, and April 30, 2023. Models were built to predict the 90- and 180-day DKA risk using EHR data from the 2 years preceding the index date. We constructed seven predictors: (1) prior DKA event, (2) number of prior DKA events, (3) average time between DKA events in years, (4) time since the most recent DKA event in years, (5) most recent HbA1c, (6) the absence of a HbA1c result in the past 2 years, and (7) insurance type. The dataset was split into discovery and prospective validation cohorts. Logistic regression models were built using the discovery cohort and validated using the prospective validation cohort.

resultsOur dataset included 7798 adults with T1D, of which 667 (8.6%) experienced ≥1 post-T1D diagnosis DKA event, totaling 1102 DKA events. The 90-day model achieved a mean area under the receiver operating characteristic curve (AUC) of 0.87 (standard deviation [SD] ± 0.02). The 180-day model achieved a mean AUC of 0.84 (SD ± 0.02). Among the 5% highest risk individuals, the 90-day model had a recall of 0.45, precision of 0.11, and specificity of 0.95, while the 180-day model had a recall of 0.42, precision of 0.17, and a specificity of 0.96.

conclusionWe developed EHR-based logistic regression models that effectively predict DKA-related hospitalizations in adults with T1D. Future work will enhance model performance by incorporating additional features and applying advanced machine learning methods.

Indexed as

Diabetes Mellitus, Type 1Diabetic KetoacidosisElectronic Health RecordsHospitalizationAdultFemaleHumansLogistic ModelsMaleMiddle AgedPrediction AlgorithmsPredictive Learning Modelsclinical decision supportdiabetic ketoacidosishealth care cost savingsmachine learningprediction modeltype 1 diabetes

Identifiers

PMID41556335
PMCPMC13102165

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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 Socratic graph.