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

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 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.