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.

From starting mechanical ventilation to ventilator-associated pneumonia, choosing the right moment to start antibiotic treatment.

BACKGROUND: Ventilator-associated pneumonia (VAP) can have a clear onset or may be a result of the gradual appearance of symptoms and signs of VAP (gradual VAP). The aim of this paper is to describe the VAP development process with the intention of discriminating between those pneumonias with a clear beginning and those that are diagnosed after a period of maturation. In addition, we evaluate the effect of the starting time of antibiotic treatment in both situations. METHODS: Consecutive ventilated patients fulfilling VAP criteria were included. The patients were monitored for clinical, microbiological, and inflammatory signs. Patients with VAP were classified into two groups: (1) nongradual VAP (patients in whom all VAP criteria were detected for the first time on the day of diagnosis) and (2) gradual VAP (progressive appearance of signs and symptoms throughout the pre-VAP period [<96 h to >24 h before VAP diagnosis]). RESULTS: A total of 71 patients with VAP were identified, of whom 43 (61 %) had gradual VAP, most of whom (n = 38, 88 %) had late-onset VAP. Antibiotic treatment was given to 34 (79 %) patients with gradual VAP in the pre-VAP period, and empirical antibiotic treatment was appropriate in 22 patients (51 %). The patients with an appropriate empirical treatment had a higher percentage of early clinical response to treatment (68 % [n = 15] vs. 28 % [n = 7]; p = 0.009). An attempt was made to find a diagnostic test capable of identifying the infectious process underway, but clinical scales and biomarkers of inflammation helped us to achieve acceptable results. CONCLUSIONS: Gradual emergence of VAP, mainly of late onset, is a common condition. Clinicians should be aware of this gradual onset of the infection to establish an early antibiotic treatment, even before the classic diagnostic criteria for VAP are applied.

Diagnosing external ventricular drain-related ventriculitis by means of local inflammatory response: soluble triggering receptor expressed on myeloid cells-1.

INTRODUCTION: External ventricular drainage (EVD)-related ventriculitis is one of the most severe complications associated with the use of EVDs. Establishing an early and certain diagnosis can be difficult in critically ill patients. We performed this prospective study to evaluate the usefulness of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) determination in cerebrospinal fluid (CSF) in the diagnosis of ventriculitis. METHODS: A prospective observational study was conducted of 73 consecutive patients with EVD. Samples of CSF for culture, cytobiochemical analysis and sTREM-1 determination were extracted three times a week. Ventriculitis diagnosis required a combination of microbiological, cytobiochemical and clinical criteria. RESULTS: Seventy-three consecutive patients were included. EVD-related ventriculitis was diagnosed in six patients and EVD-colonization in ten patients. Patients without clinical or microbiological findings were considered controls. The median CSF sTREM-1 was 4,320 pg/ml (interquartile range (IQR): 2,987 to 4,886) versus 266 pg/ml (118 to 689); P <0.001. There were no differences when comparing colonized-patients and controls. The best cut-off sTREM-1 value for the diagnosis of ventriculitis was 2,388.79 pg/ml (sensitivity 100%, specificity 98.5%, positive predictive value 85.71%, negative predictive value 100%). CSF proteins, glucose and the ratio CSF/serum glucose were also significantly different (P = 0.001). Serum biomarkers were not useful to diagnose EVD-related infection. These results were confirmed by a case-control study with ventriculitis patients (cases) and non-ventriculitis (control subjects) matched by age, comorbidities, severity scales and EVD duration (P = 0.004). CONCLUSIONS: CSF sTREM-1 was useful in the diagnosis of ventriculitis, in a similar measure to classical CSF parameters. Furthermore, CSF sTREM-1 could prove the diagnosis in uncertain cases and discriminate between EVD-colonization and infection.

Impact of microbial ecology on accuracy of surveillance cultures to predict multidrug resistant microorganisms causing ventilator-associated pneumonia.

OBJECTIVES: The objective of this study was to assess surveillance cultures (SC) prediction accuracy in two periods and settings of the same Department with a different microbiological epidemiology (high and low prevalence of multi-drug resistant microorganisms (MDRM)). METHODS: Prospective and observational study. SC were obtained twice a week in consecutive mechanically ventilated patients. Patients fulfilling VAP criteria were analyzed. RESULTS: 440 patients were followed up, 71 patients had VAP (50 in period I and 21 in period II). MDRM causing VAP were more prevalent in the first period (48% vs. 19%; p = 0.033). The rate of empirical appropriate treatment in period I was lower than in period II (52% vs.76%; p = 0.031). SC prediction accuracy was similar in the two periods (80% vs. 81%; p = 0.744). However, if antibiotic treatment had been guided by SC, the percentage of appropriate treatment would have increased by 28% in the first period but only by 5% in the second; p = 0.024. CONCLUSIONS: SC were able to predict VAP etiology in 80% of cases regardless the prevalence of MDRM. However, the potential benefit of SC in terms of appropriate empirical treatment could be only observed when MDRM were prevalent.