<b><i>Introduction:</i></b> The objective of the present study was to describe the experience of the Blood and Tissues Bank of Aragon with the Reveos® Automated Blood Processing System and Mirasol® Pathogen Reduction Technology (PRT) System, comparing retrospectively routine quality data obtained in two different observation periods. <b><i>Methods:</i></b> Comparing quality data encompassing 6,525 blood components from the period 2007–2012, when the semi-automated buffy coat method was used in routine, with 6,553 quality data from the period 2014–2019, when the Reveos system and subsequently the Mirasol system were implemented in routine. <b><i>Results:</i></b> Moving from buffy coat to Reveos led to decreased discard rates of whole blood units (1.2 to 0.1%), increased hemoglobin content (48.1 ± 7.6 to 55.4 ± 6.6 g/unit), and hematocrit (58.9 ± 6.5% to 60.0 ± 4.9%) in red blood cell concentrates. Platelet concentrates (PCs) in both periods had similar yields (3.5 ×10¹¹). Whereas in the earlier period, PCs resulted from pooling 5 buffy coats, in the second period 25% of PCs were prepared from 4 interim platelet units. The mean level of factor VIII in plasma was significantly higher with Reveos (92.8 vs. 97.3 IU). Mirasol PRT treatment of PCs reduced expiry rates to 1.2% in 2019. One septic transmission was reported with a non-PRT treated PCs, but none with PRT-treated PCs. <b><i>Conclusion:</i></b> Automation contributed to standardization, efficiency, and improvement of blood processing. Released resources enabled the effortless implementation of PRT. The combination of both technologies guaranteed the self-sufficiency and improvement of blood safety.

Comparing pandemic waves could aid in understanding the evolution of COVID-19. The objective of the present study was to compare the characteristics and outcomes of patients hospitalized for COVID-19 in different pandemic waves in terms of severity and mortality. We performed an observational retrospective cohort study of 5,220 patients hospitalized with SARS-CoV-2 infection from February to September 2020 in Aragon, Spain. We compared ICU admissions and 30-day mortality, clinical characteristics, and risk factors of the first and second waves of COVID-19. The SARS-CoV-2 genome was also analyzed in 236 samples. Patients in the first wave (n = 2,547) were older (median age 74 years [IQR 60–86] vs. 70 years [53–85]; p < 0.001) and had worse clinical and analytical parameters related to severe COVID-19 than patients in the second wave (n = 2,673). The probability of ICU admission at 30 days was 16% and 10% (p < 0.001) and the cumulative 30-day mortality rates 38% and 32% in the first and second wave, respectively (p = 0.007). Survival differences were observed among patients aged 60 to 80 years. We also found some variability among death risk factors and the viral genome between waves. Therefore, the two analyzed COVID-19 pandemic waves were different in terms of disease severity and mortality.

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.

The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787–0.854) and accurate calibration (slope = 1, intercept = −0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.

Background and Objectives Applying pathogen reduction technologies (PRT) to platelets can extend their shelf life from 5 to 7 days, but there have been few systematic studies of the repercussions of such technologies on outdate rates. Material and Methods The benefits in terms of outdate rates of applying PRT to platelets are studied via a mathematical simulation. Specifically, statistical methods are used to determine the daily production rate needed to meet demand while not exceeding a maximum amount set as a result of limitations on donations and while assuring a minimum daily stock. Results The results show that a 2‐day extension in the shelf life of platelet concentrates (PC) results in reductions in outdates ranging from 88·4% to 100% at the production centres analysed. It may be the case for budgetary reasons that only part of the PCs produced can be treated. This being so, we show that if the proportion treated per annum exceeds 25% the best option is to treat part of the output every day, otherwise, it is preferable to concentrate treatment on the last two production days of the week. Conclusions Extending the shelf life of PC from five to seven days and setting up suitable production logistics can drastically reduce outdates at production centres. If only a part of all PCs is treated, the best choices are to distribute PRT overall production days or, if the percentage of PCs treated is very low, to apply PRT on the days preceding the weekend break.