Predicting 30 days Mortality in STEMI Patients using Patient Referral Data to a Primary Percutaneous Coronary Intervention Service Primary percutaneous coronary intervention (PPCI) is a minimally invasive procedure to unblock the arteries which carry blood to the heart. This procedure is carried out once patients are accepted based on the STEMI criteria upon the assessment of 12lead ECG. This paper reports the analyses of a dataset compiled from patients accepted for PPCI. The primary objective was to explore the features which may predict 30days mortality. The 30 day mortality was?? The main features identified were a patientu0027s age, sex, door to balloon time, call time, pain time, and activation status. Together these features appear to be a predictor of 30day mortality in patients referred for PPCI (76% accuracy, 70% sensitivity and 85% specificity).

Unsupervised Machine Learning Elicits Patient Archetypes in a Primary Percutaneous Coronary Intervention Service A primary percutaneous coronary intervention (PPCI) reestablishes blood flow in an obstructed coronary artery. PPCI referrals vary in admission criteria partly on the basis of ECG findings, hence, not all the referrals are accepted. The aim of the paper is to discover archetypes of accepted patients referred to the PPCI center. Cluster analysis was performed on a PPCI referral dataset to identify patient archetypes and identify any key patterns of patients who were accepted for PPCI. A kmeans clustering algorithm was used with the elbow method for determining the optimum number of clusters (groups of patients). A silhouette plot was generated for within cluster validation. Among the accepted PPCI referrals, there were four different groups of patients. The patients within each group have similar characteristics. The largest cluster of patients include male patients being referred out of hours and with excessive door to balloon times (DTBTs) as compared to those referred in hours. Another cluster includes older female patients who are referred out of hour. Also, it was discovered that the false activation rate and DTBTs are higher in females as compared to male clusters. The smallest cluster include the most elderly patients in the whole referral dataset and mainly includes more males than femaleu0027s who are referred out of hour and have the highest false activation rate, DTBT, and 30 days mortality rate. The cluster analysis of PPCI dataset revealed different patient archetypes. Each group of patients have a different mean age, out of hours referral rate, DTBT, false activation and 30 days mortality rate compared to other group. The identified clusters could be helpful for the clinicians to better understand their patients and utilize this information to aid the clinical decision making.