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Improving early diagnosis of acute coronary syndrome and resource utilisation in acute chest pain patients

Lundager Hansen, Jakob LU (2013) In Lund University Faculty of Medicine Doctoral Dissertation Series 2013:20.
Abstract
A high volume of acute chest pain patients, poor early diagnosis and high admission rates result in high resource utilization.



In 1000 consecutively chest pain patients the majority of the direct cost was found due to admission time. The difference between mean cost of an “ACS-rule-out” admission and a discharge from the ED was 6.2 kSEK (9.7 kSEK in 2011).



Early diagnosis can be improved by 1) using the information already available better, 2) adding new diagnostic information, or 3) re-engineering the diagnostic approach. The thesis includes examples of all these strategies



A logistic regression model and an artificial neural network (ANN) model significantly predicted ACS better... (More)
A high volume of acute chest pain patients, poor early diagnosis and high admission rates result in high resource utilization.



In 1000 consecutively chest pain patients the majority of the direct cost was found due to admission time. The difference between mean cost of an “ACS-rule-out” admission and a discharge from the ED was 6.2 kSEK (9.7 kSEK in 2011).



Early diagnosis can be improved by 1) using the information already available better, 2) adding new diagnostic information, or 3) re-engineering the diagnostic approach. The thesis includes examples of all these strategies



A logistic regression model and an artificial neural network (ANN) model significantly predicted ACS better than experienced physicians applied retrospectively on 643 consecutive chest pain patients.



In a prospective study including 560 patients, our ANN models detected STEMI and the need of acute PCI on the ambulance ECG with higher sensitivity than the CCU physician. The ANN could potentially reduce the amount of ECGs transmitted to the CCU physician by 2/3.



A simple prediction model including data immediately available at presentation to the ED did not perform better than the more complex models using only the ECG.



In a convenience sample of 40 low risk patients needing admission due to the suspension of ACS, acute MPI showed a 100 % negative predictive value for ACS and was estimated to reduce overall cost.



This thesis has shown examples of strategies to improve early diagnosis of ACS. The Predictions models should be externally validated before clinical use. Further studies are needed. Such studies should include newer cardiac biomarkers and include both the diagnostic and prognostic performance and the associated resource utilisation. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Prof. Herlitz, Johan, Institutionen för vårdvetenskap, Högskolan i Borås
organization
publishing date
type
Thesis
publication status
published
subject
in
Lund University Faculty of Medicine Doctoral Dissertation Series
volume
2013:20
pages
71 pages
publisher
Medicine (Lund)
defense location
Föreläsningssal F2, Centralblocket, Skånes Universitetssjukhus, Lund
defense date
2013-03-08 14:00:00
ISSN
1652-8220
ISBN
978-91-87189-89-0
language
English
LU publication?
yes
id
936f44e2-6c76-4d91-a496-efb666aad1b0 (old id 3731267)
date added to LUP
2016-04-01 13:00:28
date last changed
2021-03-30 13:20:34
@phdthesis{936f44e2-6c76-4d91-a496-efb666aad1b0,
  abstract     = {{A high volume of acute chest pain patients, poor early diagnosis and high admission rates result in high resource utilization. <br/><br>
<br/><br>
In 1000 consecutively chest pain patients the majority of the direct cost was found due to admission time. The difference between mean cost of an “ACS-rule-out” admission and a discharge from the ED was 6.2 kSEK (9.7 kSEK in 2011). <br/><br>
<br/><br>
Early diagnosis can be improved by 1) using the information already available better, 2) adding new diagnostic information, or 3) re-engineering the diagnostic approach. The thesis includes examples of all these strategies<br/><br>
 <br/><br>
A logistic regression model and an artificial neural network (ANN) model significantly predicted ACS better than experienced physicians applied retrospectively on 643 consecutive chest pain patients. <br/><br>
<br/><br>
In a prospective study including 560 patients, our ANN models detected STEMI and the need of acute PCI on the ambulance ECG with higher sensitivity than the CCU physician. The ANN could potentially reduce the amount of ECGs transmitted to the CCU physician by 2/3.<br/><br>
<br/><br>
A simple prediction model including data immediately available at presentation to the ED did not perform better than the more complex models using only the ECG. <br/><br>
<br/><br>
In a convenience sample of 40 low risk patients needing admission due to the suspension of ACS, acute MPI showed a 100 % negative predictive value for ACS and was estimated to reduce overall cost. <br/><br>
<br/><br>
This thesis has shown examples of strategies to improve early diagnosis of ACS. The Predictions models should be externally validated before clinical use. Further studies are needed. Such studies should include newer cardiac biomarkers and include both the diagnostic and prognostic performance and the associated resource utilisation.}},
  author       = {{Lundager Hansen, Jakob}},
  isbn         = {{978-91-87189-89-0}},
  issn         = {{1652-8220}},
  language     = {{eng}},
  publisher    = {{Medicine (Lund)}},
  school       = {{Lund University}},
  series       = {{Lund University Faculty of Medicine Doctoral Dissertation Series}},
  title        = {{Improving early diagnosis of acute coronary syndrome and resource utilisation in acute chest pain patients}},
  volume       = {{2013:20}},
  year         = {{2013}},
}