Diseño de un nuevo indicador de mortalidad en el síndrome coronario agudo al ingreso en la Unidad de Cuidados Intensivos / Design of a new mortality indicator in acute coronary syndrome on admission to the Intensive Care Unit

Objetivo: Diseñar un indicador de mortalidad del síndrome coronario agudo (SCA) en el servicio de medicina intensiva (SMI). Diseño: Estudio descriptivo observacional multicéntrico. Participantes: Pacientes con SCA ingresados en SMI incluidos en el registro ARIAM- SEMICYUC entre enero del 2013 y abril del 2019. Intervenciones: Ninguna. Variables de interés principales: Las variables analizadas fueron demográficas, tiempo de acceso al sistema sanitario y estado clínico. Se analizó la terapia de revascularización, los fármacos y la mortalidad. Se realizó un análisis de regresión logística de COX y posteriormente se diseñó una red neuronal. Se elaboró una curva ROC para calcula la potencia del nuevo score. Finalmente, la utilidad clínica o relevancia del indicador ARIAM se evaluará mediante un gráfico de Fagan. Resultados: Se incluyó a 17.258 pacientes, con una mortalidad al alta del SMI del 3,5% (605). Las variables analizadas con significación estadística (p<0,001) fueron introducidas en el modelo predictivo supervisado, una red neuronal artificial. El nuevo indicador ARIAM mostro una media de 0,0257 (IC del 95%, 0,0245-0,0267) en los pacientes dados de alta de UCI y de 0,27085 (IC del 95%, 0,2533-0,2886) en los que fallecieron, p <0,001. El área ROC del modelo conseguido fue de 0,918 (IC del 95%, 0,907-0,930). En el test de Fagan se demostró que el indicador ARIAM muestra que la probabilidad de fallecimiento es del 19% (IC del 95%, 18-20%) cuando es positivo y del 0,9% (IC del 95%, 0,8-1,01%) cuando es negativo. Conclusiones: Es posible crear un nuevo indicador de mortalidad del SCA en el SMI que sea más exacto, reproducible y actualizable periódicamente. (AU)

Objective: To design a mortality indicator for acute coronary syndrome (ACS) in the intensive care unit (ICU). Design: Multicenter observational descriptive study. Participants: ACS patients admitted to SMI included in the ARIAM-SEMICYUC registry between January 2013 and April 2019. Interventions: None. Main variables of interest: Variables analyzed were demographic, time of access to the health system, and clinical condition. Revascularization therapy, drugs, and mortality were analyzed. A COX regression analysis was performed and subsequently a neural network was designed. An ROC curve was developed to calculate the power of the new score. Finally, the clinical utility or relevance of the ARIAM's indicator will be evaluated using a Fagan test. Results: 17,258 patients were included, with a 3.5% (605) mortality at discharge from the ICU. The variables analyzed with statistical significance (p<0.001) were entered into the supervised predictive model, an artificial neural network. The new ARIAM's indicator showed a mean of 0.0257 (95% CI: 0.0245–0.0267) in patients discharged from the ICU and 0.27085 (95% CI: 0.2533–0.2886) in those who died, p<0.001. The ROC area of the model achieved was 0.918 (95% CI: 0.907–0.930). The Fagan test showed that the ARIAM's Indicator shows that the probability of death is 19% (95% CI: 18%–20%) when it is positive and 0.9% (95% CI: 0.8%–1.01%) when it is negative. Conclusions: It is possible to create a new mortality indicator for ACS in the ICU that is more accurate, reproducible, and periodically updated. (AU)

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

OBJECTIVE: To design a mortality indicator in acute coronary syndrome (ACS) in the intensive care unit (ICU). DESIGN: A multicenter, observational descriptive study was carried out. PARTICIPANTS: Patients with ACS admitted to the ICUs included in the ARIAM-SEMICYUC registry between January 2013 and April 2019. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Demographic 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. RESULTS: A 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. CONCLUSIONS: A new mortality indicator for ACS in the ICU can be established that is more accurate and reproducible, and periodically updated.