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Development and evaluation of a machine learning prediction model for short-term mortality in patients with diabetes or hyperglycemia at emergency department admission

Wändell, Per LU ; Wierzbicka, Marcelina LU ; Sigurdsson, Karolina LU ; Olofsson, Anna ; Wachtler, Caroline LU ; Wessman, Torgny LU ; Melander, Olle LU orcid ; Ekelund, Ulf LU orcid ; Björkelund, Anders LU and Carlsson, Axel C. , et al. (2025) In Cardiovascular Diabetology 24(1).
Abstract

Background: Patients with diabetes admitted to emergency care face a higher risk of complications, including prolonged hospital stays, admissions to the intensive care unit and mortality. Aim: To develop a machine learning (ML) model to predict 30-day mortality in patients with diabetes admitted to the emergency department (ED). Design and setting: A cohort study utilizing data from all nine ED’s in Region Skåne 2017 to 2018. Totally 74,611 patient visits, representing 34,280 unique patients aged > 18 years with diabetes or hyperglycemia (glucose were > 11 mmol/L). The analysis focused on four groups, men and women aged 40–69 and ≥ 70 years. Methods: Stochastic gradient boosting was employed to develop a model predicting 30-day... (More)

Background: Patients with diabetes admitted to emergency care face a higher risk of complications, including prolonged hospital stays, admissions to the intensive care unit and mortality. Aim: To develop a machine learning (ML) model to predict 30-day mortality in patients with diabetes admitted to the emergency department (ED). Design and setting: A cohort study utilizing data from all nine ED’s in Region Skåne 2017 to 2018. Totally 74,611 patient visits, representing 34,280 unique patients aged > 18 years with diabetes or hyperglycemia (glucose were > 11 mmol/L). The analysis focused on four groups, men and women aged 40–69 and ≥ 70 years. Methods: Stochastic gradient boosting was employed to develop a model predicting 30-day mortality. Variable importance was assessed using normalized relative influence (NRI) scores. Variables in certain hospitals were used to train the models, and the models were tested in other hospitals. Results: Key predictors included laboratory values (pH, base excess, pCO2, standard bicarbonate, oxygen saturation, lactate, CRP, and leukocytes), as well as age, triage category, and time to doctor consultation. The sensitivity of the models ranged from 86–97%, the specificity from 86–94%, and accuracy between 86% and 94%. The area under the curve (AUC) ranged from 0.84 to 0.93 and Cohen’s kappa ranged from 0.34 to 0.45. Positive predictive values accurately identified mortality in 23% to 37% of cases across the four groups. Conclusions: A machine learning model based on routinely collected data in the ED accurately predicted 30-day mortality with high specificity and sensitivity. This approach shows promise in identifying high-risk patients requiring close monitoring and timely interventions.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial intelligence, Diabetes, Emergency medicine, Gradient boosting, Normalized relative influence, Prediction
in
Cardiovascular Diabetology
volume
24
issue
1
article number
383
publisher
BioMed Central (BMC)
external identifiers
  • scopus:105017606585
  • pmid:41044571
ISSN
1475-2840
DOI
10.1186/s12933-025-02954-8
language
English
LU publication?
yes
id
b30212f6-d781-402e-a894-6b698f18e388
date added to LUP
2025-11-21 12:51:27
date last changed
2025-11-22 03:12:32
@article{b30212f6-d781-402e-a894-6b698f18e388,
  abstract     = {{<p>Background: Patients with diabetes admitted to emergency care face a higher risk of complications, including prolonged hospital stays, admissions to the intensive care unit and mortality. Aim: To develop a machine learning (ML) model to predict 30-day mortality in patients with diabetes admitted to the emergency department (ED). Design and setting: A cohort study utilizing data from all nine ED’s in Region Skåne 2017 to 2018. Totally 74,611 patient visits, representing 34,280 unique patients aged &gt; 18 years with diabetes or hyperglycemia (glucose were &gt; 11 mmol/L). The analysis focused on four groups, men and women aged 40–69 and ≥ 70 years. Methods: Stochastic gradient boosting was employed to develop a model predicting 30-day mortality. Variable importance was assessed using normalized relative influence (NRI) scores. Variables in certain hospitals were used to train the models, and the models were tested in other hospitals. Results: Key predictors included laboratory values (pH, base excess, pCO<sub>2</sub>, standard bicarbonate, oxygen saturation, lactate, CRP, and leukocytes), as well as age, triage category, and time to doctor consultation. The sensitivity of the models ranged from 86–97%, the specificity from 86–94%, and accuracy between 86% and 94%. The area under the curve (AUC) ranged from 0.84 to 0.93 and Cohen’s kappa ranged from 0.34 to 0.45. Positive predictive values accurately identified mortality in 23% to 37% of cases across the four groups. Conclusions: A machine learning model based on routinely collected data in the ED accurately predicted 30-day mortality with high specificity and sensitivity. This approach shows promise in identifying high-risk patients requiring close monitoring and timely interventions.</p>}},
  author       = {{Wändell, Per and Wierzbicka, Marcelina and Sigurdsson, Karolina and Olofsson, Anna and Wachtler, Caroline and Wessman, Torgny and Melander, Olle and Ekelund, Ulf and Björkelund, Anders and Carlsson, Axel C. and Ruge, Toralph}},
  issn         = {{1475-2840}},
  keywords     = {{Artificial intelligence; Diabetes; Emergency medicine; Gradient boosting; Normalized relative influence; Prediction}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Cardiovascular Diabetology}},
  title        = {{Development and evaluation of a machine learning prediction model for short-term mortality in patients with diabetes or hyperglycemia at emergency department admission}},
  url          = {{http://dx.doi.org/10.1186/s12933-025-02954-8}},
  doi          = {{10.1186/s12933-025-02954-8}},
  volume       = {{24}},
  year         = {{2025}},
}