Early detection of new pandemic waves. Control chart and a new surveillance index.

The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.

Police as first reponders improve out-of-hospital cardiac arrest survival.

BACKGROUND: Police forces are abundant circulating and might arrive before the emergency services to Out-of-Hospital-Cardiac-Arrest victims. If properly trained, they can provide basic life support and early defibrillation within minutes, probably increasing the survival of the victims. We evaluated the impact of local police as first responders on the survival rates of out-of-hospital cardiac arrest victims in Navarra, Spain, over 7 years. METHODS: A retrospective analysis of an ongoing Out-of-Hospital Cardiac registry to compare the characteristics and survival of Out-of-Hospital-Cardiac-Arrest victims attended to in first place by local police, other first responders, and emergency ambulance services between 2014 and 2020. RESULTS: Of 628 cases, 73.7% were men (aged 68.9 ± 15.8), and 26.3% were women (aged 65,0 ± 14,7 years, p < 0.01). Overall survival of patients attended to by police in the first place was 17.8%, other first responders 17.4% and emergency services 13.5% with no significant differences (p > 0.1). Time to initiating cardiopulmonary resuscitation is significant for survival. When police arrived first and started CPR before the emergency services, they arrived at a mean of 5.4 ± 3 min earlier (SD = 3.10). This early police intervention showed an increase in the probability of survival by 10.1%. CONCLUSIONS: The privileged location and the sole amount of personnel of local police forces trained in life support and their fast delivery of defibrillators as first responders can improve the survival of out-of-hospital cardiac arrest victims.

Gestión de camas hospitalarias durantela pandemia en Navarra con el apoyo demétodos matemáticos de predicción / Management of hospital beds during the pandemic in Navarra with the support of mathematical prediction methods

Durante la pandemia por coronavirus, en Navarra se utilizaron modelos matemáticos depredicción para estimar las camas necesarias, convencionales y de críticos, para atender alos pacientes COVID-19. Las seis ondas pandémicas presentaron distinta incidencia en la población, ocasionandovariabilidad en los ingresos hospitalarios y en la ocupación hospitalaria. La respuesta a laenfermedad de los pacientes no fue constante en cada onda, por lo que, para la predicción decada una, se utilizaron los datos correspondientes de esa onda.El método de predicción constó de dos partes: una describió la entrada de pacientes alhospital y la otra su estancia dentro del mismo. El modelo requirió de la alimentación a tiempo real de los datos actualizados. Los resultados delos modelos de predicción fueron posteriormente volcados al sistema de información corporativotipo Business Intelligence. Esta información fue utilizada para planificar el recurso cama y lasnecesidades de profesionales asociadas a la atención de estos pacientes en el ámbito hospitalario.En la cuarta onda se realizó un análisis para cuantificar el grado de acierto de los modelospredictivos. Los modelos predijeron adecuadamente el pico, la meseta y el cambio detendencia, pero sobreestimaron los recursos necesarios para la atención de los pacientes enla parte descendente de la curva. El principal punto fuerte de la sistemática utilizada para la construcción de modelospredictivos fue proporcionar modelos en tiempo real con datos recogidos con precisión porlos sistemas de información que consiguieron un grado de acierto aceptable permitiendo unautilización inmediata.(AU)

Acuity-based rotational patient-to-physician assignment in an emergency department using electronic health records in triage.

Emergency department (ED) operational metrics generated by a new acuity-based rotational patient-to-physician assignment (ARPA) algorithm are compared with those obtained with a simple rotational patient assignment (SRPA) system aimed only at an equitable patient distribution. The new ARPA method theoretically guarantees that no two physicians' assigned patient loads can differ by more than one, either partially (by acuity levels) or in total; whereas SRPA guarantees only the latter. The performance of the ARPA method was assessed in practice in the ED of the main public hospital (Hospital Compound of Navarra) in the region of Navarre in Spain. This ED attends over 140 000 patients every year. Data analysis was conducted on 9,063 ED patients in the SRPA cohort, and 8,892 ED patients in the ARPA cohort. The metrics of interest are related both to patient access to healthcare and physician workload distribution: patient length of stay; arrival-to-provider time; ratio of patients exceeding the APT target threshold; and range of assigned patients across physicians by priority levels. The transition from SRPA to ARPA is associated with improvements in all ED operational metrics. This research demonstrates that ARPA is a simple and useful strategy for redesigning front-end ED processes.

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.

Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.

In situ visualization of large-scale turbulence simulations in Nek5000 with ParaView Catalyst.

