A Multi-Stage decision support Algorithm to Rule-Out patientswith suspected Acute Myocardial Infarction (AMI)

Objective: Provide a multi-stage rule-out algorithm to stratify patients admitted to the Emergency Room (ER) with chest pain of presumed ischemic origin. The aim is to keep at-risk patients in the ER providing a proper care while minimizing overcrowding. The algorithm uses data from biomarkers —heart-type fatty acid–binding protein (H-FABP), high sensitivity cardiac troponin T (hs-cTnT) measured at different times (presentation, 1, 2, 3, 6, 12 and 24 hours) together with ECG at presentation.Methods: Data in a randomly selected training set of 296 patients were retrospectively analysed. 182 cases comprised a test set. STEMI were not considered since biomarkers are not routinely measured for these cases. H-FABP and hs-cTnT were statistically significant for the segregation of non-MI cases over other biomarkers including CK-MB and cTnT. The multi-stage algorithm was trained and tuned looking for maximizing sensitivity (and keeping low numbers of false negative cases in the detection of AMI). Thus after each stage if the algorithm detects non-MI, the patient could be considered for release. Results: Retrospectively applying the algorithm on the whole dataset of 478 cases: 97 MI (NSTEMI) and 381 non-MI. 244 patients could have been recommended for rule-out at presentation with 3 false negatives which in turn could have been identified by other symptoms/history. Sensitivity: 0.97, specificity: 0.63, ppv: 0.40, npv: 0.99. The remaining patients would have needed to be observed and biomarkers measured again at 1 hour were the next stage algorithm would rule-out patients from AMI. The process is repeated to the following stages and the algorithms exhibit high sensitivities (0.94 at 3 hour) with moderately increasing specificity (0.80 at 3 hour). Conclusion: The algorithm serves as a rule-out test for suspected AMI patients which would allow risk stratification and a more efficient use of resources to the health care system.