Design of a new mortality indicator in acute coronary syndrome on admission to the Intensive Care Unit

Abstract

ObjectiveTo design a mortality indicator in acute coronary syndrome (ACS) in the intensive care unit (ICU). DesignA multicenter, observational descriptive study was carried out. ParticipantsPatients with ACS admitted to the ICUs included in the ARIAM-SEMICYUC registry between January 2013 and April 2019. InterventionsNone. Main variables of interestDemographic parameters, time of access to the healthcare system, and clinical condition. Revascularization therapy, drugs and mortality were analyzed. Cox regression analysis was performed, followed by the design of a neural network. A receiver operating characteristic curve (ROC) was plotted to calculate the power of the new score. Lastly, the clinical utility or relevance of the ARIAM indicator (ARIAM’s) was assessed using a Fagan test. ResultsA total of 17,258 patients were included in the study, with a mortality rate of 3.5% (n = 605) at discharge from the ICU. The variables showing statistical significance (P < .001) were entered into the supervised predictive model, an artificial neural network. The new ARIAM’s yielded a mean of 0.0257 (95%CI: 0.0245−0.0267) in patients discharged from the ICU versus 0.27085 (95%CI: 0.2533−0.2886) in those who died (P < .001). The area under the ROC curve of the model was 0.918 (95%CI: 0.907−0.930). Based on the Fagan test, the ARIAM’s showed the mortality risk to be 19% (95%CI: 18%–20%) when positive and 0.9% (95%CI: 0.8%–1.01%) when negative. ConclusionsA new mortality indicator for ACS in the ICU can be established that is more accurate and reproducible, and periodically updated.