In situ visualization on high-performance computing systems allows us to analyze simulation results that would otherwise be impossible, given the size of the simulation data sets and offline post-processing execution time. We develop an in situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computational fluid dynamics. We perform a strong scalability test up to 2048 cores on KTH's Beskow Cray XC40 supercomputer and assess in situ visualization's impact on the Nek5000 performance. In our study case, a high-fidelity simulation of turbulent flow, we observe that in situ operations significantly limit the strong scalability of the code, reducing the relative parallel efficiency to only ≈ 21 % on 2048 cores (the relative efficiency of Nek5000 without in situ operations is ≈ 99 % ). Through profiling with Arm MAP, we identified a bottleneck in the image composition step (that uses the Radix-kr algorithm) where a majority of the time is spent on MPI communication. We also identified an imbalance of in situ processing time between rank 0 and all other ranks. In our case, better scaling and load-balancing in the parallel image composition would considerably improve the performance of Nek5000 with in situ capabilities. In general, the result of this study highlights the technical challenges posed by the integration of high-performance simulation codes and data-analysis libraries and their practical use in complex cases, even when efficient algorithms already exist for a certain application scenario.

Hospital preparedness during epidemics using simulation: the case of COVID-19.

This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.

Optimal control of ICU patient discharge: from theory to implementation.

This paper deals with the management of scarce health care resources. We consider a control problem in which the objective is to minimize the rate of patient rejection due to service saturation. The scope of decisions is limited, in terms both of the amount of resources to be used, which are supposed to be fixed, and of the patient arrival pattern, which is assumed to be uncontrollable. This means that the only potential areas of control are speed or completeness of service. By means of queuing theory and optimization techniques, we provide a theoretical solution expressed in terms of service rates. In order to make this theoretical analysis useful for the effective control of the healthcare system, however, further steps in the analysis of the solution are required: physicians need flexible and medically-meaningful operative rules for shortening patient length of service to the degree needed to give the service rates dictated by the theoretical analysis. The main contribution of this paper is to discuss how the theoretical solutions can be transformed into effective management rules to guide doctors' decisions. The study examines three types of rules based on intuitive interpretations of the theoretical solution. Rules are evaluated through implementation in a simulation model. We compare the service rates provided by the different policies with those dictated by the theoretical solution. Probabilistic analysis is also included to support rule validity. An Intensive Care Unit is used to illustrate this control problem. The study focuses on the Markovian case before moving on to consider more realistic LoS distributions (Weibull, Lognormal and Phase-type distribution).

A mathematical model for simulating daily bed occupancy in an intensive care unit.

OBJECTIVES: To develop a mathematical model for simulating the daily bed occupancy in an intensive care unit. DESIGN: Data collection and retrospective analysis to develop a mathematical model for simulating daily bed occupancy in an intensive care unit. SETTING: We studied all admissions to the intensive care unit at the Hospital of Navarra over a 9-yr period. PATIENTS: Six-thousand three-hundred adult patients consecutively admitted to intensive care units at a tertiary care hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The large set of data collected comprises an arrivals file, a patient file, and a bed occupancy file. The arrival file records the number of patients admitted to the intensive care unit each day, by admission type, and by day of the week. The patient file contains records for all patients admitted to the intensive care unit during the study period: Admission type, admission and discharge dates, age, sex, Acute Physiology and Chronic Health Evaluation II score within the first 24 hrs, infections during hospitalization, and mortality. We used these two files to fit appropriate statistical models of arrival rates and length of stay by patient type. Based on this statistical analysis and the representation of the intensive care unit as a queuing problem, we built a simulation model. The bed occupancy file records the number of occupied beds at 4:00 PM each day. We used this file to validate the simulation model by testing the similarity of the real and simulated output data. The simulation model also includes bed management decisions related to patient discharge. RESULTS: We obtained a valid simulation model that reproduced on a computer the patient flow through the intensive care unit at the Hospital of Navarra. This computerized simulation model can be used to study the intensive care unit bed occupancy profile and can be used as a reliable sizing and capacity analysis tool. As an example, we present the problem of estimating the number of beds needed to meet an increase in patient arrivals at the intensive care unit because of different causes. CONCLUSIONS: It is possible to develop simulation models that can be used to predict future intensive care unit resource needs.

Effects of strength training on muscle fatigue mapping from surface EMG and blood metabolites.

PURPOSE: this study examined the effects of heavy resistance training on the relationships between power loss and surface EMG (sEMG) indices and blood metabolite concentrations on dynamic exercise-induced fatigue with the same relative load as in pretraining. METHODS: twelve trained subjects performed five sets consisting of 10 repetitions in the leg press, with 2 min of rest between sets before and after a strength training period. sEMG variables (the mean average voltage, the median spectral frequency, and the Dimitrov spectral index of muscle fatigue) from vastus medialis and lateralis muscles and metabolic responses (i.e., blood lactate, uric acid, and ammonia concentrations) were measured. RESULTS: the peak power loss after the posttraining protocol was greater (61%) than the decline observed in the pretraining protocol (46%). Similar sEMG changes were found for both protocols, whereas higher metabolic demand was observed during the posttraining exercise. The linear models on the basis of the relations found between power loss and changes in sEMG variables were significantly different between pretraining and posttraining, whereas the linear models on the basis of the relations between power loss and changes in blood metabolite concentrations were similar. CONCLUSIONS: linear models that use blood metabolites to map acute exercise-induced peak power changes were more accurate in detecting these changes before and after a short-term training period, whereas an attempt to track peak power loss using sEMG variables may fail after a strength training period.

