Systematic review of clinical decision support systems for pre-hospital acute coronary syndrome identification

Objective:
Timely pre-hospital diagnosis and treatment of acute coronary syndrome (ACS) are required to achieve optimal outcomes. Clinical decision support systems (CDSS) are platforms designed to integrate multiple data and can aid with management decisions in the pre-hospital environment. The review aim was to describe the accuracy of CDSS and individual components in the pre-hospital ACS management.

Methods:
This systematic review examined the current literature regarding the accuracy of CDSS for ACS in the pre-hospital setting, the influence of computer-aided decision making and of four components: electrocardiogram, biomarkers, patient history and examination findings. The impact of these components on sensitivity, specificity, positive and negative predictive values was assessed.

Results:
A total of 11,439 articles were identified from a search of databases, of which 199 were screened against the eligibility criteria. Eight studies were found to meet the eligibility and quality criteria. There was marked heterogeneity between studies which precluded formal meta-analysis. However, individual components analysis found that patient history led to significant improvement in the sensitivity and negative predictive values. CDSS which incorporated all four components tended to show higher sensitivities and negative predictive values. CDSS incorporating computer-aided electrocardiogram diagnosis showed higher specificities and positive predictive values.

Conclusions:
Although heterogeneity precluded meta-analysis, this review emphasises the potential of ACS CDSS in pre-hospital environments that incorporate patient history in addition to integration of multiple components. The higher sensitivity of certain components, along with higher specificity of computer-aided decision-making, highlights the opportunity for developing an integrated algorithm with computer-aided decision support.