Predicting complications in emergency department patients with acute coronary syndrome – Existing risk scores versus a new logistic regression model
Nilsson, Tsvetelina Nikolova LU ; Strömfors, Malin ; Trägårdh, Alice ; Mokhtari, Arash LU ; Khoshnood, Ardavan LU
and Ekelund, Ulf
LU
(2026)
In American Heart Journal Plus: Cardiology Research and Practice
63.
p.1-8
- Abstract
- Background Patients with acute coronary syndrome (ACS) are often admitted to monitored wards due to the risk of complications. Several risk prediction scores exist, but their use in the emergency department (ED) is limited. We aimed to compare the ability of existing risk scores with a new logistic regression model in predicting complications in ACS patients. Methods This was a secondary analysis of data from the ESC TROP trial (NCT03421873), including ACS patients from five EDs in Region Skåne, Sweden (2017–2018). Complications were identified via diagnosis and/or intervention codes and manual chart review. GRACE, GRACE FFE, TIMI, HEART, ACTION ICU, and CHA₂DS₂-VASc scores were calculated. A new logistic regression model was developed,... (More)
- Background Patients with acute coronary syndrome (ACS) are often admitted to monitored wards due to the risk of complications. Several risk prediction scores exist, but their use in the emergency department (ED) is limited. We aimed to compare the ability of existing risk scores with a new logistic regression model in predicting complications in ACS patients. Methods This was a secondary analysis of data from the ESC TROP trial (NCT03421873), including ACS patients from five EDs in Region Skåne, Sweden (2017–2018). Complications were identified via diagnosis and/or intervention codes and manual chart review. GRACE, GRACE FFE, TIMI, HEART, ACTION ICU, and CHA₂DS₂-VASc scores were calculated. A new logistic regression model was developed, and its predictive performance was assessed using the area under the ROC curve (AUROC) and a net reclassification improvement analysis (NRI). Results Among 2223 ACS patients, 164 (7.4%) experienced complications. Independent predictors for complications included age, STEMI, troponin and lactate at arrival, shock index, Killip class, and new ECG changes. The logistic regression model's AUROC 0.84 (95% CI 0.80–0.88) outperformed all known risk scores: GRACE FFE 0.79 (0.75–0.84), ACTION ICU 0.77 (0.72–0.82), GRACE 0.76 (0.70–0.81), TIMI 0.74 (0.68–0.79), HEART 0.69 (0.64–0.74), and CHA₂DS₂-VASc 0.64 (0.59–0.69). Logistic regression improved reclassification of non-events, with a positive non-event NRI compared with all other scores. Conclusions Serious complications occurred in 7% of ACS patients. A logistic regression model based on simple ED variables showed excellent predictive performance, surpassing existing risk scores. Improved risk stratification may optimize resource allocation while maintaining patient safety. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ed643836-c4f3-47f4-8574-fdc3e504df1a
- author
- Nilsson, Tsvetelina Nikolova
LU
; Strömfors, Malin
; Trägårdh, Alice
; Mokhtari, Arash
LU
; Khoshnood, Ardavan
LU
and Ekelund, Ulf
LU
- organization
- publishing date
- 2026
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Acute coronary syndrome, Emergency department, Complications, Predictors, Risk stratification
- in
- American Heart Journal Plus: Cardiology Research and Practice
- volume
- 63
- article number
- 100736
- pages
- 1 - 8
- ISSN
- 2666-6022
- DOI
- 10.1016/j.ahjo.2026.100736
- language
- English
- LU publication?
- yes
- id
- ed643836-c4f3-47f4-8574-fdc3e504df1a
- date added to LUP
- 2026-02-21 21:30:17
- date last changed
- 2026-02-23 08:30:01
@article{ed643836-c4f3-47f4-8574-fdc3e504df1a,
abstract = {{Background Patients with acute coronary syndrome (ACS) are often admitted to monitored wards due to the risk of complications. Several risk prediction scores exist, but their use in the emergency department (ED) is limited. We aimed to compare the ability of existing risk scores with a new logistic regression model in predicting complications in ACS patients. Methods This was a secondary analysis of data from the ESC TROP trial (NCT03421873), including ACS patients from five EDs in Region Skåne, Sweden (2017–2018). Complications were identified via diagnosis and/or intervention codes and manual chart review. GRACE, GRACE FFE, TIMI, HEART, ACTION ICU, and CHA₂DS₂-VASc scores were calculated. A new logistic regression model was developed, and its predictive performance was assessed using the area under the ROC curve (AUROC) and a net reclassification improvement analysis (NRI). Results Among 2223 ACS patients, 164 (7.4%) experienced complications. Independent predictors for complications included age, STEMI, troponin and lactate at arrival, shock index, Killip class, and new ECG changes. The logistic regression model's AUROC 0.84 (95% CI 0.80–0.88) outperformed all known risk scores: GRACE FFE 0.79 (0.75–0.84), ACTION ICU 0.77 (0.72–0.82), GRACE 0.76 (0.70–0.81), TIMI 0.74 (0.68–0.79), HEART 0.69 (0.64–0.74), and CHA₂DS₂-VASc 0.64 (0.59–0.69). Logistic regression improved reclassification of non-events, with a positive non-event NRI compared with all other scores. Conclusions Serious complications occurred in 7% of ACS patients. A logistic regression model based on simple ED variables showed excellent predictive performance, surpassing existing risk scores. Improved risk stratification may optimize resource allocation while maintaining patient safety.}},
author = {{Nilsson, Tsvetelina Nikolova and Strömfors, Malin and Trägårdh, Alice and Mokhtari, Arash and Khoshnood, Ardavan and Ekelund, Ulf}},
issn = {{2666-6022}},
keywords = {{Acute coronary syndrome; Emergency department; Complications; Predictors; Risk stratification}},
language = {{eng}},
pages = {{1--8}},
series = {{American Heart Journal Plus: Cardiology Research and Practice}},
title = {{Predicting complications in emergency department patients with acute coronary syndrome – Existing risk scores versus a new logistic regression model}},
url = {{http://dx.doi.org/10.1016/j.ahjo.2026.100736}},
doi = {{10.1016/j.ahjo.2026.100736}},
volume = {{63}},
year = {{2026}},
}