Changes in power curve shapes as an indicator of fatigue during dynamic contractions.

The purpose of this study was to analyze exercise-induced leg fatigue during a dynamic fatiguing task by examining the shapes of power vs. time curves through the combined use of several statistical methods: B-spline smoothing, functional principal components and (supervised and unsupervised) classification. In addition, granulometric size distributions were also computed to allow for comparison of curves coming from different subjects. Twelve physically active men participated in one acute heavy-resistance exercise protocol which consisted of five sets of 10 repetition maximum leg press with 120 s of rest between sets. To obtain a smooth and accurate representation of the data, a basis of 180 B-splines was used. Functional principal component (FPC) analysis was used to find the dominant modes of variation in the curves. A multivariate cluster over the FPC scores and a k-nearest neighbor classification led to three interpretable groups corresponding to different levels of fatigue. Fatigue-induced changes in the shapes of the power curves were evident, in which curves progressively flatten and develop a second power peak. In a practical setting FPC analysis greatly reduces dimensionality and the use of granulometries allows for comparison of the curve shapes without distorting the time scale. In contrast to the present methodology, which considers each curve as a datum, classical statistical approaches using summary parameters of time series may lead to limited information about the impact of dynamic fatiguing protocols on kinematic and kinetic time-course changes in curve shapes.

Expert judgment-based risk assessment using statistical scenario analysis: a case study-running the bulls in Pamplona (Spain).

In this article, we present a methodology to assess the risk incurred by a participant in an activity involving danger of injury. The lack of high-quality historical data for the case considered prevented us from constructing a sufficiently detailed statistical model. It was therefore decided to generate a risk assessment model based on expert judgment. The methodology is illustrated in a real case context: the assessment of risk to participants in a San Fermin bull-run in Pamplona (Spain). The members of the panel of "experts on the bull-run" represented very different perspectives on the phenomenon: runners, surgeons and other health care personnel, journalists, civil defense workers, security staff, organizers, herdsmen, authors of books on the bull-run, etc. We consulted 55 experts. Our methodology includes the design of a survey instrument to elicit the experts' views and the statistical and mathematical procedures used to aggregate their subjective opinions.

Motor unit action potential duration, I: variability of manual and automatic measurements.

The aim of this work is to analyze the variability in manual measurements of motor unit action potential (MUAP) duration and to evaluate the effectiveness of well-known algorithms for automatic measurement. Two electromyographists carried out three independent duration measurements of a set of 240 MUAPs. The intraexaminer and interexaminer variabilities were analyzed by means of the Gage Reproducibility and Repeatability method. The mean of the three closest manually marked positions was considered the gold standard of the duration markers positions (GSP). The results of four well-known automatic methods for estimating MUAP duration were compared to the GSP. Manual measurements of duration showed a lot of variability, with the combined intraoperator and interoperator variability greater than 30%. The greatest difference between manual positions was 11.2 ms. The mean differences between the GSP and those obtained with the four automatic methods ranged between 0.6 and 8.5 ms. Both manual and automatic measurements of MUAP duration show a high degree of variability. More precise methods are needed to improve the accuracy and reliability of the estimates of this parameter.

Motor unit action potential duration, II: a new automatic measurement method based on the wavelet transform.

The aim of this work is to present and evaluate a new algorithm, based on the wavelet transform, for the automatic measurement of motor unit action potential (MUAP) duration. A total of 240 MUAPs were studied. The waveform of each MUAP was wavelet-transformed, and the start and end points were estimated by regarding the maxima and minima points in a particular scale of the wavelet transform. The results of the new method were compared to the gold standard of duration marker positions obtained by manual measurement. The new method was also compared to a conventional algorithm, which we had found to be best in a previous comparative study. To evaluate the new method against manual measurements, the dispersion of automatic and manual duration markers were analyzed in a set of 19 repeatedly recorded MUAPs. The differences between the new algorithm's marker positions and the gold standard of duration marker positions were smaller than those observed with the conventional method. The dispersion of the new algorithm's marker positions was slightly less than that of the manual one. Our new method for automatic measurement of MUAP duration is more accurate than other available algorithms and more consistent than manual measurements.