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It is recommended to implement the teaching of Basic Life Support (BLS) in schools; however, studies on the best training method are limited and have been a priority in recent years. The objective of this study was to analyze the attitudes and practical skills learned during BLS training using a gamified proposal. A comparative study was carried out, consisting of Compulsory Secondary Education students [control group (CG; classical teaching) and experimental group (EG; gamified proposal)]. The instruments used were the CPR and AED action sequence observation sheet, data from the Laerdal Resusci Anne manikin and AED and Attitude Questionnaire towards Basic Life Support and the Use of the Automated External Defibrillator. Sixty-eight students (33 girls) with a mean age of 13.91 ± 0.70 years were recruited. Results were significantly better in the EG (n = 37) [i.e., breathing control (p = 0.037); call to emergency services (p = 0.049); mean compression depth (p = 0.001); self-confidence (p = 0.006); intention to perform BLS and AED (p = 0.002)]; and significantly better in the CG (n = 31) [Total percentage of CPR (p < 0.001); percentage of correct compression (p < 0.001); time to apply effective shock with AED (p < 0.001); demotivation (p = 0.005). We can conclude that the group that was trained with the training method through the gamified proposal presents better intentions and attitudes to act in the event of cardiac arrest than those of the classic method. This training method allows for similar results in terms of CPR and AED skills to classical teaching, so it should be taken into account as a method for teaching BLS to secondary education students.

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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)

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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.

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OBJECTIVES: Scarcity of liver grafts has led to the use of marginal donors, consequently increasing the number of complications posttransplant. To prevent this situation, several indicators have been developed. However, important differences remain among countries. Here, we compared an early-risk liver transplant indicator based on the Spanish Liver Transplant Registry, called the Graft Risk Index, versus the US donor risk index and the Eurotransplant donor risk index. MATERIALS AND METHODS: The new indicator was based on prospectively collected data from 600 adult liver transplants performed in our center. We considered 2 events to compare the indexes: graft survival and rejection-free graft survival, with Cox proportional regression for analyses. Power to predict graft survival was evaluated by calculating the receiver operating characteristic area under the curve. RESULTS: We found no differences between the US and Eurotransplant donor risk indexes in prediction of patients with and without early graft failure. With regard to early survival, only the Graft Risk Index allowed better survival discrimination, in which survival progressively decreased with values ≥ 3 (with probability of graft survival at 1 month of 68%; 95% confidence interval, 46.2-82.5). This increase in risk was significant compared with the standard group (hazard ratio of 10.15; 95% confidence interval, C 3.91- 26.32; P < .001). We calculated powers of prediction of 0.52 (95% confidence interval, 0.43-0.62), 0.54 (95% confidence interval, 0.45-0.65), and 0.69 (95% confidence interval, 0.61-0.77) for donor risk index, Eurotransplant donor risk index, and early Graft Risk Index, respectively. CONCLUSIONS: Neither the US donor risk index nor the Eurotransplant donor risk index was valid for our Spanish liver donation and transplant program. Therefore, an indicator to predict posttransplant graft survival that is adapted to our environment is necessary. This national Graft Risk Index can be a useful tool to optimize donor-recipient matching.

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The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combination of artificial neural networks (ANNs) and genetic algorithms (GAs), to find the optimum performance of an up-flow anaerobic sludge blanket reactor (UASB) for saline wastewater treatment. Chemical oxygen demand (COD) removal was predicted using conductivity, organic loading rate (OLR) and temperature as input variables. The ANN model was built from experimental data and performance was assessed through the maximum mean absolute percentage error (= 9.226%) computed from the measured and model predicted values of the COD. Accordingly, the ANN model was used as a fitness function in a GA to find the best operational condition. In the worst case scenario (low energy requirements, high OLR usage and high salinity) this model guaranteed COD removal efficiency values above 70%. This result is consistent and was validated experimentally, confirming that this ANN-GA model can be used as a tool to achieve the best performance of a UASB reactor with the minimum requirement of energy for saline wastewater treatment.

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The combined effect of temperature and pretreatment of the substrate on the anaerobic treatment of the organic fraction of slaughterhouse solid waste was studied. The goal of the study was to evaluate the effect of pretreating the waste on the efficiency of anaerobic digestion. The effect was analyzed at two temperature ranges (the psychrophilic and the mesophilic ranges), in order to evaluate the effect of temperature on the performance of the anaerobic digestion process for this residue. The experiments were performed in 6 L batch reactors for 30 days. Two temperature ranges were studied: the psychrophilic range (at room temperature, 18°C average) and the mesophilic range (at 37°C). The waste was pretreated with NaOH before the anaerobic treatment. The result of pretreating with NaOH was a 194% increase in the soluble chemical oxygen demand (COD) with a dose of 0.6 g NaOH per g of volatile suspended solids (VSS). In addition, the soluble chemical oxygen demand/total chemical oxygen demand ratio (sCOD/tCOD) increased from 0.31 to 0.7. For the anaerobic treatment, better results were observed in the mesophilic range, achieving 70.7%, 47% and 47.2% removal efficiencies for tCOD, total solids (TS), and volatile solids (VS), respectively.

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