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Background/Aim: The ultrasonic transit time is currently the best method for measuring the blood flow rate in the extracorporeal hemodialysis circuit. The purpose of this study was to analyze the differences between blood flow as indicated by the hemodialysis blood roller pump (prescribed blood flow) and by an ultrasonic flowmeter (delivered blood flow). Methods: The ultrasonic blood flow was measured in 20 patients on chronic hemodialysis who were dialyzed through an arteriovenous fistula. During each dialysis session the ultrasonic blood flow was measured at three different blood roller pump flow rates (300, 350, and 400 ml/min). In order to analyze the influence of inflow and outflow pressures on blood flow, this study was conducted during nine consecutive dialysis sessions during which needles of different sizes were used. Results: The ultrasonic flow was always lower than indicated by the blood roller pump: 265 ± 12, 304 ± 15, and 341 ± 19 ml/min for blood roller pump flow rates of 300, 350, and 400 ml/min, respectively (variability: –11.6, –13.1, and –14.8%, respectively). An univariate regression analysis showed that the reduction in flow recorded ultrasonically correlated with both venous blood line pressure (r = –0.2679, p < 0.001) and negative arterial blood line pressure (r = 0.6773, p < 0.001). By multivariate analysis, only the arterial blood line pressure has a predictive value. When all measurements were grouped by arterial blood line pressure ranges, the variability between ultrasonic blood flow and blood roller pump flow was found to be similar in those groups with the same arterial blood line pressure, regardless of the blood roller pump flow rate. Conclusions: The blood flow indicated by the dialysis blood roller pump is always greater than the delivered blood flow, and this difference is in turn conditioned by the negative pressure induced by the blood roller pump in the arterial blood line.

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We developed two models for real-time monitoring and forecasting of the evolution of the COVID-19 pandemic: a non-linear regression model and an error correction model. Our strategy allows us to detect pandemic peaks and make short- and long-term forecasts of the number of infected, deaths and people requiring hospitalization and intensive care. The non-linear regression model is implemented in an expert system that automatically allows the user to fit and forecast through a graphical interface. This system is equipped with a control procedure to detect trend changes and define the end of one wave and the beginning of another. Moreover, it depends on only four parameters per series that are easy to interpret and monitor along time for each variable. This feature enables us to study the effect of interventions over time in order to advise how to proceed in future outbreaks. The error correction model developed works with cointegration between series and has a great forecast capacity. Our system is prepared to work in parallel in all the Autonomous Communities of Spain. Moreover, our models are compared with a SIR model extension (SCIR) and several models of artificial intelligence.

RESUMEN El modelo de integración socio-laboral